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Tissue Forge is an open-source interactive environment for particle-based physics, chemistry and biology modeling and simulation. Tissue Forge allows users to create, simulate and explore models and virtual experiments based on soft condensed matter physics at multiple scales, from the molecular to the multicellular, using a simple, consistent interface. While Tissue Forge is designed to simplify solving problems in complex subcellular, cellular and tissue biophysics, it supports applications ranging from classic molecular dynamics to agent-based multicellular systems with dynamic populations. Tissue Forge users can build and interact with models and simulations in real-time and change simulation details during execution, or execute simulations off-screen and/or remotely in high-performance computing environments. Tissue Forge provides a growing library of built-in model components along with support for user-specified models during the development and application of custom, agent-based models. Tissue Forge includes an extensive Python API for model and simulation specification via Python scripts, an IPython console and a Jupyter Notebook, as well as C and C++ APIs for integrated applications with other software tools. Tissue Forge supports installations on 64-bit Windows, Linux and MacOS systems and is available for local installation via conda.1Author SummaryTissue Forge is a physics-based modeling and simulation software environment for research problems in physics, chemistry and biology. Tissue Forge supports modeling at a wide range of scales, from as small as the sub-nanometer, to as large as hundreds of micrometers, using particle-based models. It provides rich features for simulation development and application at all stages of model-based research, like real-time simulation visualization and interactivity, and off-screen batch execution, rendering, and GPU acceleration. Users can employ built-in models to represent a wide variety of physical processes, like chemical reactions, fluid convection and intercellular adhesion, or define their own models for agentand rule-based modeling. Tissue Forge is open-source, free and easy to install, supports simulation development in C, C++ and Python programming languages, and can be used as integrated software or in an interactive IPython console and Jupyter Notebook. Tissue Forge also provides a dedicated space for application-specific and user-contributed modeling and simulation features, and developers are welcome to contribute their custom features for distribution in future releases.

Summary The systems biology markup language (SBML) is an extensible standard format for exchanging biochemical models. One of the extensions for SBML is the SBML Layout and Render package. This allows modelers to describe a biochemical model as a pathway diagram. However, up to now, there has been little support to help users easily add and retrieve such information from SBML. In this application note, we describe a new Python package called SBMLDiagrams. This package allows a user to add a layout and render information or retrieve it using a straightforward Python API. The package uses skia-python to support the rendering of the diagrams, allowing export to commons formats such as PNG or PDF. Availability and implementation SBMLDiagrams is publicly available and licensed under the liberal MIT open-source license. The package is available for all major platforms. The source code has been deposited at GitHub (github.com/sys-bio/SBMLDiagrams). Users can install the package using the standard pip installation mechanism: pip install SBMLDiagrams. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation This article presents libRoadRunner 2.0, an extensible, high-performance, cross-platform, open-source software library for the simulation and analysis of models expressed using the systems biology markup language (SBML). Results libRoadRunner is a self-contained library, able to run either as a component inside other tools via its C++, C and Python APIs, or interactively through its Python or Julia interface. libRoadRunner uses a custom just-in-time (JIT) compiler built on the widely used LLVM JIT compiler framework. It compiles SBML-specified models directly into native machine code for a large variety of processors, making it fast enough to simulate extremely large models or repeated runs in reasonable timeframes. libRoadRunner is flexible, supporting the bulk of the SBML specification (except for delay and non-linear algebraic equations) as well as several SBML extensions such as hierarchical composition and probability distributions. It offers multiple deterministic and stochastic integrators, as well as tools for steady-state, sensitivity, stability and structural analyses. Availability and implementation libRoadRunner binary distributions for Windows, Mac OS and Linux, Julia and Python bindings, source code and documentation are all available at https://github.com/sys-bio/roadrunner, and Python bindings are also available via pip. The source code can be compiled for the supported systems as well as in principle any system supported by LLVM-13, such as ARM-based computers like the Raspberry Pi. The library is licensed under the Apache License Version 2.0.

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.

The interests in repurposing and reusing systems biology models have been growing in recent years. Semantic annotations play an important role for this, as they provide crucial information on the meanings and functions of models. However, there are a limited number of tools that evaluate the existence or quality of such annotations. In this paper, we introduce SBMate, a python package that would serve as a framework for evaluating the quality of annotations in systems biology models. Three default metrics are provided: coverage, consistency, and specificity. Coverage checks whether annotations exist in a model. Consistency tests if the annotations are appropriate for the given model element. Finally, specificity represents how detailed the annotations are. We analyzed 1,000 curated models from the BioModels repository using the three metrics and discussed the results. Additional metrics can be easily added to extend the current version of SBMate.

