Gene Networks are collections of genes that interact with each other. What does that mean, and how do genes interact?
First, we must understand that a gene does nothing but store information. The analogy most often used is a book. A closed book is of no particular interest or value, and many genes are in fact usually “closed” or turned off. What that means in biochemical terms is that the transcription process in which the sequence of bases in the DNA is converted into a matching sequence in RNA (which then goes to a protein-making factory called the ribosome) does not happen. So when a gene is turned off, the information in the gene is not expressed, and the protein that the gene codes for is not made. The gene is inactive. The book is closed, and the information that it contains (in the case of genes, the protein the gene codes for) isn’t needed.
But then something changes. A toxic material enters the cell, or a lot of energy is suddenly needed,- or the cell begins to divide because it has grown a lot. In the analogy, we might remember that the recipe for how to make chicken soup is in a cookbook, so we open the book and find the information we need. In cells, when a particular protein is needed, a signal goes to the gene that makes the protein, and the gene is turned on, activated, transcribed, expressed (all synonyms for the same thing). And the process starts.
Enzymes and other proteins copy the DNA of the appropriate gene into RNA, which is then translated into a protein sequence by an incredible process in the ribosome. This is what we do when we read the words “Cut the chicken into small pieces” in the cookbook. Those words are simply marks of ink on paper, but they are in a language that we can understand and translate into a meaningful idea. The process of protein synthesis is technically called translation, and the analogy is a very good one.
The way genes are turned on and off has been an active field of biological research for many decades. It is a very difficult field since the mechanisms are diverse, complex and hard to untangle. What we are finding is that the entire subject of gene regulation is probably far more important in all aspects of biology (including evolution) than we ever suspected. Recent research has shown that many DNA regions that were not recognized as genes actually code for proteins whose only function is to regulate other genes.
Even more fascinating is the burgeoning field of gene regulatory networks, where groups of genes regulate (or control, like switches) each other. These networks (GRNs) are very important during animal development: they are responsible for how the growing animal develops organs, limbs, and all the structures that make it what it is.
Studies on GRNs have shown that they are very stable over deep evolutionary time, and some of the key genes involved in these networks during development have not changed much in hundreds of millions of years of evolution. The complexity of GRNs includes several kinds of feedback and redundancy features that reduce the chances of disruption by mutations or random cellular events that could affect normal function. This resistance to damaging disruption is called robustness or gene buffering. At the same time, and probably as a consequence of this robustness, these networks can be a source of innovation in evolution.
One of the pioneers in theoretical modeling of GRNs is Andreas Wagner, whose book The Arrival of the Fittest presents many of his ideas about how gene regulation could impact the process of evolution, especially as related to mechanisms for variation that are outside of the standard neo-Darwinian paradigm.
I have also been working with models of such networks for the past few months in order to try to understand some of the basic dynamics and mathematical relationships that govern their behavior. This work has been funded by the John Templeton Foundation (see the page on the John Templeton Foundation grant). I am finding the work to be fun and exciting, and I am getting close to some interesting findings. But I don’t want to discuss those finding here and now (they are much too technical for this blog). What I want to do is say something about the philosophical implications of interactive networks.
The models I am using are simple mathematical matrices where each element can have an effect on every other element of the matrix. This is not at all specific to gene regulation but also applies to a large variety of systems. Probably most systems, in fact. Certainly it applies to human interactions in many ways. It’s reasonable to postulate that any group of humans constitute an interactive network. Even in small groups, like families or friends or co-workers, the complexity of these interactions and their results are beyond calculation. Like all systems that exhibit highly complex non-predictable behavior (the weather, stock market, so many other chaotic systems), interactive systems are deterministic, and end results could theoretically be calculated if one were able to have knowledge of all the parameters of the system. In other words, if you know that Uncle Jerry has no respect for his son-in-law, and that your mom is always striving for peace in the family, you might think you would be able to predict certain scenarios during Thanksgiving dinner. But you would not be able to predict that your sister’s new boyfriend would argue with Uncle Jerry all night, and even convince him that the Red Sox will never win another world Series.
It’s no surprise that gene regulatory network models (with very simple kinds of interactions) can get extremely complex, to the point of this kind of chaotic, unpredictable behavior. Wagner has published this result. What is surprising (and this is a preview of some of my results) is that this level of complexity begins to show up at some of the simplest networks with very few interactions.
Is this complexity a good thing? It might be that it is for biological systems. The brain is probably the most complex thing in the universe, and it’s a pretty powerful thing indeed. Some of the biochemical machinery that functions even in primitive cells (like how proteins are made from the DNA-based genetic code) are complex beyond our comprehension, let alone ability to imagine how they got that way. So yes, complexity seems to be the rule in life, and evolution seems to favor it whenever possible. This is not a new insight by any means, but it is something that leads to many interesting questions for our incredibly complex minds to ponder.
Even the writers of Windows can’t predict how everything will interact, or my computer wouldn’t be in a blue screen of death loop. I found myself thinking of Asimov’s Foundation trilogy as I read about Uncle Jerry, and feeling hungry as I read of the recipe book. But I love your analogies, and the things you leave me thinking about.