The Experts below are selected from a list of 129 Experts worldwide ranked by ideXlab platform
James R Faeder - One of the best experts on this subject based on the ideXlab platform.
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evaluation of parallel tempering to accelerate bayesian parameter estimation in systems biology
arXiv: Quantitative Methods, 2018Co-Authors: Sanjana Gupta, Liam Hainsworth, Justin Hogg, Robin E C Lee, James R FaederAbstract:Models of biological systems often have many unknown parameters that must be determined in order for model behavior to match experimental observations. Commonly-used methods for parameter estimation that return point estimates of the best-fit parameters are insufficient when models are high dimensional and under-constrained. As a result, Bayesian methods, which treat model parameters as random variables and attempt to estimate their probability distributions given data, have become popular in systems biology. Bayesian parameter estimation often relies on Markov Chain Monte Carlo (MCMC) methods to sample model parameter distributions, but the slow convergence of MCMC sampling can be a major bottleneck. One approach to improving performance is parallel tempering (PT), a physics-based method that uses swapping between multiple Markov chains run in parallel at different temperatures to accelerate sampling. The temperature of a Markov chain determines the probability of accepting an unfavorable move, so swapping with higher temperatures chains enables the sampling chain to escape from local minima. In this work we compared the MCMC performance of PT and the commonly-used Metropolis-Hastings (MH) algorithm on six biological models of varying complexity. We found that for simpler models PT accelerated convergence and sampling, and that for more complex models, PT often converged in cases MH became trapped in non-optimal local minima. We also developed a freely-available MATLAB package for Bayesian parameter estimation called PTempEst (this http URL), which is closely integrated with the popular BioNetGen software for Rule-Based Modeling of biological systems.
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evaluation of parallel tempering to accelerate bayesian parameter estimation in systems biology
Parallel Distributed and Network-Based Processing, 2018Co-Authors: Sanjana Gupta, Liam Hainsworth, Justin Hogg, Robin E C Lee, James R FaederAbstract:Models of biological systems often have many unknown parameters that must be determined in order for model behavior to match experimental observations. Commonly-used methods for parameter estimation that return point estimates of the best-fit parameters are insufficient when models are high dimensional and under-constrained. As a result, Bayesian methods, which treat model parameters as random variables and attempt to estimate their probability distributions given data, have become popular in systems biology. Bayesian parameter estimation often relies on Markov Chain Monte Carlo (MCMC) methods to sample model parameter distributions, but the slow convergence of MCMC sampling can be a major bottleneck. One approach to improving performance is parallel tempering (PT), a physics-based method that uses swapping between multiple Markov chains run in parallel at different temperatures to accelerate sampling. The temperature of a Markov chain determines the probability of accepting an unfavorable move, so swapping with higher temperatures chains enables the sampling chain to escape from local minima. In this work we compared the MCMC performance of PT and the commonly-used Metropolis- Hastings (MH) algorithm on six biological models of varying complexity. We found that for simpler models PT accelerated convergence and sampling, and that for more complex models, PT often converged in cases MH became trapped in non-optimal local minima. We also developed a freely-available MATLAB package for Bayesian parameter estimation called PTEMPEST (http://github.com/RuleWorld/ptempest), which is closely integrated with the popular BioNetGen software for Rule-Based Modeling of biological systems.
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rule based Modeling a computational approach for studying biomolecular site dynamics in cell signaling systems
Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 2014Co-Authors: Lily A Chylek, Leonard A Harris, James R Faeder, Changshung Tung, Carlos F Lopez, William S HlavacekAbstract:Rule-Based Modeling was developed to address the limitations of traditional approaches for Modeling chemical kinetics in cell signaling systems. These systems consist of multiple interacting biomolecules (e.g., proteins), which themselves consist of multiple parts (e.g., domains, linear motifs, and sites of phosphorylation). Consequently, biomolecules that mediate information processing generally have the potential to interact in multiple ways, with the number of possible complexes and posttranslational modification states tending to grow exponentially with the number of binary interactions considered. As a result, only large reaction networks capture all possible consequences of the molecular interactions that occur in a cell signaling system, which is problematic because traditional Modeling approaches for chemical kinetics (e.g., ordinary differential equations) require explicit network specification. This problem is circumvented through representation of interactions in terms of local rules. With this approach, network specification is implicit and model specification is concise. Concise representation results in a coarse graining of chemical kinetics, which is introduced because all reactions implied by a rule inherit the rate law associated with that rule. Coarse graining can be appropriate if interactions are modular, and the coarseness of a model can be adjusted as needed. Rules can be specified using specialized model-specification languages, and recently developed tools designed for specification of Rule-Based models allow one to leverage powerful software engineering capabilities. A Rule-Based model comprises a set of rules, which can be processed by general-purpose simulation and analysis tools to achieve different objectives (e.g., to perform either a deterministic or stochastic simulation).
