Journal article
Accurate predictions of genetic circuit behavior from part characterization and modular composition
ACS synthetic biology, Vol.4(6), pp.673-681
06/19/2015
DOI: 10.1021/sb500263b
PMID: 25369267
Abstract
A long-standing goal of synthetic biology is to rapidly engineer new regulatory circuits from simpler devices. As circuit complexity grows, it becomes increasingly important to guide design with quantitative models, but previous efforts have been hindered by lack of predictive accuracy. To address this, we developed Empirical Quantitative Incremental Prediction (EQuIP), a new method for accurate prediction of genetic regulatory network behavior from detailed characterizations of their components. In EQuIP, precisely calibrated time-series and dosage-response assays are used to construct hybrid phenotypic/mechanistic models of regulatory processes. This hybrid method ensures that model parameters match observable phenomena, using phenotypic formulation where current hypotheses about biological mechanisms do not agree closely with experimental observations. We demonstrate EQuIP's precision at predicting distributions of cell behaviors for six transcriptional cascades and three feed-forward circuits in mammalian cells. Our cascade predictions have only 1.6-fold mean error over a 261-fold mean range of fluorescence variation, owing primarily to calibrated measurements and piecewise-linear models. Predictions for three feed-forward circuits had a 2.0-fold mean error on a 333-fold mean range, further demonstrating that EQuIP can scale to more complex systems. Such accurate predictions will foster reliable forward engineering of complex biological circuits from libraries of standardized devices.
Details
- Title: Subtitle
- Accurate predictions of genetic circuit behavior from part characterization and modular composition
- Creators
- Noah Davidsohn - Massachusetts Institute of TechnologyJacob Beal - RTXSamira Kiani - Massachusetts Institute of TechnologyAaron Adler - RTXFusun Yaman - RTXYinqing Li - Massachusetts Institute of TechnologyZhen Xie - Tsinghua UniversityRon Weiss - Massachusetts Institute of Technology
- Resource Type
- Journal article
- Publication Details
- ACS synthetic biology, Vol.4(6), pp.673-681
- DOI
- 10.1021/sb500263b
- PMID
- 25369267
- ISSN
- 2161-5063
- eISSN
- 2161-5063
- Language
- English
- Date published
- 06/19/2015
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984627209002771
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