Statistical Inference from Multiscale Biological Data: theory, algorithms, applications

多尺度生物数据的统计推断：理论、算法、应用

• 批准号: EP/Y037375/1
• 负责人: Luca Ferretti
• 金额: $ 27.3万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY037375%2F1
• 关键词: Statistical; Inference; Multiscale; Biological; Data

The last two decades have witnessed giant experimental breakthroughs in different areas of the life sciences, from genomics to epidemiology. Thanks to modern high-throughput techniques, biological systems across multiple scales -from single molecules up to entire populations- can now be probed quantitatively at high spatial and temporal resolutions. Besides enhancing our basic knowledge of a system's constituents, these data potentially encode a plethora of information about the functional constraints that govern its evolution and the physical constraints that limit its performance, as well as about levels of organization, dynamical constraints or design principles that would be hard to identify from low-throughput data. Extracting this information is also crucial for applications ranging from the design of proteins with a desired functionality to the reconstruction of contacts during an epidemics. Inverse statistical mechanics attempts to do it by inferring generative models (Boltzmann distributions) from data using methods from the physics of disordered and random systems. Specific characteristics of biological data however, like strong undersampling and heterogeneity, limit the effectiveness of these tools. SIMBAD aims at developing a class of statistical inference techniques capable of overcoming these issues. In SIMBAD, theoretical work will supply concepts and methods to address four pressing problems (learning protein sequence landscapes, inverse modeling metabolic networks, inferring contact networks from epidemiological data, and improving survival analysis models), which in turn will guide the theory towards integration with the existing standards of each field. This effort promises to open new pathways for basic research to impact economic, technological and societal issues; the high-profile cross-disciplinary expertise represented in SIMBAD ensures instead for measurable and achievable objectives, placing SIMBAD in an ideal position to achieve its goals.

过去二十年见证了从基因组学到流行病学等生命科学不同领域的巨大实验突破。得益于现代高通量技术，现在可以在高空间和时间分辨率下定量探测跨多个尺度（从单个分子到整个群体）的生物系统。除了增强我们对系统组成部分的基本知识之外，这些数据还可能编码大量有关控制其演化的功能约束和限制其性能的物理约束的信息，以及有关组织级别、动态约束或设计原则的信息。很难从低通量数据中识别出来。提取这些信息对于从具有所需功能的蛋白质的设计到流行病期间的接触重建等应用也至关重要。逆统计力学试图通过使用无序和随机系统物理学的方法从数据推断生成模型（玻尔兹曼分布）来实现这一点。然而，生物数据的具体特征，例如强烈的欠采样和异质性，限制了这些工具的有效性。 SIMBAD 旨在开发一类能够克服这些问题的统计推断技术。在 SIMBAD 中，理论工作将提供解决四个紧迫问题的概念和方法（学习蛋白质序列景观、逆向建模代谢网络、从流行病学数据推断接触网络以及改进生存分析模型），这反过来又将指导理论与各领域现有标准。这项努力有望为影响经济、技术和社会问题的基础研究开辟新途径； SIMBAD 所代表的备受瞩目的跨学科专业知识确保了可衡量和可实现的目标，使 SIMBAD 处于实现其目标的理想位置。