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Miller, David J. Ghosh Avijit. "New methods in computational systems biology /." Philadelphia, Pa. : Drexel University, 2008. http://hdl.handle.net/1860/2810.
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Simoni, Giulia. "Modeling Startegies for Computational Systems Biology." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/254361.
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Mathematical models and their associated computer simulations are nowadays widely used in several research fields, such as natural sciences, engineering, as well as social sciences. In the context of systems biology, they provide a rigorous way to investigate how complex regulatory pathways are connected and how the disruption of these processes may contribute to the develop- ment of a disease, ultimately investigating the suitability of specific molecules as novel therapeutic targets. In the last decade, the launching of the precision medicine initiative has motivated the necessity to define innovative computational techniques that could be used for customizing therapies. In this context, the combination of mathematical models and computer strategies is an essential tool for biologists, which can analyze complex system pathways, as well as for the pharmaceutical industry, which is involved in promoting programs for drug discovery.
In this dissertation, we explore different modeling techniques that are used for the simulation and the analysis of complex biological systems. We analyze the state of the art for simulation algorithms both in the stochastic and in the deterministic frameworks. The same dichotomy has been studied in the context of sensitivity analysis, identifying the main pros and cons of the two approaches. Moreover, we studied the quantitative system pharmacology (QSP) modeling approach that elucidates the mechanism of action of a drug on the biological processes underlying a disease. Specifically, we present the definition, calibration and validation of a QSP model describing Gaucher disease type 1 (GD1), one of the most common lysosome storage rare disorders. All of these techniques are finally combined to define a novel computational pipeline for patient stratification. Our approach uses modeling techniques, such as model simulations, sensitivity analysis and QSP modeling, in combination with experimental data to identify the key mechanisms responsible for the stratification. The pipeline has been applied to three test cases in different biological contexts: a whole-body model of dyslipidemia, the QSP model of GD1 and a QSP model of cardiac electrophysiology. In these test cases, the pipeline proved to be accurate and robust, allowing the interpretation of the mechanistic differences underlying the phenotype classification.
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Simoni, Giulia. "Modeling Startegies for Computational Systems Biology." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/254361.
Full textAbstract:
Mathematical models and their associated computer simulations are nowadays widely used in several research fields, such as natural sciences, engineering, as well as social sciences. In the context of systems biology, they provide a rigorous way to investigate how complex regulatory pathways are connected and how the disruption of these processes may contribute to the develop- ment of a disease, ultimately investigating the suitability of specific molecules as novel therapeutic targets. In the last decade, the launching of the precision medicine initiative has motivated the necessity to define innovative computational techniques that could be used for customizing therapies. In this context, the combination of mathematical models and computer strategies is an essential tool for biologists, which can analyze complex system pathways, as well as for the pharmaceutical industry, which is involved in promoting programs for drug discovery.
In this dissertation, we explore different modeling techniques that are used for the simulation and the analysis of complex biological systems. We analyze the state of the art for simulation algorithms both in the stochastic and in the deterministic frameworks. The same dichotomy has been studied in the context of sensitivity analysis, identifying the main pros and cons of the two approaches. Moreover, we studied the quantitative system pharmacology (QSP) modeling approach that elucidates the mechanism of action of a drug on the biological processes underlying a disease. Specifically, we present the definition, calibration and validation of a QSP model describing Gaucher disease type 1 (GD1), one of the most common lysosome storage rare disorders. All of these techniques are finally combined to define a novel computational pipeline for patient stratification. Our approach uses modeling techniques, such as model simulations, sensitivity analysis and QSP modeling, in combination with experimental data to identify the key mechanisms responsible for the stratification. The pipeline has been applied to three test cases in different biological contexts: a whole-body model of dyslipidemia, the QSP model of GD1 and a QSP model of cardiac electrophysiology. In these test cases, the pipeline proved to be accurate and robust, allowing the interpretation of the mechanistic differences underlying the phenotype classification.
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BARDINI, ROBERTA. "A diversity-aware computational framework for systems biology." Doctoral thesis, Politecnico di Torino, 2019. http://hdl.handle.net/11583/2752792.
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Cong, Yang, and 丛阳. "Optimization models and computational methods for systems biology." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B47752841.
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Systems biology is a comprehensive quantitative analysis of the manner in
which all the components of a biological system interact functionally along with
time. Mathematical modeling and computational methods are indispensable
in such kind of studies, especially for interpreting and predicting the complex
interactions among all the components so as to obtain some desirable system
properties. System dynamics, system robustness and control method are three
crucial properties in systems biology. In this thesis, the above properties are
studied in four different biological systems.
The outbreak and spread of infectious diseases have been questioned and
studied for years. The spread mechanism and prediction about the disease could
enable scientists to evaluate isolation plans to have significant effects on a particular
epidemic. A differential equation model is proposed to study the dynamics
of HIV spread in a network of prisons. In prisons, screening and quarantining
are both efficient control manners. An optimization model is proposed to study
optimal strategies for the control of HIV spread in a prison system.
A primordium (plural: primordia) is an organ or tissue in its earliest recognizable
stage of development. Primordial development in plants is critical to the
proper positioning and development of plant organs. An optimization model and
two control mechanisms are proposed to study the dynamics and robustness of primordial systems.
Probabilistic Boolean Networks (PBNs) are mathematical models for studying
the switching behavior in genetic regulatory networks. An algorithm is proposed
to identify singleton and small attractors in PBNs which correspond to
cell types and cell states. The captured problem is NP-hard in general. Our
algorithm is theoretically and computationally demonstrated to be much more
efficient than the naive algorithm that examines all the possible states.
The goal of studying the long-term behavior of a genetic regulatory network is
to study the control strategies such that the system can obtain desired properties.
A control method is proposed to study multiple external interventions meanwhile
minimizing the control cost.
Robustness is a paramount property for living organisms. The impact degree
is a measure of robustness of a metabolic system against the deletion of single
or multiple reaction(s). An algorithm is proposed to study the impact degree
in Escherichia coli metabolic system. Moreover, approximation method based
on Branching process is proposed for estimating the impact degree of metabolic
networks. The effectiveness of our method is assured by testing with real-world
Escherichia coli, Bacillus subtilis, Saccharomyces cerevisiae and Homo Sapiens
metabolic systems.<br>published_or_final_version<br>Mathematics<br>Doctoral<br>Doctor of Philosophy
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Ding, Jiarui. "Computational methods for systems biology data of cancer." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58164.
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High-throughput genome sequencing and other techniques provide a cost-effective way to study cancer biology and seek precision treatment options. In this dissertation I address three challenges in cancer systems biology research: 1) predicting somatic mutations, 2) interpreting mutation functions, and 3) stratifying patients into biologically meaningful groups. Somatic single nucleotide variants are frequent therapeutically actionable mutations in cancer, e.g., the ‘hotspot’ mutations in known cancer driver genes such as EGFR, KRAS, and BRAF. However, only a small proportion of cancer patients harbour these known driver mutations. Therefore, there is a great need to systematically profile a cancer genome to identify all the somatic single nucleotide variants. I develop methods to discover these somatic mutations from cancer genomic sequencing data, taking into account the noise in high-throughput sequencing data and valuable validated genuine somatic mutations and non-somatic mutations. Of the somatic alterations acquired for each cancer patient, only a few mutations ‘drive’ the initialization and progression of cancer. To better understand the evolution of cancer, as well as to apply precision treatments, we need to assess the functions of these mutations to pinpoint the driver mutations. I address this challenge by predicting the mutations correlated with gene expression dysregulation. The method is based on hierarchical Bayes modelling of the influence of mutations on gene expression, and can predict the mutations that impact gene expression in individual patients. Although probably no two cancer genomes share exactly the same set of somatic mutations because of the stochastic nature of acquired mutations across the three billion base pairs, some cancer patients share common driver mutations or disrupted pathways. These patients may have similar prognoses and potentially benefit from the same kind of treatment options. I develop an efficient clustering algorithm to cluster high-throughput and high-dimensional bio- logical datasets, with the potential to put cancer patients into biologically meaningful groups for treatment selection.<br>Science, Faculty of<br>Computer Science, Department of<br>Graduate
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Uys, Lafras. "Computational systems biology of sucrose accumulation in sugarcane." Thesis, Link to the online version, 2006. http://hdl.handle.net/10019/245.
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Camacho, Diogo Mayo. "In silico cell biology and biochemistry: a systems biology approach." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/27960.
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In the post-"omic" era the analysis of high-throughput data is regarded as one of the major challenges faced by researchers. One focus of this data analysis is uncovering biological network topologies and dynamics. It is believed that this kind of research will allow the development of new mathematical models of biological systems as well as aid in the improvement of already existing ones. The work that is presented in this dissertation addresses the problem of the analysis of highly complex data sets with the aim of developing a methodology that will enable the reconstruction of a biological network from time series data through an iterative process.
The first part of this dissertation relates to the analysis of existing methodologies that aim at inferring network structures from experimental data. This spans the use of statistical tools such as correlations analysis (presented in Chapter 2) to more complex mathematical frameworks (presented in Chapter 3). A novel methodology that focuses on the inference of biological networks from time series data by least squares fitting will then be introduced. Using a set of carefully designed inference rules one can gain important information about the system which can aid in the inference process. The application of the method to a data set from the response of the yeast Saccharomyces cerevisiae to cumene hydroperoxide is explored in Chapter 5. The results show that this method can be used to generate a coarse-level mathematical model of the biological system at hand. Possible developments of this method are discussed in Chapter 6.<br>Ph. D.
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Small, Benjamin Gavin. "The chemical and computational biology of inflammation." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/the-chemical-and-computational-biology-of-inflammation(4de5c19c-e377-4783-acfb-ad168ad35d46).html.
