Bayesian design of synthetic biological systems
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Here we introduce a new design framework for synthetic biology that exploits the advantages of Bayesian model selection. We will argue that the difference between inference and design is that in the former we try to reconstruct the system that has given rise to the data that we observe, whereas in the latter, we seek to construct the system that produces the data that we would like to observe, i.e., the desired behavior. Our approach allows us to exploit methods from Bayesian statistics, including efficient exploration of models spaces and high-dimensional parameter spaces, and the ability to rank models with respect to their ability to generate certain types of data. Bayesian model selection furthermore automatically strikes a balance between complexity and (predictive or explanatory) performance of mathematical models. To deal with the complexities of molecular systems we employ an approximate Bayesian computation scheme which only requires us to simulate from different competing models to arrive at rational criteria for choosing between them. We illustrate the advantages resulting from combining the design and modeling (or in silico prototyping) stages currently seen as separate in synthetic biology by reference to deterministic and stochastic model systems exhibiting adaptive and switch-like behavior, as well as bacterial two-component signaling systems.
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Target conditions could include clinical settings which are generally not experimentally amenable; the current approach offers a rationale for designing therapeutic interventions into complex systems based on investigations of suitable model systems [49].
“Target conditions could, for example, include clinical settings which are generally not experimentally amenable (at least in early stage research); here the current approach offers a rationale for designing [49] therapeutic interventions into complex systems based on investigations of suitable model systems.”
From Maximizing the information content of experiments in systems biology (2013) · cites this paper as [49]
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Additional file 1 of Efficient design of synthetic gene circuits under cell-to-cell variability
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Additional file 3 of Efficient design of synthetic gene circuits under cell-to-cell variability
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- Authors
- Barnes, C. P., Silk, D., Sheng, X., & Stumpf, M. P. H.
- Affiliations
- Imperial College London (GB)
- Institute of Mathematical Sciences (IN)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2011-08-29
- Volume
- 108
- Issue
- 37
- Pages
- 15190-15195
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3174594
- OpenAlex
- W2135141237
- Cited by
- 90
- References
- 51
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 2 · 2023: 6 · 2022: 7 · 2021: 9 · 2020: 4 · 2019: 4 · 2018: 7 · 2017: 5 · 2016: 5 · 2015: 7 · 2014: 6 · 2013: 11 · 2012: 7
- FWCI
- 3.37
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Biotechnology and Biological Sciences Research Council (BB/F005210/1, BB/G020434/1, BB/C519670/1, BB/G007934/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- synthetic biology design, Bayesian model selection, approximate Bayesian computation, model ranking, model complexity, stochastic gene expression, adaptive behavior, switch-like behavior, two-component systems, bacterial signaling
- MeSH terms
- Adaptation, Physiological, Bacteria, Bayes Theorem, Stochastic Processes, Systems Biology, Synthetic Biology
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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