title: Accurate predictions of genetic circuit behavior from part characterization and modular composition.
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.
authors.affiliation: †Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.
authors.forename: Noah
authors.initials: N
authors.lastname: Davidsohn
authors.affiliation: ‡Raytheon BBN Technologies, 10 Moulton Street, Cambridge, Massachusetts 02138, United States.
authors.forename: Jacob
authors.initials: J
authors.lastname: Beal
authors.affiliation: †Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.
authors.forename: Samira
authors.initials: S
authors.lastname: Kiani
authors.affiliation: ‡Raytheon BBN Technologies, 10 Moulton Street, Cambridge, Massachusetts 02138, United States.
authors.forename: Aaron
authors.initials: A
authors.lastname: Adler
authors.affiliation: ‡Raytheon BBN Technologies, 10 Moulton Street, Cambridge, Massachusetts 02138, United States.
authors.forename: Fusun
authors.initials: F
authors.lastname: Yaman
authors.affiliation: †Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.
authors.forename: Yinqing
authors.initials: Y
authors.lastname: Li
authors.affiliation: §Bioinformatics Division/Center for Synthetic and Systems Biology, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Xie
authors.affiliation: †Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.
authors.forename: Ron
authors.initials: R
authors.lastname: Weiss
mesh_terms: D004318:Doxycycline / Q000633:toxicity; D005434:Flow Cytometry; D053263:Gene Regulatory Networks / Q000187:drug effects; D057809:HEK293 Cells; D006801:Humans; D008164:Luminescent Proteins / Q000235:genetics / Q000378:metabolism; D010957:Plasmids / Q000235:genetics / Q000378:metabolism; D058615:Synthetic Biology / Q000379:methods*
chemical_list: D008164:Luminescent Proteins; D004318:Doxycycline
keywords: genetic circuits; synthetic biology; systems biology