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compartmental rule based Modeling of biochemical systems
Winter Simulation Conference, 2009Co-Authors: Leonard A Harris, Justin S Hogg, James R FaederAbstract:Rule-Based Modeling is an approach to Modeling biochemical kinetics in which proteins and other biological components are modeled as structured objects and their interactions are governed by rules that specify the conditions under which reactions occur. BioNetGen is an open-source platform that provides a simple yet expressive language for Rule-Based Modeling (BNGL). In this paper we describe compartmental BNGL (cBNGL), which extends BNGL to enable explicit Modeling of the compartmental organization of the cell and its effects on system dynamics. We show that by making localization a queryable attribute of both molecules and species and introducing appropriate volumetric scaling of reaction rates, the effects of compartmentalization can be naturally modeled using rules. These properties enable the construction of new rule semantics that include both universal rules, those defining interactions that can take place in any compartment in the system, and transport rules, which enable movement of molecular complexes between compartments.
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rule based Modeling of biochemical systems with bionetgen
Methods of Molecular Biology, 2009Co-Authors: James R Faeder, Michael L Blinov, William S HlavacekAbstract:Rule-Based Modeling involves the representation of molecules as structured objects and molecular interactions as rules for transforming the attributes of these objects. The approach is notable in that it allows one to systematically incorporate site-specific details about protein-protein interactions into a model for the dynamics of a signal-transduction system, but the method has other applications as well, such as following the fates of individual carbon atoms in metabolic reactions. The consequences of protein-protein interactions are difficult to specify and track with a conventional Modeling approach because of the large number of protein phosphoforms and protein complexes that these interactions potentially generate. Here, we focus on how a Rule-Based model is specified in the BioNetGen language (BNGL) and how a model specification is analyzed using the BioNetGen software tool. We also discuss new developments in Rule-Based Modeling that should enable the construction and analyses of comprehensive models for signal transduction pathways and similarly large-scale models for other biochemical systems.
William S Hlavacek - One of the best experts on this subject based on the ideXlab platform.
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rule based Modeling a computational approach for studying biomolecular site dynamics in cell signaling systems
Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 2014Co-Authors: Lily A Chylek, Leonard A Harris, James R Faeder, Changshung Tung, Carlos F Lopez, William S HlavacekAbstract:Rule-Based Modeling was developed to address the limitations of traditional approaches for Modeling chemical kinetics in cell signaling systems. These systems consist of multiple interacting biomolecules (e.g., proteins), which themselves consist of multiple parts (e.g., domains, linear motifs, and sites of phosphorylation). Consequently, biomolecules that mediate information processing generally have the potential to interact in multiple ways, with the number of possible complexes and posttranslational modification states tending to grow exponentially with the number of binary interactions considered. As a result, only large reaction networks capture all possible consequences of the molecular interactions that occur in a cell signaling system, which is problematic because traditional Modeling approaches for chemical kinetics (e.g., ordinary differential equations) require explicit network specification. This problem is circumvented through representation of interactions in terms of local rules. With this approach, network specification is implicit and model specification is concise. Concise representation results in a coarse graining of chemical kinetics, which is introduced because all reactions implied by a rule inherit the rate law associated with that rule. Coarse graining can be appropriate if interactions are modular, and the coarseness of a model can be adjusted as needed. Rules can be specified using specialized model-specification languages, and recently developed tools designed for specification of Rule-Based models allow one to leverage powerful software engineering capabilities. A Rule-Based model comprises a set of rules, which can be processed by general-purpose simulation and analysis tools to achieve different objectives (e.g., to perform either a deterministic or stochastic simulation).
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rule based Modeling of biochemical systems with bionetgen
Methods of Molecular Biology, 2009Co-Authors: James R Faeder, Michael L Blinov, William S HlavacekAbstract:Rule-Based Modeling involves the representation of molecules as structured objects and molecular interactions as rules for transforming the attributes of these objects. The approach is notable in that it allows one to systematically incorporate site-specific details about protein-protein interactions into a model for the dynamics of a signal-transduction system, but the method has other applications as well, such as following the fates of individual carbon atoms in metabolic reactions. The consequences of protein-protein interactions are difficult to specify and track with a conventional Modeling approach because of the large number of protein phosphoforms and protein complexes that these interactions potentially generate. Here, we focus on how a Rule-Based model is specified in the BioNetGen language (BNGL) and how a model specification is analyzed using the BioNetGen software tool. We also discuss new developments in Rule-Based Modeling that should enable the construction and analyses of comprehensive models for signal transduction pathways and similarly large-scale models for other biochemical systems.
