Center for Computational Oncology

The last half-decade has seen an explosion in literature on mathematical and computational models of the invasion and growth of tumors in living tissue.

 What is especially intriguing is the progress toward patient-specific treatments made possible by new predictive computer simulations. The reasons for this awakening on the great potential of tumor growth models are multifaceted. Firstly, there is increasing consensus in the medical science community on the principal mechanisms leading to various cancer types.  

Secondly, progress in understanding the role of genetics in encoding proteins that form phenotypes and molecular alterations at the gene, cell, and tissue level over the last decade have lead to new families of models that could greatly increase our understanding of the origins and growth of cancer and on new therapies to combat it. These advances have lead to a flurry of new multiscale computational models that depict events at many spatial and temporal scales, from sub-cellular to cellular to tissue to organ levels. 

Thirdly, and perhaps most importantly, is the gradual emergence of predictive medical science, the body of knowledge rooted in mathematical statistics, probability theory, experimental science, and computing that addresses in depth the actual validity and, equivalently, the predictability of various models in the presence of uncertainties. This vital discipline has come to the forefront because the indispensable data needed to calibrate and validate tumor growth models have only recently become available.

Fourthly, the enormous technological and mathematical advances in high-performance computing, in big data analytics and data-intensive computing, in imaging technologies, and in new modeling paradigms, ranging from agent-based algorithms, phase-field models, models of stochastic systems, to molecular models, all have brought into play an arsenal of new tools that have great potential for developing realistic high-fidelity simulations of cancer cell behavior.

ICES has assembled a group of faculty, students, and postdocs working on tumor modeling that have produced results at the forefront of some of this work.  The Center for Computational Oncology is involved in active research in many of the foundations of modeling tumor growth and in accessing and employing relevant in vitro and in vivo data to calibrate and validate predictive models.   

Non-ICES Affliated Faculty and Staff include:

  • Yu Sheng Feng, UT San Antonio, San Antonio, Texas
  • David Fuentes, UT MD Anderson Cancer Center, Houston, Texas
  • Amy Brock, Brock Lab, at the University of Texas at Austin

CCO Website: