Progress Towards Computational 3-D Multicellular Systems Biology

Abstract

Tumors cannot be understood in isolation from their microenvironment. Tumor and stromal cells change phenotype based upon biochemical and biophysical inputs from their surroundings, even as they interact with and remodel the microenvironment. Cancer should be investigated as an adaptive, multicellular system in a dynamical microenvironment. Computational modeling offers the potential to detangle this complex system, but the modeling platform must ideally account for tumor heterogeneity, substrate and signaling factor biotransport, cell and tissue biophysics, tissue and vascular remodeling, microvascular and interstitial flow, and links between all these sub-systems. Such a platform should leverage high-throughput experimental data, while using open data standards for reproducibility. In this chapter, we review advances by our groups in these key areas, particularly in advanced models of tissue mechanics and interstitial flow, open source simulation software, high-throughput phenotypic screening, and multicellular data standards. In the future, we expect a transformation of computational cancer biology from individual groups modeling isolated parts of cancer, to coalitions of groups combining compatible tools to simulate the 3-D multicellular systems biology of cancer tissues.

Impact of force function formulations on the numerical simulation of centre-based models

“…For the piecewise quadratic force, the minimization was done jointly over µ R and the ratio m. The adhesive spring stiffness was then chosen as µ A = m × µ R . The values for r R and α where chosen according to Equations (12) and (14). Their values are stated here rounded to three decimals.…”

“…Discrete cell-based models -independent of being on-or off-lattice -can be coupled to PDE models for simulating the concentration of chemical compounds in the cellular environment or even an ODE model for simulating intracellular dynamics [13,14,15]. An extensive review of cell-based models for general tissue mechanics can be found in [7].…”

“…(d) A fixed spring stiffness value µ R = 8.0 was chosen and different ratios m. In contrast to the cubic and GLS force, the PWQ force allows for varying the repulsive and adhesive spring stiffness parameter independently within a certain range. (e-f) Intensity of the GLS force with (e) different spring stiffness values µ and (f) µ = 2.0 and different values of the bredth α.Here, for the largest α, as well as in (e) α is chosen according to Equation(14).…”

High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow

“…4 Lawrence J. Ellison Center for Transformative Medicine, University of Southern California, Los Angeles, CA USA. 5 Intelligent Systems Engineering, Indiana University, Bloomington, IN USA. Figure 1 Sample 3-D cancer-immune simulation.…”

“…Cancerhost interactions dominate throughout these scales, including interactions between tumor cells and the vas-culature (hypoxic tumor cells trigger growth of new blood vessels; new but dysfunctional blood vessels supply further growth substrates and can promote metastasis), between tumor cells and stromal cells (tumor cells can prompt tissue remodeling that facilitates tissue invasion), and between tumor cells and the immune system (immune cells can kill tumor cells, but tumor cells can co-opt inflammation to promote their survival). See the reviews in [1,2,3,4,5,6]. When designing and evaluating new cancer treatments, it is imperative to consider the impact on this complex multiscale cancer-host system.…”