Highlights:

Event:

Projects:

Large Scale Biological Network Reconstruction and Analysis for Identifying Therapeutic Targets for Alzheimer’s Disease: A Big Data Initiative

Funding Agency: Department of Science & Technology (DST), Govt. of India (Under DST-ICPS Data Science Cluster Research Program)

Duration: 3 Years Starting Date: April 2019

Cost: Rs. 60 K + (approx)

Principal Investigator: Swarup Roy

Description:

The volume of data is growing fast in bioinformatics research. The importance of ‘big data’ in biology is enormous as huge quantities of high-throughput experimental data are available in public domain and growing almost every day. DNA microarray provides a platform to analyze in-silico the expression levels of thousands of genes over different conditions, such as different environmental conditions or separate developmental stages of diseases, in turn helps in effective drug design. Gene regulatory networks (GRN) alterations underlie many anomalous conditions, such as cancer. Inferring GRN and their alterations from high-throughput microarray data is a fundamental but challenging task. Thus GRN inference offers a platform for investigations into data intensive sciences. GRNs can be merged with protein-protein interaction (PPI) networks to provide a more complete picture of cellular processes. It has been observed that central or hub genes are the influential genes and the cause of most of the diseases. Investigating genetic causes of neurodegenerative diseases like Alzheimer or Parkinson are challenging. This is because, instead of any single gene, a group of genes interact with each other causing perturbation in the molecular pathways which lead to such complex diseases. Identifying key genes responsible for the diseases may help in designing patient-specific drugs. Reconstruction of a disease regulatory network from expression data allows researchers to understand the molecular basis of complex traits and diseases, as well as the discovery of direct drug targets. By understanding the dynamics of these networks, one can shed light on the mechanisms of diseases and helps pharmacologically to identify potential drug targets.

To date, data obtained from numerous expression microarray experiments have been made publicly available. Several existing inference algorithms implemented by the original authors are also available online in R platform in open access forum such as Bioconductor. However they are not convenient for use by the non-programmers. In this proposal we intend to investigate a set of related issues in creating, comparing and analyzing large gene GRNs and PPI networks, with tens of thousands of nodes, up to genome-wide dimensions. We intend to develop an integrated web tool with a graphical user interface (GUI) and instruction manual for the tasks of inference, visualization of large scale GRNs. An integrated platform for performance evaluation of various inference methods along with inference and visualization is missing in state-of-the-art inference tools. We make the developed tool available to biomedical researchers with all the above facilities including frequent feature updates where user can choose the suitable algorithms for inference of large scale network.

This proposal seeks to extend our prior work by building large-scale gene regulatory networks using publicly available expression datasets generated from thousands of experiments in combination while addressing issues of scaling and stability. This will necessitate developing new and improved efficient algorithms for analyzing large-scale graphs. Large cellular networks will be built with heterogeneous nodes and edges and analysed using the graph algorithms we develop. Our final objective is to develop a user-friendly extensible software framework, along with a graphical user interface and thorough documentation, where existing inference algorithms implemented in R will be used in the background following good software development principles. The proposed research will provide high-valued software for the biomedical researcher and the student. We will develop algorithms that can efficiently build biological graphs from a large number of diverse inputs obtained from disparate experiments and sources. We will implement the proposed software in user friendly framework to be able to provide them in a convenient package for use by the biomedical community.

Team