+91-512-259-7629
+91-512-259-7629
Assistant Professor
skhanum[AT]iitk.ac.in
+91-512-679-2069 (Office)
FB-466 (Office)
Old Core Lab (TBD) (Lab)
Cellular metabolism is a complex process involving the consumption and production of metabolites, as well as the regulation of enzyme synthesis and activity. Modeling of metabolic processes is important to understand the underlying mechanisms, with a wide range of applications in metabolic engineering and health sciences. Computational kinetic models have proven to be valuable tools for understanding the guiding principles and uncovering novel mechanisms by capturing experimental data. However, modelling biological systems remains challenging due to unknown factors, complex regulatory processes, and difficulty quantifying interactions. While previous mathematical models rely on assumptions and incorporate known regulatory mechanisms only, the cybernetic modeling (kinetic model) approach addresses unknown control mechanisms by defining a biological goal that the system seeks to optimize and then mathematically formulating this goal.
Our research group focuses on developing mathematical models and machine learning algorithms to model the inflammatory response in mammalian cells, leveraging various concepts in chemical engineering, data science, and time series analysis. The study of inflammatory systems is fundamental to disease development, with many opportunities for chemical engineering contributions. Mathematical models for perturbations to cellular systems can improve our understanding of a cell’s function, provide insight into dysregulation in disease states, and help identify potential targets for drugs to achieve a favorable treatment outcome.
The mathematical frameworks, integrating mechanistic and machine learning models, adopt a cell-centric perspective that is pivotal for advancing our knowledge of biological complexity, as it supports developing experimental, quantitative, integrative, and predictive models of cellular function, behavior, and responses to perturbations. By focusing on cell lines and animal cells, our work aims to enable comprehensive analysis and modeling across diverse data types. Ultimately, combining experimental data with computational models will bridge the gap between cellular signaling and decision-making processes, providing new insights into how cells process and respond to environmental cues.