Maximum Entropy Production As a Framework for Understanding How Living Systems Evolve, Organize and Function

Monday, 15 December 2014
Joseph John Vallino1,2, Christopher K Algar2,3, Julie A Huber4,5 and Nuria Fernandez-Gonzalez6, (1)Ecosystems Ctr, Woods Hole, MA, United States, (2)Marine Biological Laboratory, Ecosystems Center, Woods Hole, MA, United States, (3)Marine Biological Laboratory, Woods Hole, MA, United States, (4)Marine Biological Laboratory, Josephine Bay Paul Center, Woods Hole, MA, United States, (5)Josephine Bay Paul Center, Woods Hole, MA, United States, (6)Brown University, Providence, RI, United States
The maximum entropy production (MEP) principle holds that non equilibrium systems with sufficient degrees of freedom will likely be found in a state that maximizes entropy production or, analogously, maximizes potential energy destruction rate. The theory does not distinguish between abiotic or biotic systems; however, we will show that systems that can coordinate function over time and/or space can potentially dissipate more free energy than purely Markovian processes (such as fire or a rock rolling down a hill) that only maximize instantaneous entropy production. Biological systems have the ability to store useful information acquired via evolution and curated by natural selection in genomic sequences that allow them to execute temporal strategies and coordinate function over space. For example, circadian rhythms allow phototrophs to “predict” that sun light will return and can orchestrate metabolic machinery appropriately before sunrise, which not only gives them a competitive advantage, but also increases the total entropy production rate compared to systems that lack such anticipatory control. Similarly, coordination over space, such a quorum sensing in microbial biofilms, can increase acquisition of spatially distributed resources and free energy and thereby enhance entropy production. In this talk we will develop a modeling framework to describe microbial biogeochemistry based on the MEP conjecture constrained by information and resource availability. Results from model simulations will be compared to laboratory experiments to demonstrate the usefulness of the MEP approach.