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kinetic monte carlo method for rule based Modeling of biochemical networks
Physical Review E, 2008Co-Authors: Jin Yang, James R Faeder, Michael I Monine, William S HlavacekAbstract:We present a kinetic Monte Carlo method for simulating chemical transformations specified by reaction rules, which can be viewed as generators of chemical reactions, or equivalently, definitions of reaction classes. A rule identifies the molecular components involved in a transformation, how these components change, conditions that affect whether a transformation occurs, and a rate law. The computational cost of the method, unlike conventional simulation approaches, is independent of the number of possible reactions, which need not be specified in advance or explicitly generated in a simulation. To demonstrate the method, we apply it to study the kinetics of multivalent ligand-receptor interactions. We expect the method will be useful for studying cellular signaling systems and other physical systems involving aggregation phenomena.
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bionetgen software for rule based Modeling of signal transduction based on the interactions of molecular domains
Bioinformatics, 2004Co-Authors: Michael L Blinov, James R Faeder, Byron Goldstein, William S HlavacekAbstract:Summary: BioNetGen allows a user to create a computational model that characterizes the dynamics of a signal transduction system, and that accounts comprehensively and precisely for specified enzymatic activities, potential post-translational modifications and interactions of the domains of signaling molecules. The output defines and parameterizes the network of molecular species that can arise during signaling and provides functions that relate model variables to experimental readouts of interest. Models that can be generated are relevant for rational drug discovery, analysis of proteomic data and mechanistic studies of signal transduction. Availability: http://cellsignaling.lanl.gov/bionetgen
Terrence J Sejnowski - One of the best experts on this subject based on the ideXlab platform.
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interactions between calmodulin and neurogranin govern the dynamics of camkii as a leaky integrator
PLOS Computational Biology, 2020Co-Authors: Mariam Ordyan, Thomas M Bartol, Mary B Kennedy, Padmini Rangamani, Terrence J SejnowskiAbstract:Calmodulin-dependent kinase II (CaMKII) has long been known to play an important role in learning and memory as well as long term potentiation (LTP). More recently it has been suggested that it might be involved in the time averaging of synaptic signals, which can then lead to the high precision of information stored at a single synapse. However, the role of the scaffolding molecule, neurogranin (Ng), in governing the dynamics of CaMKII is not yet fully understood. In this work, we adopt a Rule-Based Modeling approach through the Monte Carlo method to study the effect of Ca2+ signals on the dynamics of CaMKII phosphorylation in the postsynaptic density (PSD). Calcium surges are observed in synaptic spines during an EPSP and back-propagating action potential due to the opening of NMDA receptors and voltage dependent calcium channels. Using agent-based models, we computationally investigate the dynamics of phosphorylation of CaMKII monomers and dodecameric holoenzymes. The scaffolding molecule, Ng, when present in significant concentration, limits the availability of free calmodulin (CaM), the protein which activates CaMKII in the presence of calcium. We show that Ng plays an important modulatory role in CaMKII phosphorylation following a surge of high calcium concentration. We find a non-intuitive dependence of this effect on CaM concentration that results from the different affinities of CaM for CaMKII depending on the number of calcium ions bound to the former. It has been shown previously that in the absence of phosphatase, CaMKII monomers integrate over Ca2+ signals of certain frequencies through autophosphorylation (Pepke et al, Plos Comp. Bio., 2010). We also study the effect of multiple calcium spikes on CaMKII holoenzyme autophosphorylation, and show that in the presence of phosphatase, CaMKII behaves as a leaky integrator of calcium signals, a result that has been recently observed in vivo. Our models predict that the parameters of this leaky integrator are finely tuned through the interactions of Ng, CaM, CaMKII, and PP1, providing a mechanism to precisely control the sensitivity of synapses to calcium signals. Author Summary not valid for PLOS ONE submissions.
