I am an assistant member at Moffitt Cancer Center in Tampa, FL. Before that, I was a research fellow at Harvard University. My background in science is theoretical physics. I have always been fascinated by the application of mathematics to better understand the laws of nature and medicine.

I am interested in the somatic evolution of cancer, dynamics of the hematopoietic system, epidemiology, population dynamics, evolutionary game theory, and population genetics. The current focus is on dynamics of healthy and diseased tissues, in particuar of the hematopoietic system, leukemias and lymphomas.

My lab is currently looking for talented postdocs interested in the biology of cancer and evolutionary dynamics, with a background in mathematics, computer science or physics, to complement our team at the department of integrated mathematical oncology (IMO). Qualified applicants with a quantitative background and the desire to work in close collaboration with experimental and clinical oncologists are encouraged to contact me: send a cover letter, a CV, and a publication list highlighting your individual contributions. Please have three letters of recommendation sent to me independently by your references.  

 

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Recent Publications

Evolutionary games on cycles with strong selection
Altrock, P.M., Traulsen, A. & Nowak, M.A., 2017. Evolutionary games on cycles with strong selection. Physical Review E , 95 , pp. 022407. Publisher's VersionAbstract

 

Evolutionary games on graphs describe how strategic interactions and population structure determine evolutionary success, quantified by the probability that a single mutant takes over a population. Graph structures, compared to the well-mixed case, can act as amplifiers or suppressors of selection by increasing or decreasing the fixation probability of a beneficial mutant. Properties of the associated mean fixation times can be more intricate, especially when selection is strong. The intuition is that fixation of a beneficial mutant happens fast in a dominance game, that fixation takes very long in a coexistence game, and that strong selection eliminates demographic noise. Here we show that these intuitions can be misleading in structured populations. We analyze mean fixation times on the cycle graph under strong frequency-dependent selection for two different microscopic evolutionary update rules (death-birth and birth-death). We establish exact analytical results for fixation times under strong selection and show that there are coexistence games in which fixation occurs in time polynomial in population size. Depending on the underlying game, we observe inherence of demographic noise even under strong selection if the process is driven by random death before selection for birth of an offspring (death-birth update). In contrast, if selection for an offspring occurs before random removal (birth-death update), then strong selection can remove demographic noise almost entirely.

 

The mathematics of cancer: integrating quantitative models
Altrock, P.M., Liu, L. & Michor, F., 2015. The mathematics of cancer: integrating quantitative models. Nature Reviews Cancer , 15 , pp. 730-745. Publisher's VersionAbstract

Mathematical modelling approaches have become increasingly abundant in cancer research. The complexity of cancer is well suited to quantitative approaches as it provides challenges and opportunities for new developments. In turn, mathematical modelling contributes to cancer research by helping to elucidate mechanisms and by providing quantitative predictions that can be validated. The recent expansion of quantitative models addresses many questions regarding tumour initiation, progression and metastases as well as intra-tumour heterogeneity, treatment responses and resistance. Mathematical models can complement experimental and clinical studies, but also challenge current paradigms, redefine our understanding of mechanisms driving tumorigenesis and shape future research in cancer biology.

Mathematical modeling of erythrocyte chimerism informs genetic intervention strategies for sickle cell diseaseMathematical modeling of erythrocyte chimerism informs genetic intervention strategies for sickle cell disease
Altrock, P.M., et al., 2016. Mathematical modeling of erythrocyte chimerism informs genetic intervention strategies for sickle cell diseaseMathematical modeling of erythrocyte chimerism informs genetic intervention strategies for sickle cell disease. American Journal of Hematology , 91 (9) , pp. 931-937. Publisher's VersionAbstract

Recent advances in gene therapy and genome-engineering technologies offer the opportunity to correct sickle cell disease (SCD), a heritable disorder caused by a point mutation in the β-globin gene. The developmental switch from fetal γ-globin to adult β-globin is governed in part by the transcription factor (TF) BCL11A. This TF has been proposed as a therapeutic target for reactivation of γ-globin and concomitant reduction of β-sickle globin. In this and other approaches, genetic alteration of a portion of the hematopoietic stem cell (HSC) compartment leads to a mixture of sickling and corrected red blood cells (RBCs) in periphery. To reverse the sickling phenotype, a certain proportion of corrected RBCs is necessary; the degree of HSC alteration required to achieve a desired fraction of corrected RBCs remains unknown. To address this issue, we developed a mathematical model describing aging and survival of sickle-susceptible and normal RBCs; the former can have a selective survival advantage leading to their overrepresentation. We identified the level of bone marrow chimerism required for successful stem cell-based gene therapies in SCD. Our findings were further informed using an experimental mouse model, where we transplanted mixtures of Berkeley SCD and normal murine bone marrow cells to establish chimeric grafts in murine hosts. Our integrative theoretical and experimental approach identifies the target frequency of HSC alterations required for effective treatment of sickling syndromes in humans. Our work replaces episodic observations of such target frequencies with a mathematical modeling framework that covers a large and continuous spectrum of chimerism conditions.

The cancer stem cell fraction in hierarchically organized tumors can be estimated using mathematical modeling and patient-specific treatment trajectories
Werner, B., et al., 2016. The cancer stem cell fraction in hierarchically organized tumors can be estimated using mathematical modeling and patient-specific treatment trajectories. Cancer Research , 76 , pp. 1705-1713. Publisher's VersionAbstract

Many tumors are hierarchically organized and driven by a sub-population of tumor initiating cells (TICs), or cancer stem cells. TICs are uniquely capable of recapitulating the tumor and are implied to be highly resistant to radio- and chemotherapy. Macroscopic patterns of tumor expansion before treatment and tumor regression during treatment are tied to the dynamics of TICs. Until now, quantitative information about the fraction of TICs from macroscopic tumor burden trajectories could not be inferred. In this study, we generated a quantitative method based on a mathematical model that describes hierarchically organized tumor dynamics and patient-derived tumor burden information. The method identifies two characteristic equilibrium TIC regimes during expansion and regression. We show that tumor expansion and regression curves can be leveraged to infer estimates of the TIC fraction in individual patients at detection and after continued therapy. Furthermore, our method is parameter-free; it solely requires knowledge of a patient's tumor burden over multiple time points to reveal microscopic properties of the malignancy. We demonstrate proof of concept in the case of chronic myeloid leukemia (CML), wherein our model recapitulated the clinical history of the disease in two independent patient cohorts. Based on patient-specific treatment responses in CML, we predict that after one year of targeted treatment, the fraction of TICs increases 100-fold and continues to increase up to 1000-fold after five years of treatment. Our novel framework may significantly influence the implementation of personalized treatment strategies and has the potential for rapid translation into the clinic.

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