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PURPOSE OF REVIEW: Signaling pathways serve to communicate information about extracellular conditions into the cell, to both the nucleus and cytoplasmic processes to control cell responses. Genetic mutations in signaling network components are frequently associated with cancer and can result in cells acquiring an ability to divide and grow uncontrollably. Because signaling pathways play such a significant role in cancer initiation and advancement, their constituent proteins are attractive therapeutic targets. In this review, we discuss how signaling pathway modeling can assist with identifying effective drugs for treating diseases, such as cancer. An achievement that would facilitate the use of such models is their ability to identify controlling biochemical parameters in signaling pathways, such as molecular abundances and chemical reaction rates, because this would help determine effective points of attack by therapeutics. RECENT FINDINGS: We summarize the current state of understanding the sensitivity of phosphorylation cycles with and without sequestration. We also describe some basic properties of regulatory motifs including feedback and feedforward regulation. SUMMARY: Although much recent work has focused on understanding the dynamics and particularly the sensitivity of signaling networks in eukaryotic systems, there is still an urgent need to build more scalable models of signaling networks that can appropriately represent their complexity across different cell types and tumors.

Motivation An essential step in developing computational tools for the inference, optimization and simulation of biochemical reaction networks is gauging tool performance against earlier efforts using an appropriate set of benchmarks. General strategies for the assembly of benchmark models include collection from the literature, creation via subnetwork extraction and de novo generation. However, with respect to biochemical reaction networks, these approaches and their associated tools are either poorly suited to generate models that reflect the wide range of properties found in natural biochemical networks or to do so in numbers that enable rigorous statistical analysis. Results In this work, we present SBbadger, a python-based software tool for the generation of synthetic biochemical reaction or metabolic networks with user-defined degree distributions, multiple available kinetic formalisms and a host of other definable properties. SBbadger thus enables the creation of benchmark model sets that reflect properties of biological systems and generate the kinetics and model structures typically targeted by computational analysis and inference software. Here, we detail the computational and algorithmic workflow of SBbadger, demonstrate its performance under various settings, provide sample outputs and compare it to currently available biochemical reaction network generation software. Availability and implementation SBbadger is implemented in Python and is freely available at https://github.com/sys-bio/SBbadger and via PyPI at https://pypi.org/project/SBbadger/. Documentation can be found at https://SBbadger.readthedocs.io. Supplementary information Supplementary data are available at Bioinformatics online.

Summary As the number and complexity of biosimulation models grows, so do demands for tools that can help users better understand models and make those models more findable, shareable and reproducible. Consistent model annotation is a step toward these goals. Both models and tools are written in a variety of different languages; thus, the community has recognized the need for standard, language-independent methods for annotation. Based on the Computational Modeling in Biology Network community consensus, we introduce an open-source, cross-platform software library for semantic annotation of models. Availability and implementation libOmexMeta is freely available at https://github.com/sys-bio/libOmexMeta under the Apache License 2.0. A live demonstration is at github.com/sys-bio/pyomexmeta-binder-notebook. Supplementary information Supplementary data are available at Bioinformatics online.

This paper presents a report on outcomes of the 10th Computational Modeling in Biology Network (COMBINE) meeting that was held in Heidelberg, Germany, in July of 2019. The annual event brings together researchers, biocurators and software engineers to present recent results and discuss future work in the area of standards for systems and synthetic biology. The COMBINE initiative coordinates the development of various community standards and formats for computational models in the life sciences. Over the past 10 years, COMBINE has brought together standard communities that have further developed and harmonized their standards for better interoperability of models and data. COMBINE 2019 was co-located with a stakeholder workshop of the European EU-STANDS4PM initiative that aims at harmonized data and model standardization for in silico models in the field of personalized medicine, as well as with the FAIRDOM PALs meeting to discuss findable, accessible, interoperable and reusable (FAIR) data sharing. This report briefly describes the work discussed in invited and contributed talks as well as during breakout sessions. It also highlights recent advancements in data, model, and annotation standardization efforts. Finally, this report concludes with some challenges and opportunities that this community will face during the next 10 years.

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., Over the past two decades, scientists from different fields have been developing SBML, a standard format for encoding computational models in biology and medicine. This article summarizes recent progress and gives perspectives on emerging challenges.

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.

