Tapahtumakalenteri

Drug resistance and consequent tumor relapse are major clinical challenges in cancer treatment. While pre-existing subpopulations of genetically distinct drug-resistant cells have been shown to contribute to this phenomenon, recent studies reveal the role of drug-induced cell-state switching in aggravating disease outcomes by unlocking phenotypic plasticity and heterogeneity. To overcome this challenge, it is essential to understand the emergent dynamics of underlying regulatory networks enabling such switching. I will present several examples from our work integrating computational modeling of regulatory networks, single-cell high-throughput data and experimental validation to better understand what trajectories cells take to evade targeted therapy as well as immunotherapy in different cancers. We present an in silico platform to rationally identify combinatorial therapies to minimize cancer aggressiveness.

Mohit is an Associate Professor in Bioengineering at Indian Institute of Science, Bangalore. He obtained his Bachelors and master's from IIT Kanpur and PhD from Rice University, all in Bioengineering. His group integrates multi-scale mathematical models, single-cell multi-omics analysis to understand the dynamics of phenotypic plasticity and heterogeneity driving cancer metastasis and drug resistance. He has served as the co-chair of Mathematical Oncology subgroup of Society for Mathematical Biology and is the current Editor-in-Chief of NPJ Systems Biology & Applications. He has received the 2022 Young Outstanding Alumnus Award of IIT Kanpur, the 2023 ICTP (International Center for Theoretical Physics) Prize and the 2024 Young Outstanding Engineering Alumnus Award, Rice University.

Host: Jing Tang (jing.tang@helsinki.fi), ONCOSYS