An Engineering Control System Paradigm for Quantitative Understanding of Hemostasis

0925202 Ogunnaike本研究的主要目标是开发和验证一种工程控制系统范例，用于定量了解多个相互依赖的止血过程如何相互作用，以安全有效地控制血管损伤后的失血。一种新的定量模块化建模和分析技术，用于组织这个生物过程的机械细节将开发和实验验证。由此产生的数学模型将用于从控制系统的角度阐明止血障碍的机制，并生成关于有效治疗的可验证假设。具体要回答的问题是：量化，整个止血过程的各个组成部分如何协同工作，在正常情况下对血管损伤产生快速、有效、稳定的反应？将执行的具体任务是：任务1：模型开发。开发止血组件的详细控制系统框图表示，推导每个组件的数学模型，并集成到整体综合动态模型中。任务2：模型验证（实验）。在持续的合作中，PI将根据独立的实验数据验证整个模型的每个主要模块的预测。任务3：模型分析和假设生成。将进行模型的计算研究和理论分析;从控制系统的角度推导对病理性疾病的定量见解;并生成关于有效治疗方案的假设，以最佳补偿导致所识别疾病的组件故障。生命科学研究的系统性变化为数学建模创造了机会，使其在发展对复杂生物现象的定量和预测性理解方面发挥重要作用。随着不断提高的实验能力，促进最复杂的细胞和分子机制的更精细的数据的采集，越来越多的计算能力已经稳步转向系统生物学的数学建模采用？更大更复杂这些复杂系统的代表。对于止血的具体问题，目前还没有完整止血过程的整体定量模型，这可能是因为许多组成成分本身就相当复杂，而且？标准的？试图建立一个整体模型可能不会很有用。通过认识到止血的核心是一个自动生物控制系统，本研究旨在部署工程控制系统的概念，以开发一个全面的止血过程模型，实现保真度，而不牺牲分析的易处理性。PI设想了本研究的两种主要影响：（i）技术：对正常情况下如何调节该生物学过程以及如何从单个组成部分的连接中产生整体特征的定量理解得到改善，并以更精确治疗血友病和血栓形成的形式对临床实践产生影响;（ii）方法：演示如何在极其复杂的生物现象模型中同时实现高保真度和分析易处理性。在不断发展的本科生培训计划的核心是如何在经典的化学工程课程整合生物学的问题。本研究在理论上和实验验证，是完美的学生介绍生物控制系统的问题，以及如何采用SIMULINK等模拟工具建模和理解这样的系统。这项研究的结果将纳入教学课程，并通过出版物和向其他教育工作者和研究人员的介绍广泛传播。此外，PI本身作为少数民族，致力于招募代表性不足的群体进入化学工程学科，特别是系统生物学研究，并应能够吸引少数民族学生参与这一努力。

0925202OgunnaikeThe primary goal of this research is to develop and validate an engineering control system paradigm for obtaining quantitative insight into how multiple interdependent hemostatic processes interact to control blood loss safely and effectively following vessel injury. A novel quantitative modular modeling and analysis technique for organizing the mechanistic details of this biological process will be developed and validated experimentally. The resulting mathematical model will be used to elucidate mechanisms of hemostatic disorders from a control system perspective, and to generate testable hypotheses about effective treatment. The specific question to be answered is: Quantitatively, how do the various components of the entire hemostatic process work together to produce fast, effective and stable responses to vascular injury under normal conditions? The specific tasks that will be performed are: Task 1: Model Development. Develop a detailed control system block diagram representation of the components of hemostasis, derive mathematical models for each component and integrate into a holistic, comprehensive dynamic model. Task 2: Model Validation (Experimental). In a continuing collaboration the PIs will validate the predictions of each principal module of the overall model against independent experimental data. Task 3: Model Analysis and Hypothesis Generation. Computation studies and theoretical analyses of the model will be carried out; derivation of quantitative insight into pathological disorders from a control system perspective; and generation of hypothesis regarding effective treatment regimens in terms of optimal compensation for component malfunction responsible for the identified disorder. Intellectual Merit Systemic changes in life sciences research have created opportunities for mathematical modeling to play a major role in developing quantitative and predictive understanding of complex biological phenomena. With ever improving experimental capabilities facilitating the acquisition of more refined data on the most intricate cellular and molecular mechanisms, increasing computational power has steadily steered mathematical modeling in systems biology towards adopting ?bigger and more complex? representations of these complex systems. For the specific problem of hemostasis there are currently no holistic quantitative models of the complete hemostasis process perhaps because many of the constituent components are quite complex in their own right, and a ?standard? attempt at developing a holistic model is not likely to be very useful. By recognizing that at the heart of hemostasis is an automatic biological control system, this research aims to deploy concepts from engineering control systems to develop a comprehensive hemostatic process model that achieves fidelity without sacrificing analytical tractability. The PIs envision two kinds of primary impact for this research: (i) Technical: an improved quantitative understanding of how this biological process is regulated under normal circumstances, and how the characteristics of the whole emerge from the connection of the individual component parts, with implications for clinical practice in the form of more precise treatment of hemophilia and thrombophilia; (ii) Methodological: demonstrating how to achieve high-fidelity and analytical tractability simultaneously in models of extremely complex biological phenomena. Broader Impact At the heart of the evolving undergraduate training program is the issue of how to integrate biology within the classical chemical engineering curriculum. This research addresses theoretically and with experimental validation, issues that are perfect for introducing students to biological control systems, and how to employ such simulation tools as SIMULINK for modeling and understanding such systems. The results of this research will be integrated into the teaching curricula and widely disseminated through publications and presentations to other educators and researchers. In addition, the PI, as a minority himself, is committed to recruiting under-represented groups into the chemical engineering discipline in general and systems biology research in particular, and should be able to attract minority students to participate in this effort.