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AbstractThis article compares the epistemic roles of theoretical models and model organisms in science, and specifically the role of non-human animal models in biomedicine. Much of the previous literature on this topic shares an assumption that animal models and theoretical models have a broadly similar epistemic role—that of indirect representation of a target through the study of a surrogate system. Recently, Levy and Currie (2015) have argued that model organism research and theoretical modelling differ in the justification of model-to-target inferences, such that a unified account based on the widely accepted idea of modelling as indirect representation does not similarly apply to both. I defend a similar conclusion, but argue that the distinction between animal models and theoretical models does not always track a difference in the justification of model-to-target inferences. Case studies of the use of animal models in biomedicine are presented to illustrate this. However, Levy and Currie’s point can be argued for in a different way. I argue for the following distinction. Model organisms (and other concrete models) function as surrogate sources of evidence, from which results are transferred to their targets by empirical extrapolation. By contrast, theoretical modelling does not involve such an inductive step. Rather, theoretical models are used for drawing conclusions from what is already known or assumed about the target system. Codifying assumptions about the causal structure of the target in external representational media (e.g. equations, graphs) allows one to apply explicit inferential rules to reach conclusions that could not be reached with unaided cognition alone (cf. Kuorikoski and Ylikoski 2015).
Levy, Arnon, and Adrian Currie. "Model Organisms are Not (Theoretical) Models." British Journal for the Philosophy of Science 66, no. 2 (June 1, 2015): 327–48. http://dx.doi.org/10.1093/bjps/axt055.
Rine, Jasper. "A future of the model organism model." Molecular Biology of the Cell 25, no. 5 (March 2014): 549–53. http://dx.doi.org/10.1091/mbc.e12-10-0768.
Changes in technology are fundamentally reframing our concept of what constitutes a model organism. Nevertheless, research advances in the more traditional model organisms have enabled fresh and exciting opportunities for young scientists to establish new careers and offer the hope of comprehensive understanding of fundamental processes in life. New advances in translational research can be expected to heighten the importance of basic research in model organisms and expand opportunities. However, researchers must take special care and implement new resources to enable the newest members of the community to engage fully with the remarkable legacy of information in these fields.
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Maas, Richard. "Humans as Model Organisms." Cell 96, no. 4 (February 1999): 455–56. http://dx.doi.org/10.1016/s0092-8674(00)80633-9.
Sterelny, Kim. "Humans as model organisms." Proceedings of the Royal Society B: Biological Sciences 284, no. 1869 (December 13, 2017): 20172115. http://dx.doi.org/10.1098/rspb.2017.2115.
Like every other species, our species is the result of descent with modification under the influence of natural selection; a tip in an increasingly large and deep series of nested clades, as we trace its ancestry back to increasingly remote antecedents. As a consequence of shared history, our species has much in common with many others; as a consequence of its production by the general mechanisms of evolution, our species carries information about the mechanisms that shaped other species as well. For reasons unconnected to biological theory, we have far more information about humans than we do about other species. So in principle and in practice, humans should be usable as model organisms, and no one denies the truth of this for mundane physical traits, though harnessing human data for more general questions proves to be quite challenging. However, it is also true that human cognitive and behavioural characteristics, and human social groups, are apparently radically unlike those of other animals. Humans are exceptional products of evolution and perhaps that makes them an unsuitable model system for those interested in the evolution of cooperation, complex cognition, group formation, family structure, communication, cultural learning and the like. In all these respects, we are complex and extreme cases, perhaps shaped by mechanisms (like cultural evolution or group selection) that play little role in other lineages. Most of the papers in this special issue respond by rejecting or downplaying exceptionalism. I argue that it can be an advantage: understanding the human exception reveals constraints that have restricted evolutionary options in many lineages.
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ARMSTRONG, J. DOUGLAS, NIGEL H. GODDARD, and DAVID SHEPHERD. "NEUROINFORMATICS IN MODEL ORGANISMS." Journal of Neurogenetics 17, no. 2-3 (January 2003): 103–16. http://dx.doi.org/10.1080/neg.17.2-3.103.116.
Armstrong, J. Douglas, Nigel H. Goddard, and David Shepherd. "NEUROINFORMATICS IN MODEL ORGANISMS." Journal of Neurogenetics 17, no. 2 (January 1, 2003): 103–16. http://dx.doi.org/10.1080/714049411.
