Upcoming meeting

The Mathematical Biology of Human Brain Cancer
April 20-22, 2009
Babson Executive Conference Center, Babson Park, MA


Despite heroic efforts by researchers and clinicians human brain tumors, particularly the most aggressive gliomas, remain a formidable clinical and translational research challenge.      There is an opportunity to advance translational brain tumor research, and improve clinical care of patients by increasing the use of tools developed for the study of complex, non-linear, dynamical systems and incorporating such approaches into biomedical models of disease.

Notable exceptions aside, there is too little cross talk among clinicians and researchers and their mathematical colleagues.   Interest in mathematical approaches, specifically bio-informatics approaches, is growing in part due to the complexity of the large data sets generated by genomic and proteomic tools and in part to a renewed emphasis on systems biology.  Bioinformatics, however represents only one mathematical approach that could be useful to biomedical researchers and clinicians. 

The time may be right for biological mathematicians and biomedical scientists to reach beyond the typical dismissals of mathematical modeling as too simplistic to be of use in biomedical problems.   Bringing mathematicians and tumor biologists together may create new opportunities to move experiments (and treatments) away from the traditional static and linear approaches to tumor biology.

Brain cancers present a particularly thorny clinical problem.  The reasons are numerous and well known.  The assertions below are offered to deliberately spark discussions about our current state of knowledge and to what extent we have and have not fully exploited the current state of that knowledge:

The JSMF-sponsored workshop, bringing together researchers and clinicians with expertise in brain tumor biology and mathematical biological approaches to disease, intends to initiate sustained efforts pursuing new research directions leading to better clinical management.   

Examples of questions it may be fruitful to explore:

Would mathematical approaches contribute to the efficient identification of therapeutic targets and better predict the biological responses of molecular therapies aimed at complex signaling pathways? 

What are the positive and/or negative influences of current therapeutic regimens on the tumor micro-environment?    Are we trading short term gains for long term losses by selecting for more aggressive phenotypes? 

How do the multiple cell lineages contributing to tumor cellular heterogeneity co-exist?   We often act as though each cell type represents a possible “unique target”.   The hope of finding the cellular Achilles heel of tumors led to the initial excitement over tumor stem cells as a target of intervention. 

Can we better predict the adaptive response of tumors (and the surrounding host environment) to interventions?   How do we account for the dynamic nature of tumor ecosystems across multiple scales?   How can we better predict the interaction between tumor dynamics and the effects of interventions?

Are there alternate methods for assessing response to therapy that could enhance our ability to optimize the efficacy of, albeit limited, therapies that already do exist?

How do (can) we determine which of the myriad genetic and molecular changes observed in tumors and in experimental models of tumors are of causal importance from a therapeutic perspective versus those changes that are “along for the ride” and while manipulatable, unlikely to achieve the desired therapeutic outcomes?