The SBML standard is used in a number of online repositories for storing systems biology models, yet there is currently no Web–capable JavaScript library that can read and write the SBML format. This is a severe limitation since the Web has become a universal means of software distribution, and the graphical capabilities of modern web browsers offer a powerful means for building rich, interactive applications. Also, there is a growing developer population specialized in web technologies that is poised to take advantage of the universality of the web to build the next generation of tools in systems biology and other fields. However, current solutions require server– side processing in order to support existing standards in modeling. We present libsbmljs, a JavaScript / WebAssembly library for Node.js and the Web with full support for all SBML extensions. Our library is an enabling technology for online SBML editors, model–building tools, and web–based simulators, and runs entirely in the browser without the need for any dedicated server resources. We provide NPM packages, an extensive set of examples, JavaScript API documentation, and an online demo that allows users to read and validate the SBML content of any model in the BioModels and BiGG databases. We also provide instructions and scripts to allow users to build a copy of libsbmljs against any libSBML version. Although our library supports all existing SBML extensions, we cover how to add additional extensions to the wrapper, should any arise in the future. To demonstrate the utility of this implementation, we also provide a demo at https://libsbmljsdemo.github.io/ with a proof–of–concept SBML simulator that supports ODE and stochastic simulations for SBML core models. Our project is hosted at https://libsbmljs.github.io/, which contains links to examples, API documentation, and all source code files and build scripts used to create libsbmljs. Our source code is licensed under the Apache 2.0 open source license.

Biological systems are multiscale with respect to time and space, exist at the interface of biological and physical constraints, and their interactions with the environment are often nonlinear [...]

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 2.1 of SBOL that builds upon version 2.0 published in last year's JIB special issue. In particular, SBOL 2.1 includes improved rules for what constitutes a valid SBOL document, new role fields to simplify the expression of sequence features and how components are used in context, and new best practices descriptions to improve the exchange of basic sequence topology information and the description of genetic design provenance, as well as miscellaneous other minor improvements., Journal of Integrative Bioinformatics - JIB

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.

In this paper we examine the use of perturbation data to infer the underlying mechanistic dynamic model. The approach uses an evolutionary strategy to evolve networks based on a fitness criterion that measures the difference between the experimentally determined set of perturbation data and proposed mechanistic models. At present we only deal with reaction networks that use mass-action kinetics employing uni-uni, bi-uni, uni-bi and bi-bi reactions. The key to our approach is to split the algorithm into two phases. The first phase focuses on evolving network topologies that are consistent with the perturbation data followed by a second phase that evolves the parameter values. This results in almost an exact match between the evolved network and the original network from which the perturbation data was generated from. We test the approach on four models that include linear chain, feed-forward loop, cyclic pathway and a branched pathway. Currently the algorithm is implemented using Python and libRoadRunner but could at a later date be rewritten in a compiled language to improve performance. Future studies will focus on the impact of noise in the perturbation data on convergence and variability in the evolved parameter values and topologies. In addition we will investigate the effect of nonlinear rate laws on generating unique solutions.

The considerable difficulty encountered in reproducing the results of published dynamical models limits validation, exploration and reuse of this increasingly large biomedical research resource. To address this problem, we have developed Tellurium Notebook, a software system for model authoring, simulation, and teaching that facilitates building reproducible dynamical models and reusing models by 1) providing a notebook environment which allows models, Python code, and narrative to be intermixed, 2) supporting the COMBINE archive format during model development for capturing model information in an exchangeable format and 3) enabling users to easily simulate and edit public COMBINE-compliant models from public repositories to facilitate studying model dynamics, variants and test cases. Tellurium Notebook, a Python–based Jupyter–like environment, is designed to seamlessly inter-operate with these community standards by automating conversion between COMBINE standards formulations and corresponding in–line, human–readable representations. Thus, Tellurium brings to systems biology the strategy used by other literate notebook systems such as Mathematica. These capabilities allow users to edit every aspect of the standards–compliant models and simulations, run the simulations in–line, and re–export to standard formats. We provide several use cases illustrating the advantages of our approach and how it allows development and reuse of models without requiring technical knowledge of standards. Adoption of Tellurium should accelerate model development, reproducibility and reuse., Author summary There is considerable value to systems and synthetic biology in creating reproducible models. An essential element of reproducibility is the use of community standards, an often challenging undertaking for modelers. This article describes Tellurium Notebook, a tool for developing dynamical models that provides an intuitive approach to building and reusing models built with community standards. Tellurium automates embedding human–readable representations of COMBINE archives in literate coding notebooks, bringing to systems biology this strategy central to other literate notebook systems such as Mathematica. We show that the ability to easily edit this human–readable representation enables users to test models under a variety of conditions, thereby providing a way to create, reuse, and modify standard–encoded models and simulations, regardless of the user’s level of technical knowledge of said standards.

