Burkitt’s lymphoma is a highly aggressive B-cell non-Hodgkin lymphoma that accounts for about half of all childhood cancers in areas of holoendemic malaria across Africa and Papau New Guinea. This rapidly growing malignancy has a cell doubling time of every 24-48 hours, suggesting that the tumor microenvironment plays an important role in tumor evolution and progression. To study the tumor microenvironment, we used Jupyter Notebook to implement a game theoretic, ordinary differential equation-based framework. We seek to understand the dynamics between lymphoma cells, fibroblasts, and macrophages using DifferentialEquations.jl. To this end, we parameterized our mathematical model using longitudinal cell-growth data from triple culture experiments with different initial conditions of each cell type, using MonteCarloProblem with Optim.jl and L1-Loss Regularization with PenaltyFunctions.jl. Julia’s platform provided a cohesive, easy to use environment, customizable to our specific problem, allowing us to easily estimate our model parameters simultaneously over 47 different growth conditions. From our parameter estimated, we could infer the biological interactions in the lymphoma tumor microenvironment. This will enable us to propose better detection and treatment strategies for lymphoma. Julia provided an integrated ecosystem which allowed us to use one single platform to create our model, estimate parameters from experimental data, and make predictions from in silico simulations.
