Thermodynamics of Duplication Thresholds in Synthetic Protocell Systems

Abstract

Understanding the thermodynamics of the duplication process is a fundamental step towards a comprehensive physical theory of biological systems. However, the immense complexity of real cells obscures the fundamental tensions between energy gradients and entropic contributions that underlie duplication. The study of synthetic, feasible systems reproducing part of the key ingredients of living entities but overcoming major sources of biological complexity is of great relevance to deepen the comprehension of the fundamental thermodynamic processes underlying life and its prevalence. In this paper an abstract—yet realistic—synthetic system made of small synthetic protocell aggregates is studied in detail. A fundamental relation between free energy and entropic gradients is derived for a general, non-equilibrium scenario, setting the thermodynamic conditions for the occurrence and prevalence of duplication phenomena. This relation sets explicitly how the energy gradients invested in creating and maintaining structural—and eventually, functional—elements of the system must always compensate the entropic gradients, whose contributions come from changes in the translational, configurational, and macrostate entropies, as well as from dissipation due to irreversible transitions. Work/energy relations are also derived, defining lower bounds on the energy required for the duplication event to take place. A specific example including real ternary emulsions is provided in order to grasp the orders of magnitude involved in the problem. It is found that the minimal work invested over the system to trigger a duplication event is around ~ 10 - 13 J , which results, in the case of duplication of all the vesicles contained in a liter of emulsion, in an amount of energy around ~ 1 kJ . Without aiming to describe a truly biological process of duplication, this theoretical contribution seeks to explicitly define and identify the key actors that participate in it.

Fate of Duplicated Neural Structures

“…A transition to life and Darwinian evolution [ 1 , 2 , 3 ] raises the cost–benefit stakes: matter patterns need to be thermodynamically favorable and win an evolutionary contest. Interestingly, the formalism of statistical physics remains effective to describe phenomenology across biology and cognition [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 31 , 32 , 33 , 34 , 35 , 36 ]. The later is enabled by expanded computational complexity and allows organisms to increasingly integrate and predict environmental information [ 2 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ].…”

“…As a consequence, structures within organisms often operate close to computational thermodynamic limits (e.g., of effective information processing and work extraction [ 2 , 4 , 5 ]). Despite the added layer of complexity, statistical physics remains a very apt language to describe some biological processes [ 6 , 7 , 8 , 9 , 10 ]: cell cycles can be studied as thermodynamic cycles [ 11 , 12 , 13 ], aspects of organisms might stem from an effective free energy minimization (i.e., yet another cost-benefit balance) [ 14 ], and variation along relevant dimensions (e.g., organism size vs metabolic load) determines the viability of key living structures, which can be abruptly terminated as in phase transitions [ 15 , 16 ]. An organism’s computational complexity, which enables its cognition, is another one such key dimension.…”

Decomposing information into copying versus transformation

“…Several directions for future work present themselves. First, there is a large range of practical and theoretical application of our measures, from analysis of biological and neural information transmission to the study of the thermodynamics of self-replication, a fundamental and challenging problem in biophysics [65].…”

Decomposing information into copying versus transformation

“…Several directions for future work present themselves. First, there is a large range of practical and theoretical application of our measures, ranging form analysis of biological and neural information transmission to the study of the thermodynamics of self-replication, a fundamental and challenging problem in biophysics [61]. Second, we suspect our measures of copy and transformation information have further connections to existing formal treatments in information theory, in particular ratedistortion theory [2], whose connections we started to explore here.…”