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Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems is to improve the exchange of information about designed systems between laboratories. The synthetic biology open language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.3.0 of SBOL, which builds upon version 2.2.0 published in last year's JIB Standards in Systems Biology special issue. In particular, SBOL 2.3.0 includes means of succinctly representing sequence modifications, such as insertion, deletion, and replacement, an extension to support organization and attachment of experimental data derived from designs, and an extension for describing numerical parameters of design elements. The new version also includes specifying types of synthetic biology activities, unambiguous locations for sequences with multiple encodings, refinement of a number of validation rules, improved figures and examples, and clarification on a number of issues related to the use of external ontology terms.

People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species . Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.1 of SBOL Visual, which builds on the prior SBOL Visual 2.0 standard by expanding diagram syntax to include methods for showing modular structure and mappings between elements of a system, interactions arrows that can split or join (with the glyph at the split or join indicating either superposition or a chemical process), and adding new glyphs for indicating genomic context (e.g., integration into a plasmid or genome) and for stop codons.

Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems would be to improve the exchange of information about designed systems between laboratories. The synthetic biology open language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.2.0 of SBOL that builds upon version 2.1.0 published in last year's JIB special issue. In particular, SBOL 2.2.0 includes improved description and validation rules for genetic design provenance, an extension to support combinatorial genetic designs, a new class to add non-SBOL data as attachments, a new class for genetic design implementations, and a description of a methodology to describe the entire design-build-test-learn cycle within the SBOL data model.

People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species. Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.0 of SBOL Visual, which builds on the prior SBOL Visual 1.0 standard by expanding diagram syntax to include functional interactions and molecular species, making the relationship between diagrams and the SBOL data model explicit, supporting families of symbol variants, clarifying a number of requirements and best practices, and significantly expanding the collection of diagram glyphs.

Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems would be to improve the exchange of information about designed systems between laboratories. The Synthetic Biology Open Language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.0 of SBOL, introducing a standardized format for the electronic exchange of information on the structural and functional aspects of biological designs. The standard has been designed to support the explicit and unambiguous description of biological designs by means of a well defined data model. The standard also includes rules and best practices on how to use this data model and populate it with relevant design details. The publication of this specification is intended to make these capabilities more widely accessible to potential developers and users in the synthetic biology community and beyond.

While the first version of the Synthetic Biology Open Language (SBOL) has been adopted by several academic and commercial genetic design automation (GDA) software tools, it only covers a limited number of the requirements for a standardized exchange format for synthetic biology. In particular, SBOL Version 1.1 is capable of representing DNA components and their hierarchical composition via sequence annotations. This proposal revises SBOL Version 1.1, enabling the representation of a wider range of components with and without sequences, including RNA components, protein components, small molecules, and molecular complexes. It also introduces modules to instantiate groups of components on the basis of their shared function and assert molecular interactions between components. By increasing the range of structural and functional descriptions in SBOL and allowing for their composition, the proposed improvements enable SBOL to represent and facilitate the exchange of a broader class of genetic designs.

Understanding the large reaction networks found in biological systems is a daunting task. One approach is to divide a network into more manageable smaller modules, thus simplifying the problem. This is a common strategy used in engineering. However, the process of identifying biological modules is still in its infancy and very little is understood about the range and capabilities of motif structures found in biological modules. In order to delineate these modules, a library of functional motifs has been generated via in silico evolution techniques. On the basis of their functional forms, networks were evolved from four broad areas: oscillators, bistable switches, homeostatic systems and frequency filters. Some of these motifs were constructed from simple mass action kinetics, others were based on Michaelis-Menten kinetics as found in protein/protein networks and the remainder were based on Hill equations as found in gene/protein interaction networks. The purpose of the study is to explore the capabilities of different network architectures and the rich variety of functional forms that can be generated. Ultimately, the library may be used to delineate functional motifs in real biological networks.

BioModels Database (http://www.ebi.ac.uk/biomodels/), 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.

Most of the published quantitative models in biology are lost for the community because they are either not made available or they are insufficiently characterized to allow them to be reused. The lack of a standard description format, lack of stringent reviewing and authors' carelessness are the main causes for incomplete model descriptions. With today's increased interest in detailed biochemical models, it is necessary to define a minimum quality standard for the encoding of those models. We propose a set of rules for curating quantitative models of biological systems. These rules define procedures for encoding and annotating models represented in machine-readable form. We believe their application will enable users to (i) have confidence that curated models are an accurate reflection of their associated reference descriptions, (ii) search collections of curated models with precision, (iii) quickly identify the biological phenomena that a given curated model or model constituent represents and (iv) facilitate model reuse and composition into large subcellular models.