Coelho, Susana M., and J. Mark Cock. "Brown Algal Model Organisms." Annual Review of Genetics 54, no. 1 (November 23, 2020): 71–92. http://dx.doi.org/10.1146/annurev-genet-030620-093031.
Model organisms are extensively used in research as accessible and convenient systems for studying a particular area or question in biology. Traditionally, only a limited number of organisms have been studied in detail, but modern genomic tools are enabling researchers to extend beyond the set of classical model organisms to include novel species from less-studied phylogenetic groups. This review focuses on model species for an important group of multicellular organisms, the brown algae. The development of genetic and genomic tools for the filamentous brown alga Ectocarpus has led to it emerging as a general model system for this group, but additional models, such as Fucus or Dictyota dichotoma, remain of interest for specific biological questions. In addition, Saccharina japonica has emerged as a model system to directly address applied questions related to algal aquaculture. We discuss the past, present, and future of brown algal model organisms in relation to the opportunities and challenges in brown algal research.
Dissertations / Theses on the topic "Model organisms":
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Ziehm, Matthias Fritz. "Computational biology of longevity in model organisms." Thesis, University of Cambridge, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648888.
McCarthy, Linda Catherine. "New approaches to genome mapping in model organisms." Thesis, University College London (University of London), 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283335.
Atreya, Ravi Viswanathan. "Drug target prediction in pancreatic cancer using model organisms." Thesis, The University of Arizona, 2009. http://hdl.handle.net/10150/192261.
Vargas, JoseÌ Danilo. "Model organisms and human disease : from kyphoscoliosis to neurodegeneration." Thesis, University of Oxford, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275381.
Treitz, Christian [Verfasser]. "Mass Spectrometry based Bioanalytics on Model Organisms / Christian Treitz." Kiel : Universitätsbibliothek Kiel, 2018. http://d-nb.info/1154434133/34.
Recent advances in high throughput technologies have led to an increasing amount of rich and diverse biological data and related literature. Model organisms are classically selected as subjects for studying human disease based on their genotypic and phenotypic features. A significant problem with model organism identification is the determination of characteristic features related to biological processes that can provide insights into the mechanisms underlying diseases. These insights could have a positive impact on the diagnosis and management
of diseases and the development of therapeutic drugs. The increased availability of biological data presents an opportunity to develop data mining methods that can address these challenges and help scientists formulate and test data-driven hypotheses.
In this dissertation, data mining methods were developed to provide a quantitative approach for the identification of potential model organisms based on underlying features that may be correlated with disease manifestation in humans. The work encompassed three major
types of contributions that aimed to address challenges related to inferring information from biological data available from a range of sources. First, new statistical models and algorithms for graph pattern mining were developed and tested on diverse genres of data (biological networks, drug chemical compounds, and text documents). Second, data mining techniques were developed and shown to identify characteristic disease patterns (disease fingerprints), predict potentially new genetic pathways, and facilitate the assessment of organisms as potential disease models. Third, a methodology was developed that combined the application of graph-based models with information derived from natural language processing methods to identify statistically significant patterns in biomedical text. Together, the approaches developed for this dissertation show promise for summarizing the information about biological processes and phenomena associated with organisms broadly and for the potential assessment of their suitability to study human diseases.
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Cookson, Natalie Anne. "Single cell growth and gene expression dynamics in model organisms." Diss., [La Jolla] : University of California, San Diego, 2009. http://wwwlib.umi.com/cr/ucsd/fullcit?p3387196.
Thesis (Ph. D.)--University of California, San Diego, 2009. Title from first page of PDF file (viewed February 12, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 104-114).
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Konieczka, Jay, Kevin Drew, Alex Pine, Kevin Belasco, Sean Davey, Tatiana Yatskievych, Richard Bonneau, and Parker Antin. "BioNetBuilder2.0: bringing systems biology to chicken and other model organisms." BioMed Central, 2009. http://hdl.handle.net/10150/610006.
