A generic framework for computational modelling and analysis of regulatory gene networks

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.