Modern renditions of biology–many of which heavily involve data science–offer a very real opportunity to peer into the future. The interesting thing about biology is that we have millions (and billions) of years of past data at our disposal. Evolutionary biologists have traced the evolution of life on Earth since its earliest forms and can now analyze genomic data to look for connections between different generations of life. In a 2008 paper, Lawrence Kelley and Michael Scott claimed that “bioinformaticians are increasingly generating tools to make accurate predictions for a restricted range of phenomena.”
The study of evolutionary biology is as much a study of history as it is of science. A common systematic method of making predictions is to analyze events/trends of the past and use that data to construct a model for what the future will hold. Using that method, modern evolutionary biologists are continually trying to piece together the evolutionary story of many species, particularly humans. By looking at the processes that have taken humankind from its ape-like beginnings to its sophisticated, innovative contemporaries, evolutionary biologists are able to offer insight into how the human race will evolve in the future. One very key point to consider, however, is that modern humans actually have the power to shape the course of their evolution vis-à-vis technology, like gene-editing mechanisms. In a 2016 Scientific American article, some scientists predicted that humans will soon be able to engineer biological tissues to help cure diseases like Alzheimer’s and Parkinson’s. This would certainly be very beneficial to humankind, although we would effectively be altering human evolution as a result. At the same time, one could reasonably argue that such engineering techniques will make the human race “fitter” as a whole, which is evolutionarily favorable. Maybe, what we perceive as “artificial” technology-driven evolution is actually the result of natural evolutionary pressures.
One thing we might predict by looking at evolutionary history is that the human race is becoming more intelligent. After all, we see a trend of increasing brain size in hominids. A reasonable prediction would be that humans will continue to become more intelligent and develop more sophisticated technology, allowing our population to continue to grow and occupy every spot of livable land on the planet. We can also predict using population modeling that the human population will reach a carrying capacity of between 9 and 10 billion. This prediction could very well be disproven if some revolutionary new technology or environmental change suddenly provides greater access to fundamental natural resources, which could potentially increase the carrying capacity. Again, logistic models based on past data are our best guess at the future of the human population, but as Thomas Malthus found out, sometimes technological advancements can disprove even the most astute of predictions.
Another important factor to consider is that much of evolution is driven by highly random processes, such as mutation, genetic recombination, and chromosome crossover. Novel genotypes translate to novel phenotypes, which may end up giving some individuals a tremendous advantage over the rest of their kind. It is difficult (if not impossible) to foresee which novel traits will arise in a given species. For all we know, beavers may end up rapidly evolving to become more intelligent than humans and gain the ability to fly. If this were the case, then the carrying capacity of the human population would likely drop as beavers compete more fiercely with humans for limited natural resources.
My conclusion is that computational biology is one of our most powerful tools for predicting the future based on copious amounts of historical data. At the same time, random evolutionary processes can also cause massive deviations from predictive models. And so we scientists continue to forge on through the unknown wilderness of prediction.
Kelley, Lawrence, and Michael Scott. “The Evolution of Biology. A Shift towards the Engineering of Prediction-Generating Tools and Away from Traditional Research Practice.” EMBO Reports 9.12 (2008): 1163–1167. PMC. Web. 20 Sept. 2017.