BACKGROUND:Systems Biology research tools, such as Cytoscape, have greatly extended the reach of genomic research. By providing platforms to integrate data with molecular interaction networks, researchers can more rapidly begin interpretation of large data sets collected for a system of interest. BioNetBuilder is an open-source client-server Cytoscape plugin that automatically integrates molecular interactions from all major public interaction databases and serves them directly to the user's Cytoscape environment. Until recently however, chicken and other eukaryotic model systems had little interaction data available.RESULTS:Version 2.0 of BioNetBuilder includes a redesigned synonyms resolution engine that enables transfer and integration of interactions across species this engine translates between alternate gene names as well as between orthologs in multiple species. Additionally, BioNetBuilder is now implemented to be part of the Gaggle, thereby allowing seamless communication of interaction data to any software implementing the widely used Gaggle software. Using BioNetBuilder, we constructed a chicken interactome possessing 72,000 interactions among 8,140 genes directly in the Cytoscape environment. In this paper, we present a tutorial on how to do so and analysis of a specific use case.CONCLUSION:BioNetBuilder 2.0 provides numerous user-friendly systems biology tools that were otherwise inaccessible to researchers in chicken genomics, as well as other model systems. We provide a detailed tutorial spanning all required steps in the analysis. BioNetBuilder 2.0, the tools for maintaining its data bases, standard operating procedures for creating local copies of its back-end data bases, as well as all of the Gaggle and Cytoscape codes required, are open-source and freely available at http://err.bio.nyu.edu/cytoscape/bionetbuilder/ webcite.
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Simmonds, Michael Patrick. "The stress field in a suspension of swimming model micro-organisms." Thesis, University of Cambridge, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.615714.
Wagih, Omar. "Elucidating the mechanistic impact of single nucleotide variants in model organisms." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/271713.
Understanding how genetic variation propagate to differences in phenotypes in individuals is an ongoing challenge in genetics. Genome-wide association studies have allowed for the identification of many trait-associated genomic loci. However, they are limited in their inability to explain the altered cellular mechanism. Genetic variation can drive disease by altering a range of mechanisms, including signalling networks, TF binding, and protein folding. Understanding the impact of variants on such processes has key implications in therapeutics, drug development, and more. This thesis aims to utilise computational predictors to shed light on how cellular mechanisms are altered in the context of genetic variation and better understand how they drive both molecular and organism-level phenotypes. Many binding events in the cell are mediated by short stretches of sequence motifs. The ability to discover these underlying rules of binding could greatly aid our understanding of variant impact. Kinase–substrate phosphorylation is one of the most prominent post-translational modifications (PTMs) which is mediated by such motifs. We first describe a computational method which utilises interaction and phosphorylation data to predict sequence preferences of kinases. Our method was applied to 57% of human kinases capturing known well-characterised and novel kinase specificities. We experimentally validate four understudied kinases to show that predicted models closely resemble true specificities. We further demonstrate that this method can be applied to different organisms and can be used for other phospho-recognition domains. The described approach allows for an extended repertoire of sequence specificities to be generated, particularly in organisms for which little data is available. TF-DNA binding is another mechanism driven by sequence motifs, which is key for the tight regulation of gene expression and can be greatly altered by genetic variation. We have comprehensively benchmarked current methods used to predict non-coding variant effects on TF-DNA binding by employing over 20,000 compiled allele-specific ChIP-seq variants across 94 TFs. We show that machine learning-based approaches significantly outperform more rudimentary methods such as the position weight matrix. We further note that models for many TFs with distinct binding specificities were unable to accurately assess the impact of variants. For these TFs, we explore alternative mechanisms underlying TF-binding, such as methylation, co-operative binding, and DNA shape that drive poor performance. Our results demonstrate the complexity of predicting non-coding variant effects and the importance of incorporating alternative mechanisms into models. Finally, we describe a comprehensive effort to compile and benchmark state-of-the-art sequence and structure-based predictors of mutational consequences and predict the effect of coding and non-coding variants in the reference genomes of human, yeast, and E. coli. Predicted mechanisms include the impact on protein stability, interaction interfaces, and PTMs. These variant effects are provided through mutfunc, a fast and intuitive web tool by which users can interactively explore pre-computed mechanistic variant impact predictions. We validate computed predictions by analysing known pathogenic disease variants and provide mechanistic hypotheses for causal variants of unknown function. We further use our predictions to devise gene-level functionality scores in human and yeast individuals, which we then used to perform gene-phenotype associations and uncover novel gene-phenotype associations.
Brancelj, A., L. De Meester, and P. Spaak, eds. Cladocera: the Biology of Model Organisms. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-4964-8.
Larsson, Petter, and Lawrence J. Weider, eds. Cladocera as Model Organisms in Biology. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-0021-2.
Kloc, Malgorzata, and Jacek Z. Kubiak, eds. Marine Organisms as Model Systems in Biology and Medicine. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92486-1.
