These sensors can increase their sensing accuracy by expending energy, overcoming the equilibrium sensing limit. To demonstrate the usefulness of their approach, Skinner and Dunkel applied it first to active sensors, such as those used by cells to monitor the concentrations of chemicals in their environment. Their new expression complements the recently introduced thermodynamic uncertainty relation, which also uses the waiting time distributions for transitions between coarse-grained nonequilibrium steady states to calculate the bound on entropy production. Through optimization techniques, they then use those statistics to calculate the lower bound for entropy production. The theory relies on measuring the variance of the time that the system spends in each of those two states. For a cow, this state can be as general as standing or sitting, for example. Rather than focusing on transitions between microscopic steady states, they use a coarse-grained approach to calculate how the system evolves between two metastates. In their model, Skinner and Dunkel consider a mesoscale system in contact with a heat bath. As such, they reflect, for example, the thermodynamic cost of living and hence determine the capacity of an organism to survive by sensing the environmental conditions and adapting to changes in its surroundings. Nonetheless, the rates at which energy is consumed and entropy is produced while sustaining life are limited. Life, however, is associated with processes such as growth, self-organization, maintenance, and aging that occur far beyond the linear response. Complementary facets of the second law are the necessity of energy dissipation in finite-time processes and the lack of dissipation in quasistatic systems, two effects that are recovered by linear response theory. As such, entropy production quantifies the “cost” of keeping a system in a nonequilibrium steady state. The measure that indicates the availability of thermal energy for mechanical work is entropy, which, according to the second law of thermodynamics, is produced at a positive rate by every irreversible process. As such, the model provides a route to directly measuring the extent to which a mesoscopic system is out of equilibrium.Īccording to classical thermodynamics, no system-not even an ideal frictionless one-can transform all its heat into work, which limits the efficiency of thermodynamics-based devices, such as heat engines. This parameter is the waiting time distribution of transitions between the so-called metastates-the coarse-grained, observable steady states of the system. To tackle this problem, Dominic Skinner and Jörn Dunkel of the Massachusetts Institute of Technology developed a mesoscopic model that defines a lower bound on energy consumption by using an experimentally accessible parameter. Recently, however, researchers have started to consider larger-mesoscopic and macroscopic-nonequilibrium systems, such as cells, tissues, and entire organisms, for which it is impossible to identify all of a system’s microscopic states. Prototypical examples of such systems are molecular motors or the receptors responsible for cellular sensing, in which transition rates between different well-defined steady states can be fully characterized. Specifically, an exact thermodynamic framework was developed, defining entropy production, applied work, and heat exchange in small nonequilibrium systems submerged in a thermal bath. Over the last three decades, statistical physics has gone from being able to describe systems in, and close to, equilibrium to being able to describe certain classes of far-from-equilibrium systems. They tested the model using data from a number of systems, including cows transitioning from standing up to lying down. APS/ Carin Cain Figure 1: Researchers have developed a model that can place a lower bound on the energy consumption of a nonequilibrium system using the waiting time distribution of transitions between its “metastates”-the coarse grained nonequilibrium steady states of the system.
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