Cellular perception? So much for Darwinism!

Last week I asked: “Is a cell a machine or more like a mind?” Now, following up on Daniel Nicholson’s challenge to the mechanistic concept of the cell, I will examine the third and fourth points: intracellular transport and cellular behaviour.

Intracellular transport

According to Nicholson, writing in Journal of Theoretical Biology, automated cell visualization (MCC) leads to what he calls a “power stroke” understanding of how motor proteins work. As with the familiar walking protein kinesin, directional movement through 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 presents evidence that he believes makes this “power strike” model obsolete. He replaced it with the “Brownian ratchet” model.

First, Nicholson observed that cellular complexes such as protein complexes undergo coordinate bombardment by thermal agitation of other molecules. This causes the protein molecules to be pushed in random directions – a process known as Brownian motion. Second, he argues that motor proteins come in different conformational states associated with different energy states. Proteins therefore fluctuate between these different energy states and, through this process, are able to neutralize the random movements resulting from thermal agitation to create directional movement. In the power stroke model, directional motion occurs despite Brownian motion as the power stroke is able to overcome these random movements. But in the Brownian ratchet model, directional motion arises from the chaos of Brownian motion. Chaos is harnessed to produce order. But one might ask, how does the motor protein know which direction consistently biases the random Brownian motion to produce the function?

Think about playing cards for a moment. My cards are dealt to me at random. If I then played these cards randomly, I would likely never win the 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 ratchet model:

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

But motor proteins don’t just work in an organized way (random water currents can produce vortex order). The arrangement (directional movement) has a specific purpose and ultimate goal as the protein moves resources around the cell. Do we know of any examples of this kind of teleological order emerging from chaos without the input of intelligence? Nicholson simply ignores this more interesting content of his discussion.

Cellular behavior

Until recently, studying gene expression and cellular behavior could only be done by monitoring large numbers of cells. Thus, the behaviors that emerged were the average behavior of all individual cells in the population. It was simply assumed that every cell in the population behaves according to the population mean. After all, every cell has an identical genetic program, right?

According to Nicholson, experimental techniques have now emerged that allow studying the behavior of individual cells in a population. The results indicate heterogeneity of origin; Individual cells in a population do not behave in identical ways even though their genetic programs are identical. Even homogeneous cells subject to the same environmental conditions do not necessarily react in the same way. Thus cellular behavior appears probabilistic rather than deterministic. Nicholson writes:

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

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

We now know that nongenetic heterogeneity plays key roles in both microbial and eukaryotic cells, in embryonic development, and in development. On the one hand, it is a crucial generator of phenotypic diversity, which enables cell populations to rapidly adapt to changing environmental conditions. It does this by allowing the implementation of probabilistic diversification strategies within populations, such as hedging and division of labor, which can confer significant fitness advantages (122).

But then again, isn’t hedging a bet a cognitive activity? Which side of the cell “allows” the execution of a particular strategy? Nicholson simply ignores the cognitive implications of his choice of words. But if he is right, then adaptation becomes an intentional and intelligent process.

So much for Darwinism!

Given the fluid and dynamic view of the cell that emerges from current research, Nicholson says conclusively that Jacques Monod was wrong. The cell is not like a machine at all. Once again, he writes about the cell as if it were an intelligent entity in itself:

Due to its unstable nature, the cell is constantly forced to negotiate a trade-off between structural stability and functional flexibility: too much rigidity compromises physiological adaptability, and too much mixing compromises metabolic efficiency. The cell achieves this by constantly converting and reorganizing its components 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 of looking at the cell, then the death knell of materialism is ringing louder than ever. This is because matter cannot “monitor,” “negotiate,” “reorganize,” “hedge its bets,” “exploit,” “harness,” “change form,” or “prefer” self-organization to self-assembly. But smart minds can do all these things.

Nicholson concludes by examining the reasons why this new view of the cell failed to become a standard in the world of molecular biology, in light of what he considers to be the overwhelming evidence for it. For one thing, it may make studying the cell more difficult. MCC leads to a reductionist and deterministic view, which holds out hope for a future in which we will be able to fully understand and even predict cellular behavior. The new point of view calls into question these epistemological goals. Second, this new viewpoint may force biologists to accept concepts that lie outside the scope of the traditional tools of molecular biology. Although Nicholson does not say so, his opening of the horizons of cellular cognition clearly lies outside the conventional biological (and scientific) toolbox. No wonder the MCC stubbornly remains where it is.

In her Nobel Prize acceptance speech, Barbara McClintock noted:

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

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

Notes

1. Barbara McClintock, “The Importance of Genomic Responses to Challenge,” Sciences 226 (1984): 793.