Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments
Modern biological experiments are becoming increasingly complex, and designing these experiments to yield the greatest possible quantitative insight is an open challenge. Increasingly, computational models of complex stochastic biological systems are being used to understand and predict biological b...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8536365 |
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| _version_ | 1849414297025773568 |
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| author | Zachary R. Fox Gregor Neuert Brian Munsky |
| author_facet | Zachary R. Fox Gregor Neuert Brian Munsky |
| author_sort | Zachary R. Fox |
| collection | DOAJ |
| description | Modern biological experiments are becoming increasingly complex, and designing these experiments to yield the greatest possible quantitative insight is an open challenge. Increasingly, computational models of complex stochastic biological systems are being used to understand and predict biological behaviors or to infer biological parameters. Such quantitative analyses can also help to improve experiment designs for particular goals, such as to learn more about specific model mechanisms or to reduce prediction errors in certain situations. A classic approach to experiment design is to use the Fisher information matrix (FIM), which quantifies the expected information a particular experiment will reveal about model parameters. The finite state projection-based FIM (FSP-FIM) was recently developed to compute the FIM for discrete stochastic gene regulatory systems, whose complex response distributions do not satisfy standard assumptions of Gaussian variations. In this work, we develop the FSP-FIM analysis for a stochastic model of stress response genes in S. cerevisiae under time-varying MAPK induction. We verify this FSP-FIM analysis and use it to optimize the number of cells that should be quantified at particular times to learn as much as possible about the model parameters. We then extend the FSP-FIM approach to explore how different measurement times or genetic modifications help to minimize uncertainty in the sensing of extracellular environments, and we experimentally validate the FSP-FIM to rank single-cell experiments for their abilities to minimize estimation uncertainty of NaCl concentrations during yeast osmotic shock. This work demonstrates the potential of quantitative models to not only make sense of modern biological datasets but to close the loop between quantitative modeling and experimental data collection. |
| format | Article |
| id | doaj-art-742c4e942a3845309e1b5ffbe13a6038 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-742c4e942a3845309e1b5ffbe13a60382025-08-20T03:33:53ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/85363658536365Optimal Design of Single-Cell Experiments within Temporally Fluctuating EnvironmentsZachary R. Fox0Gregor Neuert1Brian Munsky2Inria Saclay Ile-de-France, Palaiseau 91120, FranceDepartment of Molecular Physiology and Biophysics, School of Medicine, Vanderbilt University, Nashville, TN 37232, USASchool of Biomedical Engineering, Colorado State University, Fort Collins, CO 80523, USAModern biological experiments are becoming increasingly complex, and designing these experiments to yield the greatest possible quantitative insight is an open challenge. Increasingly, computational models of complex stochastic biological systems are being used to understand and predict biological behaviors or to infer biological parameters. Such quantitative analyses can also help to improve experiment designs for particular goals, such as to learn more about specific model mechanisms or to reduce prediction errors in certain situations. A classic approach to experiment design is to use the Fisher information matrix (FIM), which quantifies the expected information a particular experiment will reveal about model parameters. The finite state projection-based FIM (FSP-FIM) was recently developed to compute the FIM for discrete stochastic gene regulatory systems, whose complex response distributions do not satisfy standard assumptions of Gaussian variations. In this work, we develop the FSP-FIM analysis for a stochastic model of stress response genes in S. cerevisiae under time-varying MAPK induction. We verify this FSP-FIM analysis and use it to optimize the number of cells that should be quantified at particular times to learn as much as possible about the model parameters. We then extend the FSP-FIM approach to explore how different measurement times or genetic modifications help to minimize uncertainty in the sensing of extracellular environments, and we experimentally validate the FSP-FIM to rank single-cell experiments for their abilities to minimize estimation uncertainty of NaCl concentrations during yeast osmotic shock. This work demonstrates the potential of quantitative models to not only make sense of modern biological datasets but to close the loop between quantitative modeling and experimental data collection.http://dx.doi.org/10.1155/2020/8536365 |
| spellingShingle | Zachary R. Fox Gregor Neuert Brian Munsky Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments Complexity |
| title | Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments |
| title_full | Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments |
| title_fullStr | Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments |
| title_full_unstemmed | Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments |
| title_short | Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments |
| title_sort | optimal design of single cell experiments within temporally fluctuating environments |
| url | http://dx.doi.org/10.1155/2020/8536365 |
| work_keys_str_mv | AT zacharyrfox optimaldesignofsinglecellexperimentswithintemporallyfluctuatingenvironments AT gregorneuert optimaldesignofsinglecellexperimentswithintemporallyfluctuatingenvironments AT brianmunsky optimaldesignofsinglecellexperimentswithintemporallyfluctuatingenvironments |