ITR COLLAB: Theory and Software Infrastructure for a Scalable Systems Biology

ITR COLLAB：可扩展系统生物学的理论和软件基础设施

• 批准号: 0326576
• 负责人: Mustafa Khammash
• 金额: $ 57.5万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-12-15 至 2008-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0326576&HistoricalAwards=false
• 关键词: ITR; COLLAB; Theory; Software; Infrastructure

The now well-known vision and challenge in post-genomics biology is to make the entire process of researchscalable to large networks using high-throughput techniques and large-scale computation. Computational biology and bioinformatics have focused attention on the need for sophisticated methods for handling large databases and tools for modeling and simulating complex networks. Not as widely recognized is that the scalability of the more subtle processes of drawing meaningful and reliable scientific, medical, and biological inferences from the wealth of data and computation is equally important and requires the development of fundamentally new theory and software.The research objective of this project is to develop the theoretical foundation and information technology in-frastructure necessary to accelerate progress in systems biology, with concrete demonstrations on a variety of bi-ological experiments. This ambitious goal requires augmenting bioinformatics and current modeling and simula-tion approaches with greater understanding of the organizational principles underlying network complexity, including connections with molecular details, and exploiting this understanding to advance mainstream experimental biology.Building on recent breakthroughs in theory and scalable algorithms for systematic robustness analysis and model (in)validation of nonlinear network models with uncertain rate constants, the project maps out a research path that will (1) develop the necessary rigorous and practical mathematical theory; (2) embody it in a software environment that supports the complex iterative processes involved in going from raw data to modeling, analysis, and inference, with tight feedback to experimentation and modeling throughout; and (3) apply the theory and software to specific experimental studies in biology as a way of grounding the entire endeavor.The intellectual merit combines immediate practical impact and conceptual depth. Automating and computation-ally augmenting scientific and mathematical inference from noisy and incomplete data for uncertain models has long been an elusive goal. Achieving it in the context of complex biological systems is for the first time both a necessity and an achievable goal. To do this, data and modeling assertions and questions must be described in a common framework that is biologically natural, yet can be stored, manipulated, shared, and ultimately turned over to powerful algorithms for resolution. Our objective is to create tools which make it possible to systematically answer questions such as: Is a proposed model consistent with experimental data? If so, is it robust to additional perturbations that are plausible but untested? Are different models at multiple scales of resolution consistent? What is the most promising experiment to refute or confirm a model? Traditionally, such network-level questions that arise naturally in biology have beenconsidered computationally intractable, since they are typically stochastic, nonlinear, nonequilibrium, ncertain, in-volve multiple scales, and hybrid (mixing continuous and discrete mathematics), limiting approaches to heuristic and brute-force methods, or to extreme simplification. Recently this situation changed profoundly, based on new methods developed by the research team and their collaborators. A crucial insight is that evolution favors high robustness to uncertain environments and components, yet allows severe fragility to novel perturbations, and this robust yet fragile feature must be exploited explicitly in scalable algorithmic approaches.The broader impact lies in the synergistic links this work forges with similar challenges that exist throughout science and technology, such as the Internet, aerospace systems design, materials science, multiscale physics, stochas-tic multiscale chemistry, and disturbance ecology. The theoretical foundations build broadly on robust control theory, dynamical systems, numerical analysis, operator theory, real algebraic geometry, computational complexity theory, duality and optimization, and semi-definite programming. The results will be made accessible to the broadest possible audience, both with representative and challenging experimental biology and the connections with other examples of complex systems. The preliminary progress already made by this team is striking and has been applied to under-standing,for example, the robustness of complex control systems, the performance of internet protocols, and bacterialchemotaxis and stress response. The work is creating new mathematics and algorithms, beginning to appear in the highest-impact journals, and concretely demonstrating that this research can help experimental biologists.Diversity and breadth appear at every level. In the research group of the lead PI (Doyle), 6 of 11 graduatestudents and 2 of 4 postdoctoral scholars are women, and include a broad racial and ethnic diversity. The other 5 co-PIs are from a broad spectrum of disciplines and diverse but elite academic institutions, 3 are women, and all PIs have strong and very concrete commitments to integrative, multidisciplinary research, diversity, educational innovation, and outreach at every level including K-12. The team members are frequent featured speakers at integrative conferences and in interdisciplinary colloquia at premier universities, and speakers and organizers of workshops and short courses in systems biology. This program both directly involves leading mainstream biology, and has broad contact with it through additional collaborations, creating conduits to broad dissemination of the research results in biology. The team's algorithms and software infrastructure are becoming de facto standard tools empowering research in multiple disciplines, and forming a solid foundation upon which this program builds.