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interactions between calmodulin and neurogranin govern the dynamics of camkii as a leaky integrator
bioRxiv, 2019Co-Authors: Mariam Ordyan, Thomas M Bartol, Mary B Kennedy, Padmini Rangamani, Terrence J SejnowskiAbstract:Calmodulin-dependent kinase II (CaMKII) has long been known to play an important role in learning and memory as well as long term potentiation (LTP). More recently it has been suggested that it might be involved in the time averaging of synaptic signals, which can then lead to the high precision of information stored at a single synapse. However, the role of the scaffolding molecule, neurogranin (Ng), in governing the dynamics of CaMKII is not yet fully understood. In this work, we adopt a Rule-Based Modeling approach through the Monte Carlo method to study the effect of Ca2+ signals on the dynamics of CaMKII phosphorylation in the postsynaptic density (PSD). Calcium surges are observed in synaptic spines during an EPSP and back-propagating action potential due to the opening of NMDA receptors and voltage dependent calcium channels. We study the differences between the dynamics of phosphorylation of CaMKII monomers and dodecameric holoenzymes. The scaffolding molecule Ng, when present in significant concentration, limits the availability of free calmodulin (CaM), the protein which activates CaMKII in the presence of calcium. We show that it plays an important modulatory role in CaMKII phosphorylation following a surge of high calcium concentration. We find a non-intuitive dependence of this effect on CaM concentration that results from the different affinities of CaM for CaMKII depending on the number of calcium ions bound to the former. It has been shown previously that in the absence of phosphatase CaMKII monomers integrate over calcium signals of certain frequencies through autophosphorylation (Pepke et al, Plos Comp. Bio., 2010). We also study the effect of multiple calcium spikes on CaMKII holoenzyme autophosphorylation, and show that in the presence of phosphatase CaMKII be- haves as a leaky integrator of calcium signals, a result that has been recently observed in vivo. Our models predict that the parameters of this leaky integrator are finely tuned through the interactions of Ng, CaM, CaMKII, and PP1. This is a possible mechanism to precisely control the sensitivity of synapses to calcium signals.
David Chen - One of the best experts on this subject based on the ideXlab platform.
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semantic enhanced rule driven workflow execution in collaborative virtual enterprise
International Conference on Control Automation Robotics and Vision, 2008Co-Authors: Gang Chen, Jing Bing Zhang, Zhonghua Yang, Junhong Zhou, David ChenAbstract:Globalization leads to an efficient new business paradigm generally known as collaborative virtual enterprise (CVE) which demands flexible service orchestration and robust workflow execution. As two major service orchestration strategies, OWL-S and BPEL have their own strength and deficiencies. To meet the challenge in CVE, in this paper, we propose a workflow execution system (WES) that takes advantage of the complementary strengths of these two technologies. On the one hand, having semantic support, OWL-S is used in dynamic service discovery and composition at high level. On the other hand, at the concrete level, industry-based BPEL is exploited in service execution. The description of each semantic Web service is enhanced with Rule-Based Modeling of the essential business logic behind the service interface. In order to realize interoperability in OWL-S and BPEL without loss of semantic information, we further proposed an OWLS2BPEL Mapper to facilitate the workflow robustness and support rule evaluation to increase responsiveness to customers. A concrete scenario in PC manufacturing collaborative virtual enterprise is analyzed to demonstrate the effectiveness of our workflow execution system.
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dynamic virtual enterprise integration via business rule enhanced semantic service composition framework
Conference on Industrial Electronics and Applications, 2008Co-Authors: Gang Chen, Jing Bing Zhang, Zhonghua Yang, David ChenAbstract:Effective collaboration is crucial to the success of collaborative virtual enterprise (CVE), an emerging business paradigm driven by the increasing trend of globalization. In this paper, we adopt a service-oriented architecture for enterprise integration and collaboration based on Web service standards. In order to tackle the technical challenge associated with the dynamic formation of business workflows, a service composition framework is presented and analyzed in this paper. Comparing with existing composition systems, our framework enjoys two major improvements: (1) the description of each Web service is enhanced with Rule-Based Modeling of the essential business logic behind the service interface; and (2) the divide-and-conquer strategy is explored in our framework to handle complex service composition tasks through a hierarchical composition architecture. A PC manufacturing prototyping system further presents a concrete demonstration of our framework in practical applications.
Michael L Blinov - One of the best experts on this subject based on the ideXlab platform.
