Andreas Wagner and his research group found, both in laboratory experiments and using computer models, that there is not just one way to build a protein with a particular function. There are millions (at least) of ways to do it. The same is true of gene regulatory control circuits and metabolic pathways. Wagner uses an analogy to explain the concept: a universal library containing every possible book, some of which, while containing different words, have the same meaning. We can explore the idea using a simple English sentence as a model.
Everyone knows that life is complex.
Human language is robust: making small changes in a sentence often does not change its meaning. If we substitute just one word for another, creating a one-step neighbor (a good analogy for a mutation), some of these neighbors will retain the meaning of the original. For example: Everyone understands that life is complex or Everyone believes that life is complex.
Of course, because there are so many words, we can imagine that there are a vast number of neighbors that will not make sense, or will simply make the meaning unclear: Everyone corrosive that life is complex.
Unlike human language, computer code is not robust at all. Leave out a semicolon or change a letter, and either nothing works, or it all works wrong. So how robust is life?
Wagner found that life is surprisingly robust. In life there are a lot of one-step neighbors. Most proteins, for example, are at least 100-400 amino acids long. An average protein would have almost 5000 neighbors (i.e. proteins with one amino acid changed). While a large fraction of a protein’s neighbors did not work well or at all, Wagner discovered that many of the neighbors produced the same phenotype as the original sequence. The same was true for regulatory networks and metabolic pathways.
Wagner’s research group then did an experiment that gave them an unexpected result. They continued to make single amino acid changes in the functional neighbor sequences, in a stepwise manner, and looked at all the neighbors (over 22 million) of the original neighbors. They found that even after many steps, there were still plenty of sequences that retained the original function. In fact, to their surprise, they could replace up to 80% of the amino acids in a protein structure and still get a few that worked as well as the original. Talk about robustness.
But, most importantly, they found that this process is also the source of innovation. After many steps, when the protein has a number of amino acids that differ from the original but still has the same function, just one more change can produce a protein with a brand new useful function.
Let’s get back to our sentence analogy. Here is a pathway of sentences in which each sentence is the one-step neighbor of the one before. Sentences 1, 2, and 3 have essentially the same meaning. But in sentence 4, a single word change creates an entirely new meaning:
- Everyone knows that life is complex.
- Everyone thinks that life is complex.
- Everybody thinks that life is complex.
- Everybody thinks their life is complex.
We call a one-step change in the amino acids of a protein a mutation, and we know that such mutations happen all the time. How likely is it that a mutation in a protein would produce a novel function? If there were just one protein that could perform the original function and keep the organism alive, it would be very unlikely – a miracle. But with many different candidates around, it could happen frequently. The more robust a protein is, the greater the possibility for innovation. A high degree of robustness allows for many different structures doing the same thing, and also allows for innovation through a small change in one of those robust alternative structures. Complexity leads to robustness, which leads to innovation.
So that is how the fittest arrive. Some evolution-deniers make the argument that the probability of getting a particular protein by chance is so small that it could not happen naturally. But one does not need that particular protein structure to carry out a function: there are many possible protein structures that can do the same thing. And the argument that mutations are always destructive and cannot lead to new information is also wrong, because many of these changes have no negative effect (the definition of robustness); and eventually, one change in one of the possible alternative structures can lead to an innovative function.
Andreas Wagner’s robustness/innovation hypothesis could be the basis for a new way of thinking about evolution. It provides a mechanism for innovation to occur quickly and doesn’t require the gradual accumulation of small changes, each with an adaptive advantage, of traditional Darwinism. It has the ring of truth, since it is both elegant and simple yet requires innate complexity as a prerequisite for the evolution of life.
While some traditional Darwinians insist that chance events are the dominant source of variation among individuals, there are new findings from evolutionary biologists suggesting that this might not be entirely true. Wagner’s theory adds evidence to the possibility that there is a degree of inevitability, or at least a direction, to how new life forms arise, and thus reduces the role of blind chance in evolutionary outcomes. Simon Conway Morris’ book Life’s Solution: Inevitable humans in a lonely universe describes the myriad examples of evolutionary convergence – how the same very complex features, from wings and eyes to eusociality, have evolved many times in different biological lineages. Wagner’s work could supply a mechanism to explain these observations on convergence.
I don’t know if Wagner is a Christian, although the last sentence of the book is intriguing:
And we learn that life’s creativity draws from a source that is older than life, and perhaps older than time.
Wagner’s explanation for the arrival of all of life, including us, seems to fit in quite well with the Christian belief in a Creator God who knits all creatures together, and who created all living creatures to be fruitful, robust, and innovative.