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 生物学充满了涌现行为的惊人例子--这些行为源于，但不能被减少 到，构成所考虑的系统的组成部分的相互作用。这些行为 跨越长度尺度的全谱，从不同细胞命运的出现（例如神经元、肌肉等） 由于细胞内基因的相互作用，形成了复杂的生态群落， 数千种物种的相互作用。我研究的首要目标是开发新的 概念，理论和计算工具来模拟这种紧急的，系统级的行为， 生物学要做到这一点，我们利用跨学科的方法，是在生物物理学的基础，但提请 主要来自机器学习、信息论和理论生态学。我们的工作是统一的， 我们致力于将理论与大量的生物数据相结合， 是由现代DNA测序技术和定量显微镜产生的。一个重要 这项研究的目标是找到超越传统生物学的共同概念和工具。 子学科和模型系统。本研究从四个不同的概念出发， 相关的研究方向：（1）了解不同的细胞命运是如何从双分子 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，与应用合成生物学;（3）确定生态 微生物群落中群落组装的原则和开发综合利用的技术 工程生态社区;（4）开发新的机器学习算法和技术， 生物数据分析除了为各种生物现象开发基于物理学的模型外， 提出的研究将产生一系列实用的重要工具和算法，我们将使 （1）一种新的基于线性代数的算法，用于评估有向 分化和细胞重编程方案以及可视化重编程/分化动力学 和（2）用于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 序列数据这些计算工具将使科学家们认识到， 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。