The main research focus of our lab is to move forward understanding of metabolic systems by applying mathematical modelling and data-driven artificial intelligence (AI) approaches. Manipulation of metabolism and other systems provides an opportunity to target cancer and other diseases, and also is important for biotechnological applications. To manipulate cellular systems effectively, it is crucial to identify the best set of proteins/genes and DNA regions or other players for targeting. However to gain such information one needs sufficiently to characterise the system requiring multiple molecular readouts from thousands of biological samples. Currently, we are heavily relying on molecular readouts from next-generation DNA/RNA sequencing technologies, proteomics and metabolomics, however, the existing major limitation is the complex nature of data generated by these technologies and the lack of computational approaches that are capable provide informative, interpretable and actionable information. To circumvent the problem of biological complexity, we are you developing self-taught deep learning approaches enabling interpreting and relating molecular data to complex biological phenotypes, such as gene regulation and cellular metabolism. In particular we are interested in understanding gene expression regulation and the interplay with translation where we apply state-of-the-art deep learning techniques for extraction of biological information directly from biological data.