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: 10580813
• 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/10580813
• 关键词: 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; behavior prediction; 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; model organism; novel; 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.

项目摘要 生物学充满了新兴行为的惊人例子 - 产生的行为，但不能减少 到构成正在考虑的系统的组成部分的相互作用。这些行为 从不同的细胞命运的出现（例如神经元，肌肉等）跨越长度尺度的全光谱 由于基因在细胞内的相互作用，因此是由复杂生态群落形成 数千种的相互作用。我的研究的总体目标是开发新的 概念，理论和计算工具，以模拟这种新兴的系统级行为 生物学。为此，我们利用一种基于生物学和统计的跨学科方法 物理学，但这源于机器学习，信息理论和理论生态学。我们的工作 通过我们对将理论与大量融合理论融合的深厚承诺的统一和区分 生物数据现在是通过实验生成的。拟议研究的一个重要目标是找到 超越传统生物子学科和模型系统的常见概念和工具。 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） 确定有关微生物社区社区议会的生态原则并发展 理解微生物中功能，多样性和稳定性的技术； （2）开发新数学 以及用于建模生化网络中信息处理的计算工具，尤其是基因 蜂窝身份的基础网络和控制NIH集体行为的信号网络 模型有机体distelium discoideum; （3）理解和开发新的可解释机器 针对系统和合成生物学的学习技术，特别注意独特的挑战 由生活系统提出的有关数据异质性，生物解释性和潜在来源 偏见。除了开发基于物理和机器学习启发的机器生物学模型 现象，拟议的研究将产生一系列实用和重要的计算工具， 我们将公开可用的算法，包括：（1）我们的“社区模拟器” Python软件包 为了基于新型微生物消费者资源模型框架模拟微生物群落，我们 已经发展了； （2）用于分析微生物群落和的新机器学习和统计算法 基因网络； （3）用于预测合成生物学部分行为的新计算工具和 在潜水员环境中圆圈。这些计算工具将使科学家能够利用现代的力量 理论，计算以及深度学习的进步解决与人有关的基本问题 疾病。