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: 9137947
• 负责人: Pankaj Mehta
• 金额: $ 30.27万
• 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 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9137947
• 关键词: Algorithms; Behavior; Biological; Biological Models; Biological Phenomena; Biology; Cells; Communities; Complex; Consumption; DNA Sequence; Data; Data Analyses; Discipline; Ecology; Engineering; Goals; High-Throughput Nucleotide Sequencing; Human Microbiome; Information Theory; Length; Linear Algebra; Machine Learning; Mammalian Cell; Modeling; Muscle; Neurons; Physics; Protocols documentation; Quantitative Microscopy; Research; Scientist; Series; System; Systems Biology; Techniques; Therapeutic; Transcend; Work; base; cell type; computerized tools; gene interaction; human disease; improved; interdisciplinary approach; microbial; microbial community; microorganism interaction; 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 Physics, but 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 modern DNA sequencing-based techniques and quantitative microscopy. An important goal of the proposed research is to find common concepts and tools that transcend traditional biological sub-disciplines and models systems. The proposed research pursues four distinct but conceptually interrelated research directions: (1) understanding how distinct cell fates emerge from bimolecular interactions within mammalian cells (2) investigating how bimolecular networks within cells exploit energy consumption to improve computations, with applications to Synthetic Biology; (3) identifying the ecological principles governing community assembly in microbial communities and developing techniques for synthetically engineering ecological communities; and (4) developing new machine learning algorithms and techniques for biological data analysis. In addition to developing physics-based models for diverse biological phenomena, the proposed research will yield a series of practical important tools and algorithms which we will make publically available including: (1) a new linear-algebra based algorithm for assessing the fidelity of directed differentiation and cellular reprogramming protocols and visualizing reprogramming/differentiation dynamics and (2) improved algorithms for inferring microbial interactions in the human microbiome from high-throughput sequence data. These computational tools will allow scientists to realize the immense therapeutic potential of cellular reprogramming and microbial ecology-based techniques for studying and treating 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物理学的跨学科方法，但吸引 大量来自机器学习，信息理论和理论生态学。我们的工作是统一的， 通过我们对将理论与现在大量生物学数据融入理论的深厚承诺的区别 由现代DNA测序技术和定量显微镜产生。一个重要的 拟议研究的目标是找到超越传统生物学的常见概念和工具 子学科和模型系统。拟议的研究追求四个截然不同但概念的研究 相互关联的研究方向：（1）了解如何与双分子出现不同的细胞命运 哺乳动物细胞内的相互作用（2）研究细胞内的双分子网络如何利用能量 消费以改善计算，并应用于合成生物学； （3）识别生态 在微生物社区中管理社区大会的原则并开发合成的技术 工程生态社区； （4）开发新的机器学习算法和技术 生物数据分析。除了开发用于潜水生物学现象的基于物理的模型外， 拟议的研究将产生一系列实用的重要工具和算法，我们将制作这些工具和算法 公开可用，包括：（1）一种新的基于线性代数的算法，用于评估指导的忠诚度 分化和蜂窝重新编程协议以及可视化重编程/分化动力学 （2）改进了从高通量中推断人类微生物中微生物相互作用的算法 序列数据。这些计算工具将使科学家能够实现巨大的治疗潜力 细胞重编程和基于微生物生态学的技术，用于研究和治疗人类疾病。