Rule-based modelling and simulation of biochemical systems with molecular finite automata

Yang, Jian; Meng, Xiangda; Hlavacek, William S.

doi:10.1049/iet-syb.2010.0015

Cited by 16 publications

(13 citation statements)

References 58 publications

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“…Lemons et al 65 have recently proposed conventions that allow such graphs to be used to annotate rule-based models. (Incidentally, these conventions are consistent with the related representational formalism of Yang et al 71 .) In Fig.…”

Section: Resultssupporting

confidence: 86%

Guidelines for visualizing and annotating rule-based models

Chylek

Blinov

et al. 2011

Mol. BioSyst.

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Rule-based modeling provides a means to represent cell signaling systems in a way that captures site-specific details of molecular interactions. For rule-based models to be more widely understood and (re)used, conventions for model visualization and annotation are needed. We have developed the concepts of an extended contact map and a model guide for illustrating and annotating rule-based models. An extended contact map represents the scope of a model by providing an illustration of each molecule, molecular component, direct physical interaction, post-translational modification, and enzyme-substrate relationship considered in a model. A map can also illustrate allosteric effects, structural relationships among molecular components, and compartmental locations of molecules. A model guide associates elements of a contact map with annotation and elements of an underlying model, which may be fully or partially specified. A guide can also serve to document the biological knowledge upon which a model is based. We provide examples of a map and guide for a published rule-based model that characterizes early events in IgE receptor (FcεRI) signaling. We also provide examples of how to visualize a variety of processes that are common in cell signaling systems but not considered in the example model, such as ubiquitination. An extended contact map and an associated guide can document knowledge of a cell signaling system in a form that is visual as well as executable. As a tool for model annotation, a map and guide can communicate the content of a model clearly and with precision, even for large models.

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Section: Resultssupporting

confidence: 86%

Guidelines for visualizing and annotating rule-based models

Chylek

Blinov

et al. 2011

Mol. BioSyst.

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“…Yang et al [8] have proposed a specification formalism based on finite automata . Models specified in their Molecular Finite Automata (MFA) framework can then be used to generate and simulate a system of ODEs or for stochastic simulation using a kinetic Monte Carlo algorithm.…”

Section: The Specification Problemmentioning

confidence: 99%

“…Modeling such multi-state systems poses two problems: the problem of how to describe and specify a multi-state system (the “specification problem”) and the problem of how to use a computer to simulate the progress of the system over time (the “computation problem”). To address the specification problem, modelers have in recent years moved away from explicit specification of all possible states and towards rule-based formalisms that allow for implicit model specification, including the κ-calculus [1] , BioNetGen [2] – [5] , the Allosteric Network Compiler [6] , and others [7] , [8] . To tackle the computation problem, they have turned to particle-based methods that have in many cases proved more computationally efficient than population-based methods based on ordinary differential equations , partial differential equations , or the Gillespie stochastic simulation algorithm [9] , [10] .…”

mentioning

confidence: 99%

Multi-state Modeling of Biomolecules

et al. 2014

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Multi-state modeling of biomolecules refers to a series of techniques used to represent and compute the behavior of biological molecules or complexes that can adopt a large number of possible functional states. Biological signaling systems often rely on complexes of biological macromolecules that can undergo several functionally significant modifications that are mutually compatible. Thus, they can exist in a very large number of functionally different states. Modeling such multi-state systems poses two problems: the problem of how to describe and specify a multi-state system (the “specification problem”) and the problem of how to use a computer to simulate the progress of the system over time (the “computation problem”). To address the specification problem, modelers have in recent years moved away from explicit specification of all possible states and towards rule-based formalisms that allow for implicit model specification, including the κ-calculus [1], BioNetGen [2]–[5], the Allosteric Network Compiler [6], and others [7], [8]. To tackle the computation problem, they have turned to particle-based methods that have in many cases proved more computationally efficient than population-based methods based on ordinary differential equations, partial differential equations, or the Gillespie stochastic simulation algorithm [9], [10]. Given current computing technology, particle-based methods are sometimes the only possible option. Particle-based simulators fall into two further categories: nonspatial simulators, such as StochSim [11], DYNSTOC [12], RuleMonkey [9], [13], and the Network-Free Stochastic Simulator (NFSim) [14], and spatial simulators, including Meredys [15], SRSim [16], [17], and MCell [18]–[20]. Modelers can thus choose from a variety of tools, the best choice depending on the particular problem. Development of faster and more powerful methods is ongoing, promising the ability to simulate ever more complex signaling processes in the future.

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“…Similarly rule-based formalisms are also being applied to coarse-grain patterns in chemical-kinetic models (Feret et al, 2009; Yang et al, 2010), providing scalable tools to describe complex interactions in cellular systems that begin at the molecular level.…”

Section: Computational Techniques and Advances: Systems Biology Applimentioning

confidence: 99%

Systems approaches for synthetic biology: a pathway toward mammalian design

Rekhi

Qutub

2013

Front. Physiol.

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We review methods of understanding cellular interactions through computation in order to guide the synthetic design of mammalian cells for translational applications, such as regenerative medicine and cancer therapies. In doing so, we argue that the challenges of engineering mammalian cells provide a prime opportunity to leverage advances in computational systems biology. We support this claim systematically, by addressing each of the principal challenges to existing synthetic bioengineering approaches—stochasticity, complexity, and scale—with specific methods and paradigms in systems biology. Moreover, we characterize a key set of diverse computational techniques, including agent-based modeling, Bayesian network analysis, graph theory, and Gillespie simulations, with specific utility toward synthetic biology. Lastly, we examine the mammalian applications of synthetic biology for medicine and health, and how computational systems biology can aid in the continued development of these applications.

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Rule-based modelling and simulation of biochemical systems with molecular finite automata

Cited by 16 publications

References 58 publications

Guidelines for visualizing and annotating rule-based models

Guidelines for visualizing and annotating rule-based models

Multi-state Modeling of Biomolecules

Systems approaches for synthetic biology: a pathway toward mammalian design

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