Motivation: Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively., Results: We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others., Availability: The specification of SBML Level 1 is freely available from http://www.sbml.org/

Motivation: Metabolic Control Analysis is one of many disciplines that make use of scaled derivatives. In particular, 'elasticities' are used to quantify the effect of an effector or substrate concentration on an enzyme rate under locally specified conditions. Normally an algebraic expression for the elasticity of an enzyme is obtained by differentiating its rate law, multiplying by the effector concentration and dividing by the rate law itself: this results in considerable expression expansion, and when the results are subsequently simplified it is often at the expense of biological comprehensibility., Results: We present a novel algorithm which not only circumvents the expression expansion, but preserves an elegant separation of the components in enzyme behaviour. Easily implemented, and producing gains in both performance and numerical precision, the algorithm is potentially applicable to a number of existing packages. It also greatly assists the manual derivation and evaluation of elasticities, allowing the elasticity of even quite complex enzyme systems to be written by inspection.

The control and regulation of metabolic systems are determined by their responses to changes in the internal metabolites (the internal state) and parameters of the system. In many cases, the concentrations of the intermediates are constrained by moiety conservations, for example those requiring that all intermediate forms of any enzyme sum to the conserved total concentration of that enzyme. In this study, we show how responses to changes in the internal state are related to responses to changes in the total amounts of conserved moieties. The relationship between these two different measures of control leads to a generalisation of the connectivity theorems. The results have important implications for the study of a variety of phenomena such as metabolite (coenzyme) sequestration, group-transfer and channelling. The relationships we derive make it possible to determine the control features of these pathways. As an illustration, two examples are chosen. The first shows the effect of sequestration of substrate moiety while the second deals with the sequestration of the enzyme moieties and enzyme/enzyme interactions.

This paper considers certain aspects of the analysis of moiety-conserved cycles in terms of metabolic control analysis. Two response coefficients are discussed: the response coefficient with respect to the total number of moles in a cycle (RVT), and the response coefficient with respect to perturbations to the internal state of a pathway (RVS). The relationship between these two different measures is derived and two examples are given to illustrate how the results may be used to simplify the analysis of particular complex pathways. One example considers how metabolite sequestration affects the flux summation theorem for which the analysis confirms the known result that sequestration can depress the value of the summation to below unity. The second example investigates the effect of metabolic channelling on the summation theorems. The analysis indicates that in contrast to metabolite sequestration, metabolic channelling can cause the flux summation theorem to exceed the value of unity. In addition, the maximum value that the summation theorem can reach under these conditions is shown to be equal to 2. Finally, this analysis indicates how one might use control analysis through the use of enzyme titration to determine whether metabolic channelling occurs in real systems or not.

SCAMP is a general-purpose simulator of metabolic and chemical networks. The program is written in C and is portable to all computer systems that support an ANSI C compiler. SCAMP accepts metabolic models described in a biochemical language, and this enables novice as well as experienced users rapidly to build and simulate metabolic systems. The language is sufficiently flexible to enable other types of model to be built, e.g. chemostat or ecological models. The language offers many facilities, including: the ability to describe metabolic pathways of any structure and possessing any kinetics using normal chemical notation; optionally build models directly from the differential equations; differing compartment volumes; access to flux, concentration and rate of change information; detection of conserved cycles; access to all coefficients and elasticities of metabolic control analysis; user-defined forcing functions at the model boundaries; user-defined monitoring functions; user-configurable output of any quantity. From the model description SCAMP can either generate C code for later compilation to produce fast executable stand-alone models or run-time code for input to a run-time interpreter for immediate execution. The simulator also incorporates an inbuilt symbolic differentiator for evaluating the Jacobian and elasticity matrices.

The matrix algebra procedure for determining the flux control coefficients of enzymes in metabolic pathways has been extended to allow determination of the concentration control coefficients. Although it is shown that the procedure is essentially unchanged in most cases, the presence of moiety-conserved cycles in a pathway places additional limitations on the form of the equations that can be used in the matrix formulation for concentration control coefficients. In the case of branched pathways, a new coefficient has been defined, the branch distribution control coefficient, which can be obtained via the matrix procedure. Thus a single matrix equation permits calculation or algebraic evaluation of the control coefficients for flux, concentration and distribution of flux at branches, so that the complete response of a pathway to alteration of enzyme content, or to modulation by an effector, can be determined. The relationships have been determined between flux control coefficients in isolated sections of metabolic pathways and the coefficients for the same enzymes when part of a larger metabolic system. It is shown that the control analysis of the isolated system provides useful information towards determining the control properties of the extended system.

Metabolic Control Analysis is extended to time dependent systems. It is assumed that the time derivative of the metabolite concentrations can be written as a linear combination of rate laws, each one of first order with respect to the corresponding enzyme concentration. The definitions of the control and elasticity coefficients are extended, and a new type of coefficient ("time coefficient", "T") is defined. First, we prove that simultaneous changes in all enzyme concentrations by the same arbitrary factor, is equivalent to a change in the time scale. When infinitesimal changes are considered, these arguments lead to the derivation of general summation theorems that link control and time coefficients. The comparison of two systems with identical rates, that only differ in one metabolite concentration, leads to a method for the construction of general connectivity theorems, that relate control and elasticity coefficients. A mathematical proof in matrix form, of the summation and connectivity relationships, for time dependent systems is given. Those relationships allow one to express the control coefficients in terms of the elasticity and time coefficients for the case of unbranched pathway.