Is the cell really a machine?
Authors: Daniel J Nicholson
Journal: Journal of Theoretical Biology (2019)
Discipline: BIOLOGY & BIOCHEMISTRY
Keywords: Brownian ratchet; Non-genetic heterogeneity; Pleomorphic ensembles; Self-organization; Stochasticity.
Affiliations: Konrad Lorenz Institute for Evolution and Cognition Research, Martinstraße 12, Klosterneuburg, 3400, Austria; Centre for the Study of Life Sciences (Egenis), University of Exeter, Byrne House, St. German's Road, Exeter, EX4 4PJ, United Kingdom. Electronic address: .

Abstract:  It has become customary to conceptualize the living cell as an intricate piece of machinery, different to a man-made machine only in terms of its superior complexity. This familiar understanding grounds the conviction that a cell's organization can be explained reductionistically, as well as the idea that its molecular pathways can be construed as deterministic circuits. The machine conception of the cell owes a great deal of its success to the methods traditionally used in molecular biology. However, the recent introduction of novel experimental techniques capable of tracking individual molecules within cells in real time is leading to the rapid accumulation of data that are inconsistent with an engineering view of the cell. This paper examines four major domains of current research in which the challenges to the machine conception of the cell are particularly pronounced: cellular architecture, protein complexes, intracellular transport, and cellular behaviour. It argues that a new theoretical understanding of the cell is emerging from the study of these phenomena which emphasizes the dynamic, self-organizing nature of its constitution, the fluidity and plasticity of its components, and the stochasticity and non-linearity of its underlying processes. 

Retracted: No

PubPeer comments:
 This is a very interesting theoretical paper, which questions the standard view of the cell as a machine. This is a not a new angle, of course, but this paper discusses a rich body of recent experimental literature to support its analysis. First, by u201cthe cell as a machineu201d, the author refers to the view of the cell as a set of mechanisms, with fixed parts interacting essentially deterministically with each other, as in a factory chain. Although all molecular biologists would acknowledge that there is randomness in the cell, randomness is always seen as u201cnoiseu201d, that is, as a departure from the ideal working of the cell. Essentially, the cell is conceptualized as a set of u201cmolecular machinesu201d, which are microscopic versions of our macroscopic machines. The theoretical starting point is that the world of molecules has little in common with our macroscopic world. Molecules are constantly jiggling around, not in any definite configuration. It would be hard to find parts interacting with each other in any way similar to parts of a motor, for example. The author discusses the example of transport along a microtubule. The standard model sees proteins such as kinesin and dynein as micro-motors, with energy input triggering a conformational change that produces a mechanical force (power stroke), which makes it move forward. The author discusses an alternative model, an old one in fact, but which has recently received some new supporting evidence: the Brownian ratchet. The track has two energy landscapes depending on the conformational state of the motor protein. In the flip conformation, the landscape is flat is so the protein moves by random walk. In the flop conformation, the landscape has a saw-tooth shape, so the protein moves to the nearest energy minimum and gets stuck. This produces a finite movement in a specific direction. By alternating between flip and flop, the protein moves along the track. This is very different from the standard model, because in this alternative model, the stochasticity inherent to the microscopic world is not u201cnoiseu201d (as in departure from an ideal mechanism) but on the contrary, it is harnessed to produce directed motion. The author makes a distinction between self-assembly and self-organization. In self-assembly, parts assemble themselves into a static form, as Lego bricks. Self-organization, on the other hand, views the constitution of cell parts (e.g. organelles) as stabilized processes. An example in astronomy is Jupiteru2019s great red spot. In contrast with the self-assembly of a machine, the organization is actively maintained (turn-over) by continuous exchanges with the outside. There is also a very interesting discussion of the conformation of proteins. We tend to imagine proteins as having a particular shape, that allows it to interact with other proteins like lock and key. This gives rise to the description of metabolic pathways as wiring diagrams, oriented graphs with proteins as nodes. This view appears to be a rather drastic simplification. Many, probably most proteins have a disordered region, which means that they wander in some conformation landscape rather than stabilize in some particular conformation. Importantly, this landscape is strongly affected by context. The author cites an editorial that describes how intrinsically disordered proteins interact, with the cooked spaghetti metaphor: u201cthe spaghetti uses the lock to mould itself into the shape of the key, rather than forming the key beforehandu201d. This makes the standard description of metabolic pathways questionable. Finally, the author discusses the stochasticity of genetic networks. The regulation of genetic expression is often described as a deterministic process, where expression varies continuously with a continuous factor (say, calcium concentration). This gives rise to rheostat models of genetic regulation. These models come from analyzing data from large population of genetically identical cells. The author discusses recent results that support an alternative interpretation of population results, namely that increasing the concentration of the regulating factor u201cresults in the recruitment of a rising number of cells that respond in an all-or-none fashionu201d. This is the stochastic model of gene expression: u201cWith regards to each of the genes it contains, a cell appears to exist in one of two meta-stable functional states: it is either u2018onu2019 or u2018offu2019.u201d , 
 Of course the cell isn t a machine with static parts, it s a dynamic process. Just as the brain isn t a computer with fixed parts and labelled lines. The dominance of these drastically simplistic  standard models isn t due to their rationality. , 
 Yes, the paper discusses the process view of the cell. What s interesting here is that the view is detailed, with specific examples from recent literature. , 
 #3 What would count as evidence for the non-process view? Are you saying the machine view is potentially valid, given what we already know? Machines don t spontaneously reorganize when perturbed, for example. , 
 I was just saying that I learned some new stuff by reading this paper. , 
 #5 And I was just trying to make sure that no one got the impression that the  standard view is or ever was a theoretically legitimate one, any more than the corresponding view of the brain. ,