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Non-communicable diseases (NCD) such as cancer, heart disease and cerebrovascular injury are dependent on or aggravated by inflammation. Their prevention and treatment is arguably one of the greatest challenges to medicine in the 21st century. The pleiotropic, proinflammatory cytokine; interleukin-l beta (IL-l~) is a primary, causative messenger of inflammation. Lipopolysaccharide (LPS) induction ofIL-l~ expression via toll-like receptor 4 (TLR4) in myeloid cells is a robust experimental model of inflammation and is driven in large part via p38-MAPK and NF-KB signaling networks. The control of signaling networks involved in IL-l~ expression is distributed and highly complex, so to perturb intracellular networks effectively it is often necessary to modulate several steps simultaneously. However, the number of possible permutations for intervention leads to a combinatorial explosion in the experiments that would have to be performed in a complete analysis. We used a multi-objective evolutionary algorithm (EA) to optimise reagent combinations from a dynamic chemical library of 33 compounds with established or predicted targets in the regulatory network controlling IL-l ~ expression. The EA converged on excellent solutions within 11 generations during which we studied just 550 combinations out of the potential search space of - 9 billion. The top five reagents with the greatest contribution to combinatorial effects throughout the EA were then optimised pair- wise with respect to their concentrations, using an adaptive, dose matrix search protocol. A p38a MAPK inhibitor (30 ± 10% inhibition alone) with either an inhibitor of IKB kinase (12 ± 9 % inhibition alone) or a chelator of poorly liganded iron (19 ± 8 % inhibition alone) yielded synergistic inhibition (59 ± 5 % and 59 ± 4 % respectively, n=7, p≥O.04 for both combinations, tested by one way ANOVA with Tukey's multiple test correction) of macrophage IL-l~ expression. Utilising the above data, in conjunction with the literature, an LPS-directed transcriptional map of IL-l ~ expression was constructed. Transcription factors (TF) targeted by the signaling networks coalesce at precise nucleotide binding elements within the IL-l~ regulatory DNA. Constitutive binding of PU.l and C/EBr-~ TF's are obligate for IL-l~ expression. The findings in this thesis suggest that PU.l and C/EBP-~ TF's form scaffolds facilitating dynamic control exerted by other TF's, as exemplified by c-Jun. Similarly, evidence is emerging that epigenetic factors, such as the hetero-euchromatin balance, are also important in the relative transcriptional efficacy in different cell types. Evolutionary searches provide a powerful and general approach to the discovery of novel combinations of pharmacological agents with potentially greater therapeutic indices than those of single drugs. Similarly, construction of signaling network maps aid the elucidation of pharmacological mechanism and are mandatory precursors to the development of dynamic models. The symbiosis of both approaches has provided further insight into the mechanisms responsible for IL-lβ expression, and reported here provide a - platform for further developments in understanding NCD's dependent on or aggravated by inflammation.
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Castillo, Andrea R. (Andrea Redwing). "Assessing computational methods and science policy in systems biology." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/51655.
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Thesis (S.M. in Technology and Policy)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2009.<br>Includes bibliographical references (p. 109-112).<br>In this thesis, I discuss the development of systems biology and issues in the progression of this science discipline. Traditional molecular biology has been driven by reductionism with the belief that breaking down a biological system into the fundamental biomolecular components will elucidate such phenomena. We have reached limitations with this approach due to the complex and dynamical nature of life and our inability to intuit biological behavior from a modular perspective [37]. Mathematical modeling has been integral to current system biology endeavors since detailed analysis would be invasive if performed on humans experimentally or in clinical trials [17]. The interspecies commonalities in systemic properties and molecular mechanisms suggests that certain behaviors transcend specie differentiation and therefore easily lend to generalizing from simpler organisms to more complex organisms such as humans [7, 17]. Current methodologies in mathematical modeling and analysis have been diverse and numerous, with no standardization to progress the discipline in a collaborative manner. Without collaboration during this formative period, successful development and application of systems biology for societal welfare may be at risk. Furthermore, such collaboration has to be standardized in a fundamental approach to discover generic principles, in the manner of preceding long-standing science disciplines. This study effectively implements and analyzes a mathematical model of a three-protein biochemical network, the Synechococcus elongatus circadian clock.<br>(cont.) I use mass action theory expressed in kronecker products to exploit the ability to apply numerical methods-including sensitivity analysis via boundary value formulation (BVP) and trapiezoidal integration rule-and experimental techniques-including partial reaction fitting and enzyme-driven activations-when mathematically modeling large-scale biochemical networks. Amidst other applicable methodologies, my approach is grounded in the law of mass action because it is based in experimental data and biomolecular mechanistic properties, yet provides predictive power in the complete delineation of the biological system dynamics for all future time points. The results of my research demonstrate the holistic approach that mass action method-ologies have in determining emergent properties of biological systems. I further stress the necessity to enforce collaboration and standardization in future policymaking, with reconsiderations on current stakeholder incentive to redirect academia and industry focus from new molecular entities to interests in holistic understanding of the complexities and dynamics of life entities. Such redirection away from reductionism could further progress basic and applied scientific research to embetter our circumstances through new treatments and preventive measures for health, and development of new strains and disease control in agriculture and ecology [13].<br>by Andrea R. Castillo.<br>S.M.in Technology and Policy
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Haider, Syed Abbas. "Computational systems biology-based feature selection for cancer prognosis." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610378.
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Facchetti, Giuseppe. "Computational approaches to complex biological networks." Doctoral thesis, SISSA, 2013. http://hdl.handle.net/20.500.11767/4822.
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The need of understanding and modeling the biological networks is one of the raisons d'être and of the driving forces behind the emergence of Systems Biology. Because of its holistic approach and because of the widely different level of complexity of the networks, different mathematical methods have been developed during the years. Some of these computational methods are used in this thesis in order to investigate various properties of different biological systems. The first part deals with the prediction of the perturbation of cellular metabolism induced by drugs. Using Flux Balance Analysis to describe the reconstructed genome-wide metabolic networks, we consider the problem of identifying the most selective drug synergisms for given therapeutic targets. The second part of this thesis considers gene regulatory and large social networks as signed graphs (activation/deactivation or friendship/hostility are rephrased as positive/negative coupling between spins). Using the analogy with an Ising spin glass an analysis of the energy landscape and of the content of “disorder” 'is carried out. Finally, the last part concerns the study of the spatial heterogeneity of the signaling pathway of rod photoreceptors. The electrophysiological data produced by our collaborators in the Neurobiology laboratory have been analyzed with various dynamical systems giving an insight into the process of ageing of photoreceptors and into the role diffusion in the pathway.
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Karathia, Hiren Mahendrabhai. "Development and application of computational methdologies for Integrated Molecular Systems Biology." Doctoral thesis, Universitat de Lleida, 2012. http://hdl.handle.net/10803/110518.
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L'objectiu del treball presentat en aquesta tesi va ser el desenvolupament i l'aplicació de metodologies computacionals que integren l’anàlisis de informació sobre seqüències proteiques, informació funcional i genòmica per a la reconstrucció, anotació i organització de proteomes complets, de manera que els resultats es poden comparar entre qualsevol nombre d'organismes amb genomes completament seqüenciats.
Metodològicament, m'he centrat en la identificació de l'organització molecular dins d'un proteoma complet d'un organisme de referència i comparació amb proteomes d'altres organismes, en espacial, estructural i funcional, el teixit cel • lular de desenvolupament, o els nivells de la fisiologia. La metodologia es va aplicar per abordar la qüestió de la identificació de organismes model adequats per a estudiar diferents fenòmens biològics. Això es va fer mitjançant la comparació d’un conjunt de proteines involucrades en diferents fenòmens biològics en Saccharomyces cerevisiae i Homo sapiens amb els conjunts corresponents d'altres organismes amb genomes. La tesi conclou amb la presentació d'un servidor web, Homol-MetReS, en què s'implementa la metodologia. Homol-MetReS proporciona un entorn de codi obert a la comunitat científica en què es poden realitzar múltiples nivells de comparació i anàlisi de proteomes.<br>El objetivo del trabajo presentado en esta tesis fue el desarrollo y la aplicación de metodologías computacionales que integran el análisis de la secuencia y de la información funcional y genómica, con el objetivo de reconstruir, anotar y organizar proteomas completos, de tal manera que estos proteomas se puedan comparar entre cualquier número de organismos con genomas completamente secuenciados.
Metodológicamente, I centrado en la identificación de organización molecular dentro de un proteoma completo de un organismo de referencia, vinculando cada proteína en que proteoma a las proteínas de otros organismos, de tal manera que cualquiera puede comparar los dos proteomas en espacial, estructural, funcional tejido, celular, el desarrollo o los niveles de la fisiología. La metodología se aplicó para abordar la cuestión de la identificación de organismos modelo adecuados para estudiar diferentes fenómenos biológicos. Esto se hizo comparando conjuntos de proteínas involucradas en diferentes fenómenos biológicos en Saccharomyces cerevisiae y Homo sapiens con los conjuntos correspondientes de otros organismos con genomas completamente secuenciados. La tesis concluye con la presentación de un servidor web, Homol-MetReS, en el que se implementa la metodología. Homol-MetReS proporciona un entorno de código abierto a la comunidad científica en la que se pueden realizar múltiples niveles de comparación y análisis de proteomas.<br>The aim of the work presented in this thesis was the development and application of computational methodologies that integrate sequence, functional, and genomic information to provide tools for the reconstruction, annotation and organization of complete proteomes in such a way that the results can be compared between any number of organisms with fully sequenced genomes. Methodologically, I focused on identifying molecular organization within a complete proteome of a reference organism and comparing with proteomes of other organisms at spatial, structural, functional, cellular tissue, development or physiology levels. The methodology was applied to address the issue of identifying appropriate model organisms to study different biological phenomena. This was done by comparing the protein sets involved in different biological phenomena in Saccharomyces cerevisiae and Homo sapiens. This thesis concludes by presenting a web server, Homol-MetReS, on which the methodology is implemented. It provides an open source environment to the scientific community on which they can perform multi-level comparison and analysis of proteomes.
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Fu, Yan. "Computational Systems Biology Analysis of Cell Reprogramming and Activation Dynamics." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/28414.
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In the past two decades, molecular cell biology has transitioned from a traditional descriptive science into a quantitative science that systematically measures cellular dynamics on different levels of genome, transcriptome and proteome. Along with this transition emerges the interdisciplinary field of systems biology, which aims to unravel complex interactions in biological systems through integrating experimental data into qualitative or quantitative models and computer simulations. In this dissertation, we applied various systems biology tools to investigate two important problems with respect to cellular activation dynamics and reprograming.
Specifically, in the first section of the dissertation, we focused on lipopolysaccharide (LPS)-mediated priming and tolerance: a reprogramming in cytokine production in macrophages pretreated with specific doses of LPS. Though both priming and tolerance are important in the immune systemâ s response to pathogens, the molecular mechanisms still remain unclear. We computationally investigated all network topologies and dynamics that are able to generate priming or tolerance in a generic three-node model. Accordingly, we found three basic priming mechanisms and one tolerance mechanism. Existing experimental evidence support these in silico found mechanisms.
In the second part of the dissertation, we applied stochastic modeling and simulations to investigate the phenotypic transition of bacteria E.coli between normally-growing cells and persister cells (growth-arrested phenotype), and how this process can contribute to drug resistance. We built up a complex computational model capturing the molecular mechanism on both single cell level and population level. The paper also proposed a novel way to accelerate the phenotypic transition from persister cells to normally growing cell under resonance activation. The general picture of phenotypic transitions should be applicable to a broader context of biological systems, such as T cell differentiation and stem cell reprogramming.<br>Ph. D.
