ArticleProceedings of the National Academy of Sciences of the United States of America2011
Bayesian design of synthetic biological systems.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Probability and Neurodegeneration: Alzheimer's Disease and Huntington's Disease.Brain sciences · 2025Article
- Efficient design of synthetic gene circuits under cell-to-cell variability.BMC bioinformatics · 2023Article
- Automated Design of Synthetic Gene Circuits in the Presence of Molecular Noise.ACS synthetic biology · 2023Article
- Article
- Modelling daily weight variation in honey bee hives.PLoS computational biology · 2023Article
- A group theoretic approach to model comparison with simplicial representations.Journal of mathematical biology · 2022Article
- Chaos in synthetic microbial communities.PLoS computational biology · 2022Article
- Stability selection enables robust learning of differential equations from limited noisy data.Proceedings. Mathematical, physical, and engineering sciences · 2022Article
- Mathematical Modelling in Plant Synthetic Biology.Methods in molecular biology (Clifton, N.J.) · 2022Review
- A rationally engineered decoder of transient intracellular signals.Nature communications · 2021Article
- Automated design of synthetic microbial communities.Nature communications · 2021Article
- Multi-model and network inference based on ensemble estimates: avoiding the madness of crowds.Journal of the Royal Society, Interface · 2020Article
- Modeling cell population dynamics.In silico biology · 2019Article
- Towards an Aspect-Oriented Design and Modelling Framework for Synthetic Biology.Processes (Basel, Switzerland) · 2018Article
- Rapid acquisition and model-based analysis of cell-free transcription-translation reactions from nonmodel bacteria.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- PEITH(Θ): perfecting experiments with information theory in Python with GPU support.Bioinformatics (Oxford, England) · 2018Article
- Computational Methods for Estimating Molecular System from Membrane Potential Recordings in Nerve Growth Cone.Scientific reports · 2018Article
- Computing with biological switches and clocks.Natural computing · 2018Article
- LScientific reports · 2017Article
- A computational method for the investigation of multistable systems and its application to genetic switches.BMC systems biology · 2016Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.