In cancer biology, there is a long history of utilizing quantitative models to understand tumor growth and behavior. More recently, models have been developed at various spatial and temporal scales to aid in pinpointing critical system components and parameters. From mechanistic models to machine learning approaches, there is an expanding interest in applying the tools of computational modeling to longstanding challenges in predicting cancer progression and therapeutic response.
While the field of cancer biology is now replete with genomic, cellular, patient data, it is often the case that data collection and modeling are performed as entirely separate activities. However, as a complex adaptive system, cancer exhibits numerous characteristics that challenge the bounds of human intuition. These properties include non-linear dynamics in progression, bifurcations in cell state, and adaptations and interactions among evolving subpopulations. Experimental design and preclinical testing are not often optimized to capture these behaviors. Furthermore, it is not feasible to exhaustively explore all possible parameter regimes in experimental settings. Thus, mathematical models provide an important tool to guide experimental efforts towards regimes that may be most informative and likely to yield clinical benefit. The effective integration of modeling approaches will be necessary to achieve an understanding of the underlying evolutionary and biological principles that govern cancer progression and treatment resistance.
Here we are interested in exploring how the integration of computational modelling with experimental and clinical studies is propelling new insights into cancer biology and therapy. We welcome submissions that address any aspect of computational modeling of cancer, including but not limited to modeling of: signaling and genetic networks, cell population interactions, clonal dynamics, growth/death/senescence dynamics, metabolism, therapeutic regimens, and adaptive therapy. We are also interested in articles that discuss methodologies for the effective development, standardization, testing, and resource sharing of computational models in cancer biology. We welcome original research articles, reviews, and perspective articles.
Dr. Amy Brock studies the roles of cell heterogeneity, plasticity and evolution in cancer progression and therapeutic response. Her lab develops novel technologies and experimental approaches rooted in the tools of systems biology. She is an Associate Professor in the Department of Biomedical Engineering at The University of Texas at Austin and affiliated with the Oden Institute Center for Computational Oncology. Dr. Brock’s contributions have been recognized with an AACR Breast Cancer Translational Research Award, SLAS Innovation Finalist Award, and the UT Austin Raymond F. Dawson Centennial Fellowship.
