Channel capacity of the ribosome

Translation is one of the most fundamental processes in the biological cell. Because of the central role that translation plays across all domains of life, the enzyme that carries out this process, the ribosome, is required to process information with high accuracy. This accuracy often approaches values near unity experimentally. In this paper, we model the ribosome as an information channel and demonstrate mathematically that this biological machine has information-processing capabilities that have not been recognized previously. In particular, we calculate bounds on the ribosome's theoretical Shannon capacity and numerically approximate this capacity. Finally, by incorporating estimates on the ribosome's operation time, we show that the ribosome operates at speeds safely below its capacity, allowing the ribosome to process information with an arbitrary degree of error. Our results show that the ribosome achieves a high accuracy in line with purely information-theoretic means.

Biological computation: hearts and flytraps

The original computers were people using algorithms to get mathematical results such as rocket trajectories. After the invention of the digital computer, brains have been widely understood through analogies with computers and now artificial neural networks, which have strengths and drawbacks. We define and examine a new kind of computation better adapted to biological systems, called biological computation, a natural adaptation of mechanistic physical computation. Nervous systems are of course biological computers, and we focus on some edge cases of biological computing, hearts and flytraps. The heart has about the computing power of a slug, and much of its computing happens outside of its forty thousand neurons. The flytrap has about the computing power of a lobster ganglion. This account advances fundamental debates in neuroscience by illustrating ways that classical computability theory can miss complexities of biology. By this reframing of computation, we make way for resolving the disconnect between human and machine learning.

The condensed chromatin fiber: an allosteric chemo-mechanical machine for signal transduction and genome processing

Allostery is a key concept of molecular biology which refers to the control of an enzyme activity by an effector molecule binding the enzyme at another site rather than the active site (allos = other in Greek). We revisit here allostery in the context of chromatin and argue that allosteric principles underlie and explain the functional architecture required for spacetime coordination of gene expression at all scales from DNA to the whole chromosome. We further suggest that this functional architecture is provided by the chromatin fiber itself. The structural, mechanical and topological features of the chromatin fiber endow chromosomes with a tunable signal transduction from specific (or nonspecific) effectors to specific (or nonspecific) active sites. Mechanical constraints can travel along the fiber all the better since the fiber is more compact and regular, which speaks in favor of the actual existence of the (so-called 30 nm) chromatin fiber. Chromatin fiber allostery reconciles both the physical and biochemical approaches of chromatin. We illustrate this view with two supporting specific examples. Moreover, from a methodological point of view, we suggest that the notion of chromatin fiber allostery is particularly relevant for systemic approaches. Finally we discuss the evolutionary power of allostery in the context of chromatin and its relation to modularity.