We present a Systems Biology Toolbox for the widely used general purpose mathematical software MATLAB. The toolbox offers systems biologists an open and extensible environment, in which to explore ideas, prototype and share new algorithms, and build applications for the analysis and simulation of biological and biochemical systems. Additionally it is well suited for educational purposes. The toolbox supports the Systems Biology Markup Language (SBML) by providing an interface for import and export of SBML models. In this way the toolbox connects nicely to other SBML-enabled modelling packages. Models are represented in an internal model format and can be described either by entering ordinary differential equations or, more intuitively, by entering biochemical reaction equations. The toolbox contains a large number of analysis methods, such as deterministic and stochastic simulation, parameter estimation, network identification, parameter sensitivity analysis and bifurcation analysis.

Centromeres attach chromosomes to the spindle during mitosis, thereby ensuring the equal distribution of chromosomes into daughter cells. Transcriptionally silent heterochromatin of unknown function is associated with centromeres in many organisms. In the fission yeast Schizosaccharomyces pombe, the silent mating-type loci, centromeres, and telomeres are assembled into silent heterochromatin-like domains. The Swi6 chromodomain protein affects this silencing, and now it is shown that Swi6p localizes with these three chromosomal regions. In cells lacking Swi6p, centromeres lag on the spindle during anaphase and chromosomes are lost at high rates. Thus, Swi6p is located at fission yeast centromeres and is required for their proper function.

The structural proteins of the cytoplasmic intermediate filaments (IFs) arise in the nematode Caenorhabditis elegans from eight reported genes and an additional three genes now identified in the complete genome. With the use of double-stranded RNA interference (RNAi) for all 11 C. elegans genes encoding cytoplasmic IF proteins, we observe phenotypes for the five genes A1, A2, A3, B1, and C2. These range from embryonic lethality (B1) and embryonic͞ larval lethality (A3) to larval lethality (A1 and A2) and a mild dumpy phenotype of adults (C2). Phenotypes A2 and A3 involve displaced body muscles and paralysis. They probably arise by reduction of hypodermal IFs that participate in the transmission of force from the muscle cells to the cuticle. The B1 phenotype has multiple morphogenetic defects, and the A1 phenotype is arrested at the L1 stage. Thus, at least four IF genes are essential for C. elegans development. Their RNAi phenotypes are lethal defects due to silencing of single IF genes. In contrast to C. elegans, no IF genes have been identified in the complete Drosophila genome, posing the question of how Drosophila can compensate for the lack of these proteins, which are essential in mammals and C. elegans. We speculate that the lack of IF proteins in Drosophila can be viewed as cytoskeletal alteration in which, for instance, stable microtubules, often arranged as bundles, substitute for cytoplasmic IFs.

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.

Technical possibilities are one aspect, but multi-centre trials and a consideration of the unsubstantiated fears among therapists of being replaced by machines will decide on the successful implementation of this most promising field to the benefit of patients.