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Yang, Pengyi. "Ensemble methods and hybrid algorithms for computational and systems biology." Thesis, The University of Sydney, 2012. https://hdl.handle.net/2123/28979.
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Modern molecular biology increasingly relies on the application of high-throughput technologies for studying the function, interaction, and integration of genes, proteins, and a variety of other molecules on a large scale. The application of those high throughput technologies has led to the exponential growth of biological data, making modern molecular biology a data-intensive science. Huge effort has been directed to the development of robust and efficient computational algorithms in order to make sense of these extremely large and complex biological data, giving rise to several interdisciplinary fields, such as computational and systems biology.
Machine learning and data mining are disciplines dealing with knowledge discovery from large data, and their application to computational and systems biology has been extremely fruitful. However, the ever-increasing size and complexity of the biological data require novel computational solutions to be developed. This thesis attempts to contribute to these inter-disciplinary fields by deve10ping and applying different ensemble learning methods and hybrid algorithms for solving a variety of problems in computational and systems biology. Through the study of different types of data generated from a variety of biological systems using different high-throughput approaches, we demonstrate that ensemble learning methods and hybrid algorithms are general, flexible, and highly effective tools for computational and systems biology.
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Weis, Michael Christian. "Computational Models of the Mammalian Cell Cycle." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1323278159.
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Jiang, Hao, and 姜昊. "Construction and computation methods for biological networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hub.hku.hk/bib/B50662144.
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Biological systems are complex in that they comprise large number of interacting entities, and their dynamics follow mechanic regulations for movement and biological function organization. Established computational modeling deals with studying and manipulating biologically relevant systems as a powerful approach. Inner structure and behavior of complex biological systems can be analyzed and understood by computable biological networks. In this thesis, models and computation methods are proposed for biological networks.
The study of Genetic Regulatory Networks (GRNs) is an important research topic in genomic research. Several promising techniques have been proposed for capturing the behavior of gene regulations in biological systems. One of the promising models for GRNs, Boolean Network (BN) has gained a lot of attention. However, little light has been shed on the analysis of internal connection between the dynamics of biological molecules and network systems. Inference and completion problems of a BN from a given set of singleton attractors are considered to be important in understanding the relationship between dynamics of biological molecules and network systems. Discrete dynamic systems model has been recently proposed to model time-course microarray measurements of genes, but delay effect may be modeled as a realistic factor in studying GRNs. A delay discrete dynamic systems model is developed to model GRNs.
Inference and analysis of networks is one of the grand challenges in modern statistical biology. Machine learning method, in particular, Support Vector Machine (SVM), has been successfully applied in predictions of internal connections embedded in networks. Kernels in conjunction with SVM demonstrate strong ability in performing various tasks such as biomedical diagnosis, function prediction and motif extractions. In biomedical diagnosis, data sets are always high dimensional which provide a challenging research problem in machine learning area. Novel kernels using distance-metric that are not common in machine learning framework are proposed for possible tumor differentiation discrimination problem.
Protein function prediction problem is a hot topic in bioinformatics. The K-spectrum Kernel is among the top popular models in description of protein sequences. Taking into consideration of positive-semi-definiteness in kernel construction, Eigen-matrix translation technique is introduced in novel kernel formulation to give better prediction result. In a further step, power of Eigen-matrix translation technique in feature selection is demonstrated through mathematical formulation. Due to structure complexity of carbohydrates, the study of carbohydrate sugar chains has lagged behind compared to that of DNA and proteins. A weighted q-gram kernel is constructed in classifying glycan structures with limitations in feature extractions. A biochemically-weighted tree kernel is then proposed to enhance the ability in both classification as well as motif extractions.
Finally the problem of metabolite biomarker discovery is researched. Human diseases, in particular metabolic diseases, can be directly caused by the lack of essential metabolites. Identification of metabolite biomarkers has significant importance in the study of biochemical reaction and signaling networks. A promising computational approach is proposed to identify metabolic biomarkers through integrating biomedical data and disease-specific gene expression data.<br>published_or_final_version<br>Mathematics<br>Doctoral<br>Doctor of Philosophy
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Vyshemirsky, Vladislav. "Probabilistic reasoning and inference for systems biology." Thesis, Connect to e-thesis. Move to record for print version, 2007. http://theses.gla.ac.uk/47/.
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Thesis (Ph.D.) - University of Glasgow, 2007.<br>Ph.D. thesis submitted to the Information and Mathematical Sciences Faculty, Department of Computing Science, University of Glasgow, 2007. Includes bibliographical references. Print version also available.
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Ghaffarizadeh, Ahmadreza. "COMPUTATIONAL MODELS OF INTRACELLULAR AND INTERCELLULAR PROCESSES IN DEVELOPMENTAL BIOLOGY." DigitalCommons@USU, 2014. https://digitalcommons.usu.edu/etd/3103.
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Systems biology takes a holistic approach to biological questions as it applies mathematical
modeling to link and understand the interaction of components in complex biological
systems. Multiscale modeling is the only method that can fully accomplish this aim. Mutliscale models consider processes at different levels that are coupled within the modeling framework. A first requirement in creating such models is a clear understanding of processes that operate at each level. This research focuses on modeling aspects of biological development as a complex process that occurs at many scales. Two of these scales were considered in this work: cellular differentiation, the process of in which less specialized cells acquired specialized properties of mature cell types, and morphogenesis, the process in which an organism develops its shape and tissue architecture. In development, cellular differentiation typically is required for morphogenesis. Therefore, cellular differentiation is at a lower scale than morphogenesis in the overall process of development. In this work, cellular differentiation and morphogenesis were modeled in a variety of biological contexts, with the ultimate goal of linking these different scales of developmental events into a unified model of development.
Three aspects of cellular differentiation were investigated, all united by the theme of how the dynamics of gene regulatory networks (GRNs) control differentiation. Two of the projects of this dissertation studied the effect of noise and robustness in switching between cell types during differentiation, and a third deals with the evaluation of hypothetical GRNs that allow the differentiation of specific cell types. All these projects view cell types as highdimensional attractors in the GRNs and use random Boolean networks as the modeling framework for studying network dynamics.
Morphogenesis was studied using the emergence of three-dimensional structures in biofilms as a relatively simple model. Many strains of bacteria form complex structures during growth as colonies on a solid medium. The morphogenesis of these structures was modeled using an agent-based framework and the outcomes were validated using structures of biofilm colonies reported in the literature.
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Croft, Larry. "Design of information systems in computational genomics /." [St. Lucia, Qld.], 2002. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe17545.pdf.
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Olivier, Brett Gareth. "Simulation and database software for computational systems biology : PySCes and JWS Online." Thesis, Stellenbosch : Stellenbosch University, 2005. http://hdl.handle.net/10019.1/50449.
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Thesis (PhD)--Stellenbosch University, 2005.<br>ENGLISH ABSTRACT: Since their inception, biology and biochemistry have been spectacularly successful in
characterising the living cell and its components. As the volume of information about
cellular components continues to increase, we need to ask how we should use this information
to understand the functioning of the living cell?
Computational systems biology uses an integrative approach that combines theoretical
exploration, computer modelling and experimental research to answer this question.
Central to this approach is the development of computational models, new modelling
strategies and computational tools. Against this background, this study aims to: (i) develop
a new modelling package: PySCeS, (ii) use PySCeS to study discontinuous behaviour
in a metabolic pathway in a way that was very difficult, if not impossible, with
existing software, (iii) develop an interactive, web-based repository (JWS Online) of cellular
system models.
Three principles that, in our opinion, should form the basis of any new modelling
software were laid down: accessibility (there should be as few barriers as possible to
PySCeS use and distribution), flexibility (pySCeS should be extendable by the user, not
only the developers) and usability (PySCeS should provide the tools we needed for our
research). After evaluating various alternatives we decided to base PySCeS on the freely
available programming language, Python, which, in combination with the large collection
of science and engineering algorithms in the SciPy libraries, would give us a powerful
modern, interactive development environment.<br>AFRIKAANSE OPSOMMING: Sedert hul totstandkoming was biologie en, meer spesifiek, biochemie uiters suksesvol
in die karakterisering van die lewende sel se komponente. Steeds groei die hoeveelheid
informasie oor die molekulêre bestanddele van die sel daagliks; ons moet onself dus afvra
hoe ons hierdie informasie kan integreer tot 'n verstaanbare beskrywing van die lewende
sel se werking.
Om dié vraag te beantwoord gebruik rekenaarmatige sisteembiologie 'n geïntegreerde
benadering wat teorie, rekenaarmatige modellering en eksperimenteeIe navorsing kombineer.
Sentraal tot die benadering is die ontwikkeling van nuwe modelle, strategieë vir
modellering, en sagteware. Teen hierdie agtergrond is die hoofdoelstelling van hierdie
projek: (i) die ontwikkeling van 'n nuwe modelleringspakket, PySCeS (ii) die benutting
van PySCeS om diskontinue gedrag in n metaboliese sisteem te bestudeer (iets wat
met die huidiglik beskikbare sagteware redelik moeilik is), (en iii) die ontwikkeling vann
interaktiewe, internet-gebaseerde databasis van sellulêre sisteem modelle, JWS Online.
Ons is van mening dat nuwe sagteware op drie belangrike beginsels gebaseer behoort
te wees: toeganklikheid (die sagteware moet maklik bekombaar en bruikbaar wees),
buigsaamheid (die gebruiker moet self PySCeS kan verander en ontwikkel) en bruikbaarheid
(al die funksionalitiet wat ons vir ons navorsing nodig moet in PySCeS ingebou
wees). Ons het verskeie opsies oorweeg en besluit om die vrylik verkrygbare programmeringstaal,
Python, in samehang die groot kolleksie wetenskaplike algoritmes, SciPy, te
gebruik. Hierdie kombinasie verskaf n kragtige, interaktiewe ontwikkelings- en gebruikersomgewing. PySCeS is ontwikkel om onder beide die Windows en Linux bedryfstelsels te werk
en, meer spesifiek, om gebruik te maak van 'n 'command line interface'. Dit beteken dat
PySCeS op enige interaktiewe rekenaar-terminaal Python ondersteun sal werk. Hierdie
eienskap maak ook moontlik die gebruik van PySCeS as 'n modelleringskomponent in
'n groter sagteware pakket onder enige bedryfstelsel wat Python ondersteun. PySCeS is
op 'n modulere ontwerp gebaseer, wat dit moontlik vir die eindgebruiker maak om die
sagteware se bronkode verder te ontwikkel.
