School of Science, Engineering and Information Technology

Efficient optimisation methods for robust reconstruction of gene regulatory networks

Project Title:

Efficient optimisation methods for robust reconstruction of gene regulatory networks


A/Prof Madhusudan Chetty and A/Prof Adil Baghirov

Contact person and email address:

A/Prof Madhusudan Chetty,

A brief description of the project:

In the post-genomic era, holistic understanding of biological systems in all their complexity is critical in comprehending nature's choreography of life. Biological processes and systems can be abstracted as multi-layered networks interacting with each other to create a complete biological system. Understanding the interactions of genes plays a vital role in the analysis of complex biological systems. Gene regulatory networks (GRNs) are the most important organization layer within a cell. They represent the relationship among genes of a genome. Reconstructing the GRN of a genome is a crucial step in uncovering the complete biochemical networks of cells. A GRN helps in understanding interactions at the cellular level and has immense potential for application in genetic engineering. Moreover, knowledge about GRNs provides valuable evidence for the therapeutic studies of complex diseases. GRNs are large networks and their mathematical models usually contain thousands or tens of thousands of variables. Optimisation problems from these models are large scale and highly nonlinear. Despite a significant progress in this area, the development of accurate and efficient numerical algorithms for reconstructing GRNs is still an open problem. The aim of this proposal is to develop deterministic derivative-free optimisation algorithms for solving reconstruction of large scale GRNs problems using surrogate models and novel hybrid optimisation algorithms which will incorporate deterministic derivative free and heuristic evolutionary methodologies for learning of model parameters.