Towards a whole-cell modeling approach for synthetic biology
Despite rapid advances over the last decade, synthetic biology lacks the predictive tools needed to enable rational design. Unlike established engineering disciplines, the engineering of synthetic gene circuits still relies heavily on experimental trial-and-error, a time-consuming and inefficient pr...
| Main Authors: | , , , , |
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| Other Authors: | , |
| Format: | Article |
| Language: | English |
| Published: |
American Institute of Physics (AIP),
2016-01-20T18:50:50Z.
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| Subjects: | |
| Online Access: | Get fulltext |
| Summary: | Despite rapid advances over the last decade, synthetic biology lacks the predictive tools needed to enable rational design. Unlike established engineering disciplines, the engineering of synthetic gene circuits still relies heavily on experimental trial-and-error, a time-consuming and inefficient process that slows down the biological design cycle. This reliance on experimental tuning is because current modeling approaches are unable to make reliable predictions about the in vivo behavior of synthetic circuits. A major reason for this lack of predictability is that current models view circuits in isolation, ignoring the vast number of complex cellular processes that impinge on the dynamics of the synthetic circuit and vice versa. To address this problem, we present a modeling approach for the design of synthetic circuits in the context of cellular networks. Using the recently published whole-cell model of Mycoplasma genitalium, we examined the effect of adding genes into the host genome. We also investigated how codon usage correlates with gene expression and find agreement with existing experimental results. Finally, we successfully implemented a synthetic Goodwin oscillator in the whole-cell model. We provide an updated software framework for the whole-cell model that lays the foundation for the integration of whole-cell models with synthetic gene circuit models. This software framework is made freely available to the community to enable future extensions. We envision that this approach will be critical to transforming the field of synthetic biology into a rational and predictive engineering discipline. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Advanced Undergraduate Research Program) United States. Defense Advanced Research Projects Agency National Institutes of Health (U.S.) (New Innovator Award 1DP2OD008435) National Science Foundation (U.S.) (1124247) |
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