Last week I asked, “Is the Cell a Machine, or More Like a Mind?” Now, following up in considering Daniel Nicholson’s challenge to the machine concept of the cell, I will examine his third and fourth points: intracellular transport and cellular behavior.

Intracellular Transport

According to Nicholson, writing in the Journal of Theoretical Biology, the machine conception of the cell (MCC) leads to what he calls a “power-stroke” understanding of how motor proteins work. Like with the familiar kinesin walking protein, directional movement across the cytoskeleton is driven by the chemical hydrolysis of ATP which is converted into a mechanical power stroke, much like what happens in an internal combustion engine. But Nicholson offers evidence that he believes makes this “power-stroke” model obsolete. He replaces it with a “Brownian ratchet” model. 

First, Nicholson observes that cellular compounds like protein complexes are subject to consistent bombardment by the thermal agitation of other molecules. This causes protein molecules to be pushed around in random directions — a process known as Brownian motion. Second, he argues that motor proteins come in different conformational states tied to different energy states. The proteins therefore flip and flop between these different energy states, and are able, via this process, to bias the random movements caused by thermal agitation to create directional movement. In the power-stroke model, directional movement occurs despite Brownian motion as the power stroke is able to overwhelm these random movements. But in the Brownian ratchet model, directional movement arises from the chaos of Brownian motion. Disorder is harnessed to produce order. But one might ask, how does a motor protein know what direction to consistently bias the random Brownian motion in to produce functionality?

Consider card playing for a moment. My cards are dealt to me in a random way. If I were to then play those cards randomly, I would likely never win a hand. But I can use my intelligence to overcome the randomness of the deal and play my cards strategically toward the goal of winning the hand. Likewise, Nicholson writes about the Brownian rachet model:

…it shows how the coupling of two random (or disordered) processes — namely Brownian motion and the binding of ATP — can result in a non-random (or ordered) outcome: directional movement. In this way, by providing a non-deterministic, design-free conceptualization of intracellular transport, the Brownian ratchet model strikingly illustrates how order can be generated out of chaos (118).

But motor proteins do not act in just an ordered way (as random water currents can produce the order of a whirlpool). The order (directional movement) has a specific purpose and end goal as the protein transports resources around the cell. Do we know of any examples of this kind of teleological order arising out of chaos without the input of intelligence? Nicholson simply ignores this most interesting implication of his discussion.

Cellular Behavior

Until recently, gene expression and cellular behavior could only be studied by observing large populations of cells. The behaviors that emerged were therefore the average behavior of all the individual cells across the population. It was simply assumed that each cell in the population acted in accordance with the populational average. After all, each cell possesses an identical genetic program, right? 

According to Nicholson, experimental techniques have now emerged allowing for the study of the behavior of individual cells in the population. And the results indicate isogenic heterogeneity; individual cells in a population do not all behave in identical ways despite their identical genetic programs. Even isogenic cells that are subject to the same environmental conditions do not necessarily all react in the same way. Cellular behavior thus appears to be probabilistic rather than deterministic. Nicholson writes:

Each cell in the population exhibits a specific and distinct probability to respond to a given concentration of inducer, and this probability can vary widely — even among members of the same isogenic population (120). 

Recognizing the probabilistic nature of cell behavior is now leading biologists to consider how cells exploit this probabilistic “noise” for their own benefit. Nicholson continues:

We now know that non-genetic heterogeneity plays key roles in both microbial and eukaryotic cells, in embryonic development, and in evolution. For one thing, it is a crucial generator of phenotypic diversity, which enables cell populations to adapt rapidly to changing environmental conditions. It does so by permitting the implementation of probabilistic diversification strategies within a population, such as bet-hedging and divisions of labour, which can confer considerable fitness advantages (122).

But once again, isn’t bet-hedging a cognitive activity? And what aspect of a cell “permits” the implementation of a specific strategy? Nicholson simply ignores the cognitive implications of his own choice of words. But if he is right, adaptation thus becomes an intentional and intelligent process. 

So Much for Darwinism!

Given the fluid and dynamic view of the cell emerging from current research, Nicholson flatly states that Jacques Monod was wrong. The cell is not at all like a machine. And once again, he writes about the cell as if were an intelligent entity of its own:

Given its precarious nature, the cell is constantly having to negotiate a trade-off between structural stability and functional flexibility: too much rigidity compromises physiological adaptability, and too much promiscuity compromises metabolic efficiency. The cell accomplished this by continuously turning over and reorganizing its constituents into different macromolecular complexes with diverse functional capabilities, which assemble and disassemble in order to meet the ever-changing demands of the environment (123). 

If this is the correct way to view a cell, then the death knell of materialism is sounding ever louder. For matter cannot “monitor,” “negotiate,” “reorganize,” “hedge its bets,” “exploit,” “harness,” “shape-shift,” or “favor” self-organization over self-assembly. But intelligent minds can do all these things. 

Nicholson concludes with a consideration of why this new view of the cell has failed to become the standard within the molecular biology world, given what he sees as the overwhelming evidence for it. For one thing, it might make the cell harder to study. The MCC leads to a reductionistic and deterministic view, which holds out the hope for a future in which we will be able to fully understand and even predict cellular behavior. The new view calls into question these epistemic goals. Secondly, this new view could force biologists to accept concepts that fall outside the conventional molecular biology toolbox. Though Nicholson does not say it, his opening up the prospect of cellular cognition obviously lies outside the conventional biological (and scientific) toolbox. No wonder the MCC remains stubbornly in place.

In her Nobel Prize acceptance speech, Barbara McClintock noted:

In the future, attention undeniably will be centered on the genome, with greater appreciation of its significance as a highly sensitive organ of the cell that monitors genetic activities and corrects common errors, senses unusual and unexpected events, and responds to them, often by restructuring the genome.¹

She calls this ability of the cell “truly remarkable.” If Nicholson’s presentation is at all accurate, the future McClintock envisioned may well have arrived. The difference is that McClintock embraced the scientifically heretical implications of her work; Nicholson simply ignores them. 

Notes

1. Barbara McClintock, “The Significance of Responses of the Genome to Challenge,” Science 226 (1984): 793.