Phase space characterization for gene circuit design

Genetic circuit design requires characterization of the dynamics of synthetic gene expression. This is a difficult problem since gene expression varies in complex ways over time and across different contexts. Here we present a novel method for characterizing the dynamics of gene expression with a few parameters that account for changes in cellular context (host cell physiology) and compositional context (adjacent genes). The dynamics of gene circuits were characterized by a trajectory through a multi-dimensional phase space parameterized by the expression levels of each of their constituent transcriptional units (TU). These trajectories followed piecewise linear dynamics, with each dynamical regime corresponding to different growth regimes, or cellular contexts. Thus relative expression rates were changed by transitions between growth regimes, but were constant in each regime. We present a plausible two-factor mathematical model for this behavior based on resource consumption. By analyzing different combinations of TUs, we then showed that relative expression rates were significantly affected by the neighboring TU (compositional context), but maintained piecewise linear dynamics across cellular and compositional contexts. Taken together these results show that TU expression dynamics could be predicted by a reference TU up to a context dependent scaling factor. This model provides a framework for design of genetic circuits composed of TUs. A common sharable reference TU may be chosen and measured in the cellular contexts of interest. The output of each TU in the circuit may then be predicted from a simple function of the output of the reference TU in the given cellular context. This will aid in genetic circuit design by providing simple models for the dynamics of gene circuits and their constituent TUs.

[1]  Eric Jones,et al.  SciPy: Open Source Scientific Tools for Python , 2001 .

[2]  James Alastair McLaughlin,et al.  SynBioHub: A Standards-Enabled Design Repository for Synthetic Biology. , 2018, ACS synthetic biology.

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

[4]  D. G. Gibson,et al.  Enzymatic assembly of DNA molecules up to several hundred kilobases , 2009, Nature Methods.

[5]  L. You,et al.  Emergent bistability by a growth-modulating positive feedback circuit. , 2009, Nature chemical biology.

[6]  Nathan J Hillson,et al.  The Experiment Data Depot: A Web-Based Software Tool for Biological Experimental Data Storage, Sharing, and Visualization. , 2017, ACS synthetic biology.

[7]  James Alastair McLaughlin,et al.  SBOLDesigner 2: An Intuitive Tool for Structural Genetic Design. , 2017, ACS synthetic biology.

[8]  R. Milo,et al.  Promoters maintain their relative activity levels under different growth conditions , 2013, Molecular systems biology.

[9]  Keith E. J. Tyo,et al.  N-Terminal-Based Targeted, Inducible Protein Degradation in Escherichia coli , 2016, PloS one.

[10]  Zhen Zhang,et al.  Synthetic Biology Open Language (SBOL) Version 2.1.0 , 2016, J. Integr. Bioinform..

[11]  Jacob Beal,et al.  CIDAR MoClo: Improved MoClo Assembly Standard and New E. coli Part Library Enable Rapid Combinatorial Design for Synthetic and Traditional Biology. , 2016, ACS synthetic biology.

[12]  Zhen Zhang,et al.  iBioSim 3: A Tool for Model-Based Genetic Circuit Design. , 2018, ACS synthetic biology.

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

[14]  Oleg A Igoshin,et al.  Transient heterogeneity in extracellular protease production by Bacillus subtilis , 2008, Molecular systems biology.

[15]  Xiangyu Ji,et al.  Insulated transcriptional elements enable precise design of genetic circuits , 2017, Nature Communications.

[16]  Drew Endy,et al.  Measuring the activity of BioBrick promoters using an in vivo reference standard , 2009, Journal of biological engineering.

[17]  P. Swain,et al.  Mechanistic links between cellular trade-offs, gene expression, and growth , 2015, Proceedings of the National Academy of Sciences.

[18]  Matthew R. Pocock,et al.  The SBOL Stack: A Platform for Storing, Publishing, and Sharing Synthetic Biology Designs. , 2016, ACS synthetic biology.

[19]  M. De Mey,et al.  A sigma factor toolbox for orthogonal gene expression in Escherichia coli , 2018, Nucleic acids research.

[20]  Andrew Phillips,et al.  Characterization of Intrinsic Properties of Promoters , 2015, ACS synthetic biology.

