Project title: Towards an autonomous in silico researcher: Using logic modelling to automate the explanation of unexpected results
Summary: Continuous advances in high-throughput technologies have enabled the systematic and efficient collection of measurements from diverse biological experiments, such as genomics, transcriptomics and proteomics studies. The resulting wealth of available data has boosted the potential for scientific discoveries and enhanced our understanding of biological systems in human health and disease. While laboratory experiments and data acquisition are increasingly automated, making sense of the acquired results remains the responsibility of the researcher. This has shifted the bottleneck of scientific discovery from data generation towards the complex and laborious process of data interpretation.
The aim of this PhD is to devise and implement logic programming-based algorithms capable of automatically formulating hypotheses through the combination of experimental data with prior knowledge. This novel approach is going to mimic the laborious process of a researcher manually sifting through papers and databases, to identify and rationalise unexpected results. When applied to large biological datasets, these logic models are expected to bridge the existing gap between data acquisition and interpretation, massively accelerating scientific progress.