Paul Macklin's Math Cancer Lab Website

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Recent news

Friday, Sep 9th, 2016
Coarse-graining discrete cell cycle models: Introduction One observation that often goes underappreciated in computational biology discussions is that a computational model is often a model of a model of a model ... [read more]

Tuesday, Aug 30th, 2016
Some quick math to calculate numerical convergence rates: I find myself needing to re-derive this often enough that it’s worth jotting down for current and future students. Introduction A very common task in our ... [read more]

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Welcome to Dr. Macklin's Lab!

Paul Macklin is a mathematician and an assistant professor at the Center for Applied Molecular Medicine at the University of Southern California. Our lab works to develop and validate sophisticated models of cancer in individual patients. We work in tightly-integrated teams of clinicians, modelers, and biologists to develop computational tools that will one day help improve clinical planning.

Macklin is leading development of standardizations for computational and experimental model data, which will help computational modelers to share, extend, refine, and recombine cancer models into sophisticated cancer simulators. A novel component of this work is the digital cell line: an extensible, standardized representation of a cell line, its physical and behavioral characteristics (phenotype), and necessary microenvironmental conditions. A library of digital cell lines will allow modelers and experimentalists to more easily share insights and measurements on cancer and other cells, and incorporate these into simulations.

Macklin is also leading development of two open source 3-D simulation packages: BioFVM (finite volume method for biological problems) simulates diffusive transport of dozens of substrates in large 3-D tissues. PhysiCell (physics-based cell simulator) simulates multicellular systems of 5×106 or more cells in 3-D tissues. Both simulators make heavy use of vectorization and OpenMP for parallelization. They can efficiently simulate millions of mechanics-based cells in 3-D tissues with 5-10 diffusing substrates using quad-core desktop workstations, and more with single supercomputer compute nodes.