An Omnigenic Hypothesis to Predict Human Gene Regulatory Networks

The project mainly focuses on the cellular behaviour and ways to predict human gene interactions. Using a method called Proteinfragment
Complementation Assays, we are able to compute the biochemical networks in living cells. The results of this project will
provide a novel and highly efficient way to establish hypotheses about the organization of cellular regulatory networks.
We will assign the scores for each drug onto the corresponding protein nodes of different human protein interaction networks.
Then, using a heat diffusion network propagation method, we will determine whether results reproduce known regulatory circuits,
such as signaling pathway. These scores will be applied to different directed and non-directed networks. We will also extract the
human network and assign weights to the 17,300 proteins and using the NAGA pipelines, we perform heat diffusion network
propagation. Mapping regulatory networks for each drug will be performed with the starting state of the network sets the edges to
values. Resulting networks will be tested for recovery of human disease-associated allergies.
This study will be the first to map human protein interactions and will help us in understanding how the environment and the
genome interact with each other.

Faculty Supervisor:

Stephen Michnick

Student:

Partner:

Vellore Institute of Technology

Discipline:

Engineering

Sector:

Pharmaceuticals; Biotechnology; Artificial Intelligence

University:

Université de Montréal

Program: