MODELING EMERGENT BEHAVIORS IN SYSTEMS BIOLOGY: A BIOLOGICAL PHYSICS APPROACH

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， 生物学为此，我们采用基于生物学和统计学的跨学科方法 物理学，但它大量借鉴了机器学习、信息论和理论生态学。我们的工作 是统一的，我们的深刻承诺，将理论与大量的 生物学数据正在通过实验生成。这项研究的一个重要目标是找到 超越传统生物学分支学科和模型系统的共同概念和工具。 本研究的主要研究方向有三个：（1） 确定微生物群落中控制群落组装的生态学原则， 了解微生物组的功能，多样性和稳定性的技术;（2）开发新的数学 和生物化学网络中信息处理建模的计算工具，特别是基因 网络的基础细胞身份和信号网络控制集体行为在美国国立卫生研究院 模式生物Dictyosteelium discoideum;（3）理解和开发新的可解释机器 学习系统和合成生物学技术，特别关注独特的挑战 生物系统在数据异质性、生物学可解释性和潜在的 bias.除了开发基于物理学和机器学习的模型， 现象，拟议的研究将产生一系列实用和重要的计算工具， 算法，我们将提供包括：（1）我们的“社区模拟器”Python包 为了基于新的微生物消费者资源模型框架来模拟微生物群落， 已经开发;（2）用于分析微生物群落的新机器学习和统计算法， 基因网络;（3）用于预测合成生物部件行为的新计算工具， 不同环境下的电路。这些计算工具将使科学家能够利用现代计算机的力量， 理论，计算和深度学习的进展，以解决与人类相关的基本问题 疾病

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.