Modeling Emergent Behaviors in Systems Biology: A Biological Physics Approach

项目摘要 生物学上充满了令人惊叹的紧急行为的例子--这些行为源于，但不能被减少 即，构成所考虑的系统的各组成部分之间的相互作用。这些行为 从不同的细胞命运(如神经元、肌肉等)的出现，跨越了整个长度范围。 由于细胞内基因的相互作用，形成了复杂的生态群落 数千个物种之间的相互作用。我研究的首要目标是开发新的 概念、理论和计算工具，用于建模此类紧急的系统级行为 生物学。为了做到这一点，我们利用了一种以生物物理学为基础的跨学科方法，但 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现在的大量生物数据相结合，这一点是我们的突出之处 是由现代DNA测序技术和定量显微镜产生的。一个重要的 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 分学科和模型体系。拟议的研究追求四个不同的但概念上的 相关研究方向：(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)研究细胞内双分子网络如何利用能量 消耗以改进计算，以及在合成生物学中的应用；(3)识别生态 微生物群落中群落组装的原则和综合技术的开发 工程生态社区；以及(4)开发新的机器学习算法和技术 生物数据分析。除了为不同的生物现象开发基于物理的模型外， 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 公开的内容包括：(1)一种新的基于线性代数的定向图保真度评估算法 分化和细胞重编程协议和可视化重编程/分化动力学 和(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 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 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。

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.