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3. Centralizing data to unlock whole-cell models

Author

Chew, Yin Hoon and Karr, Jonathan R.

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Despite substantial potential to transform bioscience, medicine, and bioengineering, whole-cell models remain elusive. One of the biggest challenges to whole-cell models is assembling the large and diverse array of data needed to model an entire cell. Thanks to rapid advances in experimentation, much of the necessary data is becoming available. Furthermore, investigators are increasingly sharing their data due to increased emphasis on reproducibility. However, the scattered organization of this data continues to hamper modeling. Toward more predictive models, we highlight the challenges to assembling the data needed for whole-cell modeling and outline how we can overcome these challenges by working together to build a central data warehouse., Comment: 13 pages, 1 figure

Published
2021

4. Emerging whole-cell modeling principles and methods

Author

Goldberg, Arthur P., Szigeti, Balázs, Chew, Yin Hoon, Sekar, John A. P., Roth, Yosef D., and Karr, Jonathan R.

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Whole-cell computational models aim to predict cellular phenotypes from genotype by representing the entire genome, the structure and concentration of each molecular species, each molecular interaction, and the extracellular environment. Whole-cell models have great potential to transform bioscience, bioengineering, and medicine. However, numerous challenges remain to achieve whole-cell models. Nevertheless, researchers are beginning to leverage recent progress in measurement technology, bioinformatics, data sharing, rule-based modeling, and multi-algorithmic simulation to build the first whole-cell models. We anticipate that ongoing efforts to develop scalable whole-cell modeling tools will enable dramatically more comprehensive and more accurate models, including models of human cells., Comment: 10 pages, 2 figures, 7 supplementary tables

Published
2017
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5. Multi-scale whole-plant model of Arabidopsis growth to flowering

Author

Chew, Yin Hoon, Halliday, Karen, and Williams, Mathew

Subjects
582.13, multi-scale model, crop modelling, flowering, photoperiodism, phenology
Abstract

In this study, theoretical and experimental approaches were combined, using Arabidopsis as the studied species. The multi-scale model incorporates the following, existing sub-models: a phenology model that can predict the flowering time of plants grown in the field, a gene circuit of the circadian clock network that regulates flowering through the photoperiod pathway, a process-based model describing carbon assimilation and resource partitioning, and a functional-structural module that determines shoot structure for light interception and root growth. First, the phenology model was examined on its ability to predict the flowering time of field plantings at different sites and seasons in light of the specific meteorological conditions that pertained. This analysis suggested that the synchrony of temperature and light cycles is important in promoting floral initiation. New features were incorporated into the phenology model that improved its predictive accuracy across seasons. Using both lab and field data, this study has revealed an important seasonal effect of night temperatures on flowering time. Further model adjustments to describe phytochrome (phy) mutants supported the findings and implicated phyB in the temporal gating of temperature-induced flowering. The improved phenology model was next linked to the clock gene circuit model. Simulation of clock mutants with different free-running periods highlighted the complex mechanism associated with daylength responses for the induction of flowering. Finally, the carbon assimilation and functional-structural growth modules were integrated to form the multi-component, whole-plant model. The integrated model was successfully validated with experimental data from a few genotypes grown in the laboratory. In conclusion, the model has the ability to predict the flowering time, leaf biomass and ecosystem exchange of plants grown under conditions of varying light intensity, temperature, CO2 level and photoperiod, though extensions of some model components to incorporate more biological details would be relevant. Nevertheless, this meso-scale model creates obvious application routes from molecular and cellular biology to crop improvement and biosphere management. It could provide a framework for whole-organism modelling to help address global issues such as food security and the energy crisis.

Published
2013

10. The Arabidopsis Framework Model version 2 predicts the organism-level effects of circadian clock gene mis-regulation

Author

Chew, Yin Hoon, primary, Seaton, Daniel D, additional, Mengin, Virginie, additional, Flis, Anna, additional, Mugford, Sam T, additional, George, Gavin M, additional, Moulin, Michael, additional, Hume, Alastair, additional, Zeeman, Samuel C, additional, Fitzpatrick, Teresa B, additional, Smith, Alison M, additional, Stitt, Mark, additional, and Millar, Andrew J, additional

Published
2022
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18. Linking circadian time to growth rate quantitatively via carbon metabolism

Author

Chew, Yin Hoon, Seaton, Daniel, Mengin, Virginie, Flis, Anna, Mugford, Sam T, Smith, Alison M., Stitt, Mark, and Millar, Andrew

Subjects
food and beverages, gene regulatory dynamics, genotype to phenotype, metabolism, mathematical model
Abstract

