Biological networks & systems biology LAB

Many biological pathway databases lack important information of metabolic reactions like reaction stoichiometry, mass balance, subcellular location, etc. These considerations are very important if we need to infer the catabolic routes of metabolites in pathways. For this purpose, we develop genome-scale metabolic network models (GEMs) by annotation of gene, protein, reaction, metabolite and subcellular location information and formalize the reaction-metabolite network as a constraint-based optimization problem for the prediction of metabolic reaction fluxes which can inform regarding the utilization routes of metabolites and their biochemical purposes. This project focuses on development of novel, semi-automated metabolic reconstructions of such organisms for which no prior metabolic network is available.

The innate immune system provides the first line of defense against pathogens. The interaction of innate immune cell receptors with pathogen-specific molecular patterns triggers differentially-timed signal transduction responses. These responses activate specific genes (pathways), that control cytokine/chemokine production for attracting adaptive immune cells to the site of infection and/or production of antimicrobial peptides, complement factors. The signal transduction – transcriptional responses activated upon infection also reprogram metabolism in innate immune cells positively or negatively. In order to develop beneficial immunomodulatory treatment therapies or vaccines, understanding the link between the signal transduction-induced transcriptional response and changes in host immunometabolism is paramount. The aim of this project is to develop/apply machine – learning based models for prediction of transcription factor – target interactions using transcriptomics datasets thereby obtaining genome-scale transcriptional regulatory networks.

Innate immune cells remodel their metabolism for optimal adaptation to the environment when they experience microbial pathogens. Metabolic shifts are triggered within the immune cells to support either pro- or anti-inflammatory responses during infection. Moreover, diet, pathogen-derived metabolites and cytokines can influence these responses providing a scope for therapeutic interventions. Predicted regulatory networks along with differential transcriptomics changes will be used to computationally model the influence of transcriptional regulation on existing generic human metabolic models tailored for innate immune cell metabolic behavior. The regulation-reinforced metabolic network models of human innate immune cells will facilitate different metabolic flux analyses to explain the choice of different pathways that can promote survival and proliferation of innate immune cells during fungal infection.

Organisms co-existing in an environment compete with each other for accessing metabolites and thereby survival. In a disease context, this competition can lead to an imbalance in populations of organisms, availability of metabolic resources in the microenvironment and production of specific overflow metabolites. In this project, we are interested in developing interspecies metabolic competition models to identify alterations in metabolic pathways across species in a given disease context.