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compartmental and spatial rule based Modeling with virtual cell
Biophysical Journal, 2017Co-Authors: Michael L Blinov, James C Schaff, Dan Sorin Vasilescu, Ion I Moraru, Judy E Bloom, Leslie M LoewAbstract:Abstract In Rule-Based Modeling, molecular interactions are systematically specified in the form of reaction rules that serve as generators of reactions. This provides a way to account for all the potential molecular complexes and interactions among multivalent or multistate molecules. Recently, we introduced Rule-Based Modeling into the Virtual Cell (VCell) Modeling framework, permitting graphical specification of rules and merger of networks generated automatically (using the BioNetGen Modeling engine) with hand-specified reaction networks. VCell provides a number of ordinary differential equation and stochastic numerical solvers for single-compartment simulations of the kinetic systems derived from these networks, and agent-based network-free simulation of the rules. In this work, compartmental and spatial Modeling of Rule-Based models has been implemented within VCell. To enable Rule-Based deterministic and stochastic spatial simulations and network-free agent-based compartmental simulations, the BioNetGen and NFSim engines were each modified to support compartments. In the new Rule-Based formalism, every reactant and product pattern and every reaction rule are assigned locations. We also introduce the Rule-Based concept of molecular anchors. This assures that any species that has a molecule anchored to a predefined compartment will remain in this compartment. Importantly, in addition to formulation of compartmental models, this now permits VCell users to seamlessly connect reaction networks derived from rules to explicit geometries to automatically generate a system of reaction-diffusion equations. These may then be simulated using either the VCell partial differential equations deterministic solvers or the Smoldyn stochastic simulator.
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compartmental and spatial rule based Modeling with virtual cell vcell
bioRxiv, 2017Co-Authors: Michael L Blinov, James C Schaff, Dan Sorin Vasilescu, Ion I Moraru, Judy E Bloom, Leslie M LoewAbstract:In Rule-Based Modeling, molecular interactions are systematically specified in the form of reaction rules that serve as generators of reactions. This provides a way to account for all the potential molecular complexes and interactions among multivalent or multistate molecules. Recently, we introduced Rule-Based Modeling into the Virtual Cell (VCell) Modeling framework, permitting graphical specification of rules and merger of networks generated automatically (using the BioNetGen Modeling engine) with hand-specified reaction networks. VCell provides a number of ordinary differential equation (ODE) and stochastic numerical solvers for single-compartment simulations of the kinetic systems derived from these networks, and agent-based network-free simulation of the rules. In this work, compartmental and spatial Modeling of Rule-Based models has been implemented within VCell. To enable Rule-Based deterministic and stochastic spatial simulations and network-free agent-based compartmental simulations, the BioNetGen and NFSim engines were each modified to support compartments. In the new Rule-Based formalism, every reactant and product pattern and every reaction rule are assigned locations. We also introduce the novel Rule-Based concept of molecular anchors. This assures that any species that has a molecule anchored to a predefined compartment will remain in this compartment. Importantly, in addition to formulation of compartmental models, this now permits VCell users to seamlessly connect reaction networks derived from rules to explicit geometries to automatically generate a system of reaction-diffusion equations. These may then be simulated using either the VCell partial differential equations (PDE) deterministic solvers or the Smoldyn stochastic simulator.
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rule based Modeling of biochemical systems with bionetgen
Methods of Molecular Biology, 2009Co-Authors: James R Faeder, Michael L Blinov, William S HlavacekAbstract:Rule-Based Modeling involves the representation of molecules as structured objects and molecular interactions as rules for transforming the attributes of these objects. The approach is notable in that it allows one to systematically incorporate site-specific details about protein-protein interactions into a model for the dynamics of a signal-transduction system, but the method has other applications as well, such as following the fates of individual carbon atoms in metabolic reactions. The consequences of protein-protein interactions are difficult to specify and track with a conventional Modeling approach because of the large number of protein phosphoforms and protein complexes that these interactions potentially generate. Here, we focus on how a Rule-Based model is specified in the BioNetGen language (BNGL) and how a model specification is analyzed using the BioNetGen software tool. We also discuss new developments in Rule-Based Modeling that should enable the construction and analyses of comprehensive models for signal transduction pathways and similarly large-scale models for other biochemical systems.
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bionetgen software for rule based Modeling of signal transduction based on the interactions of molecular domains
Bioinformatics, 2004Co-Authors: Michael L Blinov, James R Faeder, Byron Goldstein, William S HlavacekAbstract:Summary: BioNetGen allows a user to create a computational model that characterizes the dynamics of a signal transduction system, and that accounts comprehensively and precisely for specified enzymatic activities, potential post-translational modifications and interactions of the domains of signaling molecules. The output defines and parameterizes the network of molecular species that can arise during signaling and provides functions that relate model variables to experimental readouts of interest. Models that can be generated are relevant for rational drug discovery, analysis of proteomic data and mechanistic studies of signal transduction. Availability: http://cellsignaling.lanl.gov/bionetgen
