Towards an Aspect-Oriented Design and Modelling Framework for Synthetic Biology

Work on synthetic biology has largely used a component-based metaphor for system construction. While this paradigm has been successful for the construction of numerous systems, the incorporation of contextual design issues—either compositional, host or environmental—will be key to realising more complex applications. Here, we present a design framework that radically steps away from a purely parts-based paradigm by using aspect-oriented software engineering concepts. We believe that the notion of concerns is a powerful and biologically credible way of thinking about system synthesis. By adopting this approach, we can separate core concerns, which represent modular aims of the design, from cross-cutting concerns, which represent system-wide attributes. The explicit handling of cross-cutting concerns allows for contextual information to enter the design process in a modular way. As a proof-of-principle, we implemented the aspect-oriented approach in the Python tool, SynBioWeaver, which enables the combination, or weaving, of core and cross-cutting concerns. The power and flexibility of this framework is demonstrated through a number of examples covering the inclusion of part context, combining circuit designs in a context dependent manner, and the generation of rule, logic and reaction models from synthetic circuit designs.

[1]  J. Farrell,et al.  Temperature effects on microorganisms. , 1967, Annual review of microbiology.

[2]  G. K. Ackers,et al.  Quantitative model for gene regulation by lambda phage repressor. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

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

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

[5]  J. Elf,et al.  Selective Charging of tRNA Isoacceptors Explains Patterns of Codon Usage , 2003, Science.

[6]  Hiroaki Kitano,et al.  The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models , 2003, Bioinform..

[7]  R. Weiss,et al.  Programmed population control by cell–cell communication and regulated killing , 2004, Nature.

[8]  V. Hakim,et al.  Design of genetic networks with specified functions by evolution in silico. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Cosimo Laneve,et al.  Formal molecular biology , 2004, Theor. Comput. Sci..

[10]  D. Endy Foundations for engineering biology , 2005, Nature.

[11]  Vincent Danos,et al.  Scalable Simulation of Cellular Signaling Networks , 2007, APLAS.

[12]  G. T. Yates,et al.  On the lag phase and initial decline of microbial growth curves. , 2007, Journal of theoretical biology.

[13]  M. Elowitz,et al.  Programming gene expression with combinatorial promoters , 2007, Molecular systems biology.

[14]  Jean Peccoud,et al.  A syntactic model to design and verify synthetic genetic constructs derived from standard biological parts , 2007, Bioinform..

[15]  Michael Hucka,et al.  LibSBML: an API Library for SBML , 2008, Bioinform..

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

[17]  D. Endy,et al.  Refinement and standardization of synthetic biological parts and devices , 2008, Nature Biotechnology.

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

[19]  Herbert M. Sauro,et al.  Antimony: a modular model definition language , 2009, Bioinform..

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

[21]  Ramnivas Laddad,et al.  AspectJ in Action: Enterprise AOP with Spring Applications , 2009 .

[22]  Ernst Dieter Gilles,et al.  ProMoT: modular modeling for systems biology , 2009, Bioinform..

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

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

[25]  Christopher A. Voigt,et al.  Automated Design of Synthetic Ribosome Binding Sites to Precisely Control Protein Expression , 2009, Nature Biotechnology.

[26]  Andrew Phillips,et al.  Towards programming languages for genetic engineering of living cells , 2009, Journal of The Royal Society Interface.

[27]  Herbert M Sauro,et al.  Designing and engineering evolutionary robust genetic circuits , 2010, Journal of biological engineering.

[28]  T. Hwa,et al.  Interdependence of Cell Growth and Gene Expression: Origins and Consequences , 2010, Science.

[29]  Erika Cule,et al.  ABC-SysBio—approximate Bayesian computation in Python with GPU support , 2010, Bioinform..

[30]  R. Weiss,et al.  Automatic Compilation from High-Level Biologically-Oriented Programming Language to Genetic Regulatory Networks , 2011, PloS one.

[31]  Ruth J. Williams,et al.  Queueing up for Enzymatic Processing: Correlated Signaling through Coupled Degradation , 2022 .

[32]  Emma M. B. Weeding,et al.  Eugene – A Domain Specific Language for Specifying and Constraining Synthetic Biological Parts, Devices, and Systems , 2011, PloS one.

[33]  Baojun Wang,et al.  Engineering modular and orthogonal genetic logic gates for robust digital-like synthetic biology , 2011, Nature communications.

[34]  Erik Winfree,et al.  Bistability of an In Vitro Synthetic Autoregulatory Switch , 2011, 1101.0723.

[35]  Alfonso Jaramillo,et al.  Computational design of synthetic regulatory networks from a genetic library to characterize the designability of dynamical behaviors , 2011, Nucleic acids research.

[36]  Michael P. H. Stumpf,et al.  GPU accelerated biochemical network simulation , 2011, Bioinform..

[37]  Xia Sheng,et al.  Bayesian design of synthetic biological systems , 2011, Proceedings of the National Academy of Sciences.

[38]  Michael P H Stumpf,et al.  Bayesian design strategies for synthetic biology , 2011, Interface Focus.

[39]  Alfonso Jaramillo,et al.  Empirical model and in vivo characterization of the bacterial response to synthetic gene expression show that ribosome allocation limits growth rate. , 2011, Biotechnology journal.

[40]  Jacob Beal,et al.  An end-to-end workflow for engineering of biological networks from high-level specifications. , 2012, ACS synthetic biology.

