AI Article Synopsis
- The paper explores how various network types can effectively model biological processes through two case studies.
- The first case involves a lattice-based model simulating nonvascular tumor growth, achieving a realistic three-layer structure of cell dynamics while validating its accuracy against a fractal analysis.
- The second case examines a directed network model, where growth is limited by the cost of adding connections, introducing a unique preferential attachment method that prioritizes nodes with fewer connections, and is linked to a food-web model in simulations.
Article Abstract
This paper analyses the adequacy of different types of networks in biological process modeling. The assumptions are sustained by two case studies. The first one is a lattice-based computer model to simulate the growth of nonvascular tumors with nutrient consumption constraints. The modeling solution is able to reproduce the classic three-layer structure familiar from multicellular spheroids: cell proliferation, quiescent and necrosis. The accuracy of this model is tested by comparing it to a fractal morphometric technique of two patterns, one of them obtained by simulation, the other developed in vitro. The second application is the growth of a directed network, in which the growth is constrained by the cost of adding links to the existing nodes. This is a new preferential attachment scheme, different from those specific for the construction of scale-free graphs, because its new nodes prefer to attach to existing nodes with lower degree. We relate this mechanism to a simple food-web model studied by simulations.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5686432 | PMC |
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