As 'n toepassing is PySCeS gebruik om die oorsaak van histeretiese gedrag van 'n
lineêre, eindproduk-geïnhibeerde metaboliese pad te ondersoek. Ons het hierdie interessante
gedrag in 'n vorige studie ontdek, maar kon nie, met die sagteware wat op daardie
tydstip tot ons beskikking was, hierdie studie voortsit nie. Met PySCeS se ingeboude
vermoë om parameter kontinuering te doen, kon ons die oorsake van hierdie diskontinuë
gedrag volledig karakteriseer. Verder het ons 'n nuwe metode ontwikkel om hierdie
gedrag te visualiseer as 'n interaksie tussen die volledige sisteem se subkomponente.
Tydens PySCeS se ontwikkeling het ons opgemerk dat dit baie moeilik was om
metaboliese modelle wat in die literature gepubliseer is te herbou en te bestudeer. Hierdie
situasie is grotendeels die gevolg van die feit dat nêrens 'n sentrale databasis vir
metaboliese modelle bestaan nie (soos dit wel bestaan vir genomiese data of proteïen
strukture). Die JWS Online databasis is spesifiek ontwikkel om hierdie leemte te vul.
JWS Online maak dit vir die gebruiker moontlik om, via die internet en sonder die
installasie van enige gespesialiseerde modellerings sagteware, gepubliseerde modelle te
bestudeer en ook af te laai vir gebruik met ander modelleringspakkette soos bv. PySCeS.
JWS Online het alreeds 'n onmisbare hulpbron vir sisteembiologiese navorsing en onderwys
geword.
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Misselbeck, Karla. "Computational Systems Biology Applied To Human Metabolism. Mathematical Modelling and Network Analysis." Doctoral thesis, Università degli studi di Trento, 2019. https://hdl.handle.net/11572/369023.
Full textAbstract:
Human metabolism, an essential and highly organized process, which is required to run and maintain cellular processes and to respond to shifts in external and internal conditions, can be described as a complex and interconnected network of metabolic pathways.
Computational systems biology provides a suitable framework to study the mechanisms and interactions of this network and to address questions that are difficult to reproduce in vitro or in vivo.
This dissertation contributes to the development of computational strategies which help to investigate aspects of human metabolism and metabolic-related disorders.
In the first part, we introduce mathematical models of folate-mediated one-carbon metabolism in the cytoplasm and subsequently in the nucleus.
A hybrid-stochastic framework is applied to investigate the behavior and stability of the complete metabolic network in response to genetic and nutritional factors.
We analyse the effect of a common polymorphism of MTHFR, B12 and folate deficiency, as well as the role of the 5-formyltetrahydrofolate futile cycle on network dynamics.
Furthermore, we study the impact of multienzyme complex formation and substrate channelling, which are key aspects related to nuclear folate-mediated one-carbon metabolism.
Model simulations of the nuclear model highlight the importance of these two factors for normal functioning of the network and further identify folate status and enzyme levels as important influence factors for network dynamics.
In the second part, we focus on metabolic syndrome, a highly prevalent cluster of metabolic disorders.
We develop a computational workflow based on network analysis to characterise underlying molecular mechanisms of the disorder and to explore possible novel therapeutic strategies by means of drug repurposing.
To this end, genetic data, text mining results, drug expression profiles and drug target information are integrated in the setting of tissue-specific background networks and a proximity score based on topological distance and functional similarity measurements is defined to identify potential new therapeutic applications of already approved drugs. A filtering and prioritization analysis allow us to identify ibrutinib, an inhibitor of bruton tyrosine kinase, as the most promising repurposing candidate.
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Misselbeck, Karla. "Computational Systems Biology Applied To Human Metabolism. Mathematical Modelling and Network Analysis." Doctoral thesis, University of Trento, 2019. http://eprints-phd.biblio.unitn.it/3546/1/Thesis_Misselbeck_20190314.pdf.
Full textAbstract:
Human metabolism, an essential and highly organized process, which is required to run and maintain cellular processes and to respond to shifts in external and internal conditions, can be described as a complex and interconnected network of metabolic pathways.
Computational systems biology provides a suitable framework to study the mechanisms and interactions of this network and to address questions that are difficult to reproduce in vitro or in vivo.
This dissertation contributes to the development of computational strategies which help to investigate aspects of human metabolism and metabolic-related disorders.
In the first part, we introduce mathematical models of folate-mediated one-carbon metabolism in the cytoplasm and subsequently in the nucleus.
A hybrid-stochastic framework is applied to investigate the behavior and stability of the complete metabolic network in response to genetic and nutritional factors.
We analyse the effect of a common polymorphism of MTHFR, B12 and folate deficiency, as well as the role of the 5-formyltetrahydrofolate futile cycle on network dynamics.
Furthermore, we study the impact of multienzyme complex formation and substrate channelling, which are key aspects related to nuclear folate-mediated one-carbon metabolism.
Model simulations of the nuclear model highlight the importance of these two factors for normal functioning of the network and further identify folate status and enzyme levels as important influence factors for network dynamics.
In the second part, we focus on metabolic syndrome, a highly prevalent cluster of metabolic disorders.
We develop a computational workflow based on network analysis to characterise underlying molecular mechanisms of the disorder and to explore possible novel therapeutic strategies by means of drug repurposing.
To this end, genetic data, text mining results, drug expression profiles and drug target information are integrated in the setting of tissue-specific background networks and a proximity score based on topological distance and functional similarity measurements is defined to identify potential new therapeutic applications of already approved drugs. A filtering and prioritization analysis allow us to identify ibrutinib, an inhibitor of bruton tyrosine kinase, as the most promising repurposing candidate.
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Negron, Christopher. "Computational design of orthogonal antiparallel homodimeric coiled coils." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/93805.
Full textAbstract:
Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references.<br>Living cells integrate a vast array of protein-protein interactions (PPIs) to govern cellular functions. For instance, PPIs are critical to biosynthesis, nanostructural assembly, and in processing environmental stimuli through cell-signaling pathways. As fields such as synthetic biology and protein engineering mature they seek to mimic and expand the functions found in living systems that integrate PPIs. A critical feature to many PPIs that are integrated together to perform a complex function is orthogonality, i.e. PPIs that do not cross interact with each other. The engineering of orthogonal PPIs is thus an alluring problem. Since it not only tests our understanding of molecular specificity by having to stabilize and destabilize interactions simultaneously. The results of the design process can also have interesting applications in synthetic biology or bionanotechnology. The coiled coil, a rope-like structure made of helices, is a PPI ubiquitously found in biological systems and is an attractive fold for engineering orthogonal PPIs. Though the coiled coil is well studied, destabilization of undesired interactions still remains challenging. In this thesis I will discuss strategies for obtaining orthogonal PPIs, and describe the current sequence-to-structure relationships known about coiled coils. I will then introduce the computational multistate design framework, CLASSY, and explain how I applied it to the computational design of six orthogonal antiparallel homodimeric coiled coils. Five of these designed sequences were experimentally tested, of which only three of the sequences adopted the target antiparallel homodimer topology. All three of these sequences, as well as a previously designed antiparallel homodimer, were tested for cross reactivity in a pairwise manner. None of these sequences appeared to cross react. The sequences that failed to adopt the antiparallel topology highlight the need for improving our computational design framework. In the final chapter I will discuss strategies to improve our models, and applications for orthogonal antiparallel coiled coils.<br>by Christopher Negron.<br>Ph. D.
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Avva, Jayant. "Complex Systems Biology of Mammalian Cell Cycle Signaling in Cancer." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1295625781.
Full text
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Zwolak, Jason Walter. "Computational Tools for Molecular Networks in Biological Systems." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/30274.
Full textAbstract:
Theoretical molecular biologists try to understand the workings of
cells through mathematics. Some theoreticians use systems of ordinary
differential equations (ODEs) as the basis for mathematical modelling
of molecular networks. This thesis develops algorithms for
estimating molecular reaction rate constants within those mathematical
models by fitting the models to experimental data. An additional step is
taken to fit non-timecourse experimental data (e.g., transformations
must be performed on the ODE solutions before the experimental and
simulation data are similar, and therefore, comparable). VTDIRECT is
used to perform (a deterministic direct search) global estimation and
ODRPACK is used to perform (a trust region Levenberg-Marquardt based)
local estimation of rate constants. One such transformation performed
on the ODE solutions determines the value of the steady state of the ODE
solutions. A new algorithm was developed that finds all steady state solutions
of the ODE system given that the system has a special structure (e.g., the
right hand sides of the ODEs are rational functions). Also, since
the rate constants in the models cannot be negative and may have other
restrictions on the values, ODRPACK was modified to address this problem
of bound constraints. The new Fortran 95 version of ODRPACK is
named ODRPACK95.<br>Ph. D.
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Poirel, Christopher L. "Bridging Methodological Gaps in Network-Based Systems Biology." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/23899.
Full textAbstract:
Functioning of the living cell is controlled by a complex network of interactions among genes, proteins, and other molecules. A major goal of systems biology is to understand and explain the mechanisms by which these interactions govern the cell's response to various conditions. Molecular interaction networks have proven to be a powerful representation for studying cellular behavior. Numerous algorithms have been developed to unravel the complexity of these networks. Our work addresses the drawbacks of existing techniques. This thesis includes three related research efforts that introduce network-based approaches to bridge current methodological gaps in systems biology.
i. Functional enrichment methods provide a summary of biological functions that are overrepresented in an interesting collection of genes (e.g., highly differentially expressed genes between a diseased cell and a healthy cell). Standard functional enrichment algorithms ignore the known interactions among proteins. We propose a novel network-based approach to functional enrichment that explicitly accounts for these underlying molecular interactions. Through this work, we close the gap between set-based functional enrichment and topological analysis of molecular interaction networks.
ii. Many techniques have been developed to compute the response network of a cell. A recent trend in this area is to compute response networks of small size, with the rationale that only part of a pathway is often changed by disease and that interpreting small subnetworks is easier than interpreting larger ones. However, these methods may not uncover the spectrum of pathways perturbed in a particular experiment or disease. To avoid these difficulties, we propose to use algorithms that reconcile case-control DNA microarray data with a molecular interaction network by modifying per-gene differential expression p-values such that two genes connected by an interaction show similar changes in their gene expression values.
iii. Top-down analyses in systems biology can automatically find correlations among genes and proteins in large-scale datasets. However, it is often difficult to design experiments from these results. In contrast, bottom-up approaches painstakingly craft detailed models of cellular processes. However, developing the models is a manual process that can take many years. These approaches have largely been developed independently. We present Linker, an efficient and automated data-driven method that analyzes molecular interactomes. Linker combines teleporting random walks and k-shortest path computations to discover connections from a set of source proteins to a set of target proteins. We demonstrate the efficacy of Linker through two applications: proposing extensions to an existing model of cell cycle regulation in budding yeast and automated reconstruction of human signaling pathways. Linker achieves superior precision and recall compared to state-of-the-art algorithms from the literature.<br>Ph. D.
