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.

在过去的二十年中，从基因组学到流行病学的不同领域，见证了生命科学不同领域的巨大实验突破。借助现代的高通量技术，从单个分子到整个人群的多个尺度的生物系统现在可以在高空间和时间分辨率下进行定量探测。除了增强我们对系统组成部分的基本知识外，这些数据还可能编码有关控制其演变的功能约束以及限制其性能的物理约束的信息，以及有关组织的级别，动态约束或设计原理，这些原理很难从低通量数据中识别出来。提取此信息对于从具有所需功能的蛋白质到在流行期间的接触重建的应用也至关重要。反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中推断出生成模型（Boltzmann分布）来做到这一点。然而，生物学数据的特定特征，例如强烈采样和异质性，限制了这些工具的有效性。 Simbad旨在开发能够克服这些问题的一类统计推理技术。在Simbad中，理论工作将提供概念和方法来解决四个紧迫问题（学习蛋白质序列景观，代谢网络的反向建模，从流行病学数据中推断接触网络以及改善生存分析模型），这反过来将指导该理论朝着与每个领域的现有标准集成。这项努力有望为基础研究打开新的途径，以影响经济，技术和社会问题。 Simbad中代表的备受瞩目的跨学科专业知识可确保实现可衡量和可实现的目标，从而使Simbad处于实现其目标的理想位置。