A generic framework for computational modelling and analysis of regulatory gene networks applied to the response to wounding in arabidopsis

Plants are exposed environmental factors, many of which are detrimental, such as wounding and pathogen attack. Specific defensive responses to such challenges are critical to a plant's fitness and survival. The molecular mechanisms underlying the response to wounding, which is spatially structred into a local and a systemic response, and to other challenges have extensively been studied, and key pathways, mediated by signalling molecules including jasmonic acid, salicylic acid and ethylene, have been identified. These pathways are interlinked by crosstalk, mediated by components that participate in more than one pathway. The system that mediates defensive responses can be characterised as a regulatory gene network (RGN). Regulatory gene networks are generally a central biological mechanism of decoding genetic information that confers adaptive capabilities into phenotypic responses and other traits. RGNs are complex systems that cannot be fully understood by based either on straightforward inspection, and that can only partially be analysed mathematically. Computational modelling and analysis are tools for investigating and understanding such complex systems. Computational models of regulatory networks can be used in 'forward' simulations to generate synthetic gene expression profiles. Comparing these synthetic profiles to empirically measured gene expression data gives some indication how well a computational RGN model corresponds to the real RGN. However, discrepancies between synthetic and empirical profiles may have (at least) two causes, they may be due to an incorrect network structure, or the structure may be correct but numerical parameters (e.g. kinetic constants) were chosen incorrectly. In this project we will develop and use a statistsical approach to discriminate alternative RGN models based on the consistence of their synthetic profiles with a data set of empirical gene expression measurements. Effects resulting from parameterisation will be factored out by applying computational optimisation to find the best parameters for each of the candidate models. If this fit to the data is consistently better for one model than for an alternative one, the models are thus discriminated and the RGN structure that is more consistent with the data is identified. In the computational part of this project, a software system, called the model discrimination software platform (MDP), implementing this approach will be developed. The MDP will use transsys, a computational framework for RGN modelling. The experimental part of the project will produce a data set of gene expression measurements from various Arabidopsis mutants with altered wounding responses. The interdisciplinary project will use the MDP to produce comprehensive models of the RGNs organising the wounding response. These models will then be studied by computational simulations and analyses in order to investigate the role of crosstalk and the mechanisms by which RGNs organise the spatiotemporal structure of the defensive responses. Predictions and new hypotheses derived from these studies will be tested experimentally. This project will release MDP as an open source sofware system that is useful for RGN modelling in general, and contribute to the system-level understanding of the RGNs organising the plant wounding response.

Regulatory gene networks (RGNs) are a central mechanism of using genetic information to produce adaptive responses to environmental challenges. For a plant, it is critical to adequately respond to wounding. The wounding response has a distinct spatiotemporal structure consisting of a local and a systemic response. The transsys software framework, consists of a formal computer language which allows modelling regulatory gene networks as transsys programs. The framework provides application programming interfaces enabling generating synthetic time series of gene expression, use of transsys models as components for integrated simulations and to use optimisation to tune the numeric constants contained within a transsys model. Optimisation, e.g. gradient search and simulated annealing, can be used to parameterise the transsys program to best fit the data, keeping the program structure fixed. By applying this optimisation to transsys programs representing alternative RGN models, transsys programs for which optimisation gives results that are statistically significantly better than those for the other programs(s) can be found. Thus the RGN structure that is in best agreement with the data can be discriminated. We will develop the model discrimination platform (MDP) as an open source software system that implements this approach for general RGN modelling in a biologist-friendly way. Using microarrays we will produce a gene expression data set relevant to the wounding response, and apply the MDP to develop comprehensive models of the RGNs that organise the wounding response in Arabidopsis. We will carry out simulations with these models to investigate the role of crosstalk the generation of a spatiotemporally structured wounding response by RGNs. Predictions derived from computational simulations will be experimentally tested.