Siddhardha, Busi, Madhu Dyavaiah, and Kaviyarasu Kasinathan, eds. Model Organisms to Study Biological Activities and Toxicity of Nanoparticles. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1702-0.
Siddhardha, Busi, Madhu Dyavaiah, and Asad Syed, eds. Model Organisms for Microbial Pathogenesis, Biofilm Formation and Antimicrobial Drug Discovery. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1695-5.
Crespi, Bernard J. Evolution of ecological and behavioural diversity: Australian Acacia thrips as model organisms. Canberra, Australia: Australian Biological Resources Study, 2004.
International, Symposium on Cladocera (3rd 1993 Bergen Norway). Cladocera as model organisms in biology: Proceedings of the Third International Symposium on Cladocera, held in Bergen, Norway, 9-16 August 1993. Dordrecht: Kluwer Academic Publishers, 1995.
Arancio, Walter. "Progeria: Model Organisms." In Encyclopedia of Gerontology and Population Aging, 1–7. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-69892-2_723-1.
Jaffar Ali, H. Abdul, and M. Tamilselvi. "Ascidians—Model Organisms." In Ascidians in Coastal Water, 39–40. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29118-5_7.
Speicher, Michael R. "Model Organisms for Human Disorders." In Vogel and Motulsky's Human Genetics, 777. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-37654-5_33.
Morgan, Michael M., MacDonald J. Christie, Thomas Steckler, Ben J. Harrison, Christos Pantelis, Christof Baltes, Thomas Mueggler, et al. "Model Organisms of Hyperkinetic Syndrome." In Encyclopedia of Psychopharmacology, 790. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68706-1_3403.
Müller, Werner A. "Model Organisms in Developmental Biology." In Developmental Biology, 21–121. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-2248-4_3.
Ray Banerjee, Ena. "Model Organisms in Science and Research." In Perspectives in Regenerative Medicine, 85–104. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2053-4_6.
Kletzin, Arnulf. "General Characteristics and Important Model Organisms." In Archaea, 14–92. Washington, DC, USA: ASM Press, 2014. http://dx.doi.org/10.1128/9781555815516.ch2.
Rajkumari, Jobina, Madhu Dyavaiah, Asad Syed, and Busi Siddhardha. "Model Organisms and Antimicrobial Drug Discovery." In Model Organisms for Microbial Pathogenesis, Biofilm Formation and Antimicrobial Drug Discovery, 527–43. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1695-5_27.
Wang, Peng. "A Mathematical Model of Unknown Large Organisms." In 2019 18th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). IEEE, 2019. http://dx.doi.org/10.1109/dcabes48411.2019.00046.
Seeger, Markus, Gil Westmeyer, and Vasilis Ntziachristos. "In-vivo hybrid microscopy of small model organisms." In Opto-Acoustic Methods and Applications in Biophotonics, edited by Vasilis Ntziachristos and Roger Zemp. SPIE, 2019. http://dx.doi.org/10.1117/12.2530923.
Lin, Xiaotong, Mei Liu, and Xue-wen Chen. "Protein-Protein Interaction Prediction and Assessment from Model Organisms." In 2008 IEEE International Conference on Bioinformatics and Biomedicine. IEEE, 2008. http://dx.doi.org/10.1109/bibm.2008.26.
Ishikawa, Takuji, T. J. Pedley, and Takami Yamaguchi. "Numerical Simulation of a Suspension of Swimming Micro-Organisms." In ASME 2007 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2007. http://dx.doi.org/10.1115/sbc2007-175256.
The size of individual micro-organisms is often much smaller than that of the flow field of interest, in an oceanic plankton bloom for instance. In such cases, the suspension of micro-organisms is modelled as a continuum in which the variables are volume-averaged quantities. Continuum models for suspensions of swimming micro-organisms have been proposed for the analysis of phenomena such as bioconvection. However, the continuum models proposed so far are restricted to dilute suspensions, in which cell-cell interactions are negligible. If one wishes to analyze larger cell concentrations, it will be necessary to consider the interactions between micro-organisms. Then the particle stress tensor, the velocities of the micro-organisms and the diffusion tensor in the continuum model will need to be replaced by improved expressions. In this study, we compute the motion of interacting swimming model micro-organisms in periodic suspensions in a fluid otherwise at rest, and discuss the microstructure constructed by micro-organisms.
