Research Projects

India, as an agrarian economy, generates vast quantities of lignocellulosic biomass (LCB) such as wheat straw, rice husk, and bagasse. Despite its abundance, LCB remains underutilized due to persistent challenges including biomass recalcitrance, formation of fermentation inhibitors during pretreatment, and poor microbial performance under industrial high-solids conditions. These barriers hinder the economic viability of second-generation (2G) bioethanol and biorefinery systems. There is an urgent need for robust microbial cell factories, integrated co-product valorization, and data-driven process optimization strategies that can unlock the full potential of lignocellulosic feedstocks for sustainable bioenergy production. This project aims to address these gaps through a multi-pronged strategy combining strain development, multi-omics analyses, bioprocess optimization, and sustainability assessment. The central hypothesis is that evolved, multi-stress-tolerant yeast strains, when employed in high-gravity simultaneous saccharification and fermentation (hg-SSF) of pretreated LCB, can achieve commercially relevant 2G ethanol yields while enabling the recovery of high-value co-products. It is further hypothesized that using multi-omics analyses will identify molecular markers of stress tolerance for further strain improvement for better process control and scalability. To test these hypotheses, the project will pursue four main objectives: 1. Validate the biorefinery potential of adapted yeast strains under hg-SSF conditions using alkali-pretreated sugarcane bagasse and other regionally available residues. Fermentation performance will be evaluated in terms of ethanol yield, productivity, and inhibitor tolerance. Supervised machine learning models will be trained using process variables and fermentation outputs to predict system behavior under different operational scenarios. 2. Develop a residue-to-value framework by recovering co-products such as xylooligosaccharides from hemicellulose fractions and valorizing lignin-rich residues for potential use in packaging and UV-absorbing ingredients for cosmeceutical applications. The goal is to enhance overall biorefinery economics and reduce waste, aligning the system with circular bioeconomy principles. 3. Carry out integrated genomic, metabolomic and proteomic analyses of selected adapted and parental yeast strains to uncover key genes, regulatory pathways, and protein functions associated with multi-stress tolerance (inhibitors, high temperature, osmotic stress, and redox imbalance, etc). 4. Evaluate process integration and commercial feasibility through mass and energy balances, techno-economic analysis (TEA), and life cycle assessment (LCA). A machine learning-based sensitivity analysis will be employed to identify the most influential parameters affecting cost and environmental performance, supporting data-driven decision-making for scale-up. The experimental design includes advanced microbial screening under industrially relevant stressors, extensive fermentation trials, multi-omics profiling using next-generation sequencing and LC-MS/MS, and analytical techniques such as HPLC, FTIR, and NMR for substrate and product characterization. ML models will be built using supervised learning algorithms trained on fermentation data to achieve predictive process optimizations. If successful, this project will provide fundamental insights into yeast stress adaptation and practical tools for real-world biorefinery implementation. It will deliver a validated microbial platform for hg-SSF, enable integrated recovery of high-value co-products, and provide a modelling framework for performance prediction and sustainability assessment. Overall, this work will advance India’s capacity for 2G biofuel production, reduce agricultural residue burning, and contribute to global efforts toward low-carbon energy transitions.