The Host-Pathogen Game: An Evolutionary Approach to Biological Competitions

• Main Author: Marco Alberto Javarone
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2018-08-01
• Series: Frontiers in Physics
• Subjects: evolutionary game theory; statistical physics; numerical simulations; agent-based modeling; biological modeling
• Online Access: https://www.frontiersin.org/article/10.3389/fphy.2018.00094/full

We introduce a model called Host-Pathogen game for studying biological competitions. Notably, we focus on the invasive dynamics of external agents, like bacteria, within a host organism. The former are mapped to a population of defectors that aim to spread in the extracellular medium of the host. In turn, the latter is composed of cells, mapped to a population of cooperators, that aim to kill pathogens. The cooperative behavior of cells is fundamental for the emergence of the living functions of the whole organism, since each one contributes to a specific set of tasks. So, broadly speaking, their contribution can be viewed as a form of energy. When bacteria are spatially close to a cell, the latter can use a fraction of its energy to remove them. On the other hand, when bacteria survive an attack, they absorb the received energy, becoming stronger and more resistant to further attacks. In addition, since bacteria play as defectors, their unique target is to increase their wealth, without supporting their own kind. As in many living organisms, the host temperature plays a relevant role in the host-pathogen equilibrium. For instance, in animals like human beings, a neural mechanism triggers the increasing of the body temperature in order to activate the immune system. Here, cooperators succeed once bacteria are completely removed while, in the opposite scenario, the host undergoes a deep invasive process, like a blood poisoning. Results of numerical simulations show that the dynamics of the proposed model allow to reach a variety of states. At a very high level of abstraction, some of these states seem to be similar to those that can be observed in some living systems. Therefore, to conclude, we deem that our model might be exploited for studying further biological phenomena.