A new procedure for analyzing data developed by a University professor has provided insight into how plants have evolved on earth.

Tandy Warnow, professor in computer science and bioengineering, said her job is to “develop new methods for really hard problems in evolution.”

She, along with a team from the University of Texas at Austin, has created a software with the main goal of learning about how plants have evolved and what the most common plant ancestors are.

The research is part of a larger international effort with contributors from several countries, including Canada and China.

“My main interest is pretty basic: How did life evolve on earth,” she said.

Katy Heath, assistant professor of plant biology, said the research team is working with non-extinct plants, while attempting to find their common ancestors. The team is also trying to understand how different species of plants are connected to each other.

Plant biologists have been able to suspect things about evolution, she said, but this new research makes it possible to make confident statements about the ways plants are related to one another.

The data is gathered from the DNA of plants, then is compared and connected to other information in the database. The data computing generates millions of gene sequences that can explain, for example, how algae has turned into more complex land plants.

The information also helps to give a timeline to evolution by noticing when a certain plant stops evolving and when others start. It can connect plants like moss to trees, and can give insight into how the two are related.

This computational process also gives insight into when and how the embryo evolved for the creation of animals and later humans.

Heath said there is a great interest in learning how and when these plants changed, and this new form of data analysis is allowing researchers to come closer than ever to discovering new information about evolution. The data from the research can also help explain how adaptations evolved, she said, and it places these plants into a larger evolutionary context.

“These are really new methods that I think everyone can benefit from,” she said.

Warnow said she initially recognized the need for a new method because an existing software that was used to evaluate plant data could not handle the quantity or the complexity of finding how plants can evolve from a common ancestor.

“The use of those data to understand how plants evolved required really significant computational analysis,” she said.

Warnow said she then created a variation of the software that focused on mathematical ideas and theorems that help to accomplish her computational needs.

The team is not proposing a new idea, she said, but instead they are bringing a large amount of data to look at the same hypotheses that have been around for decades.

The challenge, then, was that scientists have never had the ability to gather this much data until recently, or to compute and use the data like people can now, Heath said.

Although this new data computation method is currently being used for diagnostic purposes in medicine, along with medicine design, it is also applicable for studying the evolution of languages, said Ruth Davidson, Ph.D. in mathematics and postdoctoral fellow.

Davidson has worked with Warnow since arriving at the University.

This technology is used to study languages to learn how they evolved from a common ancestor, she said. And, like plants found on Earth, modern languages are dissected with this software. A word is similar to a specific gene in a plant and can be traced, connected and compared to other languages.

“One of the nice things about this research area is that there is a lot of very nice mathematics in it, so even if you don’t know that much biology you can still make big contributions,” Warnow said.

Claire can be reached at [email protected].