Background: Covalent modification cycles are widely used as regulatory switches. Results: Mathematical analysis reveals, under very general assumptions, an unavoidable trade-off between switching efficiency and cell-to-cell coherence. Conclusion: Enzyme bifunctionality offers a way to circumvent this trade-off. Significance: This may explain the bifunctionality of PFK-2/FBPase-2 in controlling the switch between glycolysis and gluconeogenesis in the mammalian liver.Covalent modification provides a mechanism for modulating molecular state and regulating physiology. A cycle of competing enzymes that add and remove a single modification can act as a molecular switch between "on" and "off" and has been widely studied as a core motif in systems biology. Here, we exploit the recently developed "linear framework" for time scale separation to determine the general principles of such switches. These methods are not limited to Michaelis-Menten assumptions, and our conclusions hold for enzymes whose mechanisms may be arbitrarily complicated. We show that switching efficiency improves with increasing irreversibility of the enzymes and that the on/off transition occurs when the ratio of enzyme levels reaches a value that depends only on the rate constants. Fluctuations in enzyme levels, which habitually occur due to cellular heterogeneity, can cause flipping back and forth between on and off, leading to incoherent mosaic behavior in tissues, that worsens as switching becomes sharper. This trade-off can be circumvented if enzyme levels are correlated. In particular, if the competing catalytic domains are on the same protein but do not influence each other, the resulting bifunctional enzyme can switch sharply while remaining coherent. In the mammalian liver, the switch between glycolysis and gluconeogenesis is regulated by the bifunctional 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase (PFK-2/ FBPase-2). We suggest that bifunctionality of PFK-2/FBPase-2 complements the metabolic zonation of the liver by ensuring coherent switching in response to insulin and glucagon.An enzyme-catalyzed modification cycle is illustrated in Fig. 1A. The forward enzyme, E, catalyzes the covalent addition of an M moiety (phosphoryl, methyl, acetyl, etc.), carried by the donor, D-M, to form the modified substrate, S 1 , from the unmodified substrate, S 0 . The reverse enzyme, F, catalyzes the removal of M, returning S 1 to S 0 . Background metabolic processes continually replenish D-M from D and M. The substrate, S, can be any molecule, protein or otherwise.Such cycles can function as biological switches, in which the proportion of S 1 at steady state can be varied from low ("off") to high ("on") by altering properties of the cycle, such as the enzyme levels (1, 2). They are regarded as core motifs in cellular information processing (3,4) and have been the subject of much analysis (5-9).Application of these results to specific biological examples has been hampered, however, by the universal assumption that the enzymes E and F follow the Mich...
Recent studies of active matter have stimulated interest in the driven self-assembly of complex structures. Phenomenological modeling of particular examples has yielded insight, but general thermodynamic principles unifying the rich diversity of behaviors observed have been elusive. Here, we study the stochastic search of a toy chemical space by a collection of reacting Brownian particles subject to periodic forcing. We observe the emergence of an adaptive resonance in the system matched to the drive frequency, and show that the increased work absorption by these resonant structures is key to their stabilization. Our findings are consistent with a recently proposed thermodynamic mechanism for far-from-equilibrium self-organization.
4 5 Massachusetts Institute of Technology. ( ) . The result follows by combining equations (1) and (2) and taking p=p(0), Pp=p(τ). ORCID iDsJeremy A Owen https:/ /orcid.org/0000-0002-9180-3794 Artemy Kolchinsky https:/ /orcid.org/0000-0002-3518-9208 References[1] Shiraishi N, Funo K and Saito K 2018 Speed limit for classical stochastic processes Phys. Rev. Lett. 121 070601 AbstractWe consider the problem of how to construct a physical process over a finite state space X that applies some desired conditional distribution P to initial states to produce final states. This problem arises often in the thermodynamics of computation and nonequilibrium statistical physics more generally (e.g. when designing processes to implement some desired computation, feedback controller, or Maxwell demon). It was previously known that some conditional distributions cannot be implemented using any master equation that involves just the states in X. However, here we show that any conditional distribution P can in fact be implemented-if additional 'hidden' states not in X are available. Moreover, we show that it is always possible to implement P in a thermodynamically reversible manner. We then investigate a novel cost of the physical resources needed to implement a given distribution P: the minimal number of hidden states needed to do so. We calculate this cost exactly for the special case where P represents a single-valued function, and provide an upper bound for the general case, in terms of the non-negative rank of P. These results show that having access to one extra binary degree of freedom, thus doubling the total number of states, is sufficient to implement any P with a master equation in a thermodynamically reversible way, if there are no constraints on the allowed form of the master equation. (Such constraints can greatly increase the minimal needed number of hidden states.) Our results also imply that for certain P that can be implemented without hidden states, having hidden states permits an implementation that generates less heat.where p(0) and p(1) are column vectors whose entries sum to one, representing probability distributions at times t=0 and t=1 respectively. If our system has n states, the matrix P is an n×n stochastic matrix, representing the conditional distribution of the system state at the final time given its state at the initial time. The entry P ij is the probability that the system is in state i at the final time given that it was in state j at the initial time.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.