MODELING EMERGENT BEHAVIORS IN SYSTEMS BIOLOGY: A BIOLOGICAL PHYSICS APPROACH

系统生物学中的突发行为建模：生物物理方法

• 批准号: 10330838
• 负责人: Pankaj Mehta
• 金额: $ 39.6万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-18 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10330838
• 关键词: Algorithmic Analysis; Animal Model; Artificial Intelligence; Attention; Behavior; Biochemical; Biological; Biological Models; Biological Phenomena; Biology; Cells; Communities; Community Networks; Complex; Computational algorithm; Data; Dictyostelium discoideum; Discipline; Ecology; Goals; Information Theory; Length; Machine Learning; Mathematics; Modeling; Modernization; Muscle; Neurons; Organism; Physics; Pythons; Research; Resources; Scientist; Series; Signal Transduction; Source; Statistical Algorithm; System; Systems Biology; Techniques; Transcend; United States National Institutes of Health; Work; base; computerized tools; deep learning; experimental study; gene interaction; gene network; heterogenous data; human disease; information model; information processing; interdisciplinary approach; machine learning algorithm; microbial; microbial community; microbiome; novel; predictive tools; synthetic biology; theories; tool

Project Summary Biology is full of stunning examples of emergent behaviors – behaviors that arise from, but cannot be reduced to, the interactions of the constituent parts that make up the system under consideration. These behaviors span the full spectrum of length scales, from the emergence of distinct cell fates (e.g. neurons, muscle, etc.) due to the interactions of genes within cells, to the formation of complex ecological communities arising from the interactions of thousands of species. The overarching goal of my research is to develop new conceptual, theoretical, and computational tools to model such emergent, system-level behaviors in biology. To do so, we utilize an interdisciplinary approach that is grounded in Biological and Statistical Physics, but that draws heavily from Machine Learning, Information Theory, and Theoretical Ecology. Our work is unified and distinguished by our deep commitment to integrating theory with the vast amount of biological data now being generated by experiment. An important goal of the proposed research is to find common concepts and tools that transcend traditional biological sub-disciplines and model systems. The proposed research pursues three distinct but conceptually interrelated research directions: (1) identifying the ecological principles governing community assembly in microbial communities and developing techniques for understanding function, diversity, and stability in microbiomes; (2) developing new mathematical and computational tools for modeling information processing in biochemical networks, especially the gene networks underlying cellular identity and the signaling networks that control collective behavior in the NIH model organism Dictyostelium discoideum; and (3) understanding and developing new interpretable machine learning techniques for systems and synthetic biology, with special attention paid to the unique challenges posed by living systems with regards to data heterogeneity, biological interpretability, and potential sources of bias. In addition to developing physics-based and machine learning-inspired models for diverse biological phenomena, the proposed research will yield a series of practical and important computational tools and algorithms which we will make publically available including: (1) our “Community Simulator” Python package for simulating microbial communities based on the novel microbial consumer resource model framework we have developed; (2) new machine learning and statistical algorithms for analyzing microbial communities and gene networks; and (3) new computational tools for predicting the behavior of synthetic biological parts and circuits in diverse contexts. These computational tools will allow scientists to leverage the power of modern theory, computation, and advances in Deep Learning to tackle fundamental problems relevant to human disease.

项目概要 生物学中充满了突发行为的惊人例子——这些行为产生但无法减少 构成所考虑的系统的组成部分的相互作用。 从不同细胞命运（例如神经元、肌肉等）的出现开始，跨越整个长度尺度 由于细胞内基因的相互作用，形成了复杂的生态群落 我研究的首要目标是开发新的物种。 概念、理论和计算工具来模拟此类突发的系统级行为 为此，我们采用基于生物学和统计学的跨学科方法。 物理学，但这很大程度上依赖于机器学习、信息论和理论生态学。 统一和独特之处在于我们坚定地致力于将理论与大量的知识相结合 目前通过实验生成的生物数据是本研究的一个重要目标。 超越传统生物子学科和模型系统的通用概念和工具。 拟议的研究追求三个不同但概念上相互关联的研究方向：（1） 确定微生物群落中群落组装的生态原则并开发 (2) 开发新的数学技术来理解微生物组的功能、多样性和稳定性； 以及用于对生化网络中的信息处理（尤其是基因）进行建模的计算工具 细胞身份基础网络和控制 NIH 集体行为的信号网络 模式生物盘基网柄菌；以及（3）理解和开发新的可解释机器 学习系统和合成生物学的技术，特别关注独特的挑战 由生命系统提出的关于数据异质性、生物可解释性和潜在来源的问题 除了为不同的生物开发基于物理和机器学习启发的模型之外。 现象，所提出的研究将产生一系列实用且重要的计算工具和 我们将公开的算法包括：（1）我们的“社区模拟器”Python 包 为了基于新颖的微生物消费者资源模型框架模拟微生物群落，我们 (2) 用于分析微生物群落的新机器学习和统计算法 基因网络；（3）用于预测合成生物部件行为的新计算工具和 这些计算工具将使科学家能够利用现代的力量。 深度学习的理论、计算和进展，以解决与人类相关的基本问题 疾病。