TopoFilter: a MATLAB package for mechanistic model identification in systems biology.

BACKGROUND: To develop mechanistic dynamic models in systems biology, one often needs to identify all (or minimal) representations of the biological processes that are consistent with experimental data, out of a potentially large set of hypothetical mechanisms. However, a simple enumeration of all alternatives becomes quickly intractable when the number of model parameters grows. Selecting appropriate dynamic models out of a large ensemble of models, taking the uncertainty in our biological knowledge and in the experimental data into account, is therefore a key current problem in systems biology. RESULTS: The TopoFilter package addresses this problem in a heuristic and automated fashion by implementing the previously described topological filtering method for Bayesian model selection. It includes a core heuristic for searching the space of submodels of a parametrized model, coupled with a sampling-based exploration of the parameter space. Recent developments of the method allow to balance exhaustiveness and speed of the model space search, to efficiently re-sample parameters, to parallelize the search, and to use custom scoring functions. We use a theoretical example to motivate these features and then demonstrate TopoFilter's applicability for a yeast signaling network with more than 250'000 possible model structures. CONCLUSIONS: TopoFilter is a flexible software framework that makes Bayesian model selection and reduction efficient and scalable to network models of a complexity that represents contemporary problems in, for example, cell signaling. TopoFilter is open-source, available under the GPL-3.0 license at https://gitlab.com/csb.ethz/TopoFilter. It includes installation instructions, a quickstart guide, a description of all package options, and multiple examples.

Biologically sound formal model of Hsp70 heat induction.

A proper response to rapid environmental changes is essential for cell survival and requires efficient modifications in the pattern of gene expression. In this respect, a prominent example is Hsp70, a chaperone protein whose synthesis is dynamically regulated in stress conditions. In this paper, we expand a formal model of Hsp70 heat induction originally proposed in previous articles. To accurately capture various modes of heat shock effects, we not only introduce temperature dependencies in transcription to Hsp70 mRNA and in dissociation of transcriptional complexes, but we also derive a new formal expression for the temperature dependence in protein denaturation. We calibrate our model using comprehensive sets of both previously published experimental data and also biologically justified constraints. Interestingly, we obtain a biologically plausible temperature dependence of the transcriptional complex dissociation, despite the lack of biological constraints imposed in the calibration process. Finally, based on a sensitivity analysis of the model carried out in both deterministic and stochastic settings, we suggest that the regulation of the binding of transcriptional complexes plays a key role in Hsp70 induction upon heat shock. In conclusion, we provide a model that is able to capture the essential dynamics of the Hsp70 heat induction whilst being biologically sound in terms of temperature dependencies, description of protein denaturation and imposed calibration constraints.

Computational models of the JAK1/2-STAT1 signaling.

Despite a conceptually simple mechanism of signaling, the JAK-STAT pathway exhibits considerable behavioral complexity. Computational pathway models are tools to investigate in detail signaling process. They integrate well with experimental studies, helping to explain molecular dynamics and to state new hypotheses, most often about the structure of interactions. A relatively small amount of experimental data is available for a JAK1/2-STAT1 variant of the pathway, hence, only several computational models were developed. Here we review a dominant approach of kinetic modeling of the JAK1/2-STAT1 pathway, based on ordinary differential equations. We also give a brief overview of attempts to computationally infer topology of this pathway.

Modelling the efficacy of hyperthermia treatment.

Multimodal oncological strategies which combine chemotherapy or radiotherapy with hyperthermia, have a potential of improving the efficacy of the non-surgical methods of cancer treatment. Hyperthermia engages the heat-shock response (HSR) mechanism, the main component of which are heat-shock proteins. Cancer cells have already partially activated HSR, thereby hyperthermia may be more toxic to them relative to normal cells. On the other hand, HSR triggers thermotolerance, i.e. hyperthermia-treated cells show an impairment in their susceptibility to a subsequent heat-induced stress. This poses questions about efficacy and optimal strategy for anti-cancer therapy combined with hyperthermia treatment. To address these questions, we adapt our previous HSR model and propose its stochastic extension. We formalize the notion of a HSP-induced thermotolerance. Next, we estimate the intensity and the duration of the thermotolerance. Finally, we quantify the effect of a multimodal therapy based on hyperthermia and a cytotoxic effect of bortezomib, a clinically approved proteasome inhibitor. Consequently, we propose an optimal strategy for combining hyperthermia and proteasome inhibition modalities. In summary, by a mathematical analysis of HSR, we are able to support the common belief that the combination of cancer treatment strategies increases therapy efficacy.