[41]  Christopher A. Voigt,et al.  Genetic circuit performance under conditions relevant for industrial bioreactors. , 2012, ACS synthetic biology.

[42]  Mario di Bernardo,et al.  Temperature dependence of ssrA-tag mediated protein degradation , 2012, Journal of Biological Engineering.

[43]  Jonathan R. Karr,et al.  A Whole-Cell Computational Model Predicts Phenotype from Genotype , 2012, Cell.

[44]  James J. Collins,et al.  Iterative plug-and-play methodology for constructing and modifying synthetic gene networks , 2012, Nature Methods.

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

[46]  Marcel Geertz,et al.  Massively parallel measurements of molecular interaction kinetics on a microfluidic platform , 2012, Proceedings of the National Academy of Sciences.

[47]  Deepak Chandran,et al.  Hierarchical modeling for synthetic biology. , 2012, ACS synthetic biology.

[48]  Nicolas Pinto,et al.  PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation , 2009, Parallel Comput..

[49]  Drew Endy,et al.  Quantitative estimation of activity and quality for collections of functional genetic elements , 2013, Nature Methods.

[50]  Carlos F. Lopez,et al.  Programming biological models in Python using PySB , 2013, Molecular systems biology.

[51]  Bonny Jain,et al.  Towards a whole-cell modeling approach for synthetic biology. , 2013, Chaos.

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

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

[54]  Stefano Cardinale,et al.  Effects of genetic variation on the E. coli host-circuit interface. , 2013, Cell reports.

[55]  Domitilla Del Vecchio,et al.  Retroactivity controls the temporal dynamics of gene transcription. , 2013, ACS synthetic biology.

[56]  Christopher A. Voigt,et al.  Advances in genetic circuit design: novel biochemistries, deep part mining, and precision gene expression. , 2013, Current opinion in chemical biology.

[57]  Jeff Hasty,et al.  Translational cross talk in gene networks. , 2013, Biophysical journal.

[58]  Swapnil Bhatia,et al.  Pigeon: a design visualizer for synthetic biology. , 2013, ACS synthetic biology.

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

[60]  Herbert M Sauro,et al.  Visualization of evolutionary stability dynamics and competitive fitness of Escherichia coli engineered with randomized multigene circuits. , 2013, ACS synthetic biology.

[61]  Allan Kuchinsky,et al.  The Synthetic Biology Open Language (SBOL) provides a community standard for communicating designs in synthetic biology , 2014, Nature Biotechnology.

[62]  J Glaser,et al.  Separation of Concerns , 2014 .

[63]  Howard J. Li,et al.  Rapid and tunable post-translational coupling of genetic circuits , 2014, Nature.

[64]  Thomas E Gorochowski,et al.  Using synthetic biological parts and microbioreactors to explore the protein expression characteristics of Escherichia coli. , 2014, ACS synthetic biology.

[65]  Kresimir Josic,et al.  Engineered temperature compensation in a synthetic genetic clock , 2014, Proceedings of the National Academy of Sciences.

[66]  Andrew Phillips,et al.  A computational method for automated characterization of genetic components. , 2014, ACS synthetic biology.

[67]  Sarah Filippi,et al.  A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation , 2014, Nature Protocols.

[68]  Thomas E Gorochowski,et al.  Memory and Combinatorial Logic Based on DNA Inversions: Dynamics and Evolutionary Stability. , 2015, ACS synthetic biology.

[69]  Ilias Tagkopoulos,et al.  Fast and Accurate Circuit Design Automation through Hierarchical Model Switching. , 2015, ACS synthetic biology.

[70]  G. Stan,et al.  Quantifying cellular capacity identifies gene expression designs with reduced burden , 2015, Nature Methods.

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

[72]  Thomas E Gorochowski,et al.  A Minimal Model of Ribosome Allocation Dynamics Captures Trade-offs in Expression between Endogenous and Synthetic Genes. , 2016, ACS synthetic biology.

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

[74]  Chris P. Barnes,et al.  A Statistical Approach Reveals Designs for the Most Robust Stochastic Gene Oscillators , 2015, bioRxiv.

[75]  Thomas E Gorochowski,et al.  DNAplotlib: Programmable Visualization of Genetic Designs and Associated Data. , 2017, ACS synthetic biology.

[76]  Alec A K Nielsen,et al.  Genetic circuit characterization and debugging using RNA‐seq , 2017, Molecular systems biology.

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

[78]  Jacob Beal,et al.  Time to Get Serious about Measurement in Synthetic Biology. , 2018, Trends in biotechnology.

[79]  Richard M. Murray,et al.  Context Dependence of Biological Circuits , 2018, bioRxiv.

[80]  Chris P Barnes,et al.  Synthetic Biology and Engineered Live Biotherapeutics: Toward Increasing System Complexity. , 2018, Cell systems.

[81]  Jacob Beal,et al.  Quantification of bacterial fluorescence using independent calibrants , 2018, PloS one.

[82]  Rosa D. Hernansaiz-Ballesteros,et al.  Computing with biological switches and clocks , 2018, Natural Computing.

[83]  Brian Ingalls,et al.  New opportunities for optimal design of dynamic experiments in systems and synthetic biology , 2018, Current Opinion in Systems Biology.

[84]  Andreas Porse,et al.  Diverse genetic error modes constrain large-scale bio-based production , 2018, Nature Communications.