Alexander 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.
Noemi 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.
Renee Brady-Nicholls, PhD - Assistant Member, Integrated Mathematical Oncology
My research is primarily focused on improving patient care using accessible, minimally-invasive biomarkers through the integration of mathematical modeling, with a special interest in improving outcomes for underrepresented minorities. To accomplish this, we develop predictive mathematical models of these biomarker dynamics to propose patient-tailored treatment strategies that maximize patient response and quality of life.
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 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 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.
Aleksandra Karolak, PhD - Assistant Member, Machine Learning
We use various computational approaches led by machine learning to enhance our understanding of cancer development, phenotypic progression, and variability in the response to treatment. Particularly, we are interested in how epigenetics, DNA structure, mutagenesis, protein modifications and interactomes can inform and support diagnosis, clinical decisions, as well as drug discovery and treatment optimization.
Kasia 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.
Ariosto S. Silva, PhD - Associate Member, Cancer Physiology
During my research career, I have focused on how to improve the survival of cancer patients through optimization of therapy. Through a long-term collaboration with the Moffitt Myeloma Working Group (MMWG), I have developed a combination of ex vivo chemosensitivity assay and computational model of therapy response, capable of extrapolating six days of experimental data into months of clinical response using fresh bone marrow samples from standard-of-care biopsies. As such, we have created patient/drug-specific mathematical models that, when parameterized by drug-specific pharmacokinetics and dosing schedule, yield predictions of clinical response. In addition, we have developed a systems biology pipeline to integrate these findings with multi-omics molecular data to infer mechanisms driving disease progression and the emergence of drug resistance.
Jeffrey West, PhD - Assistant Member, Integrated Mathematical Oncology
The goal of my research group is to aid in targeting treatment resistance by constructing mathematical models of 1) tumor evolution and heterogeneity and 2) evolutionary-minded treatment strategies, employing techniques such as agent-based modeling, dose-response convexity analysis (second-order effects), and evolutionary game theory. These methods are broadly applicable to many cancer types, but recent publications have focused on applications to breast and prostate cancer.