Model-based selection of the robust JAK-STAT activation mechanism.

JAK-STAT pathway family is a principal signaling mechanism in eukaryotic cells. Evolutionary conserved roles of this mechanism include control over fundamental processes such as cell growth or apoptosis. Deregulation of the JAK-STAT signaling is frequently associated with cancerogenesis. JAK-STAT pathways become hyper-activated in many human tumors. Therefore, components of these pathways are an attractive target for drugs, which design requires as adequate models as possible. Although, in principle, JAK-STAT signaling is relatively simple, the ambiguities in a receptor activation prevent a clear explanation of the underlying molecular mechanism. Here, we compare four variants of a computational model of the JAK1/2-STAT1 signaling pathway. These variants capture known, basic discrepancies in the mechanism of activation of a cytokine receptor, in the context of all key components of the pathway. We carry out a comparative analysis using mass action kinetics. The investigated differences are so marginal that all models satisfy a goodness of fit criteria to the extent that the state of the art Bayesian model selection (BMS) method fails to significantly promote one model. Therefore, we comparatively investigate changes in a robustness of the JAK1/2-STAT1 pathway variants using the global sensitivity analysis method (GSA), complemented with the identifiability analysis (IA). Both BMS and GSA are used to analyze the models for the varying parameter values. We found out that, both BMS and GSA, narrowed down to the receptor activation component, slightly promote the least complex model. Further, insightful, comprehensive GSA, motivated by the concept of robustness, allowed us to show that the precise order of reactions of a ligand binding and a receptor dimerization is not as important as the on-membrane pre-assembly of the dimers in the absence of ligand. The main value of this work is an evaluation of the usefulness of different model selection methods in a frequently encountered, but not much discussed case of a model of a considerable size, which has several variants differing at peripheries. In such situation, all considered variants can reach nearly perfect agreement with respect to their numerical simulations results and, most often, the sufficient experimental data to test against is not available. We argue that in such an adverse setting, the GSA and IA, although not directly corresponding to the model selection problem, can be more informative than the representative, generalizability-based approaches to this task. An additional insight into how the responsibility for the network dynamics spreads among model parameters, enables more conscious, expert-mediated choice of the preferred model.

Tav4SB: integrating tools for analysis of kinetic models of biological systems.

BACKGROUND: Progress in the modeling of biological systems strongly relies on the availability of specialized computer-aided tools. To that end, the Taverna Workbench eases integration of software tools for life science research and provides a common workflow-based framework for computational experiments in Biology. RESULTS: The Taverna services for Systems Biology (Tav4SB) project provides a set of new Web service operations, which extend the functionality of the Taverna Workbench in a domain of systems biology. Tav4SB operations allow you to perform numerical simulations or model checking of, respectively, deterministic or stochastic semantics of biological models. On top of this functionality, Tav4SB enables the construction of high-level experiments. As an illustration of possibilities offered by our project we apply the multi-parameter sensitivity analysis. To visualize the results of model analysis a flexible plotting operation is provided as well. Tav4SB operations are executed in a simple grid environment, integrating heterogeneous software such as Mathematica, PRISM and SBML ODE Solver. The user guide, contact information, full documentation of available Web service operations, workflows and other additional resources can be found at the Tav4SB project's Web page: http://bioputer.mimuw.edu.pl/tav4sb/. CONCLUSIONS: The Tav4SB Web service provides a set of integrated tools in the domain for which Web-based applications are still not as widely available as for other areas of computational biology. Moreover, we extend the dedicated hardware base for computationally expensive task of simulating cellular models. Finally, we promote the standardization of models and experiments as well as accessibility and usability of remote services.