BioComp: Efficient Modeling and Analysis of Excitable Cell Networks using Hybrid Automata

BioComp：使用混合自动机对可兴奋细胞网络进行有效建模和分析

• 批准号: 0523863
• 负责人: Emilia Entcheva
• 金额: --
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-15 至 2009-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0523863&HistoricalAwards=false
• 关键词: BioComp; Efficient; Modeling; Analysis; Excitable

Systems biology is an emerging multidisciplinary field whose goal is to provide a systems-level understanding of biological systems by uncovering their structure, dynamics and control methods.While many exciting and profound advances have been made in investigating robustness, networkstructures and dynamics, and application to drug discovery, the field is still in its infancy.An important open problem in systems biology is finding appropriate computational models that scalewell for both the simulation and formal analysis of biological processes. Currently, the majority of thesemodels are given in terms of large and complex sets of nonlinear differential equations, describing in painfuldetail the underlying biological phenomena. Although an invaluable asset for integrating genomics andproteomics data to reveal local interactions, such models are often not amenable to formal analysis andrender simulation at the organ or even the cell level impractical.This proposal seeks to develop a hybrid-automata (HA) approach to modeling and analyzing complex biological systems. Excitable cell networks (heart cells in particular) will be used as an archetype of a complex biological system. Standard modeling methods capture the behavior of such cells using reaction-diffusion PDE systems, with the Hodgkin-Huxley (HH) formalism describing ion channel gating and currents. Initial results indicate that HA models, combining discrete and continuous processes, are able to successfully capture the morphology of the excitation event (action potential) of different cell types, including cardiac cells. They can also reproduce typical excitable cell characteristics, such as refractoriness (period of non-responsiveness to external stimulation) and restitution (adaptation to pacing rates). Multicellular ensembles of HA elements are used to simulate excitation wave propagation, including complex spiral waves underlying pathological conditions in the heart. The resulting simulation framework exhibits significantly improved computational efficiency, and opens the possibility to formal analysis based on HA theory.

系统生物学是一个新兴的多学科领域，其目标是通过揭示生物系统的结构、动力学和控制方法，提供对生物系统的系统级理解。虽然在研究稳健性、网络结构和动力学以及药物发现的应用方面取得了许多令人兴奋和深刻的进展，但该领域仍处于起步阶段。在系统生物学中，一个重要的开放性问题是寻找合适的计算模型，这些模型可以很好地模拟和形式化分析生物过程。目前，大多数这些模型都是根据大型和复杂的非线性微分方程集给出的，以痛苦的细节描述潜在的生物现象。尽管整合基因组学和蛋白质组学数据以揭示局部相互作用是一项宝贵的资产，但这些模型通常不适合正式分析，并且在器官甚至细胞水平上进行模拟不切实际。本提案旨在发展一种混合自动机（HA）方法来建模和分析复杂的生物系统。可兴奋细胞网络（特别是心脏细胞）将被用作复杂生物系统的原型。标准的建模方法使用反应扩散PDE系统捕捉这些细胞的行为，并用霍奇金-赫胥黎（HH）形式描述离子通道门控和电流。初步结果表明，结合离散和连续过程的HA模型能够成功捕获包括心肌细胞在内的不同细胞类型的兴奋事件（动作电位）的形态。它们还能重现典型的可兴奋细胞特征，如难治性（对外部刺激无反应的时期）和恢复性（对起搏速率的适应）。HA元素的多细胞集合用于模拟激发波的传播，包括心脏病理条件下的复杂螺旋波。由此产生的仿真框架显示出显着提高的计算效率，并为基于HA理论的形式化分析提供了可能性。