A mathematical tool for exploring the dynamics of biological networks

Barbano, Paolo Emilio; Spivak, Marina; Flajolet, Marc; Nairn, Angus C.; Greengard, Paul; Greengard, Leslie

doi:10.1073/pnas.0709955104

Cited by 35 publications

(34 citation statements)

References 27 publications

(23 reference statements)

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“…The resulting models can be very high dimensional in both states and parameters and a central concern in the field is how such complex models can be used when many of the parameter values are unknown. There are many perspectives on this problem: some properties of systems can be proved to be independent of parameter values (Feinberg 1995;Angeli et al 2004); parameters can be estimated from data in statistically meaningful ways ( Jaqaman & Danuser 2006); methods of dimensionality reduction can reduce complexity (Barbano et al 2007); some properties of systems have been found empirically to be robust to parameter variation (Alon et al 1999;von Dassow et al 2000); and both theory and empirical results suggest that, for any given phenotypic behaviour, only an exponentially small number of parameters are significant (Rand et al 2005;Gutenkunst et al 2007). We note further that both pharmaceutical and biotechnology companies are adopting modelling in drug development (Bangs & Paterson 2003;Hendriks et al 2006;Haberichter et al 2007), suggesting that model complexity is not a barrier to usefulness.…”

Section: Introductionmentioning

confidence: 99%

Programming with models: modularity and abstraction provide powerful capabilities for systems biology

Mallavarapu

Thomson

Ullian

et al. 2008

J. R. Soc. Interface.

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Mathematical models are increasingly used to understand how phenotypes emerge from systems of molecular interactions. However, their current construction as monolithic sets of equations presents a fundamental barrier to progress. Overcoming this requires modularity, enabling sub-systems to be specified independently and combined incrementally, and abstraction, enabling generic properties of biological processes to be specified independently of specific instances. These, in turn, require models to be represented as programs rather than as datatypes. Programmable modularity and abstraction enables libraries of modules to be created, which can be instantiated and reused repeatedly in different contexts with different components. We have developed a computational infrastructure that accomplishes this. We show here why such capabilities are needed, what is required to implement them and what can be accomplished with them that could not be done previously.

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Section: Introductionmentioning

confidence: 99%

Programming with models: modularity and abstraction provide powerful capabilities for systems biology

Mallavarapu

Thomson

Ullian

et al. 2008

J. R. Soc. Interface.

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“…This phenomenon is often referred to as "robustness", and simulation has begun to play a role in elucidating its molecular basis. Based on combining large-scale numerical simulation with nonlinear "dimensionality reduction" methods, Paolo et al [35] developed a mathematical approach to the study of dynamical biological networks, to detect robust features of the system in the presence of noise. Firstly, the data was reduced with their dimension using locally linear embedding (LLE) and Laplacian eigenmaps (LEs) method to simplify the complexity of subsequent data processing.…”

Section: Differential Equation Modelmentioning

confidence: 99%

Bioinformatics analyses for signal transduction networks

Liu

Zhu

et al. 2008

SCI CHINA SER C

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Research in signaling networks contributes to a deeper understanding of organism living activities. With the development of experimental methods in the signal transduction field, more and more mechanisms of signaling pathways have been discovered. This paper introduces such popular bioinformatics analysis methods for signaling networks as the common mechanism of signaling pathways and database resource on the Internet, summerizes the methods of analyzing the structural properties of networks, including structural Motif finding and automated pathways generation, and discusses the modeling and simulation of signaling networks in detail, as well as the research situation and tendency in this area. Now the investigation of signal transduction is developing from small-scale experiments to large-scale network analysis, and dynamic simulation of networks is closer to the real system. With the investigation going deeper than ever, the bioinformatics analysis of signal transduction would have immense space for development and application.

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“…A number of models of dopamine (DA) and Ca 2+ signal integration have included only Thr34 and Thr75 as major switching factors between LTP and LTD [17,18,29,105]. A few models incorporate all four phosphorylation sites [19,106].…”

Section: Biophysical Models Of Synaptic Plasticitymentioning

confidence: 99%

Analysis of proteins in computational models of synaptic plasticity

Heil

Wysocka

Sorokina

et al. 2018

Preprint

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The desire to explain how synaptic plasticity arises from interactions between ions, proteins and other signalling molecules has propelled the development of biophysical models of molecular pathways in hippocampal, striatal and cerebellar synapses. The experimental data underpinning such models is typically obtained from low-throughput, hypothesis-driven experiments. We used high-throughput proteomic data and bioinformatics datasets to assess the coverage of biophysical models.To determine which molecules have been modelled, we surveyed biophysical models of synaptic plasticity, identifying which proteins are involved in each model. We were able to map 4.2% of previously reported synaptic proteins to entities in biophysical models. Linking the modelled protein list to Gene Ontology terms shows that modelled proteins are focused on functions such as calmodulin binding, cellular responses to glucagon stimulus, G-alpha signalling and DARPP-32 events.We cross-linked the set of modelled proteins with sets of genes associated with common neurological diseases. We find some examples of disease-associated molecules that are well represented in models, such as voltage-dependent calcium channel family (CACNA1C ), dopamine D1 receptor, and glutamate ionotropic NMDA type 2A and 2B receptors. Many other disease-associated genes have not been included in models of synaptic plasticity, for example catechol-O-methyltransferase (COMT ) and MAOA. By incorporating pathway enrichment results, we identify LAMTOR, a gene uniquely associated with Schizophrenia, which is closely linked to the MAPK pathway found in some models.Our analysis provides a map of how molecular pathways underpinning neurological diseases relate to synaptic biophysical models that can in turn be used to explore how these molecular events might bridge scales into cellular processes and beyond. The map illustrates disease areas where biophysical models have good coverage as well as domain gaps that require significant further research. Author summaryThe 100 billion neurons in the human brain are connected by a billion trillion structures called synapses. Each synapse contains hundreds of different proteins. Some proteins sense the activity of the neurons connecting the synapse. Depending on what they sense, . 28, 2018; the proteins in the synapse are rearranged and new proteins are synthesised. This changes how strongly the synapse influences its target neuron, and underlies learning and memory. Scientists build computational models to reason about the complex interactions between proteins. Here we list the proteins that have been included in computational models to date. For good reasons, models do not always specify proteins precisely, so to make the list we had to translate the names used for proteins in models to gene names, which are used to identify proteins. Our translation could be used to label computational models in the future. We found that the list of modelled proteins contains only 4.2% of proteins associated with synapses, suggesting mor...

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A mathematical tool for exploring the dynamics of biological networks

Cited by 35 publications

References 27 publications

Programming with models: modularity and abstraction provide powerful capabilities for systems biology

Programming with models: modularity and abstraction provide powerful capabilities for systems biology

Bioinformatics analyses for signal transduction networks

Analysis of proteins in computational models of synaptic plasticity

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