In 2019 a paper appeared in the Journal of Theoretical Biology asking “Is the Cell Really a Machine?” The author, Daniel Nicholson of the Konrad Lorenz Institute for Evolution and Cognition Research, argues against the commonly held view of the cell as machine-like.1 If not a machine, then what? At least as we’re accustomed to thinking in our age of AI, the alternative to a machine is a mind. Does Nicholson then open up the possibility that the cell is more like an intelligent mind? He totally ignores that idea, which is ironic given that he works at an institute dedicated to cognition research. But let’s take a look.
I am in no position to pass judgement on the technicalities of molecular biology presented by Nicholson; I leave that assessment to others. But if his paper is at all scientifically accurate, the language he uses to describe cellular processes is shot through with implications of cellular cognition beyond anything envisioned by Barbara McClintock. In two posts I will analyze Nicholson’s language.
Ever Since Monod
Nicholson traces the modern machine-like view of the cell back to the work of Jacques Monod and his influential Chance and Necessity. Ever since, according to Nicholson, molecular biology has adopted an interpretive framework whereby the cell is understood as:
An intricate piece of machinery whose organization reflects a pre-existing design, whose structure is intelligible in reductionistic terms, and whose operation is governed by deterministic laws, rendering its behaviour predictable and controllable — at least in principle (108).
Nicholson calls this “the machine conception of the cell,” or MCC. Under its influence, metaphorical terms like locks, keys, gates, pumps, and circuits have come to pervade the language of molecular biology.
Nicholson finds this strange, because in his view, modern research has created a view of the cell totally at odds with this machine-like conception. His analysis revolves around four specific aspects of the cell: cellular architecture, protein complexes, intracellular transport, and cellular behavior. In each case, Nicholson introduces ideas of self-organization and stochasticity to undermine reductionist and deterministic notions of cellular behavior. But in the process, he appears to bring cellular intelligence into the picture. I will consider each of the four examples in detail.
According to Nicholson, cells have traditionally been understood as arising through the process of self-assembly, defined as:
The physical association of molecules into a static equilibrium structure in the absence of an external energy source. It is driven by local stereospecific interactions between the aggregating ‘building blocks,’ which remain unchanged throughout the process (110).
By contrast, Nicholson prefers the more dynamic idea of self-organization, whereby a collection of molecules can maintain itself in a far-from-equilibrium state by “constantly expending energy and exchanging matter with its surroundings” (110). In this more dynamic view, the cell is better characterized as a “meta-stable flux dynamically responding to changes in its environment than as a static macromolecular structure” (111).
The machine metaphor authorizes a picture of the cell as being statically structured by a genetic program, much like machines are statically structured by a blueprint. But self-organization fundamentally alters this conception:
The self-organizing nature of the cellular architecture has far reaching theoretical consequences. Most fundamentally, it leads to a view of the cell that is completely at odds with the MCC. For one thing, it dispels the notion that the ‘information’ that specifies the spatial organization of the cell is somehow encoded in the genome. Strictly speaking, there is no genetic blueprint for the cellular architecture. Self-organization generates order in the absence of an external template or global plan (112).
So, according to Nicholson, cellular architecture is in a constant state of flux as cellular components continually interact in ways to bring about the architectural structures necessary in each moment to respond to the needs of the moment, after which those structures dissipate to be replaced by new ones. But how can this level of dynamism by explained without appeal to some level of cellular cognition? If cellular architecture is not static and specified by a pre-determined genetic plan, but is rather in a dynamic state of flux that always responds appropriately to the momentary needs of the cell, intelligence would seem to be central to cellular behavior (“behavior” itself being a cognitive idea).
Interestingly, Nicholson asks why cells would favor self-organization over self-assembly, implying that cells have a conscious choice in the matter! “Would it not,” he asks, “make more sense for a cell to build static, equilibrium structures that do not require a constant expenditure of energy to maintain them?” (112). While acknowledging that self-assembly would be more economical and efficient, he concludes that self-organization “allows cells to respond rapidly and adaptively to external perturbations and other critical events that would otherwise jeopardize their systemic integrity” (112). But if a cell can choose self-organization for its greater adaptive flexibility, the cell must be some kind of an intelligent agent. Favoring one thing over another is an activity of mind, not matter.
The MCC, according to Nicholson, has led to the idea that protein complexes represent fixed entities whose structure is determined by amino acid sequences specified in the DNA code, and that perform machine-like functions in the cell determined by their unique three-dimensional shape. But recent research suggests that protein complexes are far more fluid and dynamic.
Nicholson points to the globular protein lymphotactin, which has no fixed conformation but rather undergoes major structural changes as it flickers back and forth between two different conformations. Then there is the recent discovery of intrinsically disordered proteins (IDPs) that have no ordered conformations but instead “roam the cell as unfolded polypeptide chains” (114). IDPs provide a distinct functional advantage because they are able to “interact with a broad range of binding partners (including other proteins, membranes, nucleic acids, and various smaller molecules) by adopting different configurations” (114). Rather than having a fixed function based on a fixed three-dimensional shape, the function of these proteins is determined by the environment and the interactions they have with the molecules around them. “Functional promiscuity,” as Nicholson terms it, appears to be the rule rather than the exception for proteins.
Unfortunately, Nicholson has nothing to say about how proteins perform this trick. If there is no chemical necessity driving the various functions taken on by these fluid proteins, how do proteins know what conformation and function to adopt at any given time? Again, this seems to imply the possibility of some level of cognitive ability at the level of molecules. Perhaps Sewall Wright wasn’t so far off when he speculated on what it feels like to be a hydrogen ion!2 Do proteins possess an inner life?
In this section, Nicholson also takes issue with the use of wiring diagrams to portray the complexity of cellular circuitry. In his view, the dynamic nature of proteins renders all fixed wiring diagrams obsolete. Perhaps a better metaphor is neural rather than electronic circuitry. Next, we will consider what Nicholson has to say about intracellular transport and cellular behavior.
- Daniel J. Nicholson, “Is the Cell Really a Machine?” Journal of Theoretical Biology 477 (2019): 108-126.
- Sewall Wright, “Gene and Organism,” American Naturalist 87 (1953): 17.