Philipp studied physics at the University of Leipzig, Germany. He minored in chemistry and mathematics and focused on theoretical physics. He gained first research experience in nonlinear dynamics and statistical mechanics at University of Leipzig, and went on to study evolutionary game theory, evolutionary dynamics, and population genetics. Philipp did his PhD project at the Max Planck Institute for Evolutionary Biology (MPI) in Germany, and received his PhD from University of Kiel, Germany in 2011.
From 2013 to 2017, Philipp was a research fellow at Harvard T. H. Chan School of Public Health. He worked at the Dana-Farber Cancer Institute and the Program for Evolutionary Dynamics of Harvard, funded by a grant from the German Academy of Sciences Leopoldina.
Since early 2017 Philipp has been a member of the Department of Integrated Mathematical Oncology (IMO) at Moffitt Cancer Center, with co-affiliations in Malignant Hematology and Bone Marrow Transplant and Cellular Immunotherapy.
Personal Research Statement
In my training I have gained profound expertise in modeling dynamical processes in evolving cell populations, with a particular focus on genetic disease and cancer. I have examined how complex cell-to-cell interactions shape the evolutionary outcomes, how diversity in hematopoietic stem cells shapes selection in peripheral blood, how cancer stem cell dynamics under therapy shape patient outcomes, and how co-evolution of cytokine producer and non-produce cells drive tumor growth. The insights gained in these studies are now used to better understand if and how external, as well as cell intrinsic factors drive Darwinian evolution in human tissues and tumors, and how these dynamic factors can be held accountable to quantify cancer emergence, major shifts in disease burden and tumor progression.
My current research investigates how diverse cancer cell populations are influenced by internal (genetic or epigenetic) and external (micro-environmental) changes. As these changes shape selective pressures, I seek to quantify cancer evolutionary dynamics using mathematical, computational, and statistical modeling. These analyses are based on clinical and experimental data. For predictions, I mainly use existing and further develop new methods for statistical inference and predictive modeling. Using these methods, I seek facilitate the search for cancer cures and help discover novel cancer prevention strategies.
In my lab, we use use predictive modeling to discover novel screening and treatment regimen against heterogeneous cancers. We seek to identify how interactions mediated by tumor-associated fibroblasts, macrophages, T cells, and inflammatory cytokine signals can be effective to shield cancer cells from suppression and treatment. We are also interested in how these factors can act as public goods and provide selective advantages to cancer cells, and thus drive progression and metastasis. We analyze single cell-resolution data to infer the complex determinants of disease progression in individual patients, and model disease progression and survival statistics.