Accurate prediction of genetic circuit behavior requires multidimensional characterization of parts

Mathematical models can aid the design of genetic circuits, but may yield inaccurate results if individual parts are not modeled at the appropriate resolution. To illustrate the importance of this concept, we study transcriptional cascades consisting of two inducible synthetic transcription factors connected in series. Despite the simplicity of this design, we find that accurate prediction of circuit behavior requires mapping the dose responses of each circuit component along the dimensions of both its expression level and its inducer concentration. With such multidimensional characterizations, we were able to computationally explore the behavior of 16 different circuit designs. We experimentally verified a subset of these predictions and found substantial agreement. This method of biological part characterization enables the use of models to identify (un)desired circuit behaviors prior to experimental implementation, thus shortening the design-build-test cycle for more complex circuits.

[1]  M. Khammash,et al.  Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks. , 2016, Cell systems.

[2]  R. Kwok.  Five hard truths for synthetic biology , 2010, Nature.

[3]  David Baker,et al.  Modular and tunable biological feedback control using a de novo protein switch , 2019, Nature.

[4]  Hana El-Samad,et al.  Robust Synthetic Circuits for Two-Dimensional Control of Gene Expression in Yeast. , 2017, ACS synthetic biology.

[5]  W. Lim,et al.  Defining Network Topologies that Can Achieve Biochemical Adaptation , 2009, Cell.

[6]  Benjamin L. Oakes,et al.  Synthetic gene expression perturbation systems with rapid, tunable, single-gene specificity in yeast , 2012, Nucleic acids research.

[7]  Neda Bagheri,et al.  The COMET toolkit for composing customizable genetic programs in mammalian cells , 2019, bioRxiv.

[8]  Priscilla E. M. Purnick,et al.  The second wave of synthetic biology: from modules to systems , 2009, Nature Reviews Molecular Cell Biology.

[9]  M. Elowitz,et al.  A synthetic oscillatory network of transcriptional regulators , 2000, Nature.

[10]  Carlos F. Barbas,et al.  Regulation of Endogenous Human Gene Expression by Ligand-Inducible TALE Transcription Factors , 2013, ACS synthetic biology.

[11]  Christopher A. Voigt,et al.  Genetic circuit design automation , 2016, Science.

[12]  David Botstein,et al.  Perturbation-based analysis and modeling of combinatorial regulation in the yeast sulfur assimilation pathway , 2012, Molecular biology of the cell.

[13]  Eric H Davidson,et al.  Modeling the dynamics of transcriptional gene regulatory networks for animal development. , 2009, Developmental biology.

[14]  W. Pratt.  The hsp90-based Chaperone System: Involvement in Signal Transduction from a Variety of Hormone and Growth Factor Receptors , 1998, Proceedings of the Society for Experimental Biology and Medicine. Society for Experimental Biology and Medicine.

[15]  David Botstein,et al.  Synthetic biology tools for programming gene expression without nutritional perturbations in Saccharomyces cerevisiae , 2014, Nucleic acids research.

[16]  Megan N. McClean,et al.  Fast-acting and Nearly Gratuitous Induction of Gene Expression and Protein Depletion in Saccharomyces Cerevisiae Graduate Program in Quantitative and Computational Biology, And , 2022 .

[17]  D. Toft,et al.  Minireview: the intersection of steroid receptors with molecular chaperones: observations and questions. , 2008, Molecular endocrinology.

[18]  David G Hendrickson,et al.  Learning causal networks using inducible transcription factors and transcriptome‐wide time series , 2020, Molecular systems biology.

[19]  Vikram Khipple Mulligan,et al.  De Novo Design of Bioactive Protein Switches , 2019, Nature.

[20]  Jacob Beal,et al.  Accurate predictions of genetic circuit behavior from part characterization and modular composition. , 2015, ACS synthetic biology.

[21]  William C. Deloache,et al.  A Highly Characterized Yeast Toolkit for Modular, Multipart Assembly. , 2015, ACS synthetic biology.

[22]  Drew Endy,et al.  Precise and reliable gene expression via standard transcription and translation initiation elements , 2013, Nature Methods.

[23]  J. Collins,et al.  Combinatorial promoter design for engineering noisy gene expression , 2007, Proceedings of the National Academy of Sciences.

[24]  Vivek K. Mutalik,et al.  Composability of regulatory sequences controlling transcription and translation in Escherichia coli , 2013, Proceedings of the National Academy of Sciences.

[25]  Eduardo Sontag,et al.  Modular cell biology: retroactivity and insulation , 2008, Molecular systems biology.

[26]  Christopher A. Voigt,et al.  Characterization of 582 natural and synthetic terminators and quantification of their design constraints , 2013, Nature Methods.

[27]  A. Arkin,et al.  Contextualizing context for synthetic biology – identifying causes of failure of synthetic biological systems , 2012, Biotechnology journal.

[28]  J. Collins,et al.  Construction of a genetic toggle switch in Escherichia coli , 2000, Nature.

[29]  P. Swain,et al.  Accurate prediction of gene feedback circuit behavior from component properties , 2007, Molecular systems biology.

[30]  Rob Phillips,et al.  Figure 1 Theory Meets Figure 2 Experiments in the Study of Gene Expression. , 2018, Annual review of biophysics.

[31]  M. Bennett,et al.  A fast, robust, and tunable synthetic gene oscillator , 2008, Nature.