Collaborative Research: CDI-Type II: Discovery of Succinct Dynamical Relationships in Large-Scale Biological Data Sets

合作研究：0836656（Peter Doerschuk，康奈尔大学）0836649（Bud Mishra，纽约大学）0836720（Sanjoy Mitter和Emery Brown，麻省理工学院）题目：大规模生物数据集中的简洁动态关系的发现摘要：神经科学和分子生物学中的许多类型的信息可以被描述为随着某些指标的值变化而重复进行的一组测量。 在某些情况下，例如测量基因活性的转录组数据，索引是时间，而在其他情况下，例如在遗传学关联研究中，索引是基因组DNA序列中的位置，并且在任何情况下，完整的数据集合被称为时间序列。 推理是这样一个过程，即获取可能被错误破坏的时间序列，并计算以下几类问题的答案：（1）生成时间序列的系统是什么？ 例如，如果已知系统是特定类型的微分方程，那么微分方程中的参数值是什么？(2)给定一个完全指定的系统和一个时间序列，这个系统会产生这个时间序列吗？ 例如，如果一个生物学家假设了一个系统，该系统描述了一组特定基因的基因表达，然后测量了表达数据，那么这些数据是否与该系统兼容，或者等价地，与该假设兼容？ (3)给定两个时间序列，它们是由同一个系统生成的吗？ 例如，如果在两种不同的实验情况下记录神经系统中神经放电的模式，那么模式是相同的还是不同的？ 四位首席研究员专注于三个不同生物学规模的三个不同生物学应用领域：（1）动物和人类乙醇消耗行为的表型（整个生物体规模），（2）在神经元集合上测量的动作电位模式（细胞群体规模），以及（3）由细胞调节回路控制的基因表达的时间过程（细胞规模）。 在这些应用中遇到的挑战类型包括以下特征：信息在很长一段时间内分布，而不是集中在时间上;系统包括延迟和反馈路径;系统是高度非线性的，包括切换行为，而不是线性的。 为解决这些应用领域中的推理问题而将开发和结合的主要方法是：（a）信息论和随机控制，（B）学习数据几何的多尺度方法，以及（c）计算机代数和符号计算。 例如，为了处理神经科学系统中存在的延迟和反馈，特别是在信息和随机控制相互作用的背景下，需要从根本上重新思考经典信息论，因为它被应用于基于技术的通信系统。随着计算成本的降低，计算变得越来越普遍。 普适计算的一个主要目的是实时收集高维时间序列的非常不同类型的数据，包括生物，医疗，金融，通信系统的状态，电力系统的状态等，该项目将提供计算算法和软件，以更复杂的方式分析这些数据，从而提取更复杂的信息。 根据这些更复杂的信息采取的行动，例如，基于个体化基因组信息的个性化医疗或更准确和灵活地控制电力系统从而避免停电，将对社会产生重要的人类和经济效益。 该项目的一个重要组成部分是教育，例如，三名从事该项目的研究生将获得学费和津贴，数量不受限制的本科生将通过各种方式参与，例如，项目课程。 通过吸引有才华的学生到科学和技术，并提供具有挑战性的研究经验，该项目将有重要的劳动力效益的社会。

Collaborative Research:0836656 (Peter Doerschuk, Cornell University)0836649 (Bud Mishra, NYU)0836720 (Sanjoy Mitter and Emery Brown, MIT)Title: Discovery of Succinct Dynamical Relationships in Large-Scale Biological Data SetsABSTRACT:Many types of information in neuroscience and molecular biology can be described as a set of measurements taken repeatedly as some index changes its value. In some situations, such as transcriptomic data measuring gene activities, the index is time while in other situations, such as in genetics association study, the index is position in a genomic DNA sequence and, in any case, the complete collection of data is referred to as a time series. Inference is the process of taking such time series, probably corrupted by errors, and computing answers to the following sorts of questions: (1) What is the system that generated the time series? For instance, if the system is known to be a differential equation of a specific type, what are the parameter values in the differential equation? (2) Given a completely specified system and a time series, did that system generate that time series? For instance, if a biologist has hypothesized a system that describes gene expression for a particular set of genes and then measures expression data, is the data compatible with the system, or equivalently, the hypothesis? (3) Given two time series, were they generated by the same system? For instance, if the pattern of nerve firings in a neural system is recorded in two different experimental situations, is the pattern the same or is it different? The four Principal Investigators are focused on three different biological application domains at three different biological scales: (1) the phenotyping of animal and human ethanol-consumption behavior (whole organism scale), (2) the pattern of action potentials measured on ensembles of neurons (cell-population scale), and (3) the time course of gene expressions as governed by the regulatory circuits of the cell (cellular scale). The types of challenges that are encountered in these applications include the following characteristics: the information is distributed over long periods of time rather than concentrated in time; the systems include delays and feedback paths; and the systems are highly nonlinear, including switching behavior, rather than linear. The major methodologies that will be developed and combined to solve inference problems in these application areas are: (a) information theory and stochastic control, (b) multi-scale approaches to learning the geometry of the data, and (c) computer algebra and symbolic computation. For example, to deal with the presence of delay and feedback in neuroscience systems, especially in the context of the interaction between information and stochastic control, requires a fundamental rethinking of classical information theory as it is employed in technology-based communication systems.As the cost of computing decreases, computing becomes increasingly pervasive. A major purpose of pervasive computing is the real-time collection of high-dimensional time series of very diverse types of data including biological, medical, financial, communication systems status, power systems status, etc. The project will provide computational algorithms and software to analyze this data in more sophisticated ways and thereby extract more sophisticated information. Action taken upon this more sophisticated information, e.g., personalized medicine based on individualized genomic information or more accurate and flexible control of power systems thereby avoiding blackouts, will have important human and economic benefits to society. An important component of the project is educational, e.g., three graduate students working on the project will receive tuition and stipend and an unrestricted number of undergraduates will participate through a variety of ways, e.g., project courses. By attracting talented students to science and technology and providing challenging research experiences, the project will have important work force benefits to society.