后基因组学生物学中现在众所周知的愿景和挑战是使用高通量技术和大规模计算使整个研究过程可扩展到大型网络。计算生物学和生物信息学已经将注意力集中在需要处理大型数据库的复杂方法和用于建模和模拟复杂网络的工具上。然而，从大量的数据和计算中得出有意义和可靠的科学、医学和生物学推论的更微妙过程的可扩展性同样重要，需要开发全新的理论和软件。本项目的研究目标是发展加速系统生物学进步所必需的理论基础和信息技术基础设施，用各种生物学实验的具体演示。这一雄心勃勃的目标需要增强生物信息学和当前的建模和模拟方法，更好地理解网络复杂性背后的组织原则，包括与分子细节的联系，并利用这种理解来推进主流实验生物学。基于最近在理论和可扩展算法方面的突破，用于系统鲁棒性分析和模型（in）验证具有不确定速率常数的非线性网络模型，该项目制定了一条研究路径，将（1）开发必要的严格且实用的数学理论;（2）在支持从原始数据到建模、分析和推理的复杂迭代过程的软件环境中体现它，与紧密的反馈，以实验和建模贯穿始终;和（3）应用理论和软件，以具体的实验研究，在生物学作为一种方式，接地整个奋进。智力的优点结合了直接的实际影响和概念的深度。从不确定模型的噪声和不完整数据中自动化和计算地增强科学和数学推理一直是一个难以捉摸的目标。在复杂生物系统的背景下实现这一目标第一次既是必要的，也是可以实现的目标。要做到这一点，数据和建模断言和问题必须在一个通用的框架中描述，这个框架在生物学上是自然的，但可以存储、操作、共享，并最终交给强大的算法来解决。我们的目标是创建工具，使其能够系统地回答问题，如：是一个建议的模型与实验数据一致？如果是这样，它对看似合理但未经测试的额外扰动是否具有鲁棒性？不同分辨率下的不同模型是否一致？反驳或证实一个模型最有希望的实验是什么？传统上，这种在生物学中自然出现的网络级问题被认为在计算上是棘手的，因为它们通常是随机的、非线性的、非平衡的、不确定的、涉及多个尺度的和混合的（混合连续和离散数学），限制了启发式方法和暴力方法，或极端简化。最近，基于研究团队及其合作者开发的新方法，这种情况发生了深刻的变化。一个关键的见解是，进化有利于对不确定环境和组件的高度鲁棒性，但允许对新扰动的严重脆弱性，这种鲁棒但脆弱的特性必须在可扩展的算法方法中明确利用。更广泛的影响在于这项工作与整个科学和技术中存在的类似挑战形成协同联系，例如互联网，航空航天系统设计，材料科学，多尺度物理学、随机多尺度化学和干扰生态学。理论基础广泛建立在鲁棒控制理论，动力系统，数值分析，算子理论，真实的代数几何，计算复杂性理论，对偶和优化，半定规划。结果将提供给尽可能广泛的受众，既有代表性和具有挑战性的实验生物学，也有与复杂系统的其他例子的联系。该团队已经取得的初步进展是惊人的，并已被应用于理解，例如，复杂控制系统的鲁棒性，互联网协议的性能，以及细菌趋化性和压力反应。这项工作正在创造新的数学和算法，开始出现在最具影响力的期刊上，并具体证明这项研究可以帮助实验生物学家。在首席PI（Doyle）的研究小组中，11名研究生中有6名，4名博士后学者中有2名是女性，并且包括广泛的种族和民族多样性。其他5名共同PI来自广泛的学科和多样化但精英的学术机构，3名是女性，所有PI都对综合，多学科研究，多样性，教育创新和包括K-12在内的各个层面的推广做出了坚定而具体的承诺。该团队成员经常在综合会议和一流大学的跨学科研讨会上发表演讲，并在系统生物学研讨会和短期课程中担任演讲者和组织者。该计划既直接涉及领先的主流生物学，又通过额外的合作与之广泛接触，为生物学研究成果的广泛传播创造了渠道。该团队的算法和软件基础设施正在成为事实上的标准工具，使多个学科的研究，并形成一个坚实的基础上，该计划的建设。