Biology subjects

Paul Macklin

Publications and source records attributed to Paul Macklin.

4 recordsLinked to original sources

bioRxiv2015

Estimating cell cycle model parameters using systems identification

A current challenge in data-driven mathematical modeling of cancer is identifying biologically-relevant parameters of mathematical models from sparse and often noisy experimental data of mixed types. We describe a cell cycle model and outline how to use the Optimization Toolbox in Matlab to estimate its timescale parameters, given flow cytometry and cell viability (synthetic) data, and illustrate the technique with simulated data. This technique can be similarly applied to a variety of cell cycle models, particularly as more laboratories begin to use high-content, quantitative cell screening and imaging platforms. An advanced version of this work (CellPD: cell line phenotype digitizer) will be released as open source in early 2016 at MultiCellDS.org.

Cancer Biology↗

bioRxiv2015

Simulating multi-substrate diffusive transport in 3-D tissues with BioFVM

To simulate the spatiotemporal distribution of chemical compounds, we present BioFVM, an open-source reaction-diffusion equation solver using finite volume methods with motivation for biological applications. With various numerical solvers, we can simulate the interaction of dozens of compounds, including growth substrates, drugs, and signaling compounds in 3-D tissues, with cells by treating them as various source/sink terms. BioFVM has linear computational cost scalings and demonstrates first-order accuracy in time and second-order accuracy in space. Beyond simulating the transport of drugs and growth substrates in tissues, the ability to simulate dozens of compounds should make 3-D simulations of multicellular secretomics feasible.

Cancer Biology↗

bioRxiv2015

Agent-based simulation of large tumors in 3-D microenvironments

Multicellular simulations of tumor growth in complex 3-D tissues, where data come from high content in vitro and bioengineered experiments, have gained significant attention by the cancer modeling community in recent years. Agent-based models are often selected for these problems because they can directly model and track cells states and their interactions with the microenvironment. We describe PhysiCell, a specific agent-based model that includes cell motion, cell cycling, and cell volume changes. The model has been performance tested on systems of 105 cells on desktop computers, and is expected to scale to 106 or more cells on single super-computer compute nodes. We plan an open source release of the software in early 2016 at PhysiCell.MathCancer.org

Cancer Biology↗

bioRxiv2015

Agent-based modeling of cancer stem cell driven solid tumor growth

Computational modeling of tumor growth has become an invaluable tool to simulate complex cell-cell interactions and emerging population-level dynamics. Agent-based models are commonly used to describe the behavior and interaction of individual cells in different environments. Behavioral rules can be informed and calibrated by in vitro assays, and emerging population-level dynamics may be validated with both in vitro and in vivo experiments. Here, we describe the design and implementation of a lattice-based agent-based model of cancer stem cell driven tumor growth.

Systems Biology↗