Savas Tay Lab

Savas Tay is a bioengineer and systems biologist who works at the interface of biology, physics, and nanoengineering. His main goal in science is to understand "how life works" from an engineer’s perspective, and use this knowledge to manipulate cells and pathways to help cure diseases. On the technology front, his lab develops high-throughput and high-content single-cell analysis devices by integrating microfluidics and optics.

Prof. Tay is joining University of Chicago as an Associate Professor in the summer of 2016 from ETH Zurich in Switzerland. A main focus for Savas Tay will be to understand the role of molecular pathway dynamics in environmental sensing, pathogen recognition, cell-to-cell signaling and cellular information processing. He performs precision measurements on living cells and develops predictive models of complex biological systems like the immune system. Such models can serve as a rapid test-bed for drug studies and genome editing applications.

Microfluidic technologies developed by the Tay Lab create realistic environments that mimic living tissue, and measure dynamic processes in individual cells with extreme precision and throughput, adding majorly to the systems biology push at the Tay Lab. Tay is also interested in translating such technologies to real-life biomedical applications.

His work on NF-κB, a key transcription factor that regulates thousands of immune genes, was published in leading scientific journals such as Nature, Cell and PNAS. He discovered that cells activate NF-κB in an all-or-none fashion, similar to a digital switch. Recently, he found that molecular noise improves cellular signal transmission, and showed how oscillatory inputs control transcriptional dynamics by synergizing with noise.

Before becoming interested in biological research, Dr. Tay was an optical physicist. His achievements in optics include the development of the first updateable holographic 3-D display, infrared-sensitive holographic materials for optical communications and bioimaging, tunable photonic crystal devices, and plasmonic thermal emitters for infrared imaging.

More on Tay’s research and publications can be found in his ETH website.

Ishanu Chattopadhyay Lab

The ZeD lab@UChicago, led by Professor Ishanu Chattopadhyay, investigates the core algorithmic principles behind data analysis with minimal human intervention, and little or no access to domain expertise. We are interested in unraveling complex phenomena in biology, biomedicine, clinical decision-making, epidemiology of complex diseases, and human social interactions. We are also generally interested in multi-scale dynamical systems, such as predictive modeling of weather and global seismic phenomena.

Dionysios Antonopoulos Lab

The Antonopoulos Lab is presently focused on studying complex microbial communities in natural environments, including both topsoil and subsurface environments. The group is utilizing a combination of high-throughput cultivation and next-gen sequencing-enabled approaches to study them. Many of the same metagenomics-enabled approaches to studying these systems are being used by the lab group to study human gastrointestinal microbial communities in the context of several digestive diseases.  This work is in collaboration with faculty in the Section of Gastroenterology at the University of Chicago.

Jack Gilbert Lab

My laboratory focuses on numerous key aspects of how bacteria assemble into distinct communities, the metabolic mechanisms that structure these assemblages, and whether these systems can be predictively modeled over time and space. We use next generation sequencing approaches to access phylogenetic and functional information embedded within the genes of bacteria that populate these communities.  Our goal is to use this information to reconstruct community interactions, and ultimately to build statistical models of the microbial ecosystem.  These models will help us visualize large-scale patterns, generate predictions of responses to changes in an environment, and to identify gaps in our sampling campaigns.

Robert Grossman Lab

Dr. Grossman research group focuses on bioinformatics, data mining, cloud computing, data intensive computing, and related areas.  Current research projects include: Bionimbus (, a cloud-based system for managing, analyzing and sharing genomic data and Sector/Sphere (, a cloud-based system for data intensive computing.  He is also interested in developing new algorithms for the large scale analysis of genomic and phenotypic data.

Folker Meyer Lab

Dr. Meyer builds research software to study microbial communities.

Michael Rust Lab

My lab is interested in understanding how the properties of living cells emerge from the stochastic reactions of molecular components. We use a mixture of biophysical, biochemical, genomic, mathematical modeling, and single-cell microscopy approaches to link the properties of molecules to the systems-level behavior of cells.

Andrey Rzhetsky Lab

My main interest is in (asymptotic) understanding how phenotypes, such as human healthy diversity and maladies, are implemented at the level of genes and networks of interacting molecules. To harvest as much information about known molecular interactions as possible, my group runs a large-scale text-mining effort aiming at analysis of a vast corpus of biomedical publications. Currently we can extract from text automatically about 500 distinct flavors of relations among biomedical entities (such as bind, activate, merystilate, and transport). To sharpen our text-mining axes, we are actively designing related models and computational applications. Furthermore, in cooperation with our experimentally talented colleagues, we are striving to use text-mined networks to understand, interpret and refine high- or low-throughput experimental data. We are also computationally generating biological hypotheses that our generous collaborators are attempting to test experimentally. My older (still smoldering) passion is in developing and applying computational methods related to phylogenetics and evolutionary biology.

Barbara Stranger Lab

The overriding research interest of the lab is trying to disentangle the relationship between genotype and phenotype, and to understand the forces shaping functional genetic variation in humans. We approach this broad topic through a combination of experimental and computational approaches: We employ high-throughput functional genomics and genome-wide association analysis (GWAS) to identify variable regions of the genome with functional effects on aspects of the transcription profile (mRNA expression levels, protein expression level, splicing, etc.), and to investigate how these patterns are altered in different contexts (sex, cellular activation state, tissue type). At the same time, we examine the levels and patterns of segregating nucleotide variation within human populations (population genetics) and between human and other species (comparative genomics) to identify regions of the genome with patterns of variation suggestive of natural selection or selective constraint over time, as such patterns may be indicative of function. The lab has interests in phenotypes of the immune system, neurology, cancer and reproductive health and disease.

Raymond Moellering Laboratory

Research in the Moellering Lab lies at the interface of chemistry and biology, with an eye towards understanding and intervening in human disease. By integrating chemical synthesis, cell biology and mass spectrometry platforms, our research aims to identify novel biological mechanisms underlying diseases such as diabetes and cancer, and to subsequently develop innovative diagnostic and therapeutic modalities to impact these disorders. We are specifically interested in developing new chemical tools and technologies to study complexity and dynamics in the proteome, thus enabling targeted manipulation of protein targets and the pathways they govern.

Kevin White Lab

The White lab studies the coordinated action of networks of genes that control developmental, disease and evolutionary processes. We have particular focus on discovery of genetic factors that contribute to cancer development and progression, and on building genome-wide models of transcriptional networks. We use an integrated approach that makes use of genome and transcriptome sequencing, large-scale protein-protein and protein-DNA interaction analyses, measurement of chromatin state, systematic RNAi and CRISPR mutational analysis, and high throughput functional analyses of genomic regulatory elements.