Machine Learning for Generalized Multiscale Modeling

用于广义多尺度建模的机器学习

• 批准号: 9791802
• 负责人: ERIC D MJOLSNESS
• 金额: $ 61.91万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9791802
• 关键词: Aging; Algorithms; Alzheimer' s Disease; Area; Behavior; Biochemical; Biochemistry; Biological Models; Biological Neural Networks; Biology; Brain; Calcium; Cells; Chemicals; Collaborations; Communities; Complex; Computer software; Computing Methodologies; Consequentialism; Coupling; Data Set; Development; Dimensions; Electron Microscopy; Electrophysiology (science); Environment; Equation; Equilibrium; Evolution; Fostering; Hybrids; Image; Investigation; Ion Channel; Learning; Libraries; Light; Machine Learning; Memory; Methods; Modeling; Molecular; Morphology; National Institute of General Medical Sciences; Neurons; Neuropil; Neurosciences; Pharmacologic Substance; Physics; Population; Potassium Channel; Process; Pythons; Reaction; Research Personnel; Sleep; Structure; Synapses; Synaptic plasticity; System; Techniques; Time; Tissues; United States National Institutes of Health; Vertebral column; Work; age related; base; biological systems; calmodulin-dependent protein kinase II; computer studies; experimental study; information processing; insight; interest; mathematical methods; men who have sex with men; microscopic imaging; multi-scale modeling; nervous system disorder; particle; postsynaptic; reconstruction; relating to nervous system; simulation; software development; success; tool; working group

Project Summary/Abstract This project develops machine learning approaches that describe statistical systems in biology. By combining analytic results calculated from the exact probabilistic description of the system with machine learning inference, our new methods present exciting opportunities to model previously inaccessible complex dynamics. The resulting Boltzmann machine-like learning algorithms present a new class of modeling techniques based on the powerful in- ference of arti cial neural networks. Further development of this approach will bring the groundbreaking advances from the surge of recent interest in machine learning into the biological modeling eld. The mathematical methods we develop will be used to derive e cient algorithms for multiscale simulation, directly applicable to large scale biological modeling. In particular, the algorithms will be used to study the dynamics of stochastic biochemistry at synapses, with direct relevance to learning and memory formation in the brain. Current studies of these processes are limited by the long timescales involved and the highly spatially organized structures featured. In addition to leveraging the machine learning expertise we are developing, we also employ new electron microscopy datasets to produce 3D reconstructions of neural tissue with unprecedented accuracy. Consequentially, we will be able to study the fundamental mechanisms underlying synaptic plasticity, as well as the biochemical basis of oscillatory behavior in networks of neurons that occurs during sleep. Furthermore, the interactions of these highly stochastic ion channels with electrical in neurons will be explored through groundbreaking hybrid simulation environments. The software that we will develop combines existing popular simulation tools into multiscale approaches, and will be distributed as a powerful tool to the broader biological modeling community. Its usage in further computational experiments can present a key advancement in the development of pharmaceuticals, allowing the direct study of the interactions of biochemistry and whole neuron electrophysiology without making limiting assumptions to sim- plify the simulations. This has promising implications for intervening in age-related learning de cits, as well as in neurological disorders such as Alzheimers. Finally, this proposal will bring together our existing multiscale modeling community, the National Center for Multi-scale Modeling of Biological Systems (MMBioS), with the MSM consortium. The interactions of these organizations and their communities of expert researchers will foster new collaborative work on exciting multiscale problems in biology, including applications of the machine learning frameworks and software we are developing. 1

项目总结/摘要 该项目开发描述生物学中统计系统的机器学习方法。通过组合 通过机器学习推理从系统的精确概率描述计算的分析结果， 我们的新方法提供了令人兴奋的机会来模拟以前无法实现的复杂动力学。所得 玻尔兹曼类机学习算法提出了一类新的建模技术的基础上，强大的内， 阿尔蒂神经网络。这种方法的进一步发展将带来突破性的进展， 从最近对机器学习的兴趣激增到生物建模领域。的数学方法 我们开发的算法将用于多尺度模拟，直接适用于大规模 生物建模特别是，该算法将用于研究随机生物化学的动力学， 突触，与大脑中的学习和记忆形成直接相关。目前对这些过程的研究 受到所涉及的长时间尺度和高度空间组织结构的限制。此外 为了利用我们正在开发的机器学习专业知识，我们还采用了新的电子显微镜数据集， 以前所未有的精确度对神经组织进行3D重建。因此，我们将能够 研究突触可塑性的基本机制，以及振荡的生化基础。 神经元网络在睡眠期间的行为。此外，这些高度随机的相互作用 将通过开创性的混合模拟环境探索神经元中的电离子通道。 我们将开发的软件将现有的流行模拟工具结合到多尺度方法中， 作为一个强大的工具分发给更广泛的生物建模社区。它在进一步计算中的应用 实验可以在药物开发方面取得关键进展，允许直接研究 生物化学和整个神经元电生理学的相互作用，而不对模拟进行限制性假设， 简化模拟。这对干预与年龄相关的学习缺陷以及 神经系统疾病如老年痴呆症最后，这项提案将把我们现有的多尺度 国家生物系统多尺度建模中心（MMBioS）， MSM联盟。这些组织及其专家研究人员社区的互动将促进 关于生物学中令人兴奋的多尺度问题的新合作工作，包括机器学习的应用 我们正在开发的框架和软件。 1