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.

The topology of a reaction network can have a significant influence on the network’s dynamical properties. Such influences can include constraints on network flows and concentration changes or more insidiously result in the emergence of feedback loops. These effects are due entirely to mass constraints imposed by the network configuration and are important considerations before any dynamical analysis is made. Most established simulation software tools usually carry out some kind of structural analysis of a network before any attempt is made at dynamic simulation. In this paper we describe a portable software library, libStructural, that can carry out a variety of popular structural analyses that includes conservation analysis, flux dependency analysis and enumerating elementary modes. The library employs robust algorithms that allow it to be used on large networks with more than a two thousand nodes. The library accepts either a raw or fully labeled stoichiometry matrix or models written in SBML format. The software is written in standard C/C++ and comes with documentation and a test suite. The software is available for Windows, Mac OS X, and can be compiled easily on any Linux operating system. A language binding for Python is also available through the pip package manager making it trivial to install on any standard Python distribution. As a second example, we also create a new libStructural plugin for PathwayDesigner that allows solutions to be viewed graphically. The source code is licensed under the open source BSD license and is available on GitHub (https://github.com/sys-bio/Libstructural)

The considerable difficulty encountered in reproducing the results of published dynamical models limits validation, exploration and reuse of this increasingly large biomedical research resource. To address this problem, we have developed Tellurium Notebook, a software system that facilitates building reproducible dynamical models and reusing models by 1) supporting the COMBINE archive format during model development for capturing model information in an exchangeable format and 2) enabling users to easily simulate and edit public COMBINE-compliant models from public repositories to facilitate studying model dynamics, variants and test cases. Tellurium Notebook, a Python–based Jupyter–like environment, is designed to seamlessly inter-operate with these community standards by automating conversion between COMBINE standards formulations and corresponding in–line, human–readable representations. Thus, Tellurium brings to systems biology the strategy used by other literate notebook systems such as Mathematica. These capabilities allow users to edit every aspect of the standards–compliant models and simulations, run the simulations in–line, and re–export to standard formats. We provide several use cases illustrating the advantages of our approach and how it allows development and reuse of models without requiring technical knowledge of standards. Adoption of Tellurium should accelerate model development, reproducibility and reuse.Author summaryThere is considerable value to systems and synthetic biology in creating reproducible models. An essential element of reproducibility is the use of community standards, an often challenging undertaking for modelers. This article describes Tellurium Notebook, a tool for developing dynamical models that provides an intuitive approach to building and reusing models built with community standards. Tellurium automates embedding human–readable representations of COMBINE archives in literate coding notebooks, bringing to systems biology this strategy central to other literate notebook systems such as Mathematica. We show that the ability to easily edit this human–readable representation enables users to test models under a variety of conditions, thereby providing a way to create, reuse, and modify standard–encoded models and simulations, regardless of the user’s level of technical knowledge of said standards.

Motivation: Model simulation exchange has been standardized with the Simulation Experiment Description Markup Language (SED-ML), but specialized software is needed to generate simulations in this format. Text-based languages allow researchers to create and modify experimental protocols quickly and easily, and export them to a common machine-readable format. Results: phraSED-ML language allows modelers to use simple text commands to encode various elements of SED-ML (models, tasks, simulations, and results) in a format easy to read and modify. The library can translate this script to SED-ML for use in other softwares. Availability: phraSED-ML language specification, libphrasedml library, and source code are available under BSD license from http://phrasedml.sourceforge.net/ .

