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
{"title":"Methods in quantitative biology - from analysis of single-cell microscopy images to inference of predictive models for stochastic gene expression.","authors":"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","doi":"10.1088/1478-3975/adda85","DOIUrl":null,"url":null,"abstract":"<p><p>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).</p>","PeriodicalId":20207,"journal":{"name":"Physical biology","volume":" ","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physical biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1088/1478-3975/adda85","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
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).
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.