Hardening Software for Rule-based Modeling

用于基于规则的建模的强化软件

• 批准号: 10615068
• 负责人: William S Hlavacek
• 金额: $ 34.77万
• 依托单位: NORTHERN ARIZONA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10615068
• 关键词: Acceleration; Address; Advanced Development; Algorithms; Allergic Disease; Bayesian Method; Biological; Biological Models; Cell membrane; Cell model; Chemicals; Code; Collaborations; Communication; Computer software; Coupled; Data; Derivation procedure; Differential Equation; Diffusion; Ensure; Equation; Event; Evolution; Formulation; Grain; Head; Heterogeneity; Hour; IgE Receptors; Individual; Kinetics; Laboratories; Language; Likelihood Functions; Liquid substance; Markov Chains; Markov chain Monte Carlo methodology; Mediating; Membrane; Methods; Modeling; Molecular Structure; Monte Carlo Method; Occupations; Parameter Estimation; Pattern; Performance; Phosphorylation Site; Play; Polymers; Population; Post-Translational Modification Site; Process; Property; Pythons; Reaction; Receptor Signaling; Role; Sampling; Signal Transduction; Signaling Protein; Site; Software Tools; Specific qualifier value; Standardization; System; Testing; Therapeutic; Time; Uncertainty; Update; Work; Writing; base; chemical kinetics; cluster computing; computing resources; cost; curve fitting; design; dynamic system; improved; information processing; mathematical model; model building; novel; operation; parallelization; particle; polymerization; population based; prototype; receptor; recruit; response; simulation; simulation software; software development; tool

PROJECT SUMMARY/ABSTRACT Rule-based modeling approaches, which are based on the principles of chemical kinetics and diffusion and enabled by an expanding armamentarium of sophisticated software tools (e.g., BioNetGen/NFsim), offer spe- cial advantages for studying the dynamics of interactions among multisite signaling proteins. Rule-based mod- els can capture the effects of polymerization-like reactions and multisite post-translational modifications over time scales of seconds to hours while incorporating constraints imposed by molecular structures. Furthermore, with a rule-based approach to model formulation, it is possible to construct and analyze larger, more compre- hensive models for cellular regulatory systems than with traditional modeling approaches because of the op- portunity to represent systems concisely and at a high level of abstraction using formal rules for biomolecular interactions. Rules can often be processed to automatically derive traditional model forms, such as a coupled system of ordinary differential equations (ODEs). However, when the system state space implied by rules is exceedingly large, the use of simulation engines based on network-free algorithms becomes necessary and model analysis is limited by the high computational cost of the stochastic simulations. In addition, in these cir- cumstances and others, parameter identification and uncertainty quantification (UQ) are extremely challenging. We will address these problems by improving the efficiency of simulation, fitting, and UQ tools and by leverag- ing distributed computing resources. Recently, we developed novel algorithms for accelerating stochastic simu- lations, a toolbox of parallelized metaheuristic optimization methods for fitting, and implementations of Markov chain Monte Carlo (MCMC) methods for Bayesian UQ. This toolbox, called PyBioNetFit (PyBNF), leverages standardized formats for defining and sharing models (e.g., core SBML and BNGL) and is compatible with var- ious simulators. Here, we propose to develop general-purpose software implementations for accelerated net- work-free (stochastic) simulation and for restructuring rule-based models (i.e., optimizing rules so as to mini- mize the number of rule-implied equations). We will also provide a new interface to CVODE and CVODES for numerical integration of ODEs, forward sensitivity analysis, and adjoint sensitivity analysis. Furthermore, we will extend the biological property specification language (BPSL) of PyBNF to make this means for formalizing qualitative data more expressive. In addition, we will add gradient-based optimization and MCMC methods to PyBNF and built-in support for Smoldyn, a simulator for (rule-based) spatial stochastic models. These im- 𝜀𝜀 provements will facilitate grounding of models in data. We will test and validate new tools by building models 𝜀𝜀 for IgE receptor (Fc RI) signaling in collaboration with quantitative experimentalists. We will focus on models 𝜀𝜀 for Fc RI-Lyn interaction within the context of a heterogeneous plasma membrane consisting of liquid ordered and disorded regions and Fc RI-mediated activation of Syk. These planned applications will ensure that our software development activities are directed at useful capabilities and will provide capability demonstrations.

项目总结/文摘

项目成果

Using both qualitative and quantitative data in parameter identification for systems biology models.

• DOI: 10.1038/s41467-018-06439-z
• 发表时间: 2018-09-25
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Mitra ED;Dias R;Posner RG;Hlavacek WS
• 通讯作者: Hlavacek WS

A Step-by-Step Guide to Using BioNetFit.

使用 BioNetFit 的分步指南。

• DOI: 10.1007/978-1-4939-9102-0_18
• 发表时间: 2019
• 期刊: Methods in molecular biology (Clifton, N.J.)
• 作者: Hlavacek,WilliamS;Csicsery-Ronay,JenniferA;Baker,LewisR;RamosÁlamo,MaríaDelCarmen;Ionkov,Alexander;Mitra,EshanD;Suderman,Ryan;Erickson,KeeshaE;Dias,Raquel;Colvin,Joshua;Thomas,BrandonR;Posner,RichardG
• 通讯作者: Posner,RichardG

Bayesian Inference of State-Level COVID-19 Basic Reproduction Numbers across the United States.

• DOI: 10.3390/v14010157
• 发表时间: 2022-01-15
• 期刊: Viruses
• 作者: Mallela A;Neumann J;Miller EF;Chen Y;Posner RG;Lin YT;Hlavacek WS
• 通讯作者: Hlavacek WS

Systems biology markup language (SBML) level 3 package: multistate, multicomponent and multicompartment species, version 1, release 2.

• DOI: 10.1515/jib-2020-0015
• 发表时间: 2020-07-06
• 期刊: Journal of integrative bioinformatics
• 影响因子: 1.9
• 作者: Zhang F;Smith LP;Blinov ML;Faeder J;Hlavacek WS;Juan Tapia J;Keating SM;Rodriguez N;Dräger A;Harris LA;Finney A;Hu B;Hucka M;Meier-Schellersheim M
• 通讯作者: Meier-Schellersheim M

Using RuleBuilder to Graphically Define and Visualize BioNetGen-Language Patterns and Reaction Rules.

使用 RuleBuilder 以图形方式定义和可视化 BioNetGen 语言模式和反应规则。

• DOI: 10.1007/978-1-4939-9102-0_2
• 发表时间: 2019
• 期刊: Methods in molecular biology (Clifton, N.J.)
• 作者: Suderman,Ryan;Fricke,GMatthew;Hlavacek,WilliamS
• 通讯作者: Hlavacek,WilliamS