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The role of predictive modelling in rationally re-engineering biological systems

Nature Reviews Microbiology volume 7, pages 297–305 (2009)Cite this article

Abstract

Technologies to synthesize and transplant a complete genome into a cell have opened limitless potential to redesign organisms for complex, specialized tasks. However, large-scale re-engineering of a biological circuit will require systems-level optimization that will come from a deep understanding of operational relationships among all the constituent parts of a cell. The integrated framework necessary for conducting such complex bioengineering requires the convergence of systems and synthetic biology. Here, we review the status of these rapidly developing interdisciplinary fields of biology and provide a perspective on plausible venues for their merger.

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Figure 1: Timeline of events that resulted in the development of systems biology and synthetic biology.
Figure 2: Convergence of systems and synthetic biology.

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Acknowledgements

This work was supported by grants from the National Institutes of Health (NIH; P50GM076547 and 1R01GM077398-01A2), Department of Environment (DoE; DE-FG02-07ER64327 and DE-FG02-07ER64327), National Science Foundation (NSF; EF-0313754, EIA-0220153, MCB-0425825 and DBI-0640950) and NASA (National Aeronautics and Space Administration; NNG05GN58G) to N.S.B. The authors thank D. Reiss and A. Schmid for critical reading of the manuscript and R. Vencio for helpful discussions.

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  1. Tie Koide, Wyming Lee Pang and Nitin S. Baliga are at the Institute for Systems Biology, 1441 N 34th Street, Seattle, Washington 98103, USA.,

    Tie Koide, Wyming Lee Pang & Nitin S. Baliga

  2. Nitin S. Baliga is also at the Departments of Microbiology, and Molecular and Cellular Biology, University of Washington, Seattle, Washington 98195, USA.,

    Nitin S. Baliga

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  1. Tie Koide
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  2. Wyming Lee Pang
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Correspondence to Nitin S. Baliga.

Related links

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DATABASES

Entrez Genome Project

Aspergillus nidulans

Caulobacter crescentus

Caenorhabditis elegans

Deinococcus radiodurans

Escherichia coli

Halobacterium salinarum NRC-1

Pyrococcus furiosus

Saccharomyces cerevisiae

Shewanella oneidensis MR-1

Vibrio fischeri

Yersinia pseudotuberculosis

FURTHER INFORMATION

Halobacterium systems Biology

MIT Registry of Standard Biological Parts

RegulonDB

SBML Software Matrix

Synthetic Biology community

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Koide, T., Lee Pang, W. & Baliga, N. The role of predictive modelling in rationally re-engineering biological systems. Nat Rev Microbiol 7, 297–305 (2009). https://doi.org/10.1038/nrmicro2107

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