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.

JCMB and JCRM are part of the JigCell suite available at http://jigcell.biol.vt.edu.

Abstract. Two parallel deterministic direct search algorithms are used to find improved parameters for a system of differential equations designed to simulate the cell cycle of budding yeast. Comparing the model simulation results to experimental data is difficult because most of the experimental data is qualitative rather than quantitative. An algorithm to convert simulation results to mutant phenotypes is presented. Vectors of parameters defining the differential equation model are rated by a discontinuous objective function. Parallel results on a 2200 processor supercomputer are presented for a global optimization algorithm, DIRECT, a local optimization algorithm, MADS, and a hybrid of the two.

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Theoretical molecular biologists attempt to describe cellular processes and regulatory networks with continuous and discrete mathematical models. Previous practice has been to develop models largely by hand and then to validate them primarily by comparing time-series plots versus the observed experimental results. The authors report their experiences in designing and building a modeling support environment for cell cycle models. They describe improvements to the development process for molecular network models by (a) identifying the key elements of the existing modeling process, (b) incorporating simulation methodology into a revised modeling process, and (c) building and testing software that supports the revised modeling process.