side Karthik Raman

Karthik Raman

Associate Professor

Metabolic network analysis Computational modelling Simulation of biological systems/networks Systems and synthetic biology Metabolic engineering (strain improvement through in silico modelling) High-performance GPGPU computing for systems biology

Dr. Karthik’s Ph.D. at IISc Bangalore involved the computational analysis of metabolic networks and protein-protein interaction networks in Mycobacterium tuberculosis, for the prediction of potential drug targets. Karthik’s post-doctoral research at the University of Zurich, Switzerland, involved the analysis of a complex space of signalling circuits in yeast as well as synthetic logic circuits, for their robustness and evolvability. His research interests are in the areas of computational systems biology and synthetic biology. Some pertinent research problems of interest are the reconstruction of complex biological networks from data on metabolites (metabolomics), RNA transcripts/microarrays (transcriptomics) and proteins (proteomics), and the integration of these data into biological network models. Karthik’s lab currently works on modelling and analysis of metabolic networks for applications in metabolic engineering.


Jan 20, 2021
How COVID-19 impacts population movement: A data-driven analysis to study population behavior during a pandemic
Scientific evidence available on the transmission of SARS-CoV-2, the virus that has caused the global outbreak of COVID-19, shows that the disease spreads through droplets launched from an infected person via coughing, sneezing, talking that land on a healthy person in close proximity (less than 6 feet). Epidemiological researchers have found social distancing measures to be very effective in containing the spread of virus in the absence of a proven cure or vaccine.

5 min read

Jan 13, 2021
Predicting Essential Genes through Network Approach: Deciphering basis of Life
A classic challenge in biology is to study the function of proteins. Of various functions, essential functions are very interesting, as they map to important indispensable genes in an organism. Experimentally identifying these genes is rather expensive and challenging. Computational predictions can help point in the right direction, to prioritise experiments. To date, experimental data are available for <100 organisms! On the other hand, sequencing data are available for 1000s of organisms, as also interactome (networks of interactions) data.

4 min read



Vimaladhasan Senthamizhan , Sunanda Subramaniam , Arjun Raghavan and more