Metabolic function-based normalization improves transcriptome data-driven reduction of genome-scale metabolic models.

Genome-scale metabolic models (GEMs) are extensively used to simulate cell metabolism and predict cell phenotypes. GEMs can also be tailored to generate context-specific GEMs, using omics data integration approaches. To date, many integration approaches have been developed, however, each with specific pros and cons; and none of these algorithms systematically outperforms the others.

Exploring the evolution of biochemical models at the network level.

The evolution of biochemical models is difficult to track. At present, it is not possible to inspect the differences between model versions at the network level. Biochemical models are often constructed in a distributed, non-linear process: collaborators create model versions on different branches from novel information, model extensions, during curation and adaption.

Exploring the Metabolic Heterogeneity of Cancers: A Benchmark Study of Context-Specific Models.

Metabolic heterogeneity is a hallmark of cancer and can distinguish a normal phenotype from a cancer phenotype. In the systems biology domain, context-specific models facilitate extracting physiologically relevant information from high-quality data. Here, to utilize the heterogeneity of metabolic patterns to discover biomarkers of all cancers, we benchmarked thousands of context-specific models using well-established algorithms for the integration of omics data into the generic human metabolic model Recon3D.

GEMtractor: extracting views into genome-scale metabolic models.

Summary: Computational metabolic models typically encode for graphs of species, reactions and enzymes. Comparing genome-scale models through topological analysis of multipartite graphs is challenging. However, in many practical cases it is not necessary to compare the full networks.

Data Management in Computational Systems Biology: Exploring Standards, Tools, Databases, and Packaging Best Practices.

Computational systems biology involves integrating heterogeneous datasets in order to generate models. These models can assist with understanding and prediction of biological phenomena. Generating datasets and integrating them into models involves a wide range of scientific expertise.

Harmonizing semantic annotations for computational models in biology.

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.

Evolution of computational models in BioModels Database and the Physiome Model Repository.

Background: A useful model is one that is being (re)used. The development of a successful model does not finish with its publication. During reuse, models are being modified, i.

SED-ML web tools: generate, modify and export standard-compliant simulation studies.

Summary: The Simulation Experiment Description Markup Language (SED-ML) is a standardized format for exchanging simulation studies independently of software tools. We present the SED-ML Web Tools, an online application for creating, editing, simulating and validating SED-ML documents. The Web Tools implement all current SED-ML specifications and, thus, support complex modifications and co-simulation of models in SBML and CellML formats.

A fully featured COMBINE archive of a simulation study on syncytial mitotic cycles in embryos.

COMBINE archives are standardised containers for data files related to a simulation study in computational biology. This manuscript describes a fully featured archive of a previously published simulation study, including (i) the original publication, (ii) the model, (iii) the analyses, and (iv) metadata describing the files and their origin. With the archived data at hand, it is possible to reproduce the results of the original work.

COMODI: an ontology to characterise differences in versions of computational models in biology.

Background: Open model repositories provide ready-to-reuse computational models of biological systems. Models within those repositories evolve over time, leading to different model versions. Taken together, the underlying changes reflect a model's provenance and thus can give valuable insights into the studied biology.

Toward Community Standards and Software for Whole-Cell Modeling.

Objective: Whole-cell (WC) modeling is a promising tool for biological research, bioengineering, and medicine. However, substantial work remains to create accurate comprehensive models of complex cells.

Methods: We organized the 2015 Whole-Cell Modeling Summer School to teach WC modeling and evaluate the need for new WC modeling standards and software by recoding a recently published WC model in the Systems Biology Markup Language.

The Cardiac Electrophysiology Web Lab.

Computational modeling of cardiac cellular electrophysiology has a long history, and many models are now available for different species, cell types, and experimental preparations. This success brings with it a challenge: how do we assess and compare the underlying hypotheses and emergent behaviors so that we can choose a model as a suitable basis for a new study or to characterize how a particular model behaves in different scenarios? We have created an online resource for the characterization and comparison of electrophysiological cell models in a wide range of experimental scenarios. The details of the mathematical model (quantitative assumptions and hypotheses formulated as ordinary differential equations) are separated from the experimental protocol being simulated.

An algorithm to detect and communicate the differences in computational models describing biological systems.

Motivation: Repositories support the reuse of models and ensure transparency about results in publications linked to those models. With thousands of models available in repositories, such as the BioModels database or the Physiome Model Repository, a framework to track the differences between models and their versions is essential to compare and combine models. Difference detection not only allows users to study the history of models but also helps in the detection of errors and inconsistencies.

COMBINE archive and OMEX format: one file to share all information to reproduce a modeling project.

Background: With the ever increasing use of computational models in the biosciences, the need to share models and reproduce the results of published studies efficiently and easily is becoming more important. To this end, various standards have been proposed that can be used to describe models, simulations, data or other essential information in a consistent fashion. These constitute various separate components required to reproduce a given published scientific result.

Improving the reuse of computational models through version control.

Motivation: Only models that are accessible to researchers can be reused. As computational models evolve over time, a number of different but related versions of a model exist. Consequently, tools are required to manage not only well-curated models but also their associated versions.