Computational Synthetic Biology

Computational Synthetic Biology

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Apparently, at the time of his death, the phrase "What I cannot create, I do not understand." was written on Richard Feynman's blackboard. If there is truth to that, computational synthetic biology is the ultimate test of computational biology as a whole. Beyond systems models in general, computational synthetic biology focusses on ways to integrate systems models and to support experimental design and engineering.

 

Introductory reading

Bashor et al. (2010) Rewiring cells: synthetic biology as a tool to interrogate the organizational principles of living systems. Annu Rev Biophys 39:515-37. (pmid: 20192780)

[ PubMed ] [ DOI ] The living cell is an incredibly complex entity, and the goal of predictively and quantitatively understanding its function is one of the next great challenges in biology. Much of what we know about the cell concerns its constituent parts, but to a great extent we have yet to decode how these parts are organized to yield complex physiological function. Classically, we have learned about the organization of cellular networks by disrupting them through genetic or chemical means. The emerging discipline of synthetic biology offers an additional, powerful approach to study systems. By rearranging the parts that comprise existing networks, we can gain valuable insight into the hierarchical logic of the networks and identify the modular building blocks that evolution uses to generate innovative function. In addition, by building minimal toy networks, one can systematically explore the relationship between network structure and function. Here, we outline recent work that uses synthetic biology approaches to investigate the organization and function of cellular networks, and describe a vision for a synthetic biology toolkit that could be used to interrogate the design principles of diverse systems.

MacDonald et al. (2011) Computational design approaches and tools for synthetic biology. Integr Biol (Camb) 3:97-108. (pmid: 21258712)

[ PubMed ] [ DOI ] A proliferation of new computational methods and software tools for synthetic biology design has emerged in recent years but the field has not yet reached the stage where the design and construction of novel synthetic biology systems has become routine. To a large degree this is due to the inherent complexity of biological systems. However, advances in biotechnology and our scientific understanding have already enabled a number of significant achievements in this area. A key concept in engineering is the ability to assemble simpler standardised modules into systems of increasing complexity but it has yet to be adequately addressed how this approach can be applied to biological systems. In particular, the use of computer aided design tools is common in other engineering disciplines and it should eventually become centrally important to the field of synthetic biology if the challenge of dealing with the stochasticity and complexity of biological systems can be overcome.

 

Contents

   

Further reading and resources

Standards

Müller & Arndt (2012) Standardization in synthetic biology. Methods Mol Biol 813:23-43. (pmid: 22083734)

[ PubMed ] [ DOI ] Synthetic Biology is founded on the idea that complex biological systems are built most effectively when the task is divided in abstracted layers and all required components are readily available and well-described. This requires interdisciplinary collaboration at several levels and a common understanding of the functioning of each component. Standardization of the physical composition and the description of each part is required as well as a controlled vocabulary to aid design and ensure interoperability. Here, we describe standardization initiatives from several disciplines, which can contribute to Synthetic Biology. We provide examples of the concerted standardization efforts of the BioBricks Foundation comprising the request for comments (RFC) and the Registry of Standardized Biological parts as well as the international Genetically Engineered Machine (iGEM) competition.

Cai et al. (2010) GenoCAD for iGEM: a grammatical approach to the design of standard-compliant constructs. Nucleic Acids Res 38:2637-44. (pmid: 20167639)

[ PubMed ] [ DOI ] One of the foundations of synthetic biology is the project to develop libraries of standardized genetic parts that could be assembled quickly and cheaply into large systems. The limitations of the initial BioBrick standard have prompted the development of multiple new standards proposing different avenues to overcome these shortcomings. The lack of compatibility between standards, the compliance of parts with only some of the standards or even the type of constructs that each standard supports have significantly increased the complexity of assembling constructs from standardized parts. Here, we describe computer tools to facilitate the rigorous description of part compositions in the context of a rapidly changing landscape of physical construction methods and standards. A context-free grammar has been developed to model the structure of constructs compliant with six popular assembly standards. Its implementation in GenoCAD makes it possible for users to quickly assemble from a rich library of genetic parts, constructs compliant with any of six existing standards.

Cooling et al. (2010) Standard virtual biological parts: a repository of modular modeling components for synthetic biology. Bioinformatics 26:925-31. (pmid: 20160009)

[ PubMed ] [ DOI ] MOTIVATION: Fabrication of synthetic biological systems is greatly enhanced by incorporating engineering design principles and techniques such as computer-aided design. To this end, the ongoing standardization of biological parts presents an opportunity to develop libraries of standard virtual parts in the form of mathematical models that can be combined to inform system design. RESULTS: We present an online Repository, populated with a collection of standardized models that can readily be recombined to model different biological systems using the inherent modularity support of the CellML 1.1 model exchange format. The applicability of this approach is demonstrated by modeling gold-medal winning iGEM machines. AVAILABILITY AND IMPLEMENTATION: The Repository is available online as part of http://models.cellml.org. We hope to stimulate the worldwide community to reuse and extend the models therein, and contribute to the Repository of Standard Virtual Parts thus founded. Systems Model architecture information for the Systems Model described here, along with an additional example and a tutorial, is also available as Supplementary information. The example Systems Model from this manuscript can be found at http://models.cellml.org/workspace/bugbuster. The Template models used in the example can be found at http://models.cellml.org/workspace/SVP_Templates200906.

