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.

项目摘要/摘要 基于规则的建模方法，基于化学动力学和扩散的原理 由不断扩展的复杂软件工具（例如Bionetgen/nfsim）提供的ARMAMENTAIM启用，提供SPE- 研究多站点信号蛋白之间相互作用的动力学的优势。基于规则的mod- EL可以捕获聚合样反应和多站点翻译后的影响 秒至小时的时间尺度，同时结合了由分子结构施加的约束。此外， 通过基于规则的模型制定方法，可以构建和分析更大，更综合的 对于传统的建模方法而言 使用正式的生物分子规则，portunity可以简洁地代表系统，并在高水平的抽象中表示 互动。通常可以处理规​​则以自动得出传统模型表格，例如耦合 普通微分方程（ODE）的系统。但是，当规则隐含的系统状态空间是 非常大的，基于无网络算法的模拟引擎的使用变得必要，并且 模型分析受随机模拟的高计算成本的限制。另外，在这些cir-中 混音和其他，参数识别和不确定性定量（UQ）极为挑战。 我们将通过提高模拟，拟合和UQ工具的效率以及杠杆作用来解决这些问题。 分布式计算资源。最近，我们开发了新的算法来加速随机的模拟 最新的，一种用于拟合的平行元启发式优化方法的工具箱，Markov的实现 贝叶斯uq的链蒙特卡洛（MCMC）方法。此工具箱，称为pybionetFit（pybnf），利用 定义和共享模型的标准化格式（例如，核心SBML和BNGL），与var-兼容 模拟器。在这里，我们建议开发通用软件实施，以加速网络 无工作（随机）模拟和用于恢复基于规则的模型（即优化规则 mize的规则图表的数量）。我们还将为CVODE和CVODE提供一个新的接口 ODE，正向灵敏度分析和伴随灵敏度分析的数值整合。此外，我们 将扩展pybnf的生物属性规范语言（BPSL），以使这种手段用于格式化 定性数据更具表现力。此外，我们将将基于梯度的优化和MCMC方法添加到 Pybnf和内置支持Smoldyn，Smoldyn是（基于规则的）空间随机模型的模拟器。这些 𝜀𝜀 证明将促进数据中模型的基础。我们将通过构建模型测试和验证新工具 𝜀𝜀 用于与定量实验者合作的IgE接收器（FC RI）信号传导。我们将专注于模型 𝜀𝜀 在异质质膜的背景下，用于FC Ri-Lyn的相互作用 以及扭曲的区域和FC RI介导的SYK激活。这些计划的申请将确保我们的 软件开发活动针对有用的功能，并将提供能力演示。

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

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

Parameter Estimation and Uncertainty Quantification for Systems Biology Models.

• DOI: 10.1016/j.coisb.2019.10.006
• 发表时间: 2019-12-01
• 期刊: Current opinion in systems biology
• 影响因子: 3.7
• 作者: Mitra, Eshan D;Hlavacek, William S
• 通讯作者: Hlavacek, William S