Faculty Publications
Permanent URI for this collectionhttps://hdl.handle.net/10217/179926
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Item Open Access Markov chain Monte Carlo methods: tutorial videos from the Undergraduate Quantitative Biology Summer School(Colorado State University. Libraries, 2025) Öcal, Kaan, instructor; Öcal, Kaan, author; Vo, Huy, author; Munsky, Brian, authorThe 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).Item Open Access Stochastic simulation algorithm: tutorial videos from the Undergraduate Quantitative Biology Summer School(Colorado State University. Libraries, 2025) David, Alex, instructor; Forman, Jack, instructor; Munsky, Brian, instructor; Weber, Lisa, author; May, Michael, author; Cook, Joshua, author; Munsky, Brian, authorThe 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).Item Open Access Segmentation and tracking: tutorial videos from the Undergraduate Quantitative Biology Summer School(Colorado State University. Libraries, 2025) Aguilera, Luis, instructor; Aguilera, Luis, author; Raymond, Will, author; Munsky, Brian, authorThe 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).Item Open Access Stiochiometries, propensities, and ODE models: tutorial videos from the Undergraduate Quantitative Biology Summer School(Colorado State University. Libraries, 2025) King, Connor, instructor; Munsky, Brian, instructor; May, Michael, author; Cook, Joshua, author; Munsky, Brian, authorThe 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).Item Open Access Labeling and imaging technology: tutorial videos from the Undergraduate Quantitative Biology Summer School(Colorado State University. Libraries, 2025) Ron, Eric, instructorThe 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).Item Open Access Image processing basics: tutorial videos from the Undergraduate Quantitative Biology Summer School(Colorado State University. Libraries, 2025) Aguilera, Luis, instructorThe 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).Item Open Access Chemical master equations: tutorial videos from the Undergraduate Quantitative Biology Summer School(Colorado State University. Libraries, 2025) Munsky, Brian, instructor; Popinga, Alex, authorThe 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).Item Open Access Modification of orthogonal tRNAs: unexpected consequences for sense codon reassignment(Colorado State University. Libraries, 2016-10-23) Biddle, Wil, author; Schmitt, Margaret A., author; Fisk, John D., author; Oxford University Press, publisherBreaking the degeneracy of the genetic code via sense codon reassignment has emerged as a way to incorporate multiple copies of multiple non-canonical amino acids into a protein of interest. Here, we report the modification of a normally orthogonal tRNA by a host enzyme and show that this adventitious modification has a direct impact on the activity of the orthogonal tRNA in translation. We observed nearly equal decoding of both histidine codons, CAU and CAC, by an engineered orthogonal M. jannaschii tRNA with an AUG anticodon: tRNAOpt. We suspected a modification of the tRNAOptAUG anticodon was responsible for the anomalous lack of codon discrimination and demonstrate that adenosine 34 of tRNAOptAUG is converted to inosine. We identified tRNAOptAUG anticodon loop variants that increase reassignment of the histidine CAU codon, decrease incorporation in response to the histidine CAC codon, and improve cell health and growth profiles. Recognizing tRNA modification as both a potential pitfall and avenue of directed alteration will be important as the field of genetic code engineering continues to infiltrate the genetic codes of diverse organisms.