BioModels Database (), part of the international initiative BioModels.net, provides access to published, peer-reviewed, quantitative models of biochemical and cellular systems. Each model is carefully curated to verify that it corresponds to the reference publication and gives the proper numerical results. Curators also annotate the components of the models with terms from controlled vocabularies and links to other relevant data resources. This allows the users to search accurately for the models they need. The models can currently be retrieved in the SBML format, and import/export facilities are being developed to extend the spectrum of formats supported by the resource.
The re-use of previously validated designs is critical to the evolution of synthetic biology from a research discipline to an engineering practice. Here we describe the Synthetic Biology Open Language (SBOL), a proposed data standard for exchanging designs within the synthetic biology community. SBOL represents synthetic biology designs in a communitydriven, formalized format for exchange between software tools, research groups and commercial service providers. The SBOL Developers Group has implemented SBOL as an XML/RDF serialization and provides software libraries and specification documentation to help developers implement SBOL in their own software. We describe early successes, including a demonstration of the utility of SBOL for information exchange between several different software tools and repositories from both academic and industrial partners. As a community-driven standard, SBOL will be updated as synthetic biology evolves to provide specific capabilities for different aspects of the synthetic biology workflow.Synthetic biology treats biological organisms as a new technological medium with a unique set of characteristics, such as the ability to self-repair, evolve and replicate. These characteristics create their own engineering challenges, but offer a rich and largely untapped source of potential applications across a broad range of sectors 1,2 . Applications such as biomolecular computing 3 , metabolic engineering 4 , or reconstruction and exploration of natural cell biology 5,6 commonly require the design of new genetically encoded systems. As engineers, synthetic biologists most often base their designs on previously described 'DNA segments' (see Supplementary Table 1 for definitions of selected terms) to meet their design requirements. Reuse of the DNA sequence for these segments involves their exchange between laboratories and their hierarchical composition to form devices and systems with higher level function.Every engineering field relies on a set of 'standards' 7 that practitioners follow to enable the exchange and reuse of designs for 'systems' , 'devices' and 'components' . Similarly, the representation of synthetic biology designs using computer-readable 'data standards' has the potential to facilitate the forward engineering of novel biological systems from previously characterized devices and components. For example, such standards could enable synthetic biology companies to offer catalogs of devices and components by means of computerreadable data sheets, just as modern semiconductor companies do for electronics. Such standards could also enable a synthetic biologist to develop portions of a design using one software tool, refine the design using another tool, and finally transmit it electronically to a colleague or commercial fabrication company.In order for synthetic biology designs to scale up in complexity, researchers will need to make greater use of specialized design tools and parts repositories. Seamless inter-tool communication would, for example, allow the separation of gene...
Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.
Existing theorems from the analysis of metabolic control have been taken and embedded in a simple matrix algebra procedure for calculating the flux control coefficients of enzymes (formerly known as sensitivities) in a metabolic pathway from their kinetic properties (their elasticities). New theorems governing the flux control coefficients of branched pathways and substrate cycles have been derived to allow the procedure to be applied to complex pathway configurations. Modifications to the elasticity terms used in the equations have been theoretically justified so that the method remains valid for pathways with conserved metabolites (for example, the adenine nucleotide pool or the intermediates of a catalytic cycle such as the tricarboxylic acid cycle) or with pools of metabolites kept very near to equilibrium by very rapid reactions. The matrix equations generated using these theorems and relationships may be solved algebraically or numerically. Algebraic solutions have been used to determine the factors responsible for the degree of amplification of flux control coefficients by substrate cycles and to show that it is possible to derive expressions for the elasticities of a group of enzymes.
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