Computational systems biology is the domain of understanding complex biological phenomena by incorporating experimental observations and theoretical research. Computational Systems Biology has multiple applications, including cancer pathways, cell signalling networks, etc., for the understanding of properties like robustness, stability, and well-posedness. Our group focuses on comprehending biological systems at different levels, including transcriptomics, genomics, and pathways, by using multi-omics, machine learning, and mathematical modelling i.e. Ordinary and Partial differential Equations, Generative Adversarial Networks and RNA Sequencing. The studies under the umbrella of CSB help explore the dynamics and the control therapies against the biological systems.
This research is focused on multiscale modeling of cancer via coupling the macroscale (tissue level) dynamics to the microscale (sub-cellular level) molecular interactions. We have developed a macroscale model governed by a partial differential equations (PDEs). We consider the co-evolution of healthy and mutated cell lineages distributed into three compartments with stem, progenitor and mature cells, under homeostatic regulation.
Computational Systems Biology is a multidisciplinary domain which focuses on the holistic study of complex biological systems by using and developing efficient data structures, algorithms, visualization and modelling tools to understand the dynamics of system. In this regard, machine learning is to be used to develop pipelines and work flows for efficient preprocessing and analysis of omics data to identify cancer biomarkers which can yield important pathways crucial to cancer progression.