Biological Systems: Using a Modified Game Theory
Biological systems are very complex. We observe this mostly in ourselves, in our human bodies, from our internal reactions to illness displayed externally as symptoms, to our internal neural network and its ability to control varied behaviors. So many biological agents make up the human body, such as genes, proteins, lipids, enzymes, blood cells, antibodies, and the list goes on and on the more specific we choose to be. Because our bodies need to be able to survive in changing environments, internally and externally, we have adapted the ability to self-regulate via homeostatic mechanisms. It is this adaptation that prompts the question of how so many different agents can interact as to increase survival and maintain the body at a healthy steady-state? While examining the genetic network and specific biological system that controls cancer cell migration, Manceny et al. utilized a modified game theory to answer this very question. Biological agents were modeled as the players of the game, where the game was the global biological system of the body. The strategies were the possible interactions for the agent to choose to undergo. This is when classic game theory could not hold up as a model. As stated previously, there are so many different biological agents with multiple functions in the global network of the body, so having one game that accounts for all those players and all their different functions would be impossible. However, there are localities of interaction, where set of biological agents have key local interactions with each other. Manceny et al. translated this complexity into a modified game theory – game networks’ theory.
If we think of the body as a global network of smaller games, we can see how the many local games can sum up to total a global game network. This network is one where biological agents are playing simultaneous games between localities of tight interaction. Thanks to John Nash, the idea of Nash equilibria comes into play here to describe how the body can be at such a steady-state. If each local game is in Nash equilibria, the sum of all the games will be some global equilibria, where the global network is dependent on the equilibria of the localities. Thinking in terms of biological agents as part of the game network, if one local group of agents is not in equilibrium, then the local system is not gaining its largest payoff, thus resulting in a global game network that is not at its highest payoff. With biological systems, the highest payoff is survival, so anything less is harming that system. With this logic, Manceny et al. was able to use game networks’ theory to model the global biological system that regulates cancer cell migration using the local interaction networks of each step in the regulation. Based on that model, Manceny et al. was able to identify the key biological agent that would cause the lowest payoff for the system, and thus harm the process of cancer cell migration. It is amazing to see how game theory can be applied to such complex, molecular-level processes in a biological system. The ability to model a biological system and use local Nash equilibria, we may one day be able to find those key biological agents that regulate homeostasis and, in turn, human survival.
Article Used can be found here: http://www.sciencedirect.com.proxy.library.cornell.edu/science/article/pii/S1631069106001946