Meet The Group
Below you'll find a short bio of each of the members of the Anderson lab.
Alexander R. A. Anderson (PI)
Chair of the Integrated Mathematical Oncology (IMO) department and Senior member at Moffitt Cancer Center. Dr. Anderson performed his doctoral work on hybrid mathematical models of nematode movement in heterogeneous environments at the Scottish Crop Research Institute in Dundee, UK. His postdoctoral work was on hybrid models of tumor-induced angiogenesis with Prof. Mark Chaplain at Bath University, UK. He moved back to Dundee in 1996 where he worked for the next 12 years on developing mathematical models of many different aspects of tumor progression and treatment, including anti-angiogenesis, radiotherapy, tumor invasion, evolution of aggressive phenotypes and the role of the microenvironment. He is widely recognized as one of only a handful of mathematical oncologists that develop truly integrative models that directly impact upon biological experimentation. His pioneering work using evolutionary hybrid cellular automata models has led to new insights into the role of the tumor microenvironment in driving tumor progression. Due to his belief in the crucial role of mathematical models in cancer research he moved his group to the Moffitt Cancer Center in 2008 to establish the Integrated Mathematical Oncology department.
Mark Robertson-Tessi
Mark is an Applied Research Scientist interested in tumor-immune interactions, phenotypic heterogeneity and selection, and cellular metabolism. He completed his doctoral work at the University of Arizona with Alain Goriely and Ardith El-Kareh, building a model of T-cell response to tumors subject to immunosuppression. His undergraduate work was in physics, mathematics, and astronomy. He joined the IMO department because of the rich opportunity for interdisciplinary work in oncology, and is eager to establish working relationships with colleagues in the experimental and clinical environments at Moffitt. Current projects include studying the effects of immunotherapies in lung cancer, understanding the genotype-to-metaphenotype map, and theoretical investigation of the evolution of T-cell receptor properties.
Jill A. Gallaher
Jill is a Research Scientist who joined the lab in December 2010. She has an B.S. in physics from the University of Missouri and a Ph. D. in biomedical physics from East Carolina University, where she worked with Martin Bier with developing mathematical models involving the cell membrane, ion traffic, and lipid thermodynamics. At the IMO, Jill is interested in quantifying tumor heterogeneity and its effect on treatment response, using evolutionary principles to guide treatment schedules, and integrating clinical data into models. She is specifically focused on improving treatment strategies and adaptive therapies in brain cancer and metastatic castrate resistant prostate cancer using spatial agent-based and multiscale models.
Chandler Gatenbee
Chandler received his Masters is Biological Anthropology at the University of Utah, followed by a Ph.D in Biology at the University of Louisville. He is a Research Scientist whose work is focused on tumor-immune eco-evolutionary dynamics. He uses population models, agent based models, and evolutionary game theory to study how tumors evolve the ability to escape immune predation, and the subsequent treatment implications. This is complemented by the development of tools to process and analyze histology. Ecological analyses can be conducted on the data collected by these tools, and the findings can be used to inform and verify modeling decisions and predictions, respectively. It is hoped that the integration of modeling and data will produce actionable insights that can be used treat, and ideally prevent, cancer.
Sandhya Prabhakaran
Sandhya is a Research Scientist interested in combining Machine Learning models with Deep Learning architectures and developing statistical theory, particularly to address problems in Cancer Biology and Computer Vision. Specifically, she is interested in understanding spatial relationships in the tumor-immune microenvironment using emerging multiplexed imaging technologies. These technologies will enable the precise interpretation of cellular states and the characterization of tumor-immune interactions in situ and at the single-cell level. Before joining Moffitt Cancer Center she was a Postdoctoral Research Scientist with Dr. Dana Pe’er at Memorial Sloan Kettering Cancer Center, NYC and at Columbia University in the City of New York. She received her Ph.D. from the Department of Mathematics and Computer Science, University of Basel (Switzerland) and her Masters in Intelligent Systems (Robotics) from School of Informatics, University of Edinburgh (Scotland).
Virginia Turati
Virginia is a Research Scientist who joined the lab in September 2020. Previously she was a postdoctoral researcher in the laboratory of Prof. Tariq Enver at UCL Cancer Institute in London and a PhD student at the University of Oxford. Her research interests are principally concerned with understanding the mechanisms by which tumors evolve over time, from pre-cancerous lesions to overt tumors and relapsing disease. Although her background is in wet lab biology, at Moffitt, she will deploy a more multidisciplinary approach. By combining experimental and mathematical modelling, she seeks to gain a better understanding of how inter- and intra-tumor heterogeneity shape tumor evolution and treatment response and dissect the intricate relationship between genotype and phenotype. Due to her unique multidisciplinary expertise she will be co-advised by both me and Andriy Marusyk.
M A Masud
M A Masud joined the lab in September 2023 as a Postdoctoral Research Fellow. He received his Ph. D. in Mathematics from Kyungpook National University, Daegu, South Korea. He is interested in exploring biological processes using mathematical modeling of dynamical systems. More specifically his research interest includes a data-driven understanding of tumor growth and treatment decision making. He will mostly be using differential equation-based approaches, data-fitting techniques, and agent-based models to develop treatment stratagies for pateients on evolutionary therapy trials.
Rafael Bravo
Rafael joined the lab as a Research Associate in September 2016. His focus is on designing efficient agent based models for biological research. As an undergraduate (Rutgers NB) he worked with David Axelrod on developing an agent based model of early progression of colon cancer. His Computer Science Master's thesis, mentored by Jefferey Schank, (UC Davis) explored the effects of added complexity on the evolution of an artificial life ecosystem. At the IMO, Rafael is working on creating a generalized framework to accelerate model development and unify several projects. He plans to continue collaborating and using his modeling skills to study cancer.
Paul Llamas
Paul joined the lab in October 2021 as an IMO programmer and will work closely with Mark Robertson-Tessi to assist in the designing and programming of models for the Evolution Tumor Board (ETB). Having graduated from Florida Polytechnic University in May 2021 with a BS in Computer Science, he enjoyed his experience at University in designing and creating UI’s and Applications in Java/Python. He is enthusiastic to be a part of an Moffitts pateint centric mission.
Kit Gallagher
Kit joined the lab in June 2022, as a mathematics PhD student. He is part of the Systems Approaches in Biomedical Science Doctoral Training Centre at the University of Oxford, UK, and is jointly supervised by me and Philip Maini. He previously studied for a Masters in Biophysics at the University of Cambridge, combining computational and theoretical approaches to explore the liquid crystal behaviour of DNA molecules. His primary interests are centered around improving treatment scheduling and patient outcome prediction using tools from population and agent-based modelling, statistical inference and deep learning methods. His current work focuses on treatment-resistant prostate and ovarian cancers, but develops themes applicable to a range of disease and treatment settings.