title: Diffusion rate determines balance between extinction and proliferation in birth-death processes.
abstract: We here study spatially extended catalyst induced growth processes. This type of process exists in multiple domains of biology, ranging from ecology (nutrients and growth), through immunology (antigens and lymphocytes) to molecular biology (signaling molecules initiating signaling cascades). Such systems often exhibit an extinction-proliferation transition, where varying some parameters can lead to either extinction or survival of the reactants. When the stochasticity of the reactions, the presence of discrete reactants and their spatial distribution is incorporated into the analysis, a non-uniform reactant distribution emerges, even when all parameters are uniform in space. Using a combination of Monte Carlo simulation and percolation theory based estimations; the asymptotic behavior of such systems is studied. In all studied cases, it turns out that the overall survival of the reactant population in the long run is based on the size and shape of the reactant aggregates, their distribution in space and the reactant diffusion rate. We here show that for a large class of models, the reactant density is maximal at intermediate diffusion rates and low or zero at either very high or very low diffusion rates. We give multiple examples of such system and provide a generic explanation for this behavior. The set of models presented here provides a new insight on the population dynamics in chemical, biological and ecological systems.
authors.affiliation: Department of Mathematics, Bar Ilan University, Ramat Gan, Israel. hilla.behar2@gmail.com
authors.forename: Hilla
authors.initials: H
authors.lastname: Behar
authors.forename: Alexandra
authors.initials: A
authors.lastname: Agranovich
authors.forename: Yoram
authors.initials: Y
authors.lastname: Louzoun
mesh_terms: D000818:Animals; D006790:Host-Parasite Interactions; D006801:Humans; D055641:Mathematical Concepts; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D011157:Population Dynamics; D013269:Stochastic Processes; D049490:Systems Biology