Academic literature on the topic 'Dynamic behaviour of complex biomolecular networks'
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Journal articles on the topic "Dynamic behaviour of complex biomolecular networks"
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Coveney, Peter V., and Philip W. Fowler. "Modelling biological complexity: a physical scientist's perspective." Journal of The Royal Society Interface 2, no. 4 (2005): 267–80. http://dx.doi.org/10.1098/rsif.2005.0045.
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We discuss the modern approaches of complexity and self-organization to understanding dynamical systems and how these concepts can inform current interest in systems biology. From the perspective of a physical scientist, it is especially interesting to examine how the differing weights given to philosophies of science in the physical and biological sciences impact the application of the study of complexity. We briefly describe how the dynamics of the heart and circadian rhythms, canonical examples of systems biology, are modelled by sets of nonlinear coupled differential equations, which have to be solved numerically. A major difficulty with this approach is that all the parameters within these equations are not usually known. Coupled models that include biomolecular detail could help solve this problem. Coupling models across large ranges of length- and time-scales is central to describing complex systems and therefore to biology. Such coupling may be performed in at least two different ways, which we refer to as hierarchical and hybrid multiscale modelling. While limited progress has been made in the former case, the latter is only beginning to be addressed systematically. These modelling methods are expected to bring numerous benefits to biology, for example, the properties of a system could be studied over a wider range of length- and time-scales, a key aim of systems biology. Multiscale models couple behaviour at the molecular biological level to that at the cellular level, thereby providing a route for calculating many unknown parameters as well as investigating the effects at, for example, the cellular level, of small changes at the biomolecular level, such as a genetic mutation or the presence of a drug. The modelling and simulation of biomolecular systems is itself very computationally intensive; we describe a recently developed hybrid continuum-molecular model, HybridMD, and its associated molecular insertion algorithm, which point the way towards the integration of molecular and more coarse-grained representations of matter. The scope of such integrative approaches to complex systems research is circumscribed by the computational resources available. Computational grids should provide a step jump in the scale of these resources; we describe the tools that RealityGrid, a major UK e-Science project, has developed together with our experience of deploying complex models on nascent grids. We also discuss the prospects for mathematical approaches to reducing the dimensionality of complex networks in the search for universal systems-level properties, illustrating our approach with a description of the origin of life according to the RNA world view.
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Ayadi, Ali, Cecilia Zanni-Merk, François de Bertrand de Beuvron, and Saoussen Krichen. "BNO: An ontology for describing the behaviour of complex biomolecular networks." Procedia Computer Science 112 (2017): 524–33. http://dx.doi.org/10.1016/j.procs.2017.08.159.
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Roukos, Dimitrios H. "Networks medicine: from reductionism to evidence of complex dynamic biomolecular interactions." Pharmacogenomics 12, no. 5 (2011): 695–98. http://dx.doi.org/10.2217/pgs.11.28.
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Xie, Jiang, Dongfang Lu, Jiaxin Li, et al. "Kernel differential subgraph reveals dynamic changes in biomolecular networks." Journal of Bioinformatics and Computational Biology 16, no. 01 (2018): 1750027. http://dx.doi.org/10.1142/s0219720017500275.
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Many major diseases, including various types of cancer, are increasingly threatening human health. However, the mechanisms of the dynamic processes underlying these diseases remain ambiguous. From the holistic perspective of systems science, complex biological networks can reveal biological phenomena. Changes among networks in different states influence the direction of living organisms. The identification of the kernel differential subgraph (KDS) that leads to drastic changes is critical. The existing studies contribute to the identification of a KDS in networks with the same nodes; however, networks in different states involve the disappearance of some nodes or the appearance of some new nodes. In this paper, we propose a new topology-based KDS (TKDS) method to explore the core module from gene regulatory networks with different nodes in this process. For the common nodes, the TKDS method considers the differential value (D-value) of the topological change. For the different nodes, TKDS identifies the most similar gene pairs and computes the D-value. Hence, TKDS discovers the essential KDS, which considers the relationships between the same nodes as well as different nodes. After applying this method to non-small cell lung cancer (NSCLC), we identified 30 genes that are most likely related to NSCLC and extracted the KDSs in both the cancer and normal states. Two significance functional modules were revealed, and gene ontology (GO) analyses and literature mining indicated that the KDSs are essential to the processes in NSCLC. In addition, compared with existing methods, TKDS provides a unique perspective in identifying particular genes and KDSs related to NSCLC. Moreover, TKDS has the potential to predict other critical disease-related genes and modules.
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Ayadi, Ali, François de Bertrand de Beuvron, Cecilia Zanni-Merk, and Saoussen Krichen. "CBNSimulator: a simulator tool for understanding the behaviour of complex biomolecular networks using discrete time simulation." Procedia Computer Science 112 (2017): 514–23. http://dx.doi.org/10.1016/j.procs.2017.08.157.
