Integrated Mathematical Oncology Faculty

Integrated Mathematical Oncology faculty consists of seven internationally renowned cancer researchers and mathematical modelers. The focus of all IMO research groups is to apply physical, mathematical and mechanical principles to cancer biology to decipher first order principles of tumor growth that can be exploited for novel cancer treatments.  

Anderson

Alexander Anderson, PhD – Chair

Understanding the intimate dialogue between cancer evolution and ecology is at the heart of research in my lab. Only by integrating mathematical and computational modeling approaches with experimental and clinical data can we better understand cancer growth and development and translate this understanding into novel therapies.

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Philipp AltrockPhilipp Altrock, PhD – Assistant Member

We apply theoretical evolutionary biology models, together with computational, bioinformatics and statistical approaches to cancer cell biology. Of particular interest to us is how the tumor microenvironment shapes selective pressures that play a fundamental role in the evolution of tumor progression and resistance to therapy.

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Noemi AndorNoemi Andor, PhD – Assistant Member

We 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 BasantaDavid Basanta, PhD – Associate Member

If we are going to understand and learn how to exert some control in cancer progression we need methods and approaches that can handle this evolutionary and ecological perspective of cancer. Given the complexity of this disease, mathematical and computational methods are not only useful but also necessary. For this reason, my work focuses on mathematical and computational models of cancer evolution. 

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Joel BrownJoel Brown, PhD – Senior Member

As member of the Cancer Biology and Evolution Group and the Department of Integrated Mathematical Oncology, my research and collaborations are multi-disciplinary. I have ongoing collaborations with Drs. Robert Gatenby and Sandy Alexander on applying ecological principles to understanding cancer. In terms of experimental work, I enjoy collaborative work on skin cancer cell culturing with Ken Tsai, competition experiments to explore the cost of resistance with Dr. Robert Gillies and his group, mouse model work on prostate cancer and the evolution of cancer resistance with Drs. Gatenby and Arig Ibrahim.

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Heiko EnderlingHeiko Enderling, PhD – Associate Member

My research is aimed at quantitative personalized medicine. We develop clinically motivated and experimentally calibrated quantitative models that are informable with patient-specific data for personalized treatment recommendations. In close collaboration with experimentalists and clinicians, mathematical models that are parameterized with experimental and clinical data can help estimate patient-specific disease dynamics and treatment success. This positions us at the forefront of the advent of ‘virtual trials’ that predict personalized improved treatment protocols. 

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Robert GatenbyRobert Gatenby, MD – Senior Member

Dr. Gatenby spearheaded the formation of a new program at Moffitt titled Integrative Mathematical Oncology (IMO). The IMO brings to the Cancer Center a cadre of applied mathematicians to collaborate with tumor biologists and clinical oncologists. The goal is to use the mathematics developed for other nonlinear dynamical systems to examine the physiology of a tumor incorporating factors such as phenotypic evolution, intracellular communication pathways and interactions with microenvironmental factors including therapies.

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Katarzyna RejniakKatarzyna 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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