Synthetic Biology Open Language (SBOL) Visual is a graphical standard for genetic engineering. It consists of symbols representing DNA subsequences, including regulatory elements and DNA assembly features. These symbols can be used to draw illustrations for communication and instruction, and as image assets for computer-aided design. SBOL Visual is a community standard, freely available for personal, academic, and commercial use (Creative Commons CC0 license). We provide prototypical symbol images that have been used in scientific publications and software tools. We encourage users to use and modify them freely, and to join the SBOL Visual community: http://www.sbolstandard.org/visual., This Community Page introduces SBOL Visual—a standard visual language for DNA design and synthetic biology. Researchers are encouraged to download the symbols, use them freely, and suggest additions.

In this technical note we describe modifications to Antimony (Biochemical model specification language) that allows modelers to use the SBML distributions package. In addition the article describes best practice for using distributions, including when they should and should not be used.

Biological organisms acclimatize to varying environmental conditions via active self-regulation of internal gene regulatory networks, metabolic networks, and protein signaling networks. While much work has been done to elucidate the topologies of individual networks in isolation, understanding of inter-network regulatory mechanisms remains limited. This shortcoming is of particular relevance to synthetic biology. Synthetic biological circuits tend to lose their engineered functionality over generational time, primarily due to the deleterious stress that they exert on their host organisms. To reduce this stress (and thus minimize loss of functionality) synthetic circuits must be sensitive to the health of the host organism. Development of integrated regulatory systems is therefore essential to robust synthetic biological systems. The aim of this study was to develop integrated gene-regulatory and metabolic networks which self-optimize in response to varying environmental conditions. We performed \emph{in silico} evolution to develop such networks using a two-step approach: (1) We optimized metabolic networks given a constrained amount of available enzyme. Here, we found that a proportional relationship between flux control coefficients and enzyme mass holds in all linear sub-networks of branched networks, except those sub-networks which contain allosteric regulators. Network optimization was performed by iteratively redistributing enzyme until flux through the network was maximized. Optimization was performed for a range of boundary metabolite conditions to develop a profile of optimal enzyme distributions as a function of environmental conditions. (2) We generated and evolved randomized gene regulatory networks to modulate the enzymes of a target metabolic pathway. The objective of the gene regulatory networks was to produce the optimal distribution of metabolic network enzymes given specific boundary metabolite conditions of the target network. Competitive evolutionary algorithms were applied to optimize the specific structures and kinetic parameters of the gene regulatory networks. With this method, we demonstrate the possibility of algorithmic development of integrated adaptive gene and metabolic regulatory networks which dynamically self-optimize in response to changing environmental conditions.

We describe libRoadRunner, a cross-platform, open-source, high performance C++ library for running and analyzing SBML-compliant models. libRoadRunner was created primarily to achieve high performance, ease of use, portability and an extensible architecture. libRoadRunner includes a comprehensive API, Plugin support, Python scripting and additional functionality such as stoichiometric and metabolic control analysis. To maximize collaboration, we made libRoadRunner open source and released it under the Apache License, Version 2.0. To facilitate reuse, we have developed comprehensive Python bindings using SWIG (swig.org) and a C API. LibRoadRunner uses a number of statically linked third party libraries including: LLVM, libSBML, CVODE, NLEQ2, LAPACK and Poco. LibRoadRunner is supported on Windows, Mac OS X and Linux. Online documentation, build instructions and git source repository are available at http://www.libroadrunner.org

Many biological studies are carried out on large populations of cells, often in order to obtain enough material to make measurements. However, we now know that noise is endemic in biological systems and this results in cell-to-cell variability in what appears to be a population of identical cells. Although often neglected, this noise can have a dramatic effect on system responses to environmental cues with significant and often counter-intuitive biological outcomes. A recent study in BMC Systems Biology provides an example of this, documenting a bimodal distribution of activated extracellular signal-regulated kinase in a population of cells exposed to epidermal growth factor and demonstrating that the observed bimodality of the response is induced purely by noise. See research article: http://www.biomedcentral.com/1752-0509/6/109

A great variety of software applications are now employed in the metabolic engineering field. These applications have been created to support a wide range of experimental and analysis techniques. Computational tools are utilized throughout the metabolic engineering workflow to extract and interpret relevant information from large data sets, to present complex models in a more manageable form, and to propose efficient network design strategies. In this review, we present a number of tools that can assist in modifying and understanding cellular metabolic networks. The review covers seven areas of relevance to metabolic engineers. These include metabolic reconstruction efforts, network visualization, nucleic acid and protein engineering, metabolic flux analysis, pathway prospecting, post-structural network analysis and culture optimization. The list of available tools is extensive and we can only highlight a small, representative portion of the tools from each area.

