Lovers, enemies, friends aren’t the relationships confined to human or animal societies. Tiny beings like bacteria also react to different bacteria in their vicinity, and their growth and survival depend on the kind of relationship they have with the other bacteria residing near them. Two or more types of bacteria living together constitute a microbial community, and in such a community, division of labor occurs which help in the overall growth of community, efficient production of products or teams up to get rid of chemicals which are harmful to the growth of the community. Biotechnologists are trying to understand the relationships between different bacteria better so as to develop microbial communities through which one can enhance the production of desired products of human use. However, testing the interaction between thousands of different types of bacteria is a laborious task.

Dr Karthik Raman, an Associate Professor at IIT Madras and a core member of RBCDSAI, is a scientist whose interest lies in discerning the dynamics of microbial interactions to utilize them for biotechnological application. Recently, Dr Raman and a post-doctoral fellow in his group, Dr Maziya Ibrahim proposed a computational approach named CAMP(Co-culture/Community Analyses for Metabolite Production) which suggests how to bundle two or more bacteria together, forming a community that produces the desired products in maximum quantity. Given the significance of the work, it has been published in a prestigious international Computational and Structural Biotechnology Journal.

“ To understand compatibility and interactions based on growth between the members of a microbial community, we have introduced a computational analysis framework that evaluates all possible two-species communities generated from a given set of microbial species on single or multiple substrates to achieve optimal production of a target metabolite,” says Dr Raman.

The computational model works by using the information from genome-scale metabolic models of bacteria (GSMMs), which is information of the various protein interactions taking place in bacteria leading to the production of various metabolites. The researchers decided to test their computational approach for the production of an industrially important metabolite-lactate- whose derivatives are used as a flavoring agents, preservatives and also for tanning leather and dyeing wool. The team analyzed 1176 two-species combinations searching for a combination that produces lactate in maximum quantity. Based on the model, the researchers suggest, Lactibacillus casei ATCC 334 and Lactobacillus plantarum WCFS1 as the best pair for the production of lactate when substrates are glucose and xylose.

Next, the team carried out a study to find out the genes in these bacteria, which if deleted, can increase the production of lactate. The researchers used an in silico strain optimisation approach to find such genes. They found that if genes encoding acetate kinase, phosphate acetyltransferase, and fumarate reductase are deleted in these lactic acid producing bacteria then it can increase the production of lactate. The team tested the viability of some of the predicted Lactobacillus communities in the laboratory by collaborating with Prof. Guhan Jayaraman’s research group at IIT Madras.

“The experimental results obtained corroborate our computational findings. Out of six different lactic acid bacteria cocultures studied, the best coculture with higher lactate yield was L. casei and L. plantarum. (This work has not been published as yet). These results reinforce the potential of using genome-scale metabolic models for biotechnological applications,” says Dr Maziya Ibrahim, former Post-doctoral researcher at IIT Madras.

The research by the group is of significance to various industries and can help formulate a good strategy for the use of microbial co-cultures to production of desired biotechnological products.


Maziya Ibrahim, Karthik Raman


Maziya Ibrahim and Karthik Raman (2021), Two-species community design of lactic acid bacteria for optimal production of lactates. In Computational and Structural Biotechnology Journal


Genome-scale metabolic models, Constraint-based modelling, Metabolic engineering, Cross-feeding, Microbial consortia