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"Transcriptome assembling of non-model organisms on example holothuria Eupentacta fraudatrix." In SYSTEMS BIOLOGY AND BIOINFORMATICS. Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 2019. http://dx.doi.org/10.18699/sbb-2019-05.
Zagirnyak, Mykhaylo, Volodymyr Nykyforov, Oksana Sakun, and Olga Chorna. "The industrial electrical equipment screened magnetic fields effect on model organisms." In 2017 International Conference on Modern Electrical and Energy Systems (MEES). IEEE, 2017. http://dx.doi.org/10.1109/mees.2017.8248938.
Lausser, Ludwig, Steffen Just, Wolfgang Rottbauer, and Hans Kestler. "Semantic Biomarker Selection for Functional Genomics of Heart Failure Model Organisms." In 2017 Computing in Cardiology Conference. Computing in Cardiology, 2017. http://dx.doi.org/10.22489/cinc.2017.258-195.
SARKAR, INDRA NEIL. "BIODIVERSITY INFORMATICS: MANAGING KNOWLEDGE BEYOND HUMANS AND MODEL ORGANISMS – AN INTRODUCTION." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812772435_0032.
Vourc’h, Thomas, Julien Léopoldès, Annick Méjean, and Hassan Peerhossaini. "Motion of Active Fluids: Diffusion Dynamics of Cyanobacteria." In ASME 2016 Fluids Engineering Division Summer Meeting collocated with the ASME 2016 Heat Transfer Summer Conference and the ASME 2016 14th International Conference on Nanochannels, Microchannels, and Minichannels. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/fedsm2016-7526.
Cyanobacteria are photosynthetic micro-organisms colonizing all aquatic and terrestrial environments. The motility of such living micro-organisms should make their diffusion distinct from typical Brownian motion. This diffusion can be investigated in terms of global behavior (Fickian or not) and in terms of displacement probabilities, which provide more detail about the motility process. Using cyanobacterium Synechocystis sp. PCC 6803 as the model micro-organism, we carry out time-lapse video microscopy to track and analyze the bacteria’s trajectories, from which we compute the mean-squared displacement (MSD) and the distribution function of displacement probabilities. We find that the motility of Synechocystis sp. PCC 6803 is intermittent: high-motility “run” phases are separated by low-motility “tumble” phases corresponding to trapped states. However, this intermittent motility leads to a Fickian diffusive behavior, as shown by the evolution of the MSD with time.
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Hui, Sai Chuen. "The Mathematical Model of the Natural Evolution Law of Cosmic Material Organisms." In 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/csece-18.2018.83.
Lin, Paul P., Alec J. Jaeger, Tung-Yun Wu, Sharon C. Xu, Abraxa S. Lee, Fanke Gao, Po-Wei Chen, and James C. Liao. Construction of a Robust Non-Oxidative Glycolysis in Model Organisms for n-Butanol Production. Office of Scientific and Technical Information (OSTI), April 2019. http://dx.doi.org/10.2172/1506427.
Jung, Carina, Matthew Carr, Eric Fleischman, and Chandler Roesch. Response of the green June beetle and its gut microbiome to RDX and phenanthrene. Engineer Research and Development Center (U.S.), November 2020. http://dx.doi.org/10.21079/11681/38799.
Green June beetles are a cosmopolitan pest in the United States. Adults are voracious consumers of tree and vine fruit, while their larvae can dam-age and inadvertently consume root systems, particularly those of grasses, as they move through the soil and forage for detritus. Larvae ingest and process large volumes of soil while in the process of feeding. Due to their intimate contact with the soil it was hypothesized that soil contaminants that are known animal toxins would perturb the larval and affect their overall health and survival. Studies of this kind are important contribu-tions to the development of new model organisms and our understanding of interactions between the environment, contaminants, gut microbiome, and animal development, health, and survival. It is important to continue to develop relevant model organisms for monitoring toxicity as regulations for working with vertebrates becomes more prohibitive. In this study green June beetle larvae were exposed to RDX and phenanthrene through-out their entire soil-bound development, starting within the first few days of hatching through to their emergence as adults. The overall findings included that even at high concentrations, RDX and phenanthrene (25 ppm) exerted no significant effect on body weight or survival. Also, there was lit-tle apparent effect of RDX and phenanthrene on the bacterial microbiome, and no statistical association with measurable health effects. Nevertheless, the green June beetle is an interesting model for soil toxicity experiments in the future as is it easy to collect, house, and handle.