[21]  Jacob Beal,et al.  Reducing DNA context dependence in bacterial promoters , 2017, PloS one.

[22]  G. Stan,et al.  Overloaded and stressed: whole-cell considerations for bacterial synthetic biology. , 2016, Current opinion in microbiology.

[23]  Weston R. Whitaker,et al.  Toward scalable parts families for predictable design of biological circuits. , 2008, Current opinion in microbiology.

[24]  Christian R. Boehm,et al.  Unique nucleotide sequence–guided assembly of repetitive DNA parts for synthetic biology applications , 2014, Nature Protocols.

[25]  Uri Alon,et al.  Invariant Distribution of Promoter Activities in Escherichia coli , 2009, PLoS Comput. Biol..

[26]  Adam Paul Arkin,et al.  A wise consistency: engineering biology for conformity, reliability, predictability. , 2013, Current opinion in chemical biology.

[27]  Swapnil Bhatia,et al.  Functional optimization of gene clusters by combinatorial design and assembly , 2014, Nature Biotechnology.

[28]  Andrew H. Ng,et al.  The Effect of Compositional Context on Synthetic Gene Networks , 2016, bioRxiv.

[29]  Ron Weiss,et al.  Isocost Lines Describe the Cellular Economy of Genetic Circuits , 2015, Biophysical journal.

[30]  Haley R Pipkins,et al.  Polyamine transporter potABCD is required for virulence of encapsulated but not nonencapsulated Streptococcus pneumoniae , 2017, PloS one.

[31]  Ernst Weber,et al.  A Modular Cloning System for Standardized Assembly of Multigene Constructs , 2011, PloS one.

[32]  W. Kruskal,et al.  Use of Ranks in One-Criterion Variance Analysis , 1952 .

[33]  N. Blüthgen,et al.  Molecular Systems Biology 9; Article number 675; doi:10.1038/msb.2013.32 Citation: Molecular Systems Biology 9:675 , 2022 .

[34]  Dimitra N. Stratis-Cullum,et al.  Engineered integrative and conjugative elements for efficient and inducible DNA transfer to undomesticated bacteria , 2018, Nature Microbiology.

[35]  Domitilla Del Vecchio,et al.  A quasi-integral controller for adaptation of genetic modules to variable ribosome demand , 2018, Nature Communications.

[36]  Chase L. Beisel,et al.  Synthetic control of a fitness tradeoff in yeast nitrogen metabolism , 2009, Journal of biological engineering.

[37]  Zhen Zhang,et al.  Synthetic Biology Open Language (SBOL) Version 2.2.0 , 2018, J. Integr. Bioinform..

[38]  G. Stan,et al.  Burden-driven feedback control of gene expression , 2017, Nature Methods.

[39]  T. Hwa,et al.  Growth Rate-Dependent Global Effects on Gene Expression in Bacteria , 2009, Cell.

[40]  Enoch Yeung,et al.  Biophysical Constraints Arising from Compositional Context in Synthetic Gene Networks. , 2017, Cell systems.

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

[42]  U. Sauer,et al.  Dissecting specific and global transcriptional regulation of bacterial gene expression , 2013, Molecular systems biology.

[43]  Domitilla Del Vecchio,et al.  Resource Competition Shapes the Response of Genetic Circuits. , 2017, ACS synthetic biology.

[44]  Christopher A. Voigt,et al.  Ribozyme-based insulator parts buffer synthetic circuits from genetic context , 2012, Nature Biotechnology.

[45]  Christopher A. Voigt,et al.  Principles of genetic circuit design , 2014, Nature Methods.

[46]  Terence Hwa,et al.  Bacterial growth laws and their applications. , 2011, Current opinion in biotechnology.

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

[48]  P. Mermelstein,et al.  Opposite Effects of mGluR1a and mGluR5 Activation on Nucleus Accumbens Medium Spiny Neuron Dendritic Spine Density , 2016, PloS one.

[49]  Wes McKinney,et al.  Data Structures for Statistical Computing in Python , 2010, SciPy.

[50]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[51]  Joseph H. Davis,et al.  Design, construction and characterization of a set of insulated bacterial promoters , 2010, Nucleic acids research.