Program Investigators
Antonio Auffinger
Dr. Auffinger’s research program lies at the intersection of probability theory and mathematical physics. His main focus is the study of high-dimensional complex disordered systems, such as spin glasses. These models display an exponentially large number of metastable states; their complexity, structure, and phase transitions are at the core of Auffinger’s research. He also investigates problems in random matrix theory and percolation.
Rosemary Braun
Dr. Braun’s research interests are in the areas of high-dimensional data analysis, machine learning, complex dynamical systems, and network science with application to living systems. An overarching goal is to elucidate how the structure of molecular interaction networks governs the dynamical response to perturbations. To this end, her computational group develops methods for network-based analysis of omics data and for extracting dynamics from short, sparse, and noisy time series observations. These methods are applied in collaboration with experimentalists to investigate patterns of gene regulation associated with environmental adaptation, development, and disease. REU students will explore using topological data analysis techniques to characterize changes in gene co-expression networks across circadian and developmental time.
Richard Carthew
Dr. Carthew studies the developmental biology of the fruit fly Drosophila melanogaster. An overarching goal is to understand how molecular- and cellular-scale activities manifest in the dynamic and robust formation of tissue- and organ-scale structures. To this end, his group develops methods to gain precise measurements of molecular and cellular features instrumental for development. These are applied in collaboration with theorists (including Dr. Madhav Mani) to investigate how complex and regular patterns of cell types are ordered in space and time. In the proposed REU, students will collect and analyze high-dimensional data from experiments in collaboration with graduate students and postdoctoral scholars in the Carthew group.
William Kath
Dr. Kath’s research interests are in the development of mathematical, computational, and statistical methods and their use to reveal evidence for biological mechanisms hidden within recent high-throughput biological data. Dr. Kath has extensive experience with dynamical systems and stochastic methods and a primary goal is to adapt and leverage such techniques for the study of omics data. He has collaborated or is collaborating with a number of experimental laboratories (Drs. Ravi Allada,Marco Gallio, and Carole LaBonne) investigating the genetic underpinning of circadian rhythms, mechanisms associated with thermotaxis (behavioral navigation in varying temperature environments), and early organismal development. In this project, QBREU trainees will learn to work with high-throughput data and will develop methods that shed light on the connections between cause and effect in such systems.
Carole LaBonne
Dr. LaBonne’s work is focused on understanding the gene regulatory networks that control cell-fate acquisition in vertebrate embryos using Xenopus as a model. Specifically, the lab studies the development of the neural crest, a stem cell population central to the evolution of the vertebrates and the links these cells have to the pluripotent cells of the early blastula. They seek to understand how the stemcell population forms, what controls the maintenance of its remarkable developmental potential, and how its migratory and invasive behavior is controlled. Recent work has increasingly focused on the control of pluripotency and transit to lineage restricted states at single-cell resolution. The lab deploys a range of approaches, including transcriptomics, proteomics, and quantitative imaging. The lab has collaborations with mathematical faculty including Drs. Rosemary Braun and William Kath.
Niall Mangan
Dr. Mangan’ s research interests are in the areas of numerical and analytic analyses of reaction-diffusion equations, data-driven methods for model identification, nonlinear dynamical systems, spatial-temporal organization of metabolism, and statistical learning. Her overarching interest is to find human-interpretable models and analysis to understand data from complex systems. Her group develops data-driven sparse-optimization methods to discover appropriate dynamical systems models for mass-action kinetic systems such as metabolism and regulatory networks. The group focuses on building robust methodologies to handle difficult real-world problems such as sparse manifold sampling, hidden variables, and noise. Her group also develops physics and chemistry-based models of systems to simulate behavior and interpret experiments, including work on C. elegans development with Dr. Erik Andersen. Students develop their own hypotheses, add new mechanisms to mathematical models, and apply and develop existing and new methods to data depending on their background and abilities.
Madhav Mani
Dr. Mani’s group pursues two synergistic approaches: a statistical, data-driven, approach to biology, focusing on developing new quantitative frameworks and algorithms for the analysis and understanding of live-imaging and single-cell sequencing data; and physical modeling of living systems to help parametrize and explore when a purely data-driven approach is not warranted or possible. This work spans Developmental Biology, Cell Biology, and Ecology, with the goal of achieving generality through quantitative abstractions. Students will focus on employing new quantitative approaches we have developed to a novel system.
Jiping Wang
Dr. Wang’s research mainly concerns development of statistical methods for high-throughput genomic and genetic data by collaboration with biologists. Some of his working topics include nucleosome positioning, circadian gene expression profiling, fMRI/MRI data in human brain mapping, network analysis, TCGA data analysis,DNA methylation differentiation, ChIP-seq data analysis, histone modification, and microbial species number estimation in metagenomic studies. Recently, his group has been working on machine learning methods for natural language processing, CRISPR-Cas9 efficiency, and nucleosome positioning prediction problems. His group also developed some free software tools, including NuPoP (nucleosome positioning prediction) and SPECIES (species number estimation), NuCMap (chemical mapping of nucleosome positions), DegNorm (degradation normalization for RNA-seq data), and RiboDiPA (differential pattern analysis for Ribo-seq data), which have been widely used by the scientific communities in these fields.
Example Undergraduate Summer Projects from the 2020 Virtual Program
| Olivia Dunne, The University of Chicago Studying Pattern Formation in Zebrafish Stripes Mentor: Alexandria Volkening |
| Gabriel Petersen, Northwestern University Mathematics of t-SNE and PCA Mentor: Antonio Auffinger |
| Sean Jordan, University of Maryland The Empirical Embedding Statistic Mentors: William Kath, Madhav Mani, Eric Johnson |
| Kelly Paquin, The Ohio State University Cycle Detection of Circadian genes of Drosophila through luciferase expression Mentor: William Kath, Ravi Allada, Bridget Lear, Elan Ness-Cohn |
| Nathan Burg, University of Illinois – Chicago Morphogenic Impact of Disrupting the Gene Regulatory Network Underpinning Drosophila Compound Eye Development Mentors: Richard Carthew, Kevin Gallagher |
| Saurav Kiri, New College of Florida Differential Analysis and Translation Efficiency Analysis of Ribo-Seq Data Mentors: Jiping Wang, Alec Wang, Matthew Hope, Keren Li |
| Rohan Mehra, Rutgers University Analyzing Gene Expression Level Difference in Single-Cell Hematopoietic Cells Stem Cells between Aged and Young Donors Mentors: Jonathan Desponds, William Kath |
| Karan Gowda, Northwestern University Investigation of the level of natural variance in conserved insulin signaling pathway Mentors: Eric Andersen, Gaotian Zhang |