Project description
Predicting the structure and function of RNA molecules
Recent years have witnessed an explosion in the discovery of RNA molecules, namely microRNAs, small nucleolar RNAs, piRNAs and others, generating the need for robust methods capable of predicting their structure and function. To address this need, the EU-funded RNA-RAPSODY project proposes a mathematical framework that can integrate multiple biological and structural parameters of RNA molecules and generate meaningful biological results. Implementation of such an approach in science will provide important information about RNA molecules implicated in molecular and genetic processes of medical and pharmaceutical significance.
Objective
The proposed bioinformatics project has strong ties to even several diverse disciplines, namely computer science, molecular biology, genetics, mathematics and statistics. RNA Biology plays a central role in bioinformatics research such that numerous model-driven algorithms and methods are developed to predict and calculate structures and functions or compare sequences of RNA molecules that are essential in molecular and genetic processes and thus for medical and pharmaceutical applications.
Many of these problems can be cleanly phrased as optimization problems with 'optimal substructure' and thus solved exactly and efficiently by dynamic programming (for arbitrary parametrization of the objective function). This project will systematically explore the impact of parameter changes on the quality of results of DP optimization methods, such as predictions of molecule structures or comparison of sequences. Our approach strongly relies on algebraic dynamic programming (ADP), which decouples the decomposition of the search space from the algebra used to compute a final result. Thus, the ADP framework provides a unified setting and a generic implementation to quickly test working hypotheses. Here, it enables naturally implementing the suggested parametric optimization by developing novel algebras. Those include the polytope algebra, which allows to segment the parameter space based on its impact of the final prediction, and a formal derivative algebra, which allows to compute the derivative of ensemble predictions with respect to a given parameter. Conversely, those methods can be used to learn the optimal parameter sets based on a reference set of instances. This will result in deeper insights into robustness of the algorithms to changes of parameters or input data and hence, results can be assessed based on robustness measures and leads to the calculation of biologically more meaningful results.
The overall methodology will be applied to problems in RNA Bioinformatics.
Fields of science
Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
91128 Palaiseau Cedex
France