Genetically identical cells can show phenotypic variability. This is often caused by stochastic events that originate from randomness in biochemical processes involving in gene expression and other extrinsic cellular processes. From an engineering perspective, there have been efforts focused on theory and experiments to control noise levels by perturbing and replacing gene network components. However, systematic methods for noise control are lacking mainly due to the intractable mathematical structure of noise propagation through reaction networks. Here, we provide a numerical analysis method by quantifying the parametric sensitivity of noise characteristics at the level of the linear noise approximation. Our analysis is readily applicable to various types of noise control and to different types of system; for example, we can orthogonally control the mean and noise levels and can control system dynamics such as noisy oscillations. As an illustration we applied our method to HIV and yeast gene expression systems and metabolic networks. The oscillatory signal control was applied to p53 oscillations from DNA damage. Furthermore, we showed that the efficiency of orthogonal control can be enhanced by applying extrinsic noise and feedback. Our noise control analysis can be applied to any stochastic model belonging to continuous time Markovian systems such as biological and chemical reaction systems, and even computer and social networks. We anticipate the proposed analysis to be a useful tool for designing and controlling synthetic gene networks., Author Summary Stochastic gene expression at the single cell level can lead to significant phenotypic variation at the population level. To obtain a desired phenotype, the noise levels of intracellular protein concentrations may need to be tuned and controlled. Noise levels often decrease in relative amount as the mean values increase. This implies that the noise levels can be passively controlled by changing the mean values. In an engineering perspective, the noise levels can be further controlled while the mean values can be simultaneously adjusted to desired values. Here, systematic schemes for such simultaneous control are described by identifying where and by how much the system needs to be perturbed. The schemes can be applied to the design process of a potential therapeutic HIV-drug that targets a certain set of reactions that are identified by the proposed analysis, to prevent stochastic transition to the lytic state. In some cases, the simultaneous control cannot be performed efficiently, when the noise levels strongly change with the mean values. This problem is shown to be resolved by applying extra noise and feedback.

Background One problem with engineered genetic circuits in synthetic microbes is their stability over evolutionary time in the absence of selective pressure. Since design of a selective environment for maintaining function of a circuit will be unique to every circuit, general design principles are needed for engineering evolutionary robust circuits that permit the long-term study or applied use of synthetic circuits. Results We first measured the stability of two BioBrick-assembled genetic circuits propagated in Escherichia coli over multiple generations and the mutations that caused their loss-of-function. The first circuit, T9002, loses function in less than 20 generations and the mutation that repeatedly causes its loss-of-function is a deletion between two homologous transcriptional terminators. To measure the effect between transcriptional terminator homology levels and evolutionary stability, we re-engineered six versions of T9002 with a different transcriptional terminator at the end of the circuit. When there is no homology between terminators, the evolutionary half-life of this circuit is significantly improved over 2-fold and is independent of the expression level. Removing homology between terminators and decreasing expression level 4-fold increases the evolutionary half-life over 17-fold. The second circuit, I7101, loses function in less than 50 generations due to a deletion between repeated operator sequences in the promoter. This circuit was re-engineered with different promoters from a promoter library and using a kanamycin resistance gene (kanR) within the circuit to put a selective pressure on the promoter. The evolutionary stability dynamics and loss-of-function mutations in all these circuits are described. We also found that on average, evolutionary half-life exponentially decreases with increasing expression levels. Conclusions A wide variety of loss-of-function mutations are observed in BioBrick-assembled genetic circuits including point mutations, small insertions and deletions, large deletions, and insertion sequence (IS) element insertions that often occur in the scar sequence between parts. Promoter mutations are selected for more than any other biological part. Genetic circuits can be re-engineered to be more evolutionary robust with a few simple design principles: high expression of genetic circuits comes with the cost of low evolutionary stability, avoid repeated sequences, and the use of inducible promoters increases stability. Inclusion of an antibiotic resistance gene within the circuit does not ensure evolutionary stability.

