A key challenge in modern biology is to understand how cellular behavior is controlled by an integrated network (genetic, signalling, metabolic and microRNA). This requires construction/design of networks based on inferences from available data on multiple properties and translating them into computational/mathematical models to study the dynamic state of the cell. We explore the structure and dynamics of complex biological networks using methods from systems biology, dynamical systems theory, and network theory to generate systems-level understanding and novel hypothesis regarding cellular decision making. The focus areas include regulation of cell growth, division, differentiation, and cell death. We also develop machine learning models for integrating molecular (omics data) and phenotypic data with a focus on the applications in medicine and drug discovery.

Nanomaterials are at the forefront of the emerging science and technology. We synthesize novel inorganic nanomaterials of controlled compositions and tunable conducting and magnetic properties and explore their applications in catalysis, biosensors, and detection and destruction of microbes and environmental pollutants.

Theoretical chemistry combines mathematical and computational techniques with the fundamental laws of physics and chemistry to characterize the properties of chemical and biochemical systems. Our research covers a broad range of topics including noise-induced nonlinear processes, quantum control of dynamics of molecules and quantum chemical calculations of organic and biomolecules and nanosystems. We develop novel equilibrium and non-equilibrium methods and algorithms to compute thermodynamic and kinetic properties of these systems. The applications of quantum computing in electronic structure theory and of computational electrodynamics in biosensor development are also explored.

Machine Learning methods have several prominent successes in various areas of scientific research in recent times, leading to an outlook that these methods will become significantly important research tools. During the past few years, artificial intelligence, sometimes referred to as the ‘fourth industrial revolution’ has been enabling innovative applications in various areas of fundamental sciences such as drug design, material design, retrosynthetic pathway prediction, molecule characterization, etc. Our center has taken a major lead in this area with several publications and projects.

Understanding the fundamental principles of biological systems at the systems level is one the main focuses of biology in the new era. Mathematical modeling and quantitative experimental techniques have fostered the investigation of such design principles. Our lab studies biological networks using combined theoretical and computational approaches in close collaboration with experiments, aiming to uncover underlying principles that govern their functioning and evolution. We apply techniques from physics and engineering ranging from dynamical systems, stochastic processes, information theory to explore different underlying features of biological networks such as robustness, optimality, adaptability etc.. Nowadays, due to availability of several genome scale high throughputs datasets, some of the predictions from theoretical models can be validated. We apply different machine learning techniques to analyze and integrate multiomics datasets. Furthermore, evolutionary algorithms are used to investigate in silico evolution of different biological networks.

Computational materials science exploits the power of high-performance scientific computing to better understand the atomic-level inner-workings of materials and to design improved materials for future applications in energy, environment, and medicine. Our current research is focused on the design and characterization of materials used in polymer-based ultrathin solid batteries for lightweight electronic applications and of cost-effective, naturally occurring layered inorganic materials for leakage-proof storage of various environmental pollutants including toxic compounds, nuclear contaminants, and greenhouse gases. We also investigate how complex biomolecular machines made of proteins and nucleic acids drive complex life processes in living organisms.

The elementary particles are the fundamental building blocks of matter that make up the universe. We investigate the nature of elementary particles and their interactions using the physics beyond the “Standard Model” and explore the theoretical basis for new discoveries made using high-energy particle accelerators, including the Large Hadron Collider (LHC).

Next generation sequencing technology has revolutionized biological research and there is need for developing tools and databases for the analysis of high-throughput data. We develop end-to-end solutions for sequence and structural variation analysis to investigate the role of genomic variants in cancer pathogenesis and in identifying diagnostic and prognostic biomarkers. We are also developing deep learning models for cancer classification and identifying biomarkers by integrating various omics and variation data, and in image analysis of chest X-rays and CT scans for detection of COVID-19. Graph theoretic approaches are now being successfully applied in understanding biological systems and in protein structure analysis and in understanding abiotic/biotic stress response in plants. Various web-based tools and databases have been developed in our group.