28
Smillie, Christopher Scott. "Computational insights into the ecology of the human microbiota." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/103273.
Full textAbstract:
Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, February 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 99-110).<br>The vast community of microbes that inhabit the human body, the human microbiota, is important to human health and disease. These microbes contribute to human metabolism, the development of the immune system and pathogen resistance, while imbalances among them have been associated with several diseases. In this work, I develop computational methods to gain key insights into the ecological principles that shape these communities. In the first chapter, I develop an evolutionary rate heuristic that leads to the discovery of a massive network of recently exchanged genes, connecting diverse bacteria throughout the human microbiota. Using this network, I examine the roles of phylogenetic distance, geographic proximity and ecological overlap in shaping rates of horizontal gene transfer. Of these factors, ecological similarity is the principal force shaping gene exchange. In the second chapter, I focus on the microbial communities within a person, identifying the factors that affect the stability of the human microbiota. Alpha-diversity is strongly correlated with stability, but the direction of this correlation changes depending on the body site or subject being examined. In contrast, beta-diversity is consistently negatively correlated to stability. I show that a simple equilibrium model explains these results and accurately predicts the correlation between diversity and stability in every body site, thus reconciling these seemingly contradictory relationships into a single model. In the final chapter, I explore the use of fecal microbiota transplantation (FMT) to treat recurrent Clostridium difficile infection. I develop a new method to infer the genotypes and frequencies of bacterial strains in metagenomics samples. I apply this method to a dataset covering twenty patients before and after FMT, uncovering key ecological rules that govern the colonization and growth of bacteria in human subjects after FMT.<br>by Christopher Scott Smillie.<br>Ph. D.
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Rodrigo, Tarrega Guillermo. "Computational design and designability of gene regulatory networks." Doctoral thesis, Universitat Politècnica de València, 2011. http://hdl.handle.net/10251/14179.
Full textAbstract:
Nuestro conocimiento de las interacciones moleculares nos ha conducido hoy hacia una perspectiva ingenieril, donde diseños e implementaciones de sistemas artificiales de regulación intentan proporcionar instrucciones fundamentales para la reprogramación celular. Nosotros aquí abordamos el diseño de redes de genes como una forma de profundizar en la comprensión de las regulaciones naturales. También abordamos el problema de la diseñabilidad dada una genoteca de elementos compatibles. Con este fin, aplicamos métodos heuríticos de optimización que implementan rutinas para resolver problemas inversos, así como herramientas de análisis matemático para estudiar la dinámica de la expresión genética. Debido a que la ingeniería de redes de transcripción se ha basado principalmente en el ensamblaje de unos pocos elementos regulatorios usando principios de diseño racional, desarrollamos un marco de diseño computacional para explotar este enfoque. Modelos asociados a genotecas fueron examinados para descubrir el espacio genotípico asociado a un cierto fenotipo. Además, desarrollamos un procedimiento completamente automatizado para diseñar moleculas de ARN no codificante con capacidad regulatoria, basándonos en un modelo fisicoquímico y aprovechando la regulación alostérica. Los circuitos de ARN resultantes implementaban un mecanismo de control post-transcripcional para la expresión de proteínas que podía ser combinado con elementos transcripcionales. También aplicamos los métodos heurísticos para analizar la diseñabilidad de rutas metabólicas. Ciertamente, los métodos de diseño computacional pueden al mismo tiempo aprender de los mecanismos naturales con el fin de explotar sus principios fundamentales. Así, los estudios de estos sistemas nos permiten profundizar en la ingeniería genética. De relevancia, el control integral y las regulaciones incoherentes son estrategias generales que los organismos emplean y que aquí analizamos.<br>Rodrigo Tarrega, G. (2011). Computational design and designability of gene regulatory networks [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/14179<br>Palancia
30
Gu, Xu. "Systems biology approaches to the computational modelling of trypanothione metabolism in Trypanosoma brucei." Thesis, University of Glasgow, 2010. http://theses.gla.ac.uk/1618/.
Full textAbstract:
This work presents an advanced modelling procedure, which applies both structural modelling and kinetic modelling approaches to the trypanothione metabolic network in the bloodstream form of Trypanosoma brucei, the parasite responsible for African Sleeping sickness. Trypanothione has previously been identified as an essential compound for parasitic protozoa, however the underlying metabolic processes are poorly understood. Structural modelling allows the study of the network metabolism in the absence of sufficient quantitative information of target enzymes. Using this approach we examine the essential features associated with the control and regulation of intracellular trypanothione level. The first detailed kinetic model of the trypanothione metabolic network is developed, based on a critical review of the relevant scientific papers. Kinetic modelling of the network focuses on understanding the effect of anti-trypanosomal drug DFMO and examining other enzymes as potential targets for anti-trypanosomal chemotherapy. We also consider the inverse problem of parameter estimation when the system is defined with non-linear differential equations. The performance of a recently developed population-based PSwarm algorithm that has not yet been widely applied to biological problems is investigated and the problem of parameter estimation under conditions such as experimental noise and lack of information content is illustrated using the ERK signalling pathway. We propose a novel multi-objective optimization algorithm (MoPSwarm) for the validation of perturbation-based models of biological systems, and perform a comparative study to determine the factors crucial to the performance of the algorithm. By simultaneously taking several, possibly conflicting aspects into account, the problem of parameter estimation arising from non-informative experimental measurements can be successfully overcome. The reliability and efficiency of MoPSwarm is also tested using the ERK signalling pathway and demonstrated in model validation of the polyamine biosynthetic pathway of the trypanothione network. It is frequently a problem that models of biological systems are based on a relatively small amount of experimental information and that extensive in vivo observations are rarely available. To address this problem, we propose a new and generic methodological framework guided by the principles of Systems Biology. The proposed methodology integrates concepts from mathematical modelling and system identification to enable physical insights about the system to be accounted for in the modelling procedure. The framework takes advantage of module-based representation and employs PSwarm and our proposed multi-objective optimization algorithm as the core of this framework. The methodological framework is employed in the study of the trypanothione metabolic network, specifically, the validation of the model of the polyamine biosynthetic pathway. Good agreements with several existing data sets are obtained and new predictions about enzyme kinetics and regulatory mechanisms are generated, which could be tested by in vivo approaches.
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Dominy, James Gilmour. "PySUNDIALS : Providing python bindings to a robust suite of mathematical tools for computational systems biology." Thesis, Stellenbosch : University of Stellenbosch, 2009. http://hdl.handle.net/10019.1/2029.
Full textAbstract:
Thesis (MSc (Biochemistry))--University of Stellenbosch, 2009.<br>A Python package called PySUNDIALS has been developed which provides
an interface to the suite of nonlinear di erential/algebraic equation solvers
(SUNDIALS) using ctypes as a foreign function interface (FFI). SUNDIALS is
a C implementation of a set of modern algorithms for integrating and solving
various forms of the initial value problem (IVP). Additionally, arbitrary root
nding capabilities, time dependent sensitivity analysis, and the solution of
di erential and algebraic systems are available in the various modules provided
by SUNDIALS. A signi cant focus of the project was to ensure the python
package conforms to Python language standards and syntactic expectations.
Multiple examples of the SUNDIALS modules (CVODE, CVODES, IDA and
KINSOL) are presented comparing PySUNDIALS to C SUNDIALS (for veri cation
of correctness), and comparing PySUNDIALS to various other comparable
software packages. The examples presented also provide benchmark comparisons
for speed, and code length. Speci c uses of the features of the SUNDIALS
package are illustrated, including the modelling of discontinuous events using
root nding, time dependent sensitivity analysis of oscillatory systems, and
the modelling of equilibrium blocks using a complete set of implicit di erential
and algebraic equations.
PySUNDIALS is available as open source software for download. It is being
integrated into the systems biology software PySCeS as an optional solver set,
on an ongoing basis. A brief discussion of potential methods of optimization
and the continuation of the project to wrap the parallel processing modules
of SUNDIALS is presented.
32
Lee, Yun. "Computational modeling reveals new control mechanisms for lignin biosynthesis." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45774.
Full textAbstract:
Lignin polymers provide natural rigidity to plant cell walls by forming complex molecular networks with polysaccharides such as cellulose and hemicellulose. This evolved strategy equips plants with recalcitrance to biological and chemical degradation. While naturally beneficial, recalcitrance complicates the use of inedible plant materials as feedstocks for biofuel production. Genetically modifying lignin biosynthesis is an effective way to generate varieties of bioenergy crops with reduced recalcitrance, but certain lignin-modified plants display undesirable phenotypes and/or unexplained effects on lignin composition, suggesting that the process and regulation of lignin biosynthesis is not fully understood. Given the intrinsic complexities of metabolic pathways in plants and the technical hurdles in understanding them purely with experimental methods, the objective of this dissertation is to develop novel computational tools combining static, constraint-based, and dynamic, kinetics-based modeling approaches for a systematic analysis of lignin biosynthesis in wild-type and genetically engineered plants. Pathway models are constructed and analyzed, yielding insights that are difficult to obtain with traditional molecular and biochemical approaches and allowing the formulation of new, testable hypotheses with respect to pathway regulation. These model-based insights, once they are verified experimentally, will form a solid foundation for the rational design of genetic modification strategies towards the generation of lignin-modified crops with reduced recalcitrance. More generically, the methods developed in this dissertation are likely to have wide applicability in similar studies of complex, ill-characterized pathways where regulation occurring at the metabolic level is not entirely known.
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Octavio, Leah M. (Leah Mae Manalo). "Molecular systems analysis of a cis-encoded epigenetic switch." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/68433.