Smolke (2009) Building outside of the box: iGEM and the BioBricks Foundation. Nat Biotechnol 27:1099-102. (pmid: 20010584)

[ PubMed ] [ DOI ]

Examples

Rekhi & Qutub (2013) Systems approaches for synthetic biology: a pathway toward mammalian design. Front Physiol 4:285. (pmid: 24130532)

[ PubMed ] [ DOI ] We review methods of understanding cellular interactions through computation in order to guide the synthetic design of mammalian cells for translational applications, such as regenerative medicine and cancer therapies. In doing so, we argue that the challenges of engineering mammalian cells provide a prime opportunity to leverage advances in computational systems biology. We support this claim systematically, by addressing each of the principal challenges to existing synthetic bioengineering approaches-stochasticity, complexity, and scale-with specific methods and paradigms in systems biology. Moreover, we characterize a key set of diverse computational techniques, including agent-based modeling, Bayesian network analysis, graph theory, and Gillespie simulations, with specific utility toward synthetic biology. Lastly, we examine the mammalian applications of synthetic biology for medicine and health, and how computational systems biology can aid in the continued development of these applications.

Chen et al. (2013) Programmable chemical controllers made from DNA. Nat Nanotechnol 8:755-62. (pmid: 24077029)

[ PubMed ] [ DOI ] Biological organisms use complex molecular networks to navigate their environment and regulate their internal state. The development of synthetic systems with similar capabilities could lead to applications such as smart therapeutics or fabrication methods based on self-organization. To achieve this, molecular control circuits need to be engineered to perform integrated sensing, computation and actuation. Here we report a DNA-based technology for implementing the computational core of such controllers. We use the formalism of chemical reaction networks as a 'programming language' and our DNA architecture can, in principle, implement any behaviour that can be mathematically expressed as such. Unlike logic circuits, our formulation naturally allows complex signal processing of intrinsically analogue biological and chemical inputs. Controller components can be derived from biologically synthesized (plasmid) DNA, which reduces errors associated with chemically synthesized DNA. We implement several building-block reaction types and then combine them into a network that realizes, at the molecular level, an algorithm used in distributed control systems for achieving consensus between multiple agents.

Hallinan, JS. (2013) Computational Intelligence in the Design of Synthetic Microbial Genetic Systems. Methods in Microbiology 40:1-37.

(pmid: None) [ Source URL ][ DOI ] The large-scale engineering of novel bacterial systems is a complex, challenging task. Although small circuits can be designed manually, using the domain knowledge of the designer, this approach is not feasible for designs involving multiple pathways or even complete genomes. In this chapter, we address the value of computational intelligence approaches to the design of synthetic genetic circuits. Computational intelligence algorithms were designed to operate in complex, poorly understood domains in which the quality of a solution is more important than the route taken to achieve it and, as such, are potentially valuable to synthetic biology. To date, evolutionary computation has been used extensively in this field, but other computational intelligence algorithms, of potentially equal value, have been neglected. We review the basic principles of these algorithms and the way in which they have been, and may in the future be, of value in synthetic biology.

Myers, CJ. (2013) Platforms for Genetic Design Automation. Methods in Microbiology 40:177-202.

(pmid: None) [ Source URL ][ DOI ] Crucial to the success of synthetic biology is the development of platforms for genetic design automation (GDA). This chapter presents the current state-of-the-art in GDA tools. This chapter also briefly describes the standards used for data representation that enable these GDA tools to work together to complete a genetic design task and the emerging repositories that are available to archive and share these data. Finally, this chapter compares tool capabilities and discusses future requirements for a complete GDA workflow.

Marchisio (2012) In silico implementation of synthetic gene networks. Methods Mol Biol 813:3-21. (pmid: 22083733)

[ PubMed ] [ DOI ] Computational synthetic biology has borrowed methods, concepts, and techniques from systems biology and electrical engineering. Features of tools for the analysis of biochemical networks and the design of electric circuits have been combined to develop new software, where Standard Biological Parts (physically stored at the MIT Registry) have a mathematical description, based on mass action or Hill kinetics, and can be assembled into genetic networks in a visual, "drag & drop" fashion. Recent tools provide the user with databases, simulation environments, formal languages, and even algorithms for circuit automatic design to refine and speed up gene network construction. Moreover, advances in automation of DNA assembly indicate that synthetic biology software soon will drive the wet-lab implementation of DNA sequences.

