Hardening Software for Rule-based Modeling

用于基于规则的建模的强化软件

• 批准号: 10165739
• 负责人: William S Hlavacek
• 金额: $ 34.71万
• 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 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10165739
• 关键词: Address; Advanced Development; Algorithms; Allergic Disease; Bayesian Method; Biological; Biological Models; Cell membrane; Cell model; Chemicals; Collaborations; Computer software; Coupled; Data; Derivation procedure; Differential Equation; Diffusion; Ensure; Equation; Event; Evolution; Formulation; Grain; 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; Pattern; Performance; Phosphorylation; Play; Population; Post-Translational Protein Processing; 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; improved; information processing; mathematical model; model building; novel; operation; 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/摘要 基于规则的建模方法，基于化学动力学和扩散原理， 通过不断扩展的复杂软件工具（例如，BioNetGen/NFsim），提供特殊- 研究多位点信号蛋白之间相互作用的动力学的社会优势。基于规则的模式- ELS可以捕获聚合样反应和多位点翻译后修饰的影响， 秒到小时的时间尺度，同时结合由分子结构施加的约束。此外，委员会认为， 有了基于规则的方法来制定模型，就有可能构建和分析更大、更全面的 细胞调节系统的hensive模型比传统的建模方法，因为操作， 使用生物分子的形式规则，在高抽象层次上简洁地表示系统的能力 交互.通常可以处理规则以自动派生传统的模型形式，例如耦合的 常微分方程（ODE）然而，当规则所隐含的系统状态空间为 非常大，使用基于无网络算法的仿真引擎变得必要， 模型分析受到随机模拟的高计算成本的限制。此外，在这些cir- 在复杂的环境和其他情况下，参数识别和不确定性量化（UQ）是极具挑战性的。 我们将通过提高模拟、拟合和UQ工具的效率，并通过以下方式解决这些问题： 分布式计算资源。最近，我们开发了新的算法，用于加速随机模拟， 一个用于拟合的并行元启发式优化方法的工具箱，以及马尔可夫模型的实现。 链蒙特卡罗（MCMC）方法的贝叶斯UQ。这个名为PyBioNetFit（PyBNF）的工具箱利用了 用于定义和共享模型的标准化格式（例如，核心SBML和BNGL），并兼容var- ious模拟器。在这里，我们建议开发通用的软件实现加速网络， 无工作（随机）模拟和用于重构基于规则的模型（即，优化规则，以减少 米泽规则隐含方程的数量）。我们还将为CVODE和CVODES提供新的接口， 常微分方程的数值积分、前向灵敏度分析和伴随灵敏度分析。而且我们 我将扩展PyBNF的生物属性规范语言（BPSL），使其形式化 定性数据更有表现力。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。这些IM- 𝜀𝜀 这些改进将有助于将模型建立在数据基础上。我们将通过构建模型来测试和验证新工具 𝜀𝜀 IgE受体（Fc RI）信号转导与定量实验学家合作。我们将专注于模型 𝜀𝜀 对于Fc RI-Lyn相互作用，在由有序液体组成的异质质膜的背景下， 和无序区以及Fc RI介导的Syk活化。这些计划中的应用将确保我们的 软件开发活动针对有用的功能，并将提供功能演示。