Research

Welcome to the Network Modeling Research Group – NETLAB

Our research interests are in the broad area of computational systems biology and bioinformatics. Specifically, we are interested in

  • network medicine and network modeling
  • multi-omic data integration
  • single cell and bulk omic data analysis,
  • the discovery of latent cancer driver mutations.

Our research focus is on how the interaction networks between proteins and genes are altered in diseases by using multi-omic data integration and computational techniques.  Ultimately, we aim to transform big multi-omic datasets into clinically interpretable knowledge.[

 

Discovery of Latent Drivers from Double Mutations in Pan-Cancer Data

Despite massive advancements in cancer genomics, to date driver mutations whose frequencies are low, and whose observable translational potential is minor have escaped identification. Yet, when paired with other mutations in cis, such ‘latent driver’ mutations can drive cancer. Here, we discover potential ‘latent driver’ double mutations. Our screening reveals tumor-type specific double mutations on the same gene which may promote tumorigenesis and alter the response to treatments. It also reveals that tumors having at least one double mutation pair may lead to changes in response to drugs.

Network-based Comparison of Cancer Drugs

Our research interests are in the broad area of computational systems biology and bioinformatics. Specifically, we are interested in

  • network medicine and network modeling
  • multi-omic data integration
  • single cell and bulk omic data analysis,
  • the discovery of latent cancer driver mutations.

Our research focus is on how the interaction networks between proteins and genes are altered in diseases by using multi-omic data integration and computational techniques.  Ultimately, we aim to transform big multi-omic datasets into clinically interpretable knowledge.[/vc_column_text][/vc_column][vc_column width=”1/2″][vc_column_text][/vc_column_text][vc_column_text]

Pseudo-Temporal Network Simulation

The main motivation of this project is to develop an integrative methodology that will simulate the accumulation of molecular signatures in cancer using graph theory, optimization and simulations together with informatics techniques. In addition, during this project, we aim to transform the already available large datasets to gain new clinically relevant insights and improve personalized medicine.

NDDs and cancer networks share pathways; but differ in mechanisms and signaling strength

NDDs and cancer have shared mutations, but their presentation and phenotypic damage differ. These mutations tend to be on the weaker side in terms of a driver effect. NDDs- and cancer-specific networks regulate common pathways with different signaling outcomes supporting our premise that NDD mutations offer modest but prolonged signaling, whereas cancer mutations are associated with high signaling levels. Despite the common signaling pathways, their regulation and differences in signal levels enhance different cell states: proliferation in cancer and differentiation in NDDs.