Nandagopal & Elowitz (2011) Synthetic biology: integrated gene circuits. Science 333:1244-8. (pmid: 21885772)

[ PubMed ] [ DOI ] A major goal of synthetic biology is to develop a deeper understanding of biological design principles from the bottom up, by building circuits and studying their behavior in cells. Investigators initially sought to design circuits "from scratch" that functioned as independently as possible from the underlying cellular system. More recently, researchers have begun to develop a new generation of synthetic circuits that integrate more closely with endogenous cellular processes. These approaches are providing fundamental insights into the regulatory architecture, dynamics, and evolution of genetic circuits and enabling new levels of control across diverse biological systems.

Liang et al. (2011) Synthetic biology: putting synthesis into biology. Wiley Interdiscip Rev Syst Biol Med 3:7-20. (pmid: 21064036)

[ PubMed ] [ DOI ] The ability to manipulate living organisms is at the heart of a range of emerging technologies that serve to address important and current problems in environment, energy, and health. However, with all its complexity and interconnectivity, biology has for many years been recalcitrant to engineering manipulations. The recent advances in synthesis, analysis, and modeling methods have finally provided the tools necessary to manipulate living systems in meaningful ways and have led to the coining of a field named synthetic biology. The scope of synthetic biology is as complicated as life itself--encompassing many branches of science and across many scales of application. New DNA synthesis and assembly techniques have made routine customization of very large DNA molecules. This in turn has allowed the incorporation of multiple genes and pathways. By coupling these with techniques that allow for the modeling and design of protein functions, scientists have now gained the tools to create completely novel biological machineries. Even the ultimate biological machinery--a self-replicating organism--is being pursued at this moment. The aim of this article is to dissect and organize these various components of synthetic biology into a coherent picture.

Zheng & Sriram (2010) Mathematical modeling: bridging the gap between concept and realization in synthetic biology. J Biomed Biotechnol 2010:541609. (pmid: 20589069)

[ PubMed ] [ DOI ] Mathematical modeling plays an important and often indispensable role in synthetic biology because it serves as a crucial link between the concept and realization of a biological circuit. We review mathematical modeling concepts and methodologies as relevant to synthetic biology, including assumptions that underlie a model, types of modeling frameworks (deterministic and stochastic), and the importance of parameter estimation and optimization in modeling. Additionally we expound mathematical techniques used to analyze a model such as sensitivity analysis and bifurcation analysis, which enable the identification of the conditions that cause a synthetic circuit to behave in a desired manner. We also discuss the role of modeling in phenotype analysis such as metabolic and transcription network analysis and point out some available modeling standards and software. Following this, we present three case studies-a metabolic oscillator, a synthetic counter, and a bottom--up gene regulatory network--which have incorporated mathematical modeling as a central component of synthetic circuit design.

Grünberg & Serrano (2010) Strategies for protein synthetic biology. Nucleic Acids Res 38:2663-75. (pmid: 20385577)

[ PubMed ] [ DOI ] Proteins are the most versatile among the various biological building blocks and a mature field of protein engineering has lead to many industrial and biomedical applications. But the strength of proteins-their versatility, dynamics and interactions-also complicates and hinders systems engineering. Therefore, the design of more sophisticated, multi-component protein systems appears to lag behind, in particular, when compared to the engineering of gene regulatory networks. Yet, synthetic biologists have started to tinker with the information flow through natural signaling networks or integrated protein switches. A successful strategy common to most of these experiments is their focus on modular interactions between protein domains or domains and peptide motifs. Such modular interaction swapping has rewired signaling in yeast, put mammalian cell morphology under the control of light, or increased the flux through a synthetic metabolic pathway. Based on this experience, we outline an engineering framework for the connection of reusable protein interaction devices into self-sufficient circuits. Such a framework should help to 'refacture' protein complexity into well-defined exchangeable devices for predictive engineering. We review the foundations and initial success stories of protein synthetic biology and discuss the challenges and promises on the way from protein- to protein systems design.

Rothschild (2010) A powerful toolkit for synthetic biology: Over 3.8 billion years of evolution. Bioessays 32:304-13. (pmid: 20349441)

[ PubMed ] [ DOI ] The combination of evolutionary with engineering principles will enhance synthetic biology. Conversely, synthetic biology has the potential to enrich evolutionary biology by explaining why some adaptive space is empty, on Earth or elsewhere. Synthetic biology, the design and construction of artificial biological systems, substitutes bio-engineering for evolution, which is seen as an obstacle. But because evolution has produced the complexity and diversity of life, it provides a proven toolkit of genetic materials and principles available to synthetic biology. Evolution operates on the population level, with the populations composed of unique individuals that are historical entities. The source of genetic novelty includes mutation, gene regulation, sex, symbiosis, and interspecies gene transfer. At a phenotypic level, variation derives from regulatory control, replication and diversification of components, compartmentalization, sexual selection and speciation, among others. Variation is limited by physical constraints such as diffusion, and chemical constraints such as reaction rates and membrane fluidity. While some of these tools of evolution are currently in use in synthetic biology, all ought to be examined for utility. A hybrid approach of synthetic biology coupled with fine-tuning through evolution is suggested.

