The next software revolution: programming biological cells

David Marr was a brilliant mathematician and neuroscientist, who died at a tragically young age, but had a huge impact on the field of computational neuroscience. Marr sought to understand a critically important process in the brain — vision — from an information processing perspective. His motivation was that the "how" is more likely to be understood by considering the problem that is being solved, rather than by only collecting more data about the various parts of the system. Marr's work almost single-handedly revolutionized thinking in neuroscience and challenged nearly every prevailing assumption and idea of the day.

A wry analogy of a how a biologist would attempt to repair a broken radio, which remains both a pertinent and highly entertaining description of prevailing biological science. The comparison was posed to convey the difficulty, or impossibility, of attempting to understand a complex system by cataloguing, removing and modifying parts, and hypothesizing causes and connections from knock-on effects. Lazebnik argues that such information alone is insufficient to explain and understand how a radio works, less still to repair one. Rather it is necessary, first, to understand what the object does (is for), and secondly, to understand the processes at work, which explain how the parts work together to produce a functioning radio.

Sydney Brenner was one of the titans of molecular biology, who won the Nobel Prize for Physiology and Medicine in 2002 together with Robert Horvitz and Sir John Sulston. In this perspective article, Dr. Brenner highlights the gap between data and understanding in biology, and offers a critique of systems biology as an approach to make sense of living systems. He argues that cells should be considered as molecular information processing systems, and offers his insight into how to organize biological information.

In this short but powerful perspective, Regev and Shapiro propose that "cells as computation" offers a much-needed abstraction for biomolecular systems.

Nicholson examines a prevailing and historical metaphor in biology: the machine concept of the organism, arguing that it is a failed metaphor and highlighting the risk of being drawn into treating analogies too literally as we seek explanations of living systems. While metaphors offer valuable tools in science to communicate our knowledge, or offer abstractions that allow us to make sense of experimental data, this article exposes how we can assign too much significance or meaning to them, which can distort our understanding. His arguments hold significance as we must confront the limitations of alternative frameworks or abstractions for cellular decision-making, such as computation. Ultimately it underscores that while one can say that cells carry out computation, this does not mean that it is a type of computation that bears any resemblance to our silicon-based devices.