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.

项目概要/摘要 基于规则的建模方法，基于化学动力学和扩散原理 通过扩展复杂的软件工具（例如 BioNetGen/NFsim），提供特殊 研究基于规则的信号蛋白之间相互作用的动态具有显着优势。 els 可以捕捉类聚合反应和多位点翻译后修饰的影响 时间尺度从几秒到几小时，同时考虑到分子结构所施加的限制。 通过基于规则的模型制定方法，可以构建和分析更大、更全面的模型。 与传统建模方法相比，细胞调节系统的模型更为丰富，因为 有机会使用生物分子的正式规则来简洁和高抽象地表示系统 通常可以处理规​​则以自动派生传统模型形式，例如耦合模型。 常微分方程组 (ODE) 然而，当规则隐含的系统状态空间为 由于规模非常大，因此需要使用基于无网络算法的仿真引擎 此外，在这些情况下，模型分析受到随机模拟的高计算成本的限制。 在各种情况下，参数识别和不确定性量化（UQ）极具挑战性。 我们将通过提高模拟、拟合和 UQ 工具的效率以及利用 最近，我们开发了用于加速随机模拟的新算法。 lations，用于拟合的并行元启发式优化方法的工具箱，以及马尔可夫的实现 用于贝叶斯 UQ 的链蒙特卡罗 (MCMC) 方法 该工具箱称为 PyBioNetFit (PyBNF)，利用了该工具箱。 用于定义和共享模型的标准化格式（例如，核心 SBML 和 BNGL），并且与 var- 兼容 在这里，我们建议开发用于加速网络的通用软件实现。 无工作（随机）模拟和重构基于规则的模型（即优化规则以最小化 减少规则隐含方程的数量）我们还将为 CVODE 和 CVODES 提供新的接口。 ODE 的数值积分、正向敏感性分析和伴随敏感性分析。 将扩展 PyBNF 的生物属性规范语言 (BPSL)，使这种方法形式化 此外，我们将添加基于梯度的优化和MCMC方法。 PyBNF 和对 Smoldyn 的内置支持，Smoldyn 是（基于规则的）空间随机模型的模拟器。 𝜀𝜀 证明将有助于模型在数据中的落地。我们将通过构建模型来测试和验证新工具。 𝜀𝜀 我们将与定量实验人员合作研究 IgE 受体 (Fc RI) 信号传导。 𝜀𝜀 用于由液体有序组成的异质质膜背景下的 Fc RI-Lyn 相互作用 和紊乱的区域和 Fc RI 介导的 Syk 激活这些计划的应用将确保我们。 软件开发活动针对有用的功能，并将提供功能演示。

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

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

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

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

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