Methods in quantitative biology - from analysis of single-cell microscopy images to inference of predictive models for stochastic gene expression.

IF 2 4区 生物学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY
Luis Aguilera, Lisa M Weber, Eric Ron, Connor R King, Kaan Öcal, Alex Popinga, Joshua Cook, Michael P May, William S Raymond, Zachary Fox, Linda S Forero-Quintero, Jack R Forman, Alexandre David, Brian Munsky
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引用次数: 0

Abstract

The field of quantitative biology (q-bio) seeks to provide precise and testable explanations for observed biological phenomena by applying mathematical and computational methods. The central goals of q-bio are to (1) systematically propose quantitative hypotheses in the form of mathematical models, (2) demonstrate that these models faithfully capture a specific essence of a biological process, and (3) correctly forecast the dynamics of the process in new, and previously untested circumstances. Achieving these goals depends on accurate analysis and incorporating informative experimental data to constrain the set of potential mathematical representations. In this introductory tutorial, we provide an overview of the state of the field and introduce some of the computational methods most commonly used in q-bio. In particular, we examine experimental techniques in single-cell imaging, computational tools to process images and extract quantitative data, various mechanistic modeling approaches used to reproduce these quantitative data, and techniques for data-driven model inference and model-driven experiment design. All topics are presented in the context of additional online resources, including open-source Python notebooks and open-ended practice problems that comprise the technical content of the annual Undergraduate Quantitative Biology Summer School (UQ-Bio).

定量生物学的方法-从单细胞显微镜图像的分析到随机基因表达预测模型的推断。
定量生物学(q-bio)旨在通过应用数学和计算方法为观察到的生物现象提供精确和可测试的解释。q-bio的核心目标是:(1)以数学模型的形式系统地提出定量假设,(2)证明这些模型忠实地捕捉了生物过程的特定本质,(3)正确地预测了新的和以前未经测试的情况下该过程的动态。实现这些目标依赖于准确的分析和结合翔实的实验数据来约束潜在的数学表示集。在本入门教程中,我们概述了该领域的现状,并介绍了q-bio中最常用的一些计算方法。特别是,我们研究了单细胞成像的实验技术,处理图像和提取定量数据的计算工具,用于再现这些定量数据的各种机制建模方法,以及数据驱动模型推理和模型驱动实验设计的技术。所有主题都在额外的在线资源的背景下呈现,包括开源Python笔记本和开放式实践问题,这些问题构成了年度本科定量生物学暑期学校(UQ-Bio)的技术内容。
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来源期刊
Physical biology
Physical biology 生物-生物物理
CiteScore
4.20
自引率
0.00%
发文量
50
审稿时长
3 months
期刊介绍: Physical Biology publishes articles in the broad interdisciplinary field bridging biology with the physical sciences and engineering. This journal focuses on research in which quantitative approaches – experimental, theoretical and modeling – lead to new insights into biological systems at all scales of space and time, and all levels of organizational complexity. Physical Biology accepts contributions from a wide range of biological sub-fields, including topics such as: molecular biophysics, including single molecule studies, protein-protein and protein-DNA interactions subcellular structures, organelle dynamics, membranes, protein assemblies, chromosome structure intracellular processes, e.g. cytoskeleton dynamics, cellular transport, cell division systems biology, e.g. signaling, gene regulation and metabolic networks cells and their microenvironment, e.g. cell mechanics and motility, chemotaxis, extracellular matrix, biofilms cell-material interactions, e.g. biointerfaces, electrical stimulation and sensing, endocytosis cell-cell interactions, cell aggregates, organoids, tissues and organs developmental dynamics, including pattern formation and morphogenesis physical and evolutionary aspects of disease, e.g. cancer progression, amyloid formation neuronal systems, including information processing by networks, memory and learning population dynamics, ecology, and evolution collective action and emergence of collective phenomena.
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