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Revealing mechanisms of specificity and adaptability in molecular information processing through data-driven models

通过数据驱动模型揭示分子信息处理的特异性和适应性机制

基本信息

批准号：
10715575
负责人：
Arvind Murugan
金额：
$ 38.51万
依托单位：
UNIVERSITY OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-01 至 2028-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10715575
关键词：
Algorithms Antibody Specificity Architecture Atlases Binding Biological Process Biophysics Buffers Cells Chemistry Circadian Rhythms Coupling Cytokine Signaling Data Development Directed Molecular Evolution EGF gene Engineering Environment Epitopes Goals Immune system Information Theory Life Ligands Machine Learning Metabolism Modeling Modernization Molecular Mutation NF-kappa B Nutrient Organism Pathway interactions Pattern Planet Earth Proteins Research Sea Signal Transduction Signaling Protein Specificity Statistical Models System Systems Theory Time Transforming Growth Factor beta Viral Viral Proteins Work antibody mimetics biophysical model circadian pacemaker combinatorial computerized tools cytokine data-driven model dynamic system experimental study information processing insight model building molecular modeling novel predictive modeling programs receptor success theories

项目摘要

Project summary/abstract The success of life on earth derives from its use of molecules to carry information and implement algorithms that control chemistry, allowing organisms to respond adaptively to their environment. The ability to transduce information and respond adaptively ultimately relies on molecular systems being able to selectively recognize one molecular signal from among many other similar signals. The signal could be a molecule (molecular specificity), a combination of molecules (combinatorial specificity), or a time varying concentration pattern (temporal specificity). Further, these molecular systems need to remain adaptable to switch their specificity as needed. The central goal of this proposal is to understand the molecular basis of information processing by building predictive models of molecular, combinatorial and temporal specificity and adaptability of such specificity. We will combine biophysically grounded models, information theory and dynamical systems frameworks for signaling to create data-driven models of molecular, combinatorial and temporal specificity. We will pursue questions on three scales: (1) molecular specificity: how do proteins like antibodies recognize a specific partner, such as an epitope on a viral spike protein, and yet can rapidly change its specificity through mutations? We will develop a biophysically informed machine learning-based toolbox to exploit evolutionary trajectories observed in directed evolution experiments to understand the origin of such adaptability. (2) combinatorial specificity: how do developmental pathways like BMP and TGF-beta resolve specific ligand combinations to determine cell fate, even though each ligand promiscuously binds multiple receptors? We will use an information theory framework for molecular cooperativity to build models of many-many signaling architectures and validate using cell atlas data and experiments that co-express novel combinations of receptor subunits. (3) temporal specificity: how do molecular circuits respond to specific time-varying patterns of concentrations but not others in cytokine signaling and in circadian rhythms? We will develop dynamical systems-theory guided models of stochastic resonance that allow NF-kB to respond to otherwise undetectable levels of cytokines and models of circadian clock-metabolism coupling to understand how cells buffer nutrient fluctuations. Our work is distinguished by combining biophysical models which provide understanding and insight with statistical models that are better able to leverage modern high- throughput data and provide predictive power. In addition, our inference toolboxes and related theory-experiment workflows can used by other labs for similar conceptual questions about alternate systems, such as, molecular specificity for antibodies and spike proteins, combinatorial specificity in the TGF-beta pathway or temporal specificity in EGF signaling respectively for the three thrusts above.

项目概要/摘要地球上生命的成功源于它使用分子来携带信息，实现控制化学的算法，允许生物体适应性地响应它们的环境识别信息和自适应响应的能力最终依赖于能够从许多分子信号中选择性地识别一种分子信号的分子系统类似的信号。信号可以是一种分子（分子特异性），分子（组合特异性）或时变浓度模式（时间特异性）。此外，这些分子系统需要保持适应性以切换它们的根据需要的特殊性。这项提案的中心目标是了解分子通过建立分子预测模型，组合和时间特异性以及这种特异性的适应性。我们将联合收割机结合生物医学基础模型，信息理论和动力系统框架用于信号传导，以创建分子、组合和时间特异性的数据驱动模型。我们将在三个尺度上探讨问题：（1）分子特异性：蛋白质如何抗体识别特定的配偶体，例如病毒刺突蛋白上的表位，但还可以通过突变迅速改变其特异性我们将开发一个生物医学信息基于机器学习的工具箱，用于利用定向中观察到的进化轨迹进化实验来理解这种适应性的起源。(2)组合特异性：像BMP和TGF-β这样发育途径是如何分解特异性配体的组合以确定细胞命运，即使每个配体混杂地结合多个受体？我们将使用分子协同性的信息论框架来构建多-多信号结构的模型，并使用细胞图谱数据和实验进行验证共同表达新的受体亚单位组合。(3)时间特异性：如何分子电路响应特定的时变模式的浓度，但不是其他人，细胞因子信号和昼夜节律？我们将发展动力系统-理论指导随机共振模型，允许NF-kB响应，否则检测不到的水平，细胞因子和生物钟代谢耦合模型，以了解细胞如何缓冲营养波动我们的工作的特点是结合生物物理模型，理解和洞察力与统计模型，能够更好地利用现代高，吞吐量数据并提供预测能力。此外，我们的推理工具箱和相关的理论实验工作流程可以被其他实验室用于类似的概念关于替代系统的问题，例如抗体和加标物的分子特异性蛋白质，TGF-β途径的组合特异性或EGF的时间特异性分别为上述三个推力发信号。