Predicting a multicellular organism's phenotype quantitatively from its genotype is challenging, as genetic effects must propagate up time and length scales. Circadian clocks are intracellular regulators that control temporal gene expression patterns and hence metabolism, physiology and behaviour, from sleep/wake cycles in mammals to flowering in plants1-3. Clock genes are rarely essential but appropriate alleles can confer a competitive advantage4,5 and have been repeatedly selected during crop domestication3,6. Here we quantitatively explain and predict canonical phenotypes of circadian timing in a multicellular, model organism. We used metabolic and physiological data to combine and extend mathematical models of rhythmic gene expression, photoperiod-dependent flowering, elongation growth and starch metabolism within a Framework Model for growth of Arabidopsis thaliana7-9. The model predicted the effect of altered circadian timing upon each particular phenotype in clock-mutant plants. Altered night-time metabolism of stored starch accounted for most but not all of the decrease in whole-plant growth rate. Altered mobilisation of a secondary store of organic acids quantitatively explained the remaining defect. Our results link genotype through specific processes to higher-level phenotypes, formalising our understanding of a subtle, pleiotropic syndrome at the whole-organism level, and validating the systems approach to understand complex traits starting from intracellular circuits.

Published
2017
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20. The Arabidopsis Framework Model version 2 predicts the organism-level effects of circadian clock gene mis-regulation

Author

Chew, Yin Hoon, primary, Seaton, Daniel D., additional, Mengin, Virginie, additional, Flis, Anna, additional, Mugford, Sam T., additional, George, Gavin M., additional, Moulin, Michael, additional, Hume, Alastair, additional, Zeeman, Samuel C., additional, Fitzpatrick, Teresa B., additional, Smith, Alison M., additional, Stitt, Mark, additional, and Millar, Andrew J., additional

Published
2017
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22. Toward Community Standards and Software for Whole-Cell Modeling

Author

Waltemath, Dagmar, primary, Karr, Jonathan R., additional, Bergmann, Frank T., additional, Chelliah, Vijayalakshmi, additional, Hucka, Michael, additional, Krantz, Marcus, additional, Liebermeister, Wolfram, additional, Mendes, Pedro, additional, Myers, Chris J., additional, Pir, Pinar, additional, Alaybeyoglu, Begum, additional, Aranganathan, Naveen K, additional, Baghalian, Kambiz, additional, Bittig, Arne T., additional, Burke, Paulo E. Pinto, additional, Cantarelli, Matteo, additional, Chew, Yin Hoon, additional, Costa, Rafael S., additional, Cursons, Joseph, additional, Czauderna, Tobias, additional, Goldberg, Arthur P., additional, Gomez, Harold F., additional, Hahn, Jens, additional, Hameri, Tuure, additional, Gardiol, Daniel F. Hernandez, additional, Kazakiewicz, Denis, additional, Kiselev, Ilya, additional, Knight-Schrijver, Vincent, additional, Knupfer, Christian, additional, Konig, Matthias, additional, Lee, Daewon, additional, Lloret-Villas, Audald, additional, Mandrik, Nikita, additional, Medley, J. Kyle, additional, Moreau, Bertrand, additional, Naderi-Meshkin, Hojjat, additional, Palaniappan, Sucheendra K., additional, Priego-Espinosa, Daniel, additional, Scharm, Martin, additional, Sharma, Mahesh, additional, Smallbone, Kieran, additional, Stanford, Natalie J., additional, Song, Je-Hoon, additional, Theile, Tom, additional, Tokic, Milenko, additional, Tomar, Namrata, additional, Toure, Vasundra, additional, Uhlendorf, Jannis, additional, Varusai, Thawfeek M, additional, Watanabe, Leandro H., additional, Wendland, Florian, additional, Wolfien, Markus, additional, Yurkovich, James T., additional, Zhu, Yan, additional, Zardilis, Argyris, additional, Zhukova, Anna, additional, and Schreiber, Falk, additional

Published
2016
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25. Mechanistic Model-Driven Biodesign in Mammalian Synthetic Biology.

Author

Chew YH and Marucci L

Subjects
Animals, Mammals, Research Design, Synthetic Biology, Machine Learning
Abstract

Mathematical modeling plays a vital role in mammalian synthetic biology by providing a framework to design and optimize design circuits and engineered bioprocesses, predict their behavior, and guide experimental design. Here, we review recent models used in the literature, considering mathematical frameworks at the molecular, cellular, and system levels. We report key challenges in the field and discuss opportunities for genome-scale models, machine learning, and cybergenetics to expand the capabilities of model-driven mammalian cell biodesign., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Published
2024
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