Sergei Maslov

Signals from the environment set off a cascade of changes that affect different genes in different ways. Therefore, traditionally it has been difficult to study how such signals influence an organism. In a new study, researchers have developed a machine learning approach called FUN-PROSE to predict how genes react to different environmental conditions.

Human bodies contain trillions of microbes, so much that the number of microbes rival the number of human cells in a body. These microbes help shape many of our biological functions. For example, microbes in the gut break down food into small molecules called metabolites, many of which are important for human health. Measuring species composition of the microbial community using metagenomics has become a quick and automated process, while measuring the concentrations of metabolites produced by those microbes, a process called metabolomics, is much more difficult and expensive.

A new Center for Artificial Intelligence and Modeling will be established at the Carl R. Woese Institute for Genomic Biology. It will be led by Sergei Maslov (CABBI), a professor of bioengineering and Bliss Faculty Scholar and Olgica Milenkovic (BSD/CGD/GNDP), a Donald Biggar Willett Scholar and Franklin Woeltge Professor of Electrical and Computer Engineering. The goal of CAIM is to provide biological groups with appropriate expertise in computational sciences.

During the earliest months of 2020, COVID-19 seemed like an innocuous event that was too geographically distant to affect the Illinois community. In fact, by March 10th there were only 19 confirmed cases. Nevertheless, Nigel Goldenfeld (BCXT leader/GNDP), former Swanlund Endowed Chair and professor of physics, and Sergei Maslov (BCXT/CABBI), a professor of bioengineering and Bliss Faculty Scholar, were worried. The news from China and Italy was concerning and in four days a significant portion of students, faculty, and staff were going to leave for spring break.

Many aspects of economic productivity, including gross domestic product measurements, have been steadily rising over the past three decades. Although one would assume that the products that are available in the economy have become more diverse, a recent study has suggested otherwise. The results may help economists rethink how they measure the health of the economy.

The COVID-19 pandemic has gone on much longer than many predicted in its earliest months. The world has closely watched its progression, with infection rates measured out on graphs in large waves that sometimes taper to extended plateaus, rather than disappearing as traditional epidemiological models would have suggested they should. Meanwhile, scientists have been working to better understand the factors governing the wave and plateau dynamics of the spread of COVID-19, to be able to better forecast future outbreaks in this pandemic and future epidemics.

Microbial communities often contain several species that coexist even though they share similar metabolic abilities. How they do so is unclear. Researchers have now developed a model to show that if these species have complementary preferences for what they consume, they can more easily coexist.

Scientists at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory and the University of Illinois Urbana-Champaign (UIUC) have developed a new mathematical model for predicting how epidemics such as COVID-19 spread. This model not only accounts for individuals’ varying biological susceptibility to infection but also their levels of social activity, which naturally change over time. Using their model, the team showed that a temporary state of collective immunity—which they termed “transient collective immunity”—emerged during the early, fast-paced stages of the epidemic.

The human gut consists of a complex community of microbes that consume and secrete hundreds of small molecules—a phenomenon called cross-feeding. However, it is challenging to study these processes experimentally. A new study, published in Nature Communications, uses models to predict cross-feeding interactions between microbial species in the gut. Predictions from such computational methods could eventually help doctors get a more complete understanding of gut health.