Bethany Korom

Bethany Korom

  • Undergraduate Student

Quantifying the heterogeneity of viral gene expression during infection at the single-cell level

Sailendharan Sudakaran

Sailendharan Sudakaran

Madison Microbiome Hub Manager / Multi Omics Hub Coordinat

  • Research Staff

Campus resource to support researchers interested in tackling a broad spectrum of microbiome research

Bradley Schwab

Bradley Schwab

  • Undergraduate Student

In silico modeling of viral infections and pre-biotic chemical reaction networks

Manuel Garavito

Manuel Garavito

  • Postdoctoral Associate

Genetic bases of multi-species bacterial communities

Page Lab

Algorithms for data mining and machine learning, and their applications to biomedical data, especially clinical and high-throughput genetic and other molecular data. Of particular interest are inductive logic programming (ILP) and other multi-relational learning techniques capable of dealing with complex data points (such as molecules or clinical histories) and producing logical rules.

Vetsigian Lab

Dynamics of microbial interactions in natural and synthetic microbial communities. The lab develops protocols for quantifying the community dynamics at the phenotypic and genetic levels, and seek simplified theoretical models that reproduce aspects of the experimentally measured dynamics.

Roy Lab

A computational biology group interested in developing statistical computational methods to understand regulatory networks driving cellular functions. The lab works to identify networks under different environmental, developmental and evolutionary contexts, comparing these networks across contexts, and construct predictive models from these networks.

Loewe Lab

Envisioning new ways to help biologists capture their ideas as models in the larger context of Evolutionary Systems Biology. Our lab aims to improve the quality of these models by quantifying evolution with increasing precision.

Yin Research Group

Investigating how living organisms cooperate or compete in diverse and changing environments. Methods and perspectives are drawn from many fields, including ecology, evolution, molecular biology, physics, chemistry, engineering, mathematics, and computer science. The lab uses data-driven mechanistic and statistical models to predict when microbes or other organisms will persist or perish, with a broad goal of promoting human health through effective management of microbe-host interactions.

Sridharan Lab

The main scientific focus of the lab is in defining how the epigenome controls cell identity. We want to know how non-genetic information controls functional specialization of a cell and use this knowledge to direct efficient conversion of desired cell types with the ultimate goal of improving stem cell based therapy.