By using statistical methods to compare genomic data across species, such as chimpanzees and humans, the Przeworski Lab is gaining insights into the origins of genetic variation and adaptation. (Photo: Common chimpanzee at the Leipzig Zoo. Thomas Lersch, Wikimedia Commons.)

Launched approximately 100 years ago, population genetics is a subfield within evolutionary biology that seeks to explain how processes such as mutation, natural selection, and random genetic drift lead to genetic variation within and between species. Population genetics was originally born from the convergence of Mendelian genetics and biostatistics, but with the recent availability of genome sequencing data and high-performance computing technologies, it has bloomed into a mature computational science that is providing increasingly high-resolution models of the processes that drive evolution.

Molly Przeworski, a professor in the Columbia University Departments of Biological Sciences and Systems Biology, majored in mathematics at Princeton before beginning her PhD in evolutionary biology at the University of Chicago in the mid-1990s. While there, she realized that the availability of increasingly large data sets was changing population genetics, and has since been interested in using statistical approaches to investigate questions such as how genetic variation drives adaptation and why mutation rate and recombination rate differ among species. In the following interview, she describes how population genetics is itself evolving, as well as some of her laboratory’s contributions to the field.

The Columbia University Center for Topology of Cancer Evolution and Heterogeneity will combine mathematical approaches from topological data analysis with new single-cell experimental technologies to study cellular diversity in solid tumors. Image courtesy of Raul Rabadan.

The National Cancer Institute’s Physical Sciences in Oncology program has announced the creation of a new center for research and education based at Columbia University. The Center for Topology of Cancer Evolution and Heterogeneity will develop and utilize innovative mathematical and experimental techniques to explore how genetic diversity emerges in the cells that make up solid tumors. In this way it will address a key challenge facing cancer research in the age of precision medicine — how to identify the clonal variants within a tumor that are responsible for its growth, spread, and resistance to therapy. Ultimately, the strategies the Center develops could be used to identify more effective biomarkers of disease and new therapeutic strategies.

Image courtesy of Germán Plata and Dennis Vitkup.

Columbia News has just published an article covering recent research by associate professor Dennis Vitkup and postdoctoral research scientist Germán Plata that uses simulations of bacterial metabolism as a lens for studying how phenotypes adapt and diversify across evolutionary time scales. The article reports:

Despite their omnipresence, microbial evolutionary adaptations are often challenging to study, partly due to the difficulty of growing diverse bacteria in the lab. “Probably less than a dozen bacteria are really well studied in the laboratory,” Vitkup says.

Writing in the journal Nature this past January, Vitkup and Plata applied computational tools to investigate bacterial evolutionary adaptations by simulating metabolism for more than 300 bacterial species, covering the entire microbial tree of life.

A graph representing the sequence of genomic alterations in chronic lymphocytic leukemia (CLL). Each node represents a mutation, with arrows indicating temporal relationships between them. The size of the nodes indicates the number of patients in the study who exhibited the alteration, while the thickness of the lines shows how often the temporal relationships between nodes were seen. The method the researchers use enabled them to identify multiple, distinct evolutionary patterns in CLL.

As biologists have gained a better understanding of cancer, it has become clear that tumors are often driven not by a single mutation, but by a series of genetic changes that correspond to particular stages of cancer progression. In this sense, a tumor is constantly evolving, with different groups of cells that harbor distinctive mutations multiplying at different rates, depending on their fitness for particular disease states. As the search for more effective cancer diagnostics and therapies continues, one key question is how to disentangle the order in which mutations occur in order to understand how tumors change over time. Being able to predict how a tumor will behave based on signs seen early in the course of disease could enable the development of new diagnostics that could better inform treatment planning.

In a paper just published in the journal eLife, a team of investigators led by Department of Systems Biology Associate Professor Raul Rabadan reports on a new computational strategy for addressing this challenge. Their framework, called tumor evolutionary directed graphs (TEDG), considers next-generation sequencing data from tumor samples from a large number of patients. Using TEDG to analyze cancer cells in patients with chronic lymphocytic leukemia (CLL), they were able to develop a model of how the disease’s mutational landscape changes from its initial onset to its late stages. Their findings suggest that CLL may not be just the result of a single evolutionary path, but can evolve in alternative ways.