I am an applied mathematician, focusing my research on interdisciplinary multi-scale approaches to utilize mathematics to understand the underlying complexity of various biological and biomedical problems in medicine and in particular, cancer.
Currently, I am working on developing multiscale models of cancer growth and treatment protocols to study various optimum treatment strategies; eventually to devise much needed predictive patient specific multimodality treatment regimes. These mathematical and computational models can be very helpful in gaining valuable insights into the mechanisms and consequences of various complex intracellular and intercellular changes during and after therapy.
I am a member of Centre for Biomathematics, Biomathematics Group and Computational Mathematics Group. Some of my recent research projects include:
1) Multiscale modelling of glioma growth and treatments:
Gliomas, the most common primary brain tumours, are diffusive and highly invasive. The standard treatment for brain tumours consists of a combination of surgery, radiation therapy and chemotherapy. Currently, I am developing a multiscale mathematical model for glioma growth using several patient-specific information to assist its treatment planning and delivery.
2) Modelling the effects of tumour heterogeneities on tumour growth and treatment responses:
It is necessary to understand the tumour heterogeneities in order to study cancer progression and plan effective treatment strategies. A growing tumour can change its microenvironment in its own favour by suppressing anti‐tumour factors and producing excess growth factors. There is also increasing evidence to support the hypothesis that the tumour microenvironment plays an important role in conferring drug resistance, a major cause of relapse contributing to the incurability of cancer.
3) Modelling drug resistance and its implications in a cell-cycle phase specific chemotherapy:
The development of drug resistance by cancer cells continues to be a key impediment in the successful delivery of these multi-drug therapies. Recent studies have indicated that intra-tumoural heterogeneity has a significant role in driving resistance to chemotherapy in many human malignancies. Multiple factors, including the internal cell-cycle dynamics and external microenvironment contribute to the intra-tumoral heterogeneity. Our recent studies have indicated the role of slow-cycling tumour sub-populations in developing resistance to the conventional chemotherapeutic drug.
4) Modelling radiation bystander effects and its implications in clinical radiotherapy:
Radiation-induced bystander effects are defined as those biological effects expressed, after the irradiation, by cells that are not directly exposed to the radiation. As a consequence of these bystander signals, the affected cells may die or show chromosomal instability as well as further abnormalities. Consequently, the bystander effect has several important implications for radiation protection, radiotherapy and diagnostic radiology. Currently, I am developing a hybrid model incorporating the multiple effects of radiation and radiation-induced bystander effects.
5) Modelling intracellular signalling pathways involved in cancer progression:
Cancer is a heterogeneous disease often requiring complex alterations of a normal cell to drive it to malignancy and ultimately to a metastatic state. These alterations are largely due to aberrant expression of a set of genes or pathways such as p53 pathways and hypoxia pathways rather than a single gene. We have recently studied the effects of the miR-451-AMPK-mTOR pathway to study how up- or down-regulation of components in these pathways affects cell proliferation and migration.