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，我们采用了一种以生物物理学为基础的跨学科方法，但借鉴了这些方法。 我们的工作主要来自机器学习、信息论和理论生态学。 因我们坚定地致力于将理论与大量生物数据相结合而闻名 由现代 DNA 测序技术和定量显微镜产生。 拟议研究的目标是找到超越传统生物学的共同概念和工具 拟议的研究追求四个不同但概念上的子学科和模型系统。 相互关联的研究方向：（1）了解双分子如何产生不同的细胞命运 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 消耗以改进计算，并应用于合成生物学（3）识别生态； 微生物群落群落组装的管理原则和综合开发技术 工程生态社区；（4）开发新的机器学习算法和技术 除了为各种生物现象开发基于物理的模型之外， 所提出的研究将产生一系列实用的重要工具和算法，我们将制作它们 公开可用的包括：（1）一种新的基于线性代数的算法，用于评估定向的保真度 分化和细胞重编程方案以及可视化重编程/分化动态 (2) 从高通量推断人类微生物组中微生物相互作用的改进算法 这些计算工具将使科学家认识到序列数据的巨大治疗潜力。 用于研究和治疗人类疾病的细胞重编程和基于微生物生态学的技术。