Patil Group

The Patil group uses a combination of modelling, bioinformatics, and experimental approaches to study metabolic networks and how they are controlled.

Previous and current research

Regulation of metabolic network activity in response to environmental and genetic changes is fundamental to the survival and evolution of organisms. Disorders and malfunctions of metabolic networks are at the root of complex, systemic diseases such as diabetes and obesity. On the other hand, microbial metabolic capabilities are crucial for sustainable production of chemicals and pharmaceutical compounds of socio-economic importance. What are the thermodynamic and regulatory principles underlying the architecture and operation of metabolic networks? What are the mechanisms by which metabolic responses are linked to sensing and signalling networks? Biochemical principles dictating the metabolic phenotype are emerging through various genome-wide molecular abundance and interaction studies. At the scale of genome and evolutionary time span, mechanistic answers to these questions, however, have still remained largely elusive. A main goal of our group is to tackle these questions through a combination of modelling, bioinformatics and experimental approaches.

We develop in silico models and design algorithms for quantitatively predicting metabolic phenotypes given a certain genotype. These models exploit the principle of conservation of mass as well as our understanding of the biological objective functions underlying the network functionality. Several microbial metabolic engineering problems have been used by our group for successful in vivo testing of the in silico model-guided predictions. To further the predictive power of metabolic models, we are actively researching the integration of genomic, transcriptomic, proteomic and metabolomic information. This has led to the discovery of new regulatory principles and, in some cases, underlying mechanisms. For example, we have previously shown that the transcriptional regulation within a metabolic network is organised around perturbation-specific key metabolites crucial for adjusting the network state (see figure). Using such integrative data analysis approaches, we are also studying the human metabolic network, working towards the development of a framework for rationally designing clinical intervention strategies and diagnostics for type-2 diabetes.

Future Projects and Goals

Designing novel modelling strategies for incorporating non-linear regulatory constraints into genome-scale metabolic models will be a major goal of our future projects. Understanding of metabolic changes during development and adaptive evolution is another aspect that we wish to investigate in order to gain insight into the dynamic nature of metabolic network operation in these fundamental biological processes. To this end, we are actively seeking collaborative projects within EMBL and elsewhere.