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Efficient protein synthesis depends on the availability of charged tRNA molecules. With 61 different codons, shifting the balance among the tRNA abundances can lead to large changes in the protein synthesis rate. Previous theoretical work has asked about the optimization of these abundances, and there is some evidence that regulatory mechanisms bring cells close to this optimum, on average. We formulate the tradeoff between the precision of control and the efficiency of synthesis, asking for the maximum entropy distribution of tRNA abundances consistent with a desired mean rate of protein synthesis. Our analysis, using data from E. coli, indicates that reasonable synthesis rates are consistent only with rather low entropies, so that the cells regulatory mechanisms must encode a large amount of information about the correct tRNA abundances.
Biological sensory systems react to changes in their surroundings. They are characterized by fast response and slow adaptation to varying environmental cues. Insofar as sensory adaptive systems map environmental changes to changes of their internal d
Due to the stochastic nature of biochemical processes, the copy number of any given type of molecule inside a living cell often exhibits large temporal fluctuations. Here, we develop analytic methods to investigate how the noise arising from a bursti
Genetically identical cells under the same environmental conditions can show strong variations in protein copy numbers due to inherently stochastic events in individual cells. We here develop a theoretical framework to address how variations in enzym
In this article, we study the kinetics of reversible ligand binding to receptors on a spherical cell surface using a self-consistent stochastic theory. Binding, dissociation, diffusion and rebinding of ligands are incorporated into the theory in a sy
Proteins are essential components of living systems, capable of performing a huge variety of tasks at the molecular level, such as recognition, signalling, copy, transport, ... The protein sequences realizing a given function may largely vary across