Computational simulation experiments increasingly inform modern biological research, and bring with them the need to provide ways to annotate, archive, share and reproduce the experiments performed. These simulations increasingly require extensive collaboration among modelers, experimentalists, and engineers. The Minimum Information About a Simulation Experiment (MIASE) guidelines outline the information needed to share simulation experiments. SED-ML is a computer-readable format for the information outlined by MIASE, created as a community project and supported by many investigators and software tools. The first versions of SED-ML focused on deterministic and stochastic simulations of models. Level 1 Version 4 of SED-ML substantially expands these capabilities to cover additional types of models, model languages, parameter estimations, simulations and analyses of models, and analyses and visualizations of simulation results. To facilitate consistent practices across the community, Level 1 Version 4 also more clearly describes the use of SED-ML constructs, and includes numerous concrete validation rules. SED-ML is supported by a growing ecosystem of investigators, model languages, and software tools, including eight languages for constraint-based, kinetic, qualitative, rule-based, and spatial models, over 20 simulation tools, visual editors, model repositories, and validators. Additional information about SED-ML is available at https://sed-ml.org/.

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.

Although reproducibility is a core tenet of the scientific method, it remains challenging to reproduce many results. Surprisingly, this also holds true for computational results in domains such as systems biology where there have been extensive standardization efforts. For example, Tiwari et al. recently found that they could only repeat 50% of published simulation results in systems biology. Toward improving the reproducibility of computational systems research, we identified several resources that investigators can leverage to make their research more accessible, executable, and comprehensible by others. In particular, we identified several domain standards and curation services, as well as powerful approaches pioneered by the software engineering industry that we believe many investigators could adopt. Together, we believe these approaches could substantially enhance the reproducibility of systems biology research. In turn, we believe enhanced reproducibility would accelerate the development of more sophisticated models that could inform precision medicine and synthetic biology., Comment: 11 pages, 1 figure

Publishing repeatable and reproducible computational models is a crucial aspect of the scientific method in computational biology and one that is often forgotten in the rush to publish. The pressures of academic life and the lack of any reward system at institutions, granting agencies and journals means that publishing reproducible science is often either non-existent or, at best, presented in the form of an incomplete description. In the article, we will focus on repeatability and reproducibility in the systems biology field where a great many published models cannot be reproduced and in many cases even repeated. This review describes the current landscape of software tooling, model repositories, model standards and best practices for publishing repeatable and reproducible kinetic models. The review also discusses possible future remedies including working more closely with journals to help reviewers and editors ensure that published kinetic models are at minimum, repeatable. Contact: hsauro@uw.edu

Here we describe Tellurium, a Python based platform for supporting the development of reproducible models in systems biology. The tool exploits a number of available standards, including SBML, SED-ML and COMBINE archives to achieve its goal.

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.

Linear metabolic pathways are the simplest network architecture we find in metabolism and are a good starting point to gain insight into the operating principles of metabolic control. Linear pathways possess some well-known properties, such as a bias of flux control towards the first few steps of the pathway as well as the lack of flux control at reactions close to equilibrium. In both cases, a rationale for these behaviors is given in terms of how elasticities transmit changes through a pathway. A discussion is given on the fundamental role that two reaction step sections play in a linear pathway when transmitting changes. For a pathway with irreversible steps, the deconstruction is straight forward and includes a product of local response coefficients that cascade along the pathway. When reversibility is included, the picture became more complex but a relationship in terms of the local response coefficients if derived that includes the reverse response coefficients and highlights the interplay between the forward and backward transmission of changes during a perturbation.

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.

This paper presents pySBOL, a software library for computer-aided design of synthetic biological systems in the Python scripting language. This library provides an easy-to-use, object-oriented, application programming interface (API) with low barrier of entry for synthetic biology application developers. The pySBOL library enables reuse of genetic parts and designs through standardized data exchange with biological parts repositories and software tools that communicate using the Synthetic Biology Open Language (SBOL). In addition, pySBOL supports data management of design-build-test-learn workflows for individual laboratories as well as large, distributed teams of synthetic biologists. PySBOL also lets users add custom data to SBOL files to support the specific data requirements of their research. This extensibility helps users integrate software tool chains and develop workflows for new applications. These features and others make the pySBOL library a valuable tool for supporting engineering practices in synthetic biology. Documentation and installation instructions can be found at pysbol2.readthedocs.io .

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 extensive on-line 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 simple to install on any standard Python distribution. The bulk of the source code is licensed under the open source BSD license with other parts using as either the MIT license or more simply public domain. All source is available on GitHub ( https://github.com/sys-bio/Libstructural ).

Summary Like many scientific disciplines, dynamical biochemical modeling is hindered by irreproducible results. This limits the utility of biochemical models by making them difficult to understand, trust, or reuse. We comprehensively list the best practices that biochemical modelers should follow to build reproducible biochemical model artifacts—all data, model descriptions, and custom software used by the model—that can be understood and reused. The best practices provide advice for all steps of a typical biochemical modeling workflow in which a modeler collects data; constructs, trains, simulates, and validates the model; uses the predictions of a model to advance knowledge; and publicly shares the model artifacts. The best practices emphasize the benefits obtained by using standard tools and formats and provides guidance to modelers who do not or cannot use standards in some stages of their modeling workflow. Adoption of these best practices will enhance the ability of researchers to reproduce, understand, and reuse biochemical models.

