Machines of life: catalogue, stochastic process modeling, probabilistic reverse engineering and the PIs- from Aristotle to Alberts

TL;DR

This paper provides a broad, non-technical overview of stochastic modeling and statistical inference techniques for understanding molecular machines, emphasizing their energy transduction mechanisms and historical context.

Contribution

It introduces interdisciplinary strategies for modeling molecular machines' mechano-chemical processes and discusses model selection based on experimental data analysis, from a broad scientific perspective.

Findings

Different mechanisms of energy transduction in molecular machines are discussed.

Stochastic models help understand noisy power strokes versus Brownian ratchets.

Model selection techniques are highlighted for analyzing experimental data.

Abstract

Molecular machines consist of either a single protein or a macromolecular complex composed of protein and RNA molecules. Just like their macroscopic counterparts, each of these nano-machines has an engine that "transduces" input energy into an output form which is then utilized by its coupling to a transmission system for appropriate operations. The theory of heat engines, pioneered by Carnot, rests on the second law of equilibrium thermodynamics. However, the engines of molecular machines, operate under isothermal conditions far from thermodynamic equilibrium. Moreover, one of the possible mechanisms of energy transduction, popularized by Feynman and called Brownian ratchet, does not even have any macroscopic counterpart. But, {\it molecular machine is not synonymous with Brownian ratchet}; a large number of molecular machines actually execute a noisy power stroke, rather than operating as Brownian ratchet. The man-machine analogy, a topic of intense philosophical debate in which many leading philosophers like Aristotle and Descartes participated, was extended to similar analogies at the cellular and subcellular levels after the invention of optical microscope. The idea of molecular machine, pioneered by Marcelo Malpighi, has been pursued vigorously in the last fifty years. It has become a well established topic of current interdisciplinary research as evident from the publication of a very influential paper by Alberts towards the end of the twentieth century. Here we give a non-technical overview of the strategies for (a) stochastic modeling of mechano-chemical kinetic processes, and (b) model selection based on statistical inference drawn from analysis of experimental data. It is written for non-experts and from a broad perspective, showing overlapping concepts from several different branches of physics and from other areas of science and technology.