Full textAbstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2011.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references.<br>An ability to control the degree of heterogeneity in cellular phenotypes may be important for cell populations to survive uncertain and ever-changing environments or make cell-fate decisions in response to external stimuli. Cells may control the degree of gene expression heterogeneity and ultimately levels of phenotypic heterogeneity by modulating promoter switching dynamics. In this thesis, I investigated various mechanisms by which heterogeneity in the expression of FLO 11 in S. cerevisiae could be generated and controlled. First, we show that two copies of the FLOJ1 locus in S. cerevisiae switch between a silenced and competent promoter state in a random and independent fashion, implying that the molecular event leading to the transition occurs in cis. Through further quantification of the effect of trans regulators on both the slow epigenetic transitions between a silenced and competent promoter state and the fast promoter transitions associated with conventional regulation of FLO11, we found different classes of regulators affect epigenetic, conventional, or both forms of regulation. Distributing kinetic control of epigenetic silencing and conventional gene activation offers cells flexibility in shaping the distribution of gene expression and phenotype within a population. Next, we demonstrate how multiple molecular events occurring at a gene's promoter could lead to an overall slow step in cis. At the FLO] 1 promoter, we show that at least two pathways that recruit histone deacetylases to the promoter and in vivo association between the region -1.2 kb from the ATG start site of the FLO11 ORF and the core promoter region are all required for a stable silenced state. To generate bimodal gene expression, the activator Msnlp forms an alternate looped conformation, where the core promoter associates with the non-coding RNA PWR1's promoter and terminator regions, located at -2.1 kb and -3.0 kb from the ATG start site of the FLO]1 ORF respectively. Formation of the active looped conformation is required for Msnlp's ability to stabilize the competent state without destabilizing the silenced state and generate a bimodal response. Our results support a model where multiple stochastic steps at the promoter are required to transition between the silenced and active states, leading to an overall slow step in cis. Finally, preliminary investigations of heterozygous diploids revealed possible transvection occurring at FLO] 1, where a silenced allele of FLO 11 appeared to transfer silencing factors to a desilenced FLO11 allele on the homologous chromosome. These observations suggest a new mechanism through which heterogeneity in FL011 expression could be further controlled, in addition to the molecular events at the FL011 promoter we elucidated previously.<br>by Leah M. Octavio.<br>Ph.D.
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Cleary, Brian(Brian Lowman). "Leveraging latent patterns in the study of living systems." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122720.
Full textAbstract:
Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2019<br>Cataloged from PDF version of thesis. "June 2019."<br>Includes bibliographical references.<br>The development of high-throughput techniques to observe and perturb biological systems has led to remarkable progress in the last several decades. From the tremendous amounts of data being accumulated, new opportunities have emerged, including the possibility of finding latent patterns in high-dimensional variables that are reflective of underlying biological processes. While these methods have led to countless discoveries and innovations, it is clear there is much more we could learn by measuring and perturbing at far greater scales. Here, I advance methods to understand and utilize latent patterns in new types of high-dimensional data. I devise a method of analyzing networks of 'frequency interactions' in 16S/18S time series data, showing that these can be used to identify microbial communities and associated environmental factors.<br>Then, as part of a highly collaborative project, I show how latent patterns in single cell RNA-Seq can be used together with optimal transport analysis to identify cell types and cell type trajectories, regulatory pathways, and cell-cell interactions in a time-course of developmental reprogramming. I then step back to ask a fundamental question: how do we choose which observations and perturbations to make, and how many of each are necessary? I approach this question on the basis of the inherency of latent structure in biology, and on foundational mathematical results concerning the analysis of highly-structured data. I present the beginnings of a framework to formalize how random composite experiments can make biological discovery more efficient by leveraging latent patterns. I first show how to recover individual genomes using covariance patterns in a series of composite (meta-) genomic data.<br>I then describe how random composite measurements and compressed sensing can be used to make gene expression profiling more efficient. Finally, I apply this idea to in situ imaging transcriptomics, demonstrating how many individual gene images can be efficiently recovered from a small number of composite gene images.<br>by Brian Cleary.<br>Ph. D.<br>Ph.D. Massachusetts Institute of Technology, Computational and Systems Biology Program
35
MAJ, CARLO. "Sensitivity analysis for computational models of biochemical systems." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/50494.
Full textAbstract:
Systems biology is an integrated area of science which aims at the analysis of biochemical systems using an holistic perspective. In this context, sensitivity analysis, a technique studying how the output variation of a computational model can be associated to its input state plays a pivotal role. In the thesis it is described how to properly apply the different sensitivity analysis techniques according to the specific case study (i.e., continuous deterministic rather than discrete stochastic output). Moreover, we explicitly consider aspects that have been often neglected in the analysis of computational biochemical models, among others, we propose an exploratory analysis of spatial effects in diffusion processes in crowded environments. Furthermore, we developed an innovative pipeline for the partitioning of the input factor space according with the different qualitative dynamics that may be attained by a model (focusing on steady state and oscillatory behavior). Finally, we describe different implementation methods for the reduction of the computational time required to perform sensitivity analysis by evaluating distribute and parallel approaches of model simulations.
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Henning, Peter Allen. "Computational Parameter Selection and Simulation of Complex Sphingolipid Pathway Metabolism." Thesis, Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/16202.
Full textAbstract:
Systems biology is an emerging field of study that seeks to provide systems-level understanding of biological systems through the integration of high-throughput biological data into predictive computational models. The integrative nature of this field is in sharp contrast as compared to the Reductionist methods that have been employed since the advent of molecular biology. Systems biology investigates not only the individual components of the biological system, such as metabolic pathways, organelles, and signaling cascades, but also considers the relationships and interactions between the components in the hope that an understandable model of the entire system can eventually be developed. This field of study is being hailed by experts as a potential vital technology in revolutionizing the pharmaceutical development process in the post-genomic era. This work not only provides a systems biology investigation into principles governing de novo sphingolipid metabolism but also the various computational obstacles that are present in converting high-throughput data into an insightful model.
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Kleiman, Laura B. "Experimental and computational analysis of epidermal growth factor receptor pathway phosphorylation dynamics." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/57796.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2010.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 157-168).<br>The epidermal growth factor receptor (EGFR, also known as ErbB 1) is a prototypical receptor tyrosine kinase (RTK) that activates multi-kinase phosphorylation cascades to regulate diverse cellular processes, including proliferation, migration and differentiation. ErbB 1 heterooligomerizes with three close homologues: ErbB2, ErbB3 and ErbB4. ErbB1-3 receptors are frequently mutated, overexpressed or activated by autocrine or paracrine ligand production in solid tumors and have been the target of extensive drug discovery efforts. Multiple small molecule kinase inhibitors and therapeutic antibodies against ErbB receptors are in clinical use or development. Despite their importance as RTKs, oncogenes and drug targets, regulation of ErbB receptors by the interplay of conformational change, phosphorylation, phosphatases and receptor trafficking remains poorly understood, and the impact of these dynamics on physiological activity and cellular responses to anti-ErbB drugs is largely unknown. This thesis investigates the dynamic opposition of kinases and phosphatases within the ErbB pathway. By standard biochemical analysis, ErbB receptors and downstream proteins appear to become phosphorylated and then dephosphorylated in approximately 30 minutes. However, pulse chase experiments where cells are exposed to ligand and then to small molecule kinase inhibitors reveal that individual proteins must in fact cycle rapidly between being phosphorylated and dephosphorylated in seconds. We construct a succession of differential equation-based models of varying biochemical resolution, each model appropriate for analyzing a different aspect of ErbB regulation, to help interpret the data and gain quantitative insight into receptor and drug biology. Rapid phosphorylation and dephosphorylation of receptors has important implications for the assembly dynamics of signalosomes. We find that signals are rapidly propagated through some downstream pathways but slowly through others, resulting in prolonged activation in the absence of upstream signal. We show that fast phosphorylation/dephosphorylation may provide cells with the flexibility necessary to rapidly detect and respond to changes in their extracellular environment. These fast dynamics also play a crucial role in determining the response to ErbB 1-targeting cancer therapies, which we find to vary significantly between drugs with different mechanisms of action. We show that treatment with one class of these drugs results in sustained signaling, instead of inhibition, and thus may actually promote tumor proliferation or invasion. Our work may help explain why certain drugs have been more effective in patients than others and suggests new approaches for evaluating biochemical signaling networks and targeted therapeutics.<br>By Laura B. Kleiman.<br>Ph.D.
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Belyaeva, Anastasiya. "Computational methods for analyzing and modeling gene regulation and 3D genome organization." Thesis, Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130828.
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Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, February, 2021<br>Cataloged from the official PDF of thesis.<br>Includes bibliographical references (pages 261-281).<br>Biological processes from differentiation to disease progression are governed by gene regulatory mechanisms. Currently large-scale omics and imaging data sets are being collected to characterize gene regulation at every level. Such data sets present new opportunities and challenges for extracting biological insights and elucidating the gene regulatory logic of cells. In this thesis, I present computational methods for the analysis and integration of various data types used for cell profiling. Specifically, I focus on analyzing and linking gene expression with the 3D organization of the genome. First, I describe methodologies for elucidating gene regulatory mechanisms by considering multiple data modalities. I design a computational framework for identifying colocalized and coregulated chromosome regions by integrating gene expression and epigenetic marks with 3D interactions using network analysis.<br>Then, I provide a general framework for data integration using autoencoders and apply it for the integration and translation between gene expression and chromatin images of naive T-cells. Second, I describe methods for analyzing single modalities such as contact frequency data, which measures the spatial organization of the genome, and gene expression data. Given the important role of the 3D genome organization in gene regulation, I present a methodology for reconstructing the 3D diploid conformation of the genome from contact frequency data. Given the ubiquity of gene expression data and the recent advances in single-cell RNA-sequencing technologies as well as the need for causal modeling of gene regulatory mechanisms, I then describe an algorithm as well as a software tool, difference causal inference (DCI), for learning causal gene regulatory networks from gene expression data.<br>DCI addresses the problem of directly learning differences between causal gene regulatory networks given gene expression data from two related conditions. Finally, I shift my focus from basic biology to drug discovery. Given the current COVID19 pandemic, I present a computational drug repurposing platform that enables the identification of FDA approved compounds for drug repurposing and investigation of potential causal drug mechanisms. This framework relies on identifying drugs that reverse the signature of the infection in the space learned by an autoencoder and then uses causal inference to identify putative drug mechanisms.<br>by Anastasiya Belyaeva.<br>Ph. D.<br>Ph.D. Massachusetts Institute of Technology, Computational and Systems Biology Program
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Randall, Adrian Joseph. "A systems approach to uncovering the adaptive response of cancer to targeted therapies." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/72967.
