One of the hottest debates among evangelicals today centers on the question of whether there was a literal Adam and Eve. Some believers adhere to theistic evolution, which rejects the notion that humanity descended from a single man (Adam) and a single woman (Eve) as Scripture describes. This view instead accepts the evolutionary paradigm, including its hypothesis that humanity emerged from a population of at least 10,000 individuals.
Though it may not be immediately evident, theistic evolution has enormous theological implications for the central tenets of historical Christianity. Reasons To Believe scholars have devoted considerable effort to explaining our scientific and theological concerns regarding theistic evolution. As we continue to question the prevailing naturalistic human origins model, we need to delve into bioinformatics (defined as “the application of computer science and information technology to the field of biology and medicine”). Much of the interpretation of the data supporting the evolutionary theory of humanity’s origin comes from this discipline.
An Introduction to Bioinformatics
In the 1980s and 1990s, life scientists began to exploit the power of computational methods in their fields and bioinformatics was born. My first encounters with computer science occurred between my undergraduate and PhD programs. As someone who has always loved logic and math problems, I decided to give this relatively new area of technology a try and soon discovered I really liked it. It had the same precision and neatness as chemistry and math and it was so enormously powerful.
When I began graduate school, bioinformatics was still in its youth. Most of my work had to be done using workstations running the UNIX operating system. It was difficult to use bioinformatics successfully unless you could write some computer programs yourself. Some of the best software for bioinformatics was created by computer science graduate students at the request of biochemists in their universities.
Since I had worked in both computer science and biochemistry and understood the terminology of both disciplines, I was equipped to “translate” the original user guide for a powerful RNA structure prediction software package written by a computer scientist into verbiage accessible to other biochemists. I used software products produced by companies and products produced by computer scientists for the academic world extensively in my thesis research. I also wrote some computer programs specifically for my own research.
The Decision Maker’s Role
Lately, I’ve been refreshing my skills in bioinformatics and I’ve discovered that the new generation of bioinformatics software is much more “user friendly.” Still, scientists using these tools must make a lot of decisions about which pieces of data to include in (or exclude from) their analysis and about the specific mathematical approaches that should be used to analyze the selected data. These decisions can have a major impact on the results of the analysis.
As an example of a decision maker’s impact on outcome, I drew the images in figure 1 using the “Insert/Shape” function in Microsoft Word. Though I started each image with the same circle, they all look different because I used different features to shape and color the image. The decisions I made generated a very different final image.
But the decision maker isn’t the only factor impacting results. The limitations of the model and how it is designed can also influence results.
The Limitations of Models
Though our great-grandfathers may have predicted the next day’s weather based on the color of the sunset, today’s forecasters use complex computer programs to forecast weather. The predicted weather is actually a computer model. The programs that generate these models incorporate our best knowledge of what determines the weather, but, as we all know, they aren’t 100 percent accurate.
Inaccuracy is especially evident in computer models for projected hurricane paths. When Hurricane Camille threatened the Gulf Coast in 1969, my family gathered around the radio in my uncle’s sturdy brick house in Baton Rouge, Louisiana, waiting to hear whether Camille would come into Louisiana through New Orleans or head into Mississippi. It really wasn’t clear which direction Camille would take until just shortly before it made landfall.
Reviewing the story of Camille reminds one that, even today, hurricane forecasting models are often wrong. As a result, hurricane predictions often include alternative paths and the percentage likelihood associated with each possible path. The computer models’ accuracy is limited by the accuracy of the assumptions and the data used to create the model. Changing the assumptions generally changes the predictions of a hurricane’s strength and likely path.
Bioinformatics and Theistic Evolution
Currently, theistic evolutionists’ argument against a literal Adam and Eve is frequently based on phylogenetic (“the study of evolutionary relatedness among groups of organisms”) computer models. The results of these models are presented as absolute truth. In reality, their accuracy, like weather forecasting, is limited by the assumptions on which the model is built, the data used to build the model, and the mathematical approach used to generate the phylogenetic trees.
As I’ll explain in future Today’s New Reason to Believe posts, the person constructing the phylogenetic computer model makes many assumptions and decisions in the course of building the model that ultimately affect the result, just as I can dramatically change the appearance of a circle by adjusting its shape, depth, and color. Additionally, just as hurricane forecasting models predict not just one path but multiple paths, phylogenetic models generate multiple distinct (and sometimes contradictory) theories regarding the evolutionary relationships between species.
In summary, when you hear theistic evolutionists say that the church must accept evolution because it is a fact, remember, what they refer to as “fact” is instead a theory. Remember that the phylogenetic models on which the rejection of a literal Adam and Eve are urged are just that—models. And like any model, they are only as good as the assumptions, the data, and the decisions on which they are built.
Dr. Patricia Fanning
Patricia Fanning is an RNA biochemist with a PhD from North Carolina State University and formerly a consultant for software companies. As a visiting scholar to Reasons To Believe in 2011, she specialized in human embryology and evolutionary development and regularly contributed to RTB’s podcasts and publications.