New players in the repertoire: Antigen-presenting cells, such as macrophages and dendritic cells, activate immunological T cells by presenting them with antigens bound by major histocompatibility complexes (MHCs). The proteasome typically processes these antigens, which include peptides derived from both self and microbial origins. Liepe et al. now report that, surprisingly, a large fraction of peptides bound to class I MHC on multiple human cell types are spliced together by the proteasome from two different fragments of the same protein. Such merged peptides might turn out to be useful in vaccine or cancer immunotherapy development. Science , this issue p. 354

Our understanding of most biological systems is in its infancy. Learning their structure and intricacies is fraught with challenges, and often side-stepped in favour of studying the function of different gene products in isolation from their physiological context. Constructing and inferring global mathematical models from experimental data is, however, central to systems biology. Different experimental setups provide different insights into such systems. Here we show how we can combine concepts from Bayesian inference and information theory in order to identify experiments that maximize the information content of the resulting data. This approach allows us to incorporate preliminary information; it is global and not constrained to some local neighbourhood in parameter space and it readily yields information on parameter robustness and confidence. Here we develop the theoretical framework and apply it to a range of exemplary problems that highlight how we can improve experimental investigations into the structure and dynamics of biological systems and their behavior. For most biological signalling and regulatory systems we still lack reliable mechanistic models. And where such models exist, e.g. in the form of differential equations, we typically have only rough estimates for the parameters that characterize the biochemical reactions. In order to improve our knowledge of such systems we require better estimates for these parameters and here we show how judicious choice of experiments, based on a combination of simulations and information theoretical analysis, can help us. Our approach builds on the available, frequently rudimentary information, and identifies which experimental set-up provides most additional information about all the parameters, or individual parameters. We will also consider the related but subtly different problem of which experiments need to be performed in order to decrease the uncertainty about the behaviour of the system under altered conditions. We develop the theoretical framework in the necessary detail before illustrating its use and applying it to the repressilator model, the regulation of Hes1 and signal transduction in the Akt pathway.

Immunoproteasomes are considered to be optimised to process Ags and to alter the peptide repertoire by generating a qualitatively different set of MHC class I epitopes. Whether the immunoproteasome at the biochemical level, influence the quality rather than the quantity of the immuno‐genic peptide pool is still unclear. Here, we quantified the cleavage‐site usage by human standard‐ and immunoproteasomes, and proteasomes from immuno‐subunit‐deficient mice, as well as the peptides generated from model polypeptides. We show in this study that the different proteasome isoforms can exert significant quantitative differences in the cleavage‐site usage and MHC class I restricted epitope production. However, independent of the proteasome isoform and substrates studied, no evidence was obtained for the abolishment of the specific cleavage‐site usage, or for differences in the quality of the peptides generated. Thus, we conclude that the observed differences in MHC class I restricted Ag presentation between standard‐ and immunoproteasomes are due to quantitative differences in the proteasome‐generated antigenic peptides.

Motivation: The growing field of systems biology has driven demand for flexible tools to model and simulate biological systems. Two established problems in the modeling of biological processes are model selection and the estimation of associated parameters. A number of statistical approaches, both frequentist and Bayesian, have been proposed to answer these questions. Results: Here we present a Python package, ABC-SysBio, that implements parameter inference and model selection for dynamical systems in an approximate Bayesian computation (ABC) framework. ABC-SysBio combines three algorithms: ABC rejection sampler, ABC SMC for parameter inference and ABC SMC for model selection. It is designed to work with models written in Systems Biology Markup Language (SBML). Deterministic and stochastic models can be analyzed in ABC-SysBio. Availability:http://abc-sysbio.sourceforge.net Contact:christopher.barnes@imperial.ac.uk; ttoni@imperial.ac.uk

Motivation: Mathematical modelling is central to systems and synthetic biology. Using simulations to calculate statistics or to explore parameter space is a common means for analysing these models and can be computationally intensive. However, in many cases, the simulations are easily parallelizable. Graphics processing units (GPUs) are capable of efficiently running highly parallel programs and outperform CPUs in terms of raw computing power. Despite their computational advantages, their adoption by the systems biology community is relatively slow, since differences in hardware architecture between GPUs and CPUs complicate the porting of existing code. Results: We present a Python package, cuda-sim, that provides highly parallelized algorithms for the repeated simulation of biochemical network models on NVIDIA CUDA GPUs. Algorithms are implemented for the three popular types of model formalisms: the LSODA algorithm for ODE integration, the Euler–Maruyama algorithm for SDE simulation and the Gillespie algorithm for MJP simulation. No knowledge of GPU computing is required from the user. Models can be specified in SBML format or provided as CUDA code. For running a large number of simulations in parallel, up to 360-fold decrease in simulation runtime is attained when compared to single CPU implementations. Availability:http://cuda-sim.sourceforge.net/ Contact:christopher.barnes@imperial.ac.uk; m.stumpf@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.