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Thesis (S.M.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2012.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 47-53).<br>Tyrosine kinase inhibitors have significant promise in the fight to develop agents that can target cancer in a tumor-specific manner. A number of drugs have been and are currently in development to inhibit specific kinases that can mediate uncontrolled proliferation; however, an unfortunate eventuality for most patients receiving these treatments is the development of resistance that renders these drugs almost completely ineffective. While a number of mechanisms can evolve within a tumor to mitigate effects of kinase inhibitors, we sought to uncover what changes are occurring in the tyrosine phosphorylation network at both short timescales (minutes to 72 hours) and long timescales (120 hours+) that can be playing a role in helping a tumor become resistant to driver-kinase inhibition. It is our hypothesis that specific feedback networks are able to detect and overcome driver kinase inhibition through activation of potential other pathways, which can go on to mediate a longer term resistance phenotype. In order to probe dynamics in the tyrosine phosphorylation network, we employed mass spectrometry to analyze peptides derived from six non-small cell lung cancer cell lines that we classify as either EGFR+ or EML4-ALK+. From both mass spectrometry data and growth assays, we identified an unintuitive boost in signaling and growth in response to low inhibitor concentrations, suggestive of a cellular mechanism that is adaptive to driver kinase inhibition. Studies of EML4-ALK driven H3122 cells showed that this short-term response is not the same as the known long-term resistance mechanism to ALK inhibition, leading support to the notion that the short-term "adaptive response" may be a novel type of mechanism to aid tumor adaptation to targeted therapies. In an effort to better probe signaling events occurring downstream of the phosphotyrosine network, a new pull down technique for mass spectrometry using 14-3-3 protein against phosphoserine and phosphothreonine peptides is described. The results of these studies open up many potential avenues for further exploration into the immediate and long-term signaling response of cancer to targeted therapies.<br>by Adrian Joseph Randall.<br>S.M.
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Matus, García Mariana Guadalupe. "Analysis of fecal biomarkers to impact clinical care and public health." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119603.
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Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references.<br>DNA sequencing and metabolomics technologies have accelerated the discovery of novel biomarkers in clinical samples. In this thesis, I explore the potential of fecal biomarkers to impact clinical and public health practice through non-invasive assessments. First, I highlight the potential of the gut microbiome to provide novel diagnostic and therapeutic targets. By analyzing the gut microbiome and metabolome of mice exposed to a high salt diet, we identified Lactobacillus as a potential probiotic to counteract salt-sensitive conditions such as high blood pressure. Next, I present preliminary validation of wipe samples as a patient-friendly alternative to standard stool collection methods, in particular for the clinical management of Inflammatory Bowel Disease patients. By comparing paired stool and wipe samples, I show that wipe samples capture the same gut microbiome profiles as standard stool samples, and can also be used to quantify fecal calprotectin. Finally, I present the first ever analysis of the microbiome and metabolome of wastewater collected from a residential neighborhood. By testing samples collected hourly over one day, we identified thousands of bacteria and metabolites derived from human activity. Glucuronide compounds that directly reflect consumption of pharmaceutical products and drugs were identified for the first time in a wastewater epidemiology study. Our results highlight the potential of testing wastewater in geo-localized residential areas to produce high-quality data to inform public health practice. Together, these results show the potential of leveraging high-throughput technologies to create seamless readouts of human and population health.<br>by Mariana Guadalupe Matus García.<br>Ph. D.
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Friedman, Robin Carl. "The specificity and evolution of gene regulatory elements." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/61790.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2010.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references.<br>The regulation of gene expression underlies the morphological, physiological, and functional differences between human cell types, developmental stages, and healthy and disease states. Gene regulation in eukaryotes is controlled by a complex milieu including transcription factors, microRNAs (miRNAs), cis-regulatory DNA and RNA. It is the quantitative and combinatorial interactions of these regulatory elements that defines gene expression, but these interactions are incompletely understood. In this thesis, I present two new methods for determining the quantitative specificity of gene regulatory factors. First, I present a comparative genomics approach that utilizes signatures of natural selection to detect the conserved biological relevance of miRNAs and their targets. Using this method, I quantify the abundance of different conserved miRNA target types, including different seed matches and 30-compensatory targets. I show that over 60% of mammalian mRNAs are conserved targets of miRNAs and that a surprising amount of conserved miRNA targeting is mediated by seed matches with relatively low efficacy. Extending this method from mammals to other organisms, I find that miRNA targeting rules are mostly conserved, although I show evidence for new types of miRNA targets in nematodes. Taking advantage of variations in 30 UTR lengths between species, I describe general properties of miRNA targeting that are affected by 30 UTR length. Finally, I introduce a new, high-throughput assay for the quantification of transcription factor in vitro binding affinity to millions of sequences. I apply this method to GCN4, a yeast transcription factor, and reconstruct all known properties of its binding preferences. Additionally, I discover some new subtleties in its specificity and estimate dissociation constants for hundreds of thousands of sequences. I verify the utility of the binding affinities by comparing to in vivo binding data and to the regulatory response following GCN4 induction.<br>by Robin Carl Friedman.<br>Ph.D.
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Agarwal, Vikram. "MicroRNAs : principles of target recognition and developmental roles." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/101295.
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Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2015.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references.<br>MicroRNAs (miRNAs) are ~21-24 nt non-coding RNAs that mediate the degradation and translational repression of target mRNAs. The genomes of vertebrate organisms encode hundreds of miRNAs, each of which may regulate hundreds of mRNA targets. Thus, miRNAs are crucial post-transcriptional regulators engaged in vast regulatory networks. To date, the characteristics of these networks remain mysterious due to the difficulty of identifying miRNA targets through either experimental or computational means. To understand the physiological roles of miRNAs in animal species, it is of fundamental importance to elucidate the structure of the targeting networks in which they participate. The recognition of a miRNA target is guided largely by perfect Watson-Crick base pairing interactions between nucleotides 2-7 from the 5' end of the miRNA (i.e., the "seed" region) and complementary motifs embedded in the 3' UTRs of the target mRNAs. The prevalence of these motifs throughout the transcriptome poses a challenge to our understanding of how specificity emerges: since the presence of a motif is not sufficient to mediate target repression, what contextual features discriminate effective target sites from ineffective ones? Further complicating this is the proposition that "noncanonical" sites lacking perfect seed pairing might mediate repression, which would expand the potential number of functional target sites by orders of magnitude. In the second chapter of this work, we define the features that predict effective miRNA target sites, incorporating their relative influence into a quantitative model which can outperform existing computational models and experimental approaches in target identification. Though the molecular roles of miRNAs in gene regulation have long been appreciated, the functions of most miRNAs in living organisms has remained elusive. In the third chapter of this work, we discuss the consequences of genetic ablation of miR-196, a deeply conserved miRNA that is predicted to simultaneously repress many HOX genes, in the mouse. We propose a role for miR-196 in the spatial patterning of the vertebrate axial skeleton. Isolating the cell populations that express the miRNA during early mammalian development, we attempt to characterize the direct in vivo targets of miR-196 and dissect the molecular underpinnings of the phenotypes observed.<br>by Vikram Agarwal.<br>Ph. D.
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Chan, Michelle M. (Michelle Mei Wah). "DNA methylation in early mammalian development." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/81580.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references.<br>All the cells in the body contain the same genome yet showcase drastically different phenotypes. This is the result of different transcriptional programs, which are partly controlled by epigenetic modifications, including DNA methylation. In this thesis, I analyze genome-scale DNA methylation profiles across pre-implantation development to identify the targets and characterize the dynamics of global demethylation that lead to totipotency and the subsequent changes to embryonic specification. In Chapter 1, I validate and refine the decades old model for DNA methylation in mouse embryogenesis, identify many retrotransposons with active DNA methylation signatures at fertilization, and discover many, novel differentially methylated regions between the gametes that exist transiently during early development. Notably, the majority of epigenetic events unique to mammalian pre-implantation development are characterized in mouse. In Chapter 2, 1 describe the DNA methylation dynamics in human preimplantation development and show that the regulatory principles that operate in mouse are conserved, though some of their targets are species-specific and define regions of local divergence. Finally, in Chapter 3, I compare DNA methylation dynamics of fertilization to an artificial reprogramming process, somatic cell nuclear transfer, in mouse, and find that most dynamics are conserved but occur at a smaller magnitude after artificial reprogramming. I conclude this thesis with a summary of the chapters and a brief discussion of ongoing and future work.<br>by Michelle M. Chan.<br>Ph.D.
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Rameseder, Jonathan. "Multivariate methods for the statistical analysis of hyperdimensional high-content screening data." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/92957.
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Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2014.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references.<br>In the post-genomic era, greater emphasis has been placed on understanding the function of genes at the systems level. To meet these needs, biologists are creating larger, and increasingly complex datasets. In recent years, high-content screening (HCS) using RNA interference (RNAi) or other perturbation techniques in combination with automated microscopy has emerged as a promising investigative tool to explore intricate biological processes. Image-based HC screens produce massive hyperdimensional data sets. To identify novel components of the DNA damage response (DDR) after ionizing radiation, we recently performed an image-based HC RNAi screen in an osteosarcoma cell line. Robust univariate hit identication methods and manual network analysis identied an isoform of BRD4, a bromodomain and extra-terminal domain family member, as an endogenous inhibitor of DDR signaling. However, despite the plethora of data generated from our and other HC screens, little progress has been made in analyzing HC data using multivariate computational methods that exploit the full richness of hyperdimensional data and identify more than just the most salient knockdown phenotypes to gain a detailed understanding of how gene products cooperate to regulate complex cellular processes. We developed a novel multivariate method using logistic regression models and least absolute shrinkage and selection operator regularization for analyzing hyperdimensional HC data. We applied this method to our HC screen to identify genes that exhibit subtle but consistent phenotypic changes upon knockdown that would have been missed by conventional univariate hit identication approaches. Our method automatically selects the most predictive features at the most predictive time points to facilitate the more ecient design of follow-up experiments and puts the identied hits in a network context using the Prize-Collecting Steiner Tree algorithm. This method offers superior performance over the current gold standard for the analysis of HC RNAi screens. A surprising finding from our analysis is that training sets of genes involved in complex biological phenomena used to train predictive models must be broken down into functionally coherent subsets in order to enhance new gene discovery. Additionally, we found that in the case of RNAi screening, statistical cell-to-cell variation in phenotypic responses in a well of cells targeted by a single shRNA is an important predictor of gene dependent events.<br>by Jonathan Rameseder.<br>Ph. D.
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Robertson, Alexander De Jong. "Understanding regulation of mRNA by RNA binding proteins." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/87474.
