Sudeepto Bhattacharya

Network Measures for Chemical Library Design

Preclinical Research

Genetic Algorithms in Drug Design: A Not-So-Old Story in a Newer Bottle

The application of computational tools can alleviate the challenges (viz. time consumption and cost intensiveness) in drug design. Metaheuristics, a collection of such diverse computational and mathematical tools, whose first application was dated back to the early 1970s, caters to several expectations of rational drug designing. Among the population-based metaheuristics, genetic algorithms most closely mimic natural selection. As a consequence, they are rapidly garnering popularity over other members of evolutionary computation as the most preferred simulation processes for drug trials and designing. This chapter discusses various applications of genetic algorithms in drug design, from designing a combinatorial library, QSAR/QSPR study, and designing lead candidacy in drug discovery to solutions for genetic disease. Genetic algorithms find their place in all.

Comparative transcriptome meta-analysis of Arabidopsis thaliana under drought and cold stress

Multiple environmental stresses adversely affect plant growth and development. Plants under multiple stress condition trigger cascade of signals and show response unique to specific stress as well as shared responses, common to individual stresses. Here, we aim to identify common and unique genetic components during stress response mechanisms liable for cross-talk between stresses. Although drought and cold stress have been widely studied, insignificant information is available about how their combination affects plants. To that end, we performed meta-analysis and co-expression network comparison of drought and cold stress response in Arabidopsis thaliana by analyzing 390 microarray samples belonging to 29 microarray studies. We observed 6120 and 7079 DEGs (differentially expressed genes) under drought and cold stress respectively, using Rank Product methodology. Statistically, 28% (2890) DEGs were found to be common in both the stresses (i.e.; drought and cold stress) with most of them having similar expression pattern. Further, gene ontology-based enrichment analysis have identified shared biological processes and molecular mechanisms such as—‘photosynthesis’, ‘respiratory burst’, ‘response to hormone’, ‘signal transduction’, ‘metabolic process’, ‘response to water deprivation’, which were affected under cold and drought stress. Forty three transcription factor families were found to be expressed under both the stress conditions. Primarily, WRKY, NAC, MYB, AP2/ERF and bZIP transcription factor family genes were highly enriched in all genes sets and were found to regulate 56% of common genes expressed in drought and cold stress. Gene co-expression network analysis by WGCNA (weighted gene co-expression network analysis) revealed 21 and 16 highly inter-correlated gene modules with specific expression profiles under drought and cold stress respectively. Detection and analysis of gene modules shared between two stresses revealed the presence of four consensus gene modules.

Self-organized Bacterial Evolutionary Dynamics: Fractal Characteristics

In recent years, a new theory of the evolution of microbes under normal and stressed conditions has emerged, mainly in reference with the development of fractals and of self-organized mapping (SOM) concepts. This theory has much improved our understanding of the growth pattern of microbes like bacteria which determine their dynamics of evolution. In the first part of this paper, the main ideas in the theory of microbial self-organization are outlined, and some remarkable features of the resulting growth patterns are presented. In the second part, we apply conceptual tools developed in the context of fractal geometry to the study of the scaling properties of the bacterial growth in context with SOM patterns. We observe that such growth patterns appear to be more complex than simple fractals, although in some cases a simple fractal framework may be adequate for their description.

A Computational Approach for Designing Tiger Corridors in India

Wildlife corridors are components of landscapes, which facilitate the movement of organisms and processes between intact habitat areas, and thus provide connectivity between the habitats within the landscapes. Corridors are thus regions within a given landscape that connect fragmented habitat patches within the landscape. The major concern of designing corridors as a conservation strategy is primarily to counter, and to the extent possible, mitigate the effects of habitat fragmentation and loss on the biodiversity of the landscape, as well as support continuance of land use for essential local and global economic activities in the region of reference. In this paper, we use game theory, graph theory, membership functions and chain code algorithm to model and design a set of wildlife corridors with tiger (Panthera tigris tigris) as the focal species. We identify the parameters which would affect the tiger population in a landscape complex and using the presence of these identified parameters construct a graph using the habitat patches supporting tiger presence in the landscape complex as vertices and the possible paths between them as edges. The passage of tigers through the possible paths have been modelled as an Assurance game, with tigers as an individual player. The game is played recursively as the tiger passes through each grid considered for the model. The iteration causes the tiger to choose the most suitable path signifying the emergence of adaptability. As a formal explanation of the game, we model this interaction of tiger with the parameters as deterministic finite automata, whose transition function is obtained by the game payoff.

Innovative techniques to discover novel antimalarials

Malaria a global pandemic has engulfed nearly 0.63 million people globally. It is high time that a cure for malaria is required to stop its ever increasing menace. Our commentary discusses the advent and contribution of genetic algorithm (GA) in the drug discovery efforts towards developing cure for malaria. GAs are computational models of Darwinian evolution, ideally capture and mimic the principles of genetic variation and natural selection to evolve good solutions through multiple iterations on the space of all possible candidate solutions, called the search space, to a given optimization problem. Herein we will discuss the applications, advantages, disadvantages and future directions of GA with respect to malaria.