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Section 2: Physics and Life

Here's a question from a biologist, Don Coffey at Johns Hopkins University: Is a chicken egg in your refrigerator alive? We face a problem right away: What does being alive actually mean from a physics perspective? Nothing. The concept of aliveness has played no role in anything you have been taught yet in this course. It is a perfectly valid biological question; yet physics would seem to have little to say about it. It is an emergent property arising from the laws of physics, which presumably are capable of explaining the physics of the egg.

A chicken egg. Is it alive or dead?

Figure 5: A chicken egg. Is it alive or dead?

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The chicken egg is a thing of elegant geometric beauty. But its form is not critical to its state of aliveness (unless, of course, you smash it). However, you can ask pertinent physical questions about the state of the egg to determine whether it is alive: Has the egg been cooked? It's pretty easy to tell from a physics perspective: Spin the egg around the short axis of the ellipse rapidly, stop it suddenly, and then let it go. If it starts to spin again, it hasn't been cooked because the yolk proteins have not been denatured by heat and so remain as a viscous fluid. If your experiment indicates the egg hasn't been cooked it might be alive, but this biological physics experiment wouldn't take you much closer to an answer.

Assuming you haven't already broken the egg, you can now drop it. If you were right that it has not been cooked, the egg will shatter into hundreds of pieces. Is it dead now? If this were your laptop computer, you could pick up all the pieces and—if you are good enough—probably get it working again. However, all of the king's horses and all the king's men can't put Humpty Dumpty back together again and make him alive once more; we don't know how to do it. The egg's internal mechanical structure is very complex and rather important to the egg's future. It, too, is part of being alive, but surely rather ancillary to the main question of aliveness.

Aliveness is probably not a yes-no state of a system with a crisp binary answer, but rather a matter of degree. One qualitative parameter is the extent to which the egg is in thermodynamic equilibrium with its surroundings. If it is even slightly warmer, then I would guess that the egg is fertilized and alive, because it is out of thermodynamic equilibrium and radiating more energy than it absorbs. That would imply that chemical reactions are running inside the egg, maintaining the salt levels, pH, metabolites, signaling molecules, and other factors necessary to ensure that the egg has a future some day as a chicken.

Wait, the egg has a future? No proton has a future unless, as some theories suggest, it eventually decays. But if the egg is not dropped or cooked and is kept at exactly the right temperature for the right time, the miracle of embryonic development will occur: The fertilized nucleus within the egg will self-assemble in an intricate dance of physical forces and eventually put all the right cells into all the right places for a chick to emerge. Can the laws of physics ever hope to predict such complex emergent phenomena?

Emergent and adaptive behavior in bacteria

Here's an explicit example of what we are trying to say about emergent behavior in biology. Let's move from the complex egg where the chick embryo may be developing inside to the simple example of bacteria swimming around looking for food. It's possible that each bacterium follows a principle of every bug for itself: They do not interact with each other and simply try to eat as much food as possible in order to reproduce in an example of Darwinian competition at its most elemental level. But food comes and food goes at the bacterial level; and if there is no food, an individual bacterium will starve and not be able to survive. Thus, we should not be surprised that many bacteria do not exist at the level as rugged individuals but instead show quite startling collective behavior, just like people build churches.

Figure 6: Random motion of gas molecules—bottom up.

Source: © Wikimedia Commons, GNU Free Documentation License. Author: Greg L., 25 August 2006. More info

If bacteria acted as rugged individuals, then we would expect their movement through space looking for food to resemble what is called a random walk, which is different from the Brownian motion that occurs due to thermal fluctuations. In a random walk there is a characteristic step size L, which is how far the bacterium swims in one direction before it tumbles and goes off randomly in a new direction. Howard Berg at Harvard University has beautiful videos of this random movement of bacteria. The effect of this random motion is that we can view individual bacteria rather like the molecules of a gas, as shown in Figure 6. If that were all there is to bacterial motion, we would be basically done, and we could use the mathematics of the random walk to explain bacterial motion.

However, bacteria can be much more complicated than a gas when viewed collectively. In the Introduction, we discussed the chemotaxis of a population of individual Dictyostelium cells in response to a signal created and received by the collective population of the Dictyostelium cells. Bacteria do the same thing. Under stress, they also begin signaling to each other in various ways, some quite scary. For example, if one bacterium mutates and comes up with a solution to the present problem causing the stress, in a process called "horizontal gene transfer" they secrete the gene and transfer it to their buddies. Another response is to circle the wagons: The bacteria signal to each other and move together to form a complex community called a "biofilm." Figure 7 shows a dramatic example of the growth of a complex biofilm, which is truly a city of bacteria.

Figure 7: Growth of a biofilm of the bacteria Bacillis subtilis over four days.

Source: © Time-lapse movie by Dr. Remco Kort—published in J. Bacteroi. (2006) 188:3099-109. More info

The mystery is how the supposedly simple bacteria communicate with each other to form such a complex and adapted structure. There is a set of equations, called the "Keller-Segel equations," which are usually the first steps in trying to puzzle out emergent behavior in a collection of swimming agents such as bacteria. These equations are not too hard to understand, at least in principle. Basically, they take the random walk we discussed above and add in the generation and response of a chemoattractant molecule. A sobering aspect of these equations is that they are very difficult to solve exactly: They are nonlinear in the density of the bacteria, and one of the great secrets of physics is that we have a very hard time solving nonlinear equations.

Principles of a complex adaptive system

We are just skimming the surface of a monumental problem in biological physics: How agents that communicate with each other and adapt to the structures that they create can be understood. A biological system that communicates and adapts like the film-forming bacteria is an example of a complex adaptive system. In principle, a complex adaptive system could appear almost anywhere, but biological systems are the most extreme cases of this general phenomenon.

A schematic view of what constitutes a complex adaptive system.

Figure 8: A schematic view of what constitutes a complex adaptive system.

Source: © Wikimedia Commons, Creative Commons Attribution ShareAlike 3.0. More info

The computer scientist John Holland and the physicist Murray Gell-Mann, who played a major role in the physics developments you read about in Units 1 through 4, have tried to define what makes a complex adaptive system. We can select a few of the key properties as presented by Peter Freyer that are most germane to biological systems:

  1. Emergence: We have already discussed this concept, both in this unit and in Unit 8.
  2. Co-evolution: We will talk about evolution later. Coevolution refers to how the evolution of one agent (say a species, or a virus, or a protein) affects the evolution of another related agent, and vice versa.
  3. Connectivity: This is concerned with biological networks, which we will discuss later.
  4. Iteration: As a system grows and evolves, the succeeding generations learn from the previous ones.
  5. Nested Systems: There are multiple levels of control and feedback.

These properties will appear time and again throughout this unit as we tour various complex adaptive systems in biology, and ask how well we can understand them using the investigative tools of physics.


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