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Irigoyen, Eloy, Antonio Javier Barragán, Mikel Larrea, and José Manuel Andújar. "About Extracting Dynamic Information of Unknown Complex Systems by Neural Networks." Complexity 2018 (July 8, 2018): 1–12. http://dx.doi.org/10.1155/2018/3671428.
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This work presents a straightforward methodology based on neural networks (NN) which allows to obtain relevant dynamic information of unknown nonlinear systems. It provides an approach for cases in which the complex task of analyzing the dynamic behaviour of nonlinear systems makes it excessively challenging to obtain an accurate mathematical model. After reviewing the suitability of multilayer perceptrons (MLPs) as universal approximators to replace a mathematical model, the first part of this work presents a system representation using a model formulated with state variables which can be exported to a NN structure. Considering the linearization of the NN model in a mesh of operating points, the second part of this work presents the study of equilibrium states in such points by calculating the Jacobian matrix of the system through the NN model. The results analyzed in three case studies provide representative examples of the strengths of the proposed method. Conclusively, it is feasible to study the system behaviour based on MLPs, which enables the analysis of the local stability of the equilibrium points, as well as the system dynamics in its environment, therefore obtaining valuable information of the system dynamic behaviour.
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Bryden, John, Sebastian Funk, Nicholas Geard, Seth Bullock, and Vincent A. A. Jansen. "Stability in flux: community structure in dynamic networks." Journal of The Royal Society Interface 8, no. 60 (2010): 1031–40. http://dx.doi.org/10.1098/rsif.2010.0524.
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The structure of many biological, social and technological systems can usefully be described in terms of complex networks. Although often portrayed as fixed in time, such networks are inherently dynamic, as the edges that join nodes are cut and rewired, and nodes themselves update their states. Understanding the structure of these networks requires us to understand the dynamic processes that create, maintain and modify them. Here, we build upon existing models of coevolving networks to characterize how dynamic behaviour at the level of individual nodes generates stable aggregate behaviours. We focus particularly on the dynamics of groups of nodes formed endogenously by nodes that share similar properties (represented as node state) and demonstrate that, under certain conditions, network modularity based on state compares well with network modularity based on topology. We show that if nodes rewire their edges based on fixed node states, the network modularity reaches a stable equilibrium which we quantify analytically. Furthermore, if node state is not fixed, but can be adopted from neighbouring nodes, the distribution of group sizes reaches a dynamic equilibrium, which remains stable even as the composition and identity of the groups change. These results show that dynamic networks can maintain the stable community structure that has been observed in many social and biological systems.
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Kim, Youngchan, Federico Bertagna, Edeline M. D’Souza, et al. "Quantum Biology: An Update and Perspective." Quantum Reports 3, no. 1 (2021): 80–126. http://dx.doi.org/10.3390/quantum3010006.
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Understanding the rules of life is one of the most important scientific endeavours and has revolutionised both biology and biotechnology. Remarkable advances in observation techniques allow us to investigate a broad range of complex and dynamic biological processes in which living systems could exploit quantum behaviour to enhance and regulate biological functions. Recent evidence suggests that these non-trivial quantum mechanical effects may play a crucial role in maintaining the non-equilibrium state of biomolecular systems. Quantum biology is the study of such quantum aspects of living systems. In this review, we summarise the latest progress in quantum biology, including the areas of enzyme-catalysed reactions, photosynthesis, spin-dependent reactions, DNA, fluorescent proteins, and ion channels. Many of these results are expected to be fundamental building blocks towards understanding the rules of life.
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Kiss, Istvan Z., Luc Berthouze, Timothy J. Taylor, and Péter L. Simon. "Modelling approaches for simple dynamic networks and applications to disease transmission models." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 468, no. 2141 (2012): 1332–55. http://dx.doi.org/10.1098/rspa.2011.0349.
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In this paper a random link activation–deletion (RLAD) model is proposed that gives rise to a stochastically evolving network. This dynamic network is then coupled to a simple susceptible-infectious-suceptible ( SIS ) dynamics on the network, and the resulting spectrum of model behaviour is explored via simulation and a novel pairwise model for dynamic networks. First, the dynamic network model is systematically analysed by considering link-type independent and dependent network dynamics coupled with globally constrained link creation. This is done rigorously with some analytical results and we highlight where such analysis can be performed and how these simpler models provide a benchmark to test and validate full simulations. The pairwise model is used to study the interplay between SIS -type dynamics on the network and link-type-dependent activation–deletion. Assumptions of the pairwise model are identified and their implications interpreted in a way that complements our current understanding. Furthermore, we also discuss how the strong assumptions of the closure relations can lead to disagreement between the simulation and pairwise model. Unlike on a static network, the resulting spectrum of behaviour is more complex with the prevalence of infections exhibiting not only a single steady state, but also bistability and oscillations.