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Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 167-187).<br>Posttranscriptional regulation of mRNA by RNA-binding proteins plays key roles in regulating the transcriptome over the course of development, between tissues and in disease states. The specific interactions between mRNA and protein are controlled by the proteins' inherent affinities for different RNA sequences as well as other features such as translation and RNA structure which affect the accessibility of mRNA. The stabilities of mRNA transcripts are regulated by nonsense-mediated mRNA decay (NMD), a quality control degradation pathway. In this thesis, I present a novel method for high throughput characterization of the binding affinities of proteins for mRNA sequences and an integrative analysis of NMD using deep sequencing data. This thesis describes RNA Bind-n-Seq (RBNS), which comprehensively characterizes the sequence and structural specificity of RNA binding proteins (RBPs), and application to the developmentally-regulated splicing factors RBFOX2, MBNL1 and CELF1/CUGBP1. For each factor, the canonical motifs are recovered as well as additional near-optimal binding motifs. RNA secondary structure inhibits binding of RBFOX2 and CELF1, while MBNL1 favors unpaired Us but tolerates C/G pairing in UGC-containing motifs. In a project investigating how NMD shapes the embryonic transcriptome, this thesis presents integrated genome-wide analyses of UPF1 binding locations, NMD-regulated gene expression, and translation in murine embryonic stem cells (mESCs). Over 200 direct UPF1 binding targets are identified using crosslinking/immunoprecipitation-sequencing (CLIP-seq). Results from ribosome foot printing show that actively translated upstream open reading frames (uORFs) are enriched in transcription factor mRNAs and predict mRNA repression by NMD, while poorly translated mRNAs escape repression.<br>by Alexander De Jong Robertson.<br>Ph. D.
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Cheng, Wu Albert. "Epigenetic and post-transcriptional regulation of gene expression in pluripotent stem cells, differentiation and metastasis." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91122.
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Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references.<br>Transmission of information from DNA to RNA to protein underlies the core of modem life forms. The advance in sequencing and genetic technologies has revolutionized the study of molecular biology, genetics and developmental biology enabling delineation of biological processes in unprecedented details. Through the study of epigenetics and posttranscriptional regulation of gene expression by high-throughput sequencing technologies in several biological processes, namely embryonic stem cells, somatic reprogramming, erythroid differentiation, epithelial-mesenchymal transition and cancer metastasis, this thesis work has identified novel players and regulatory mechanisms underlying these developmental processes and diseases. Furthermore, an attempt to engineer CRISPRzymes - protein fusions of RNA-guided DNA binding dCas9 - will enable experiments to directly test biological processes at defined genomic loci and expands the toolbox for synthetic biology and potentially opens up opportunities for novel therapeutics.<br>by Wu Albert Cheng.<br>Ph. D.
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Romer, Katherine A. "Deciphering the mitotic and meiotic phases of spermatogenesis in the mouse." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107076.
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Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references.<br>Mammalian spermatogenesis includes two types of cell divisions. First, germ cells undergo transit-amplifying mitotic divisions, which enable prodigious output of mature spermatozoa. Second, they undergo reductive meiotic divisions to produce haploid gametes. In this thesis, I examine gene expression and regulation during the mitotic and meiotic phases of spermatogenesis. Chapter 2 describes how RA-STRA8 signaling regulates two key transitions: spermatogonial differentiation, which begins the transit-amplifying mitotic divisions, and meiotic initiation, which ends them. First, in mice lacking the RA (retinoic acid) target gene Stra8, undifferentiated spermatogonia accumulated; thus, Stra8 promotes spermatogonial differentiation as well as meiotic initiation. Second, injection of RA into wild-type males induced precocious spermatogonial differentiation and meiotic initiation; thus, RA acts instructively on germ cells at both transitions. Finally, competencies of germ cells to undergo spermatogonial differentiation or meiotic initiation in response to RA were found to be distinct and periodic. Chapter 3 describes a novel method for isolating precise populations of mitotic and meiotic germ cells from the mouse testis. We first synchronize germ cell development in vivo, and perform histological staging to verify synchronization. We then separate these germ cells from contaminating somatic and stem cells by FACS, to achieve ~90% purity of each distinct germ cell type, from the stem cell pool through mid/late meiotic prophase. Utilizing this "3S" method (synchronize, stage, and sort), we can robustly and efficiently separate germ cell types that were previously challenging or impossible to distinguish, with sufficient yield for transcriptomic and epigenetic studies. Chapter 4 presents a systematic comparison of the male and female gene expression programs of meiotic prophase. We performed transcriptional profiling of postnatal testes synchronized in precise stages of meiotic prophase, and compared to the same stages in the fetal ovary. We identified 260 genes up-regulated during both male and female prophase; this shared gene set represents a core meiotic program, composed of known and potential novel meiotic players. We also identified over two thousand genes that are up-regulated during meiotic prophase specifically in the male. These comprise both a male-specific meiotic program, and a preparatory program for cellular differentiation of spermatozoa.<br>by Katherine A. Romer.<br>Ph. D.
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Al-Obeidi, Arshed. "Single-cell reporters for inflammatory caspase activity." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/93044.
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Thesis: S.M., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (page 29).<br>Caspases are a 12-member family of human proteases that regulate apoptosis and inflammation. They serve as key effectors downstream of diverse signaling receptors and shape cell fate. Inflammatory caspases mediate the proteolytic processing of inflammatory cytokines and are essential in maintaining immune function, but also lead to disease when deregulated. In order to examine the activity of inflammatory caspases, we generated 2 inflammatory caspase reporters: a fluorescence resonance energy transfer (FRET) inflammatory caspase activity reporter as well as a fluorescent translocation inflammatory caspase reporter. These reporters were then used to study inflammatory caspase activity in vitro using recombinant caspases and in vivo using a simplified cell culture model. The inflammatory caspase activity reporters have the potential to capture inflammatory caspase activation under a variety of stimuli. They also have several advantages compared to existing methods: they are non-destructive and can be used for live single cell measurements; they do not require the addition of exogenous chemicals or cofactors; and they do not covalently modify the inflammatory caspases. Inflammatory caspase activation is a rapid, asynchronous process, and detecting the activity of the mature inflammatory caspase molecules is made difficult due to the short half-life of the enzyme. The reporters we have developed can fill this need.<br>by Arshed Al-Obeidi.<br>S.M.
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Zheng, Grace Xinying. "Exploring the regulatory roles of microRNAs in mammalian development." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/57556.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2010.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (p. 159-176).<br>microRNAs (miRNAs) are ~22-nt long short RNAs that regulate gene expression in organisms ranging from plants to animals. In mammals, miRNAs post-transcriptionally repress gene expression by primarily binding to the 3' untranslated region (3' UTR) of target mRNAs. Although hundreds of miRNAs have been discovered, targets of most miRNAs and the method by which they affect their biological function remain elusive. To better understand the role of miRNAs in fundamental cellular processes, we characterized enriched miRNA populations in three distinct murine developmental programs, T lymphocytes, embryonic stem cells, and the placenta. We started exploring the role of miRNAs in T lymphocytes by globally characterizing short RNA expression during key developmental stages of T lymphocytes. Our results showed that a distinct set of miRNAs is enriched in each stage. In particular, miR-181 is elevated at the double positive (DP) stage, when thymocytes expressing both CD4 and CD8 undergo positive and negative selection. We found that miR-181 can repress the expression of Bcl-2, CD69, and the T cell receptor, all of which are involved in positive selection. Analysis of short RNAs in T lymphocytes also revealed a novel miRNA cluster, the Sfmbt2 miRNA cluster, named as such since it maps to an intron of the Sfmbt2 gene, a Polycomb Group gene. Instead of studying this cluster in T lymphocytes, we decided to use embryonic stem (ES) cells as this cluster is also expressed in ES cells and the cells are more conducive to lab experimentation. This cluster contains several miRNA families, and we addressed the function of one miRNA family, miR-467a, as it shares target specificity with other highly abundant miRNAs in ES cells. Gain and loss of function assays showed that this family of miRNAs can promote cell survival by advancing the G1 to S phase transition. In addition, they target certain proapoptotic factors to buffer ES cells from apoptosis, especially in the context of genotoxic stress. The Sfmbt2 cluster is a mouse-specific miRNA cluster, and individual members have been uniquely amplified in the Sfmbt2 locus. We developed a method to explore the impact of species-specific miRNAs on the evolution of 3' UTRs, and found that target sites of many miRNAs show positive selection. In particular, mouse target sites have evolved to specifically gain binding sites (mouse-specific targets) for some Sfmbt2 miRNAs, several of which are enriched in the placenta. These mouse-specific targets are enriched in pathways regulating cell survival, implicating the Sfmbt2 miRNA cluster as a possible promoter to placental growth. Our studies in T lymphocytes, ES cells and the placenta have revealed important roles of miRNAs in shaping 3' UTR evolution, and mammalian development. Several novel miRNA targets we uncovered are important regulators of differentiation, cell cycle, and apoptosis. Understanding their functions will not only shed light on their roles in normal physiology, but also generate useful insights that can be applied to cancer and reprogramming.<br>by Grace Xinying Zheng.<br>Ph.D.
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Fan, Zi Peng. "Transcriptional and structural control of cell identity genes." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98641.
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Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references.<br>Mammals contain a wide array of cell types with distinct functions, yet nearly all cell types have the same genomic DNA. How the genetic instructions in DNA are selectively interpreted by cells to specify various cellular functions is a fundamental question in biology. This thesis work describes two genome-wide studies designed to study how transcriptional control of gene expression programs defines cell identity. Recent studies suggest that a small number of transcription factors, called "master" transcription factors, dominate the control of gene expression programs. These master transcription factors and the transcriptional regulatory circuitry they produce, however, are not known for all cell types. Ectopic expression of these factors can, in principle, direct transdifferentiation of readily available cells into medically relevant cell types for applications in regenerative medicine. Limited knowledge of these factors is a roadblock to generation of many medically relevant cell types. Chapter 2 presents a study in which a novel computational approach was undertaken to generate an atlas of candidate master transcriptional factors for 100+ human tissue/cell types. The candidate master transcription factors in retinal pigment epithelial (RPE) cells were then used to guide the investigation of the regulatory circuitry of RPE cells and to reprogram human fibroblasts into functional RPE-like cells. Master transcription factors define cell-type-specific gene expression through binding to enhancer elements in the genome. These enhancer-bound transcription factors regulate genes by contacting target gene promoters via the formation of DNA loops. It is becoming increasingly clear that transcription factors operate and regulate gene expression within a larger three-dimensional (3D) chromatin architecture, but these structures and their functions are poorly understood. Chapter 3 presents a study in which Cohesin ChIA-PET data was generated to identify the local chromosomal structures at both active and repressed genes across the genome in embryonic stem cells. The results led to the discovery of functional insulated neighborhood structures that are formed by two CTCF interaction sites occupied by Cohesin. The integrity of these looped structures contributes to the transcriptional control of super-enhancer-driven active genes and repressed genes encoding lineage-specifying developmental regulators.<br>by Zi Peng Fan.<br>Ph. D.