This is just one example of what we'll be able to do as we discover the circuitry and the logic -- and therefore the dynamical behavior -- of cells. We will know which gene we have to perturb with what, or which sequences of genes we have to perturb in what temporal order, to guide the differentiation of a cancer cell to nonmalignant behavior or to apoptosis. Or to guide the regeneration of some tissue. I can imagine the time when we'll be able to regenerate cardiac tissue from surrounding normal tissue instead of having a scar in place, and the scar serves as the focus for getting recurrent electrical activity in the heart, sending up little spirals of electrical activity, which make your heart beat unstable and which makes people post-heart attack subject to sudden cardiac death because they go into ventricular fibrillation.
Suppose what we could do instead of getting scar tissue, suppose we could get those cells to differentiate into perfectly normal myofibrils. Nothing says we can't do that since the muscle cells and fibrotic tissue are cousins of one another developmentally. So you could begin to imagine treating all kinds of diseases by controlling cell differentiation, tissue differentiation and so on. And to do that we're going to have to know what the circuitry is, and we're going to have to know what small molecules or molecules in general can be added to a person that will specifically treat the diseased tissues and not have undue side effects.
SA: How does complexity theory, disorganization/self-organizing systems, come into play? How do computers and algorithms and data from many different places need to be integrated?
There are three ways we will understand genetic regulatory networks, all of which involve computational work, as well as piles of data. One of them has already been pioneered. It is the following: I have a small genetic circuit, for example, bacteriophage lambda -- or something like that, which has 20 or 30 genes in it and one major switch. And I know all of the genes; I know which genes make which products, which bind to which genes; I know the binding constants by which those gene products bind to the gene. And what I do is, I make in effect an engineering model of that specific circuit, sort of like electrical engineering, except it's molecular-biology-chemical engineering to make a specific circuit for that bacterium. One is inclined to do the same thing with the human genome.
Suppose I pick out 10 genes that I know regulate one another. And I try to build a circuit about their behavior. It's a perfectly fine thing, and we should do it. But the downside is the following: those 10 genes have inputs from other genes outside that circuit. So you're taking a little chunk of the circuitry that's embedded in a much larger circuit with thousands of genes in it. You're trying to figure out the behavior of that circuit when you do not know the outside genes it impacted. And that makes that direct approach hard because you never know what the other inputs are. Evidence that it's hard comes from a parallel, looking at neural circuits, at neural ganglia. We've known for years what every neuron is in, say, the lobster gastric ganglia; what all of the synaptic connections are; what the neurotransmitters are; and you have maybe 13 or 20 neurons in the ganglion, and you still can't figure out the behavior of the ganglion. So no mathematician would ever think that understanding a system with 13 variables is going to be an easy thing to do. And we want to do it with 100,000 variables. That scales the problem.
Molecular biologists have thought they're going to be able to work out how 100,000 genes work with one another without having to write down the mathematical equations by which genes govern one another, and then figuring out from that what the behavior is. That's why this is a stunning transition that we're going through, and there's a lot of stumbling around going on. It's because molecular biologists don't know any mathematics, by and large.