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Vernon, Matthew C., and Matt J. Keeling. "Representing the UK's cattle herd as static and dynamic networks." Proceedings of the Royal Society B: Biological Sciences 276, no. 1656 (2008): 469–76. http://dx.doi.org/10.1098/rspb.2008.1009.
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Network models are increasingly being used to understand the spread of diseases through sparsely connected populations, with particular interest in the impact of animal movements upon the dynamics of infectious diseases. Detailed data collected by the UK government on the movement of cattle may be represented as a network, where animal holdings are nodes, and an edge is drawn between nodes where a movement of animals has occurred. These network representations may vary from a simple static representation, to a more complex, fully dynamic one where daily movements are explicitly captured. Using stochastic disease simulations, a wide range of network representations of the UK cattle herd are compared. We find that the simpler static network representations are often deficient when compared with a fully dynamic representation, and should therefore be used only with caution in epidemiological modelling. In particular, due to temporal structures within the dynamic network, static networks consistently fail to capture the predicted epidemic behaviour associated with dynamic networks even when parameterized to match early growth rates.
Dissertations / Theses on the topic "Dynamic behaviour of complex biomolecular networks"
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Ayadi, Ali. "Semantic approaches for the meta-optimization of complex biomolecular networks." Thesis, Strasbourg, 2018. http://www.theses.fr/2018STRAD035/document.
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Les modèles de la biologie des systèmes visent à comprendre le comportement d’une cellule à travers un réseau biomoléculaire complexe. Dans a littérature, la plupart des études ne se sont intéressés qu’à la modélisation des parties isolées du réseau biomoléculaire com les réseaux métaboliques, etc. Cependant, pour bien comprendre le comportement d’une cellule, nous devons modéliser et analyser le réseau biomoléculaire dans son ensemble. Les approches existantes ne répondent pas suffisamment à ces exigences. Dans ce projet de recherche,nous proposons une plate-forme qui permet aux biologistes de simuler les changements d’état des réseaux biomoléculaires dans le but de piloter leurs comportements et de les faire évoluer d’un état non désiré vers un état souhaitable. Cette plate-forme utilise des règles, des connaissances et de l’expérience, un peu comme celles que pourrait en tirer un biologiste expert. La plate-forme comprend quatre modules : un module de modélisation logique, un module de modélisation sémantique, un module de simulation qualitative à événements discrets etun module d’optimisation. Dans ce but, nous présentons d’abord une approche logique pour la modélisation des réseaux biomoléculaires complexes, incluant leurs aspects structurels, fonctionnels et comportementaux. Ensuite, nous proposons une approche sémantique basée sur quatre ontologies pour fournir une description riche des réseaux biomoléculaires et de leurs changements d’état. Ensuite, nous présentons une méthode de simulation qualitative à événements discrets pour simuler le comportement du réseau biomoléculaire dans le temps. Enfin, nous proposons une méthode d’optimisation multi-objectifs pour optimiser la transitabilité des réseaux biomoléculaires complexes dans laquelle nous prenons en compte différents critères tels que la minimisation du nombre de stimuli externes, la minimisation du coût de ces stimuli, la minimisation du nombre de noeuds cibles et la minimisation de l’inconfort du patient. En se fondant sur ces quatre contributions, un prototype appelé CBN-Simulateur a été développé. Nous décrivons nos approches et montrons leurs applications sur des études de cas réels, le bactériophage T4 gene 32, le phage lambda et le réseau de signalisation p53. Les résultats montrent que ces approches fournissent les éléments nécessaires pour modéliser, raisonner et analyser le comportement dynamique et les états de transition des réseaux biomoléculaires complexes<br>Systems biology models aim to understand the behaviour of a cell trough a complex biomolecular network. In the literature, most research focuses on modelling isolated parts of this network, such as metabolic networks.However, to fully understand the cell’s behaviour we should analyze the biomolecular network as a whole. Avail-able approaches do not address these requirements sufficiently. In this context, we aim at developing a platform that enables biologists to simulate the state changes of biomolecular networks with the goal of steering their be-haviours. The platform employs rules, knowledge and experience, much like those that an expert biologist mightderive. This platform consists of four modules: a logic-based modelling module, a semantic modelling module,a qualitative discrete-event simulation module and an optimization module. For this purpose, we first present alogic-based approach for modelling complex biomolecular networks including the structural, functional and be-havioural aspects. Next, we propose a semantic approach based on four ontologies to provide a rich description of biomolecular networks and their state changes. Then, we present a method of qualitative discrete-event simulation to simulate the biomolecular network behaviour over time. Finally, we propose a multi-objective optimization method for optimizing the transittability of complex biomolecular networks in which we take into account various criteria such as minimizing the number of external stimuli, minimizing the cost of these stimuli, minimizing the number of target nodes and minimizing patient discomfort. Based on these four contributions, a prototype called the CBNSimulator was developed. We describe our approaches and show their applicability through real cases studies, the bacteriophage T4 gene 32, the phage lambda, and the p53 signaling network. Results demonstrate that these approaches provide the necessary elements to model, reason and analyse the dynamic behaviour and the transition states of complex biomolecular networks
Book chapters on the topic "Dynamic behaviour of complex biomolecular networks"
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Dragos, Valentina. "From Finding to Explaining." In Information Retrieval and Management. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5191-1.ch069.