Motivation: The SBML Render Extension enables coloring and shape information of biochemical models to be stored in the Systems Biology Markup Language (SBML). Rendering of this stored graphical information in a portable and well supported system such as TeX would be useful for researchers preparing documentation and presentations. In addition, since the Render Extension is not yet supported by many applications, it is helpful for such rendering functionality be extended to the more popular CellDesigner annotation as well. Results: SBML2TikZ supports automatic generation of graphics for biochemical models in the popular TeX typesetting system. The library generates a script of TeX macro commands for the vector graphics languages PGF/TikZ that can be compiled into scalable vector graphics described in a model. Availability: Source code, documentation and compiled binaries for the SBML2TikZ library can be found at http://www.sbml2tikz.org. In addition, a web application is available at http://www.sys-bio.org/layout Contact: hsauro@u.washington.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Model exchange in systems and synthetic biology has been standardized for computers with the Systems Biology Markup Language (SBML) and CellML, but specialized software is needed for the generation of models in these formats. Text-based model definition languages allow researchers to create models simply, and then export them to a common exchange format. Modular languages allow researchers to create and combine complex models more easily. We saw a use for a modular text-based language, together with a translation library to allow other programs to read the models as well. Summary: The Antimony language provides a way for a researcher to use simple text statements to create, import, and combine biological models, allowing complex models to be built from simpler models, and provides a special syntax for the creation of modular genetic networks. The libAntimony library allows other software packages to import these models and convert them either to SBML or their own internal format. Availability: The Antimony language specification and the libAntimony library are available under a BSD license from http://antimony.sourceforge.net/ Contact: lpsmith@u.washington.edu

Probably one of the most characteristic features of a living system is its continual propensity to change as it juggles the demands of survival with the need to replicate. Internally these adjustments are manifest as changes in metabolite, protein, and gene activities. Such changes have become increasingly obvious to experimentalists, with the advent of high-throughput technologies. In this chapter we highlight some of the quantitative approaches used to rationalize the study of cellular dynamics. The chapter focuses attention on the analysis of quantitative models based on differential equations using biochemical control theory. Basic pathway motifs are discussed, including straight chain, branched, and cyclic systems. In addition, some of the properties conferred by positive and negative feedback loops are discussed, particularly in relation to bistability and oscillatory dynamics.

Motivation: Simulations are an essential tool when analyzing biochemical networks. Researchers and developers seeking to refine simulation tools or develop new ones would benefit greatly from being able to compare their simulation results. Summary: We present an approach to compare simulation results between several SBML capable simulators and provide a website for the community to share simulation results. Availability: The website with simulation results and additional material can be found under: http://sys-bio.org/sbwWiki/compare. The software used to generate the simulation results is available on the website for download. Contact: fbergman@u.washington.edu

Motivation: Biochemical networks often yield interesting behavior such as switching, oscillation and chaotic dynamics. This article describes a tool that is capable of searching for bifurcation points in arbitrary ODE-based reaction networks by directing the user to regions in the parameter space, where such interesting dynamical behavior can be observed. Results: We have implemented a genetic algorithm that searches for Hopf bifurcations, turning points and bistable switches. The software is implemented as a Systems Biology Workbench (SBW) enabled module and accepts the standard SBML model format. The interface permits a user to choose the parameters to be searched, admissible parameter ranges, and the nature of the bifurcation to be sought. The tool will return the parameter values for the model for which the particular behavior is observed. Availability: The software, tutorial manual and test models are available for download at the following website: http:/www.sys-bio.org/ under the bifurcation link. The software is an open source and licensed under BSD. Contact: vijay_chickarmane@kgi.edu

Researchers in quantitative systems biology make use of a large number of different software packages for modelling, analysis, visualization, and general data manipulation. In this paper, we describe the Systems Biology Workbench (SBW), a software framework that allows heterogeneous application components--written in diverse programming languages and running on different platforms--to communicate and use each others' capabilities via a fast binary encoded-message system. Our goal was to create a simple, high performance, opensource software infrastructure which is easy to implement and understand. SBW enables applications (potentially running on separate, distributed computers) to communicate via a simple network protocol. The interfaces to the system are encapsulated in client-side libraries that we provide for different programming languages. We describe in this paper the SBW architecture, a selection of current modules, including Jarnac, JDesigner, and SBWMeta-tool, and the close integration of SBW into BioSPICE, which enables both frameworks to share tools and compliment and strengthen each others capabilities.