Biological engineers often find it useful to communicate using diagrams. These diagrams can include information both about the structure of the nucleic acid sequences they are engineering and about the functional relationships between features of these sequences and/or other molecular species. A number of conventions and practices have begun to emerge within synthetic biology for creating such diagrams, and the Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard to organize, systematize, and extend such conventions in order to produce a coherent visual language. Here, we describe SBOL Visual version 2, which expands previous diagram standards to include new functional interactions, categories of molecular species, support for families of glyph variants, and the ability to indicate modular structure and mappings between elements of a system. SBOL Visual 2 also clarifies a number of requirements and best practices, significantly expands the collection of glyphs available to describe genetic features, and can be readily applied using a wide variety of software tools, both general and bespoke.

Mechanistic kinetic models of biological pathways are an important tool for understanding biological systems. Constructing kinetic models requires fitting the parameters to experimental data. However, parameter fitting on these models is a non–convex, non–linear optimization problem. Many algorithms have been proposed to addressing optimization for parameter fitting including globally convergent, population–based algorithms. The computational complexity of the this optimization for even modest models means that parallelization is essential. Past approaches to parameter optimization have focused on parallelizing a particular algorithm. However, this requires re–implementing the algorithm usinga distributed computing framework, which requires a significant investment of time and effort. There are two major drawbacks of this approach: First, the choice of best algorithm may depend on the model. Given the large variety of optimization algorithms available, it is difficult to re–implement every potentially useful algorithm. Second, when new advances are made in a given optimization algorithm, the parallel implementation must be updated to take advantage of these advantages. Thus, there is a continual burden placed on the parallel implementation. The drawbacks of re–implementing algorithms lead us to a different approach to parallelizing parameter optimization. Instead of parallelizing the algorithms themselves, we run many instances of the algorithm on single cores. This provides great flexibility as to the choice of algorithms by allowing us to reuse previous implementations. Also, it does not require the creation and maintenance of parallel versions of optimization algorithms. This approach is known as the island method. To our knowledge, the utility of the island method for parameter fitting in systems biology has not been previously demonstrated. For the parameter fitting problem, we allow islands to exchange information about their “best” solutions so that all islands leverage the discoveries of the few. This turns out to be avery effective in practice, leading to super–linear speedups. That is, if a single processor finds the optimal value of parameters in time t, then N processors exchanging information in this way find the optimal value much faster than t/N. We show that the island method is able to consistently provide good speedups for these problems. We also benchmark the island method against a variety of large, challenging kinetic models and show that it is able to consistently improve the quality of fit in less time than a single–threaded implementation.Our software is available at https://github.com/sys-bio/sabaody under a Apache 2.0 license.Contactmailto:medjk@comcast.net

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

The Synthetic Biology Open Language (SBOL) is a standard that enables collaborative engineering of biological systems across different institutions and tools. SBOL is developed through careful consideration of recent synthetic biology trends, real use cases, and consensus among leading researchers in the field and members of commercial biotechnology enterprises. We demonstrate and discuss how a set of SBOL-enabled software tools can form an integrated, cross-organizational workflow to recapitulate the design of one of the largest published genetic circuits to date, a 4-input AND sensor. This design encompasses the structural components of the system, such as its DNA, RNA, small molecules, and proteins, as well as the interactions between these components that determine the system's behavior/function. The demonstrated workflow and resulting circuit design illustrate the utility of SBOL 2.0 in automating the exchange of structural and functional specifications for genetic parts, devices, and the biological systems in which they operate.

The explosive growth in semiconductor integrated circuits was made possible in large part by design automation software. The design and/or analysis of synthetic and natural circuits in living cells could be made more scalable using the same approach. We present a compiler which converts standard representations of chemical reaction networks and circuits into hardware configurations that can be used to simulate the network on specialized cytomorphic hardware. The compiler also creates circuit–level models of the target configuration, which enhances the versatility of the compiler and enables the validation of its functionality without physical experimentation with the hardware. We show that this compiler can translate networks comprised of mass–action kinetics, classic enzyme kinetics (Michaelis–Menten, Briggs–Haldane, and Botts–Morales formalisms), and genetic repressor kinetics, thereby allowing a large class of models to be transformed into a hardware representation. Rule–based models are particularly well–suited to this approach, as we demonstrate by compiling a MAP kinase model. Development of specialized hardware and software for simulating biological networks has the potential to enable the simulation of larger kinetic models than are currently feasible or allow the parallel simulation of many smaller networks with better performance than current simulation software., Author summary We present a “silicon compiler” that is capable of translating biochemical models encoded in the SBML standard into specialized analog cytomorphic hardware and transfer function–level simulations of such hardware. We show how the compiler and hardware address challenges in analog computing: 1) We ensure that the integration of errors due to the mismatch between analog circuit parameters does not become infinite over time but always remains finite via the use of total variables (the solution of the “divergence problem”); 2) We describe the compilation process through a series of examples using building blocks of biological networks, and show the results of compiling two SBML models from the literature: the Elowitz repressilator model and a rule–based model of a MAP kinase cascade. Source code for the compiler is available at https://doi.org/10.5281/zenodo.3948393.