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Supporting anomaly analysis in the maritime field is a challenging problem because of the dynamic nature of the task: the definition of abnormal or suspicious behaviour is subject to change and depends on user interests. This paper provides a novel approach to support anomaly analysis in the maritime domain through the exploration of large collections of interpretation reports. Based on observables or more sophisticated patterns, the approach provides information retrieval strategies going from basic facts retrieval that guide short-term corrective actions to more complex networks of related concepts that help domain experts to understand or to explain abnormal vessel behaviours. Semantic integration is used to link various information sources, by using a commonly adopted standard. The paper seeks to explore different aspects of using information retrieval to support the analysis and interpretation of abnormal vessel behaviours for maritime surveillance.
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Mohanty, Itishree, and Dabashish Bhattacherjee. "Artificial Neural Network and Its Application in Steel Industry." In Advances in Chemical and Materials Engineering. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0290-6.ch010.
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The recent developments in computational intelligence has enhances the applicability of empirical modelling in different areas particularly in the area of machine learning. These new approaches are based on analysing the data about a system, in particular finding connections between the system state variables (input, internal and output variables) without having precise knowledge about the physical behaviour of the system. These data driven methods explain advances on conventional empirical modelling and include contributions from many overlapping fields like Artificial Intelligence (AI), Computational Intelligence (CI), Soft Computing (SC), Machine Learning (ML), Intelligent Data Analysis (IDA), and Data Mining (DM). The most popular computational intelligence techniques used in process modelling of steel industry includes neural networks, fuzzy rule-based systems, genetic algorithms as well as approaches to model integration. This chapter describes mainly the application of Artificial Neural Network (ANN) in steel industry. ANN has extensively used in improving and controlling different processes of steel industry like steel making, casting and rolling which lead to indirect energy savings through reduced product rejects, improved productivity and reduced down time. The efficiency of artificial neural network tool in handling steel plant processes has been discussed in detail. ANN based models are found to be very potential to handle very complex, dynamic and non-linear problems.
Conference papers on the topic "Dynamic behaviour of complex biomolecular networks"
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Ayadi, Ali, Cecilia Zanni-Merk, Francois de Bertrand de Beuvron, and Saoussen Krichen. "Ontological Reasoning for Understanding the Behaviour of Complex Biomolecular Networks." In 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA). IEEE, 2017. http://dx.doi.org/10.1109/aiccsa.2017.182.
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Ayadi, Ali, Cecilia Zanni-Merk, and François de Beuvron de Bertrand. "Qualitative Reasoning for Understanding the Behaviour of Complex Biomolecular Networks." In 8th International Conference on Knowledge Engineering and Ontology Development. SCITEPRESS - Science and and Technology Publications, 2016. http://dx.doi.org/10.5220/0006065901440149.
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Anthony, Richard John, and Mona Ghassemian. "Mobility Status as Dynamic Context for Behaviour Optimisation in Self-Organised Networks." In 2009 International Conference on Complex, Intelligent and Software Intensive Systems (CISIS). IEEE, 2009. http://dx.doi.org/10.1109/cisis.2009.98.
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Fiala, Petr, and Martina Kuncová. "Simulation model of supply networks development." In The 19th International Conference on Modelling and Applied Simulation. CAL-TEK srl, 2019. http://dx.doi.org/10.46354/i3m.2019.mas.003.
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The paper is dedicated to network development in the network economy. The current economy needs to look not only at networks with only dynamic flows and with a fixed structure, but as a dynamic system its structure evolves and changes. Structure and behaviour dynamics of network systems can be modelled as complex adaptive systems and use agent-oriented simulation to demonstrate origin, perturbation effects, and sensitivity with regard to initial conditions. Survival of firms is associated with the value of so-called fitness function. Firms whose fitness value falls below a certain threshold will be extinguished. In this way, it is possible to partially model network growth. A simulation model in SIMUL8 is proposed.