Integrated Mathematical Oncology Faculty

Alexander AndersonAlexander RA Anderson, PhD - Senior Member

The Anderson Lab is focused on integrating mathematical and computational modeling approaches with experimental and clinical data to better understand cancer growth and development and translate this understanding into novel therapies.

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Noemi Andor, PhD - Assistant Member

Noemi AndorWe develop and integrate algorithms to quantify the clonal composition of a tumor from different perspectives, including its genome, transcriptome or morphology. The goal is using these clone characteristics to inform how a tumor’s environment can be altered to favor certain tumor subclones over others. A first instance of this translates into testing the potential of a clone’s genomic instability as biomarker of its sensitivity to DNA damaging drugs.

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David Basanta

David Basanta Gutierrez, PhD - Associate Member

To understand the evolutionary dynamics of cancer using integrative approaches so that one day we will be able to exert some control on cancer progression. My work will focus on mathematical and computational models of cancer evolution.


Joel BrownJoel Brown, PhD - Senior Member

We apply theoretical evolutionary biology models, together with computational, bioinformatics and statistical approaches to cancer cell biology. I use a combination of mathematical and experimental approaches to understand how organisms interact with and shape their environments.


Heiko EnderlingHeiko Enderling, PhD – Associate Member

Dr. Enderling's research interests are focused on developing clinically and experimentally motivated and quantitative models of cell-cell interactions within a tumor as well as at the tumor-host interface. In particular, the work in his laboratory focuses on the role of cancer stem cells in tumor progression and treatment response, with the ultimate goal to improve patient-specific treatment design.

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Kasia RejniakKasia Rejniak, PhD - Associate Member

My lab's research is focused on understanding how the heterogeneous and dynamically changing tumor microenvironment can be harnessed to design more effective treatment protocols. In close collaboration with experimentalists and clinicians, we develop mathematical models of in silico organoids and micro-pharmacology based on tumor-specific histology and quantitative image analyses to predict tumor response to combined (chemo-, immuno- and targeted) therapies and to optimize their schedules. 

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