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

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

[ PubMed ] [ DOI ] Abstract 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

Cf. http://www.csb.ethz.ch/research/synthetic

Further reading and resources

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

[ PubMed ] [ DOI ] Abstract 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.

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

[ PubMed ] [ DOI ]

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

[ PubMed ] [ DOI ] Abstract 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.

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 ] Abstract 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.

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

[ PubMed ] [ DOI ] Abstract 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.

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

[ PubMed ] [ DOI ] Abstract 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.

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

[ PubMed ] [ DOI ] Abstract 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.

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

[ PubMed ] [ DOI ] Abstract 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.

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

[ PubMed ] [ DOI ] Abstract 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.