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Johnson, M. E., A. R. Montoro Bustos, S. K. Hanna, E. J. Petersen, K. E. Murphy, L. L. Yu, B. C. Nelson, and M. R. Winchester. Sucrose density gradient centrifugation for efficient separation of engineered nanoparticles from a model organism, Caenorhabditis elegans. Gaithersburg, MD: National Institute of Standards and Technology, October 2017. http://dx.doi.org/10.6028/nist.sp.1200-24.
Lovley, Derek R. Final Report Coupled In Silico Microbial and Geochemical Reactive Transport Models: Extension to Multi-Organism Communities, Upscaling, and Experimental Validation. Office of Scientific and Technical Information (OSTI), March 2014. http://dx.doi.org/10.2172/1122519.
DIPLANOAGAP: Distribution of Plastics in the North Atlantic Garbage Patch Ponta Delgada (Portugal) – Malaga (Spain) 17.08. – 12.09.2019 The expedition POS 536 is part of a multi-disciplinary research initiative of GEOMAR investigating the origin, transport and fate of plastic debris from estuaries to the oceanic garbage patches. The main focus will be on the vertical transfer of plastic debris from the surface and near-surface waters to the deep sea and on the processes that mediate this transport. The obtained data will help to develop quantitative models that provide information about the level of plastic pollution in the different compartments of the open ocean (surface, water column, seafloor). Furthermore, the effects of plastic debris on marine organisms in the open ocean will be assessed. The cruise will provide data about the: (1) abundance of plastic debris with a minimum size of 100 μm as well as the composition of polymer types in the water column at different depths from the sea surface to the seafloor including the sediment, (2) abundance and composition of plastic debris in organic aggregates (“marine snow”), (3) in pelagic and benthic organisms (invertebrates and fish) and in fecal pellets, (4) abundance and the identity of biofoulers (bacteria, protozoans and metazoans) on the surface of plastic debris from different water depths, (5) identification of chemical compounds (“additives”) in the plastic debris and in water samples.
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Hawkins, Brian T., and Sonia Grego. A Better, Faster Road From Biological Data to Human Health: A Systems Biology Approach for Engineered Cell Cultures. RTI Press, June 2017. http://dx.doi.org/10.3768/rtipress.2017.rb.0015.1706.
Traditionally, the interactions of drugs and toxicants with human tissue have been investigated in a reductionist way—for example, by focusing on specific molecular targets and using single-cell-type cultures before testing compounds in whole organisms. More recently, “systems biology” approaches attempt to enhance the predictive value of in vitro biological data by adopting a comprehensive description of biological systems and using computational tools that are sophisticated enough to handle the complexity of these systems. However, the utility of computational models resulting from these efforts completely relies on the quality of the data used to construct them. Here, we propose that recent advances in the development of bioengineered, three-dimensional, multicellular constructs provide in vitro data of sufficient complexity and physiological relevance to be used in predictive systems biology models of human responses. Such predictive models are essential to maximally leveraging these emerging bioengineering technologies to improve both therapeutic development and toxicity risk assessment. This brief outlines the opportunities presented by emerging technologies and approaches for the acceleration of drug development and toxicity testing, as well as the challenges lying ahead for the field.
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Seale, Maria, Natàlia Garcia-Reyero, R. Salter, and Alicia Ruvinsky. An epigenetic modeling approach for adaptive prognostics of engineered systems. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41282.
Prognostics and health management (PHM) frameworks are widely used in engineered systems, such as manufacturing equipment, aircraft, and vehicles, to improve reliability, maintainability, and safety. Prognostic information for impending failures and remaining useful life is essential to inform decision-making by enabling cost versus risk estimates of maintenance actions. These estimates are generally provided by physics-based or data-driven models developed on historical information. Although current models provide some predictive capabilities, the ability to represent individualized dynamic factors that affect system health is limited. To address these shortcomings, we examine the biological phenomenon of epigenetics. Epigenetics provides insight into how environmental factors affect genetic expression in an organism, providing system health information that can be useful for predictions of future state. The means by which environmental factors influence epigenetic modifications leading to observable traits can be correlated to circumstances affecting system health. In this paper, we investigate the general parallels between the biological effects of epigenetic changes on cellular DNA to the influences leading to either system degradation and compromise, or improved system health. We also review a variety of epigenetic computational models and concepts, and present a general modeling framework to support adaptive system prognostics.