Bashor et al. (2010) Rewiring cells: synthetic biology as a tool to interrogate the organizational principles of living systems. Annu Rev Biophys 39:515-37. (pmid: 20192780)

[ PubMed ] [ DOI ] The living cell is an incredibly complex entity, and the goal of predictively and quantitatively understanding its function is one of the next great challenges in biology. Much of what we know about the cell concerns its constituent parts, but to a great extent we have yet to decode how these parts are organized to yield complex physiological function. Classically, we have learned about the organization of cellular networks by disrupting them through genetic or chemical means. The emerging discipline of synthetic biology offers an additional, powerful approach to study systems. By rearranging the parts that comprise existing networks, we can gain valuable insight into the hierarchical logic of the networks and identify the modular building blocks that evolution uses to generate innovative function. In addition, by building minimal toy networks, one can systematically explore the relationship between network structure and function. Here, we outline recent work that uses synthetic biology approaches to investigate the organization and function of cellular networks, and describe a vision for a synthetic biology toolkit that could be used to interrogate the design principles of diverse systems.

Stricker et al. (2008) A fast, robust and tunable synthetic gene oscillator. Nature 456:516-9. (pmid: 18971928)

[ PubMed ] [ DOI ] One defining goal of synthetic biology is the development of engineering-based approaches that enable the construction of gene-regulatory networks according to 'design specifications' generated from computational modelling. This approach provides a systematic framework for exploring how a given regulatory network generates a particular phenotypic behaviour. Several fundamental gene circuits have been developed using this approach, including toggle switches and oscillators, and these have been applied in new contexts such as triggered biofilm development and cellular population control. Here we describe an engineered genetic oscillator in Escherichia coli that is fast, robust and persistent, with tunable oscillatory periods as fast as 13 min. The oscillator was designed using a previously modelled network architecture comprising linked positive and negative feedback loops. Using a microfluidic platform tailored for single-cell microscopy, we precisely control environmental conditions and monitor oscillations in individual cells through multiple cycles. Experiments reveal remarkable robustness and persistence of oscillations in the designed circuit; almost every cell exhibited large-amplitude fluorescence oscillations throughout observation runs. The oscillatory period can be tuned by altering inducer levels, temperature and the media source. Computational modelling demonstrates that the key design principle for constructing a robust oscillator is a time delay in the negative feedback loop, which can mechanistically arise from the cascade of cellular processes involved in forming a functional transcription factor. The positive feedback loop increases the robustness of the oscillations and allows for greater tunability. Examination of our refined model suggested the existence of a simplified oscillator design without positive feedback, and we construct an oscillator strain confirming this computational prediction.

Fange & Elf (2006) Noise-induced Min phenotypes in E. coli. PLoS Comput Biol 2:e80. (pmid: 16846247)

[ PubMed ] [ DOI ] The spatiotemporal oscillations of the Escherichia coli proteins MinD and MinE direct cell division to the region between the chromosomes. Several quantitative models of the Min system have been suggested before, but no one of them accounts for the behavior of all documented mutant phenotypes. We analyzed the stochastic reaction-diffusion kinetics of the Min proteins for several E. coli mutants and compared the results to the corresponding deterministic mean-field description. We found that wild-type (wt) and filamentous (ftsZ-) cells are well characterized by the mean-field model, but that a stochastic model is necessary to account for several of the characteristics of the spherical (rodA-) and phospathedylethanolamide-deficient (PE-) phenotypes. For spherical cells, the mean-field model is bistable, and the system can get trapped in a non-oscillatory state. However, when the intrinsic noise is considered, only the experimentally observed oscillatory behavior remains. The stochastic model also reproduces the change in oscillation directions observed in the spherical phenotype and the occasional gliding of the MinD region along the inner membrane. For the PE- mutant, the stochastic model explains the appearance of randomly localized and dense MinD clusters as a nucleation phenomenon, in which the stochastic kinetics at low copy number causes local discharges of the high MinD(ATP) to MinD(ADP) potential. We find that a simple five-reaction model of the Min system can explain all documented Min phenotypes, if stochastic kinetics and three-dimensional diffusion are accounted for. Our results emphasize that local copy number fluctuation may result in phenotypic differences although the total number of molecules of the relevant species is high.