Understanding how biological cells adapt and evolve

Living beings need to constantly sense their surroundings in order to get nutrients and escape from hazards or predators. A large amount of information is stored in every organism's DNA, such as in the genes, to help it adapt, and survive. However, very little of this is used at any particular point in time. The mechanism that selects which information is required and which is not for adaptation and survival is called the gene regulatory network. This network senses the present condition of cells and the external environment and adapts the cell response to allow a particular cell to grow and thrive.

Fig. 1: The simplified structure of a gene regulatory network. (Courtesy: Ajay Nair
, licensed under CC BY)

Ajay Nair, a researcher with the IITB-Monash Research Academy, is working on a project titled 'Modelling and Analysis of Gene Regulatory Networks', under the supervision of Prof Madhu Chetty, Prof Pramod Wangikar. Decoding this process, he says, has huge benefits for mankind. "To cite just two, it will enable us to make better medicines and therapeutics and also improve the industrial production of biochemical products such as bio-fuels".

The IITB-Monash Research Academy is a pioneering joint-venture research partnership between two leading institutions in India and Australia. It offers research scholars the opportunity to study for a dually-badged PhD from both IIT Bombay in India and Monash University in Australia. Students spend time in both countries over the course of their research, and many, like Ajay, work on projects that are strongly-interdisciplinary in nature and with an applied research focus.

Mathematical modelling is ubiquitous in Engineering and Physical Sciences, but not so in Biology. One of the long term objectives of computational biology is to create a mathematical model of a biological cell—the basic building block of a living organism. "However, mathematical modelling is a challenge in biology for a variety of reasons," explains Ajay. "Firstly, we lack knowledge of many fundamental processes, secondly, the processes involved are highly complex, and thirdly, they occur at a minuscule scale and at such a rapid rate that they are difficult to observe and measure."

Fig. 2: The prior biological knowledge is combined with high-throughput experimental data to predict more accurate gene regulatory networks efficiently (Courtesy: Ajay Nair, licensed under CC BY)

It is difficult to model the structure of the gene regulatory networks from the experimental data using known machine-learning approaches.

Explaining his work so far, Ajay says, "We are looking at how to efficiently include the knowledge that biologists have acquired over centuries of research with modern high throughput experimental data. Our work has helped to improve the accuracy as well as speed of predicting the gene regulatory networks so far. These results were accepted in leading conferences and journals in the area.

The results have relevance in many fields which require the elucidation of gene regulatory networks. For example Ajay's group is working on elucidation of gene regulatory networks in cyanobacteria, popularly known as blue-green algae. This will help make them more efficient at converting sunlight and atmospheric CO2 (carbon-dioxide) to useful components like bio-fuels. Another big application of this work is in the study of cancer.

Says Prof Murali Sastry, CEO, the IITB-Monash Research Academy, "The Academy was conceived as a unique model for how two leading, globally focused academic organisations can come together in the spirit of collaboration to deliver solutions and outcomes to grand challenge research questions facing industry and society."

Ajay Nair hopes that he can soon offer solutions to some of those questions.

Research scholar: Ajay Nair, IITB-Monash Research Academy

Project title: Modelling and analysis of gene regulatory networks

Supervisors: Prof Madhu Chetty, Prof Pramod Wangikar

Contact details: ajaynair@iitb.ac.in

This story was written by Mr Krishna Warrier based on inputs from the research student and IITB-Monash Research Academy. Copyright IITB-Monash Research Academy.