Mathematical modelling of cancer cell evolution and plasticity

Area of Science:

• Oncology
• Computational Biology
• Evolutionary Biology

Background:

• Cancer cell evolution is complex, involving phenotypic plasticity.
• Understanding tumor dynamics requires integrating biological data with theoretical frameworks.

Purpose of the Study:

• To highlight the essential role of mathematical modeling in cancer research.
• To demonstrate how mathematical models aid in understanding tumor evolutionary dynamics and treatment resistance.
• To show how models can guide experimental design and clinical trials.

Main Methods:

• Review of mathematical modeling approaches in cancer research.
• In silico experimentation using mathematical models.
• Reconstruction of time-dependent tumor evolutionary dynamics from biological data.

Main Results:

• Mathematical models are crucial for reconstructing tumor evolutionary dynamics.
• Models facilitate in silico experimentation to test biological hypotheses.
• Models generate experimentally-testable predictions for phenotype evolution and treatment resistance.

Conclusions:

• Mathematical modeling is indispensable for deciphering cancer cell evolution and phenotypic plasticity.
• Models enable robust in silico experimentation and prediction generation.
• Mathematical models are vital for guiding future experimental and clinical research in oncology.