Explore ARCExplore ARC

Patrick Schloss

By |

The Schloss lab is broadly interested in beneficial and pathogenic host-microbiome interactions with the goal of improving our understanding of how the microbiome can be used to reach translational outcomes in the prevention, detection, and treatment of colorectal cancer, Crohn’s disease, and Clostridium difficile infection. To address these questions, we test traditional ecological theory in the microbial context using a systems biology approach. Specifically, the laboratory specializes in using studies involving human subjects and animal models to understand how biological diversity affects community function using a variety of culture-independent genomics techniques including sequencing 16S rRNA gene fragments, metagenomics, and metatranscriptomics. In addition, they use metabolomics to understand the functional role of the gut microbiota in states of health and disease. To support these efforts, they develop and apply bioinformatic tools to facilitate their analysis. Most notable is the development of the mothur software package (https://www.mothur.org), which is one of the most widely used tools for analyzing microbiome data and has been cited more than 7,300 times since it was initially published in 2009. The Schloss lab deftly merges the ability to collect data to answer important biological questions using cutting edge wet-lab techniques and computational tools to synthesize these data to answer their biological questions.

Given the explosion in microbiome research over the past 15 years, the Schloss lab has also stood at the center of a major effort to train interdisciplinary scientists in applying computational tools to study complex biological systems. These efforts have centered around developing reproducible research skills and applying modern data visualization techniques. An outgrowth of these efforts at the University of Michigan has been the institutionalization of The Carpentries organization on campus (https://carpentries.org), which specializes in peer-to-peer instruction of programming tools and techniques to foster better reproducibility and build a community of practitioners.

The Schloss lab uses computational tools to integrate multi-omics tools in a culture-independent approach to understand how bacteria interact with each other and their host to drive processes such as colorectal cancer and susceptibility to Clostridium difficile infections.

Ho-Joon Lee

By |

Dr. Lee’s research in data science concerns biological questions in systems biology and network medicine by developing algorithms and models through a combination of statistical/machine learning, information theory, and network theory applied to multi-dimensional large-scale data. His projects have covered genomics, transcriptomics, proteomics, and metabolomics from yeast to mouse to human for integrative analysis of regulatory networks on multiple molecular levels, which also incorporates large-scale public databases such as GO for functional annotation, PDB for molecular structures, and PubChem and LINCS for drugs or small compounds. He previously carried out proteomics and metabolomics along with a computational derivation of dynamic protein complexes for IL-3 activation and cell cycle in murine pro-B cells (Lee et al., Cell Reports 2017), for which he developed integrative analytical tools using diverse approaches from machine learning and network theory. His ongoing interests in methodology include machine/deep learning and topological Kolmogorov-Sinai entropy-based network theory, which are applied to (1) multi-level dynamic regulatory networks in immune response, cell cycle, and cancer metabolism and (2) mass spectrometry-based omics data analysis.

Figure 1. Proteomics and metabolomics analysis of IL-3 activation and cell cycle (Lee et al., Cell Reports 2017). (A) Multi-omics abundance profiles of proteins, modules/complexes, intracellular metabolites, and extracellular metabolites over one cell cycle (from left to right columns) in response to IL-3 activation. Red for proteins/modules/intracellular metabolites up-regulation or extracellular metabolites release; Green for proteins/modules/intracellular metabolites down-regulation or extracellular metabolites uptake. (B) Functional module network identified from integrative analysis. Red nodes are proteins and white nodes are functional modules. Expression profile plots are shown for literature-validated functional modules. (C) Overall pathway map of IL-3 activation and cell cycle phenotypes. (D) IL-3 activation and cell cycle as a cancer model along with candidate protein and metabolite biomarkers. (E) Protein co-expression scale-free network. (F) Power-low degree distribution of the network E. (G) Protein entropy distribution by topological Kolmogorov-Sinai entropy calculated for the network E.

 

Samuel K Handelman

By |

Samuel K Handelman, Ph.D., is Research Assistant Professor in the department of Internal Medicine, Gastroenterology, of Michigan Medicine at the University of Michigan, Ann Arbor. Prof. Handelman is focused on multi-omics approaches to drive precision/personalized-therapy and to predict population-level differences in the effectiveness of interventions. He tends to favor regression-style and hierarchical-clustering approaches, partially because he has a background in both statistics and in cladistics. His scientific monomania is for compensatory mechanisms and trade-offs in evolution, but he has a principled reason to focus on translational medicine: real understanding of these mechanisms goes all the way into the clinic. Anything less that clinical translation indicates that we don’t understand what drove the genetics of human populations.

Srijan Sen

By |

Srijan Sen, MD, PhD, is the Frances and Kenneth Eisenberg Professor of Depression and Neurosciences. Dr. Sen’s research focuses on the interactions between genes and the environment and their effect on stress, anxiety, and depression. He also has a particular interest in medical education, and leads a large multi-institution study that uses medical internship as a model of stress.

Jun Li

By |

Jun Li, PhD, is Professor and Chair for Research in the department of Computational Medicine and Bioinformatics and Professor of Human Genetics in the Medical School at the University of Michigan, Ann Arbor.

 Prof. Li’s areas of interest include genetic and genomic analyses of complex phenotypes, including bipolar disorder, cancer, blood clotting disease, and traits involving animal models and human microbiomes. Our approach emphasizes statistical analysis of genome-scale datasets (e.g, gene expression and genotyping data, results from next-generation sequencing), evolutionary history, bioinformatics, and pattern recognition.

Sriram Chandrasekaran

By |

Sriram Chandrasekaran, PhD, is Assistant Professor of Biomedical Engineering in the College of Engineering at the University of Michigan, Ann Arbor.

Dr. Chandrasekaran’s Systems Biology lab develops computer models of biological processes to understand them holistically. Sriram is interested in deciphering how thousands of proteins work together at the microscopic level to orchestrate complex processes like embryonic development or cognition, and how this complex network breaks down in diseases like cancer. Systems biology software and algorithms developed by his lab are highlighted below and are available at http://www.sriramlab.org/software/.

– INDIGO (INferring Drug Interactions using chemoGenomics and Orthology) algorithm predicts how antibiotics prescribed in combinations will inhibit bacterial growth. INDIGO leverages genomics and drug-interaction data in the model organism – E. coli, to facilitate the discovery of effective combination therapies in less-studied pathogens, such as M. tuberculosis. (Ref: Chandrasekaran et al. Molecular Systems Biology 2016)

– GEMINI (Gene Expression and Metabolism Integrated for Network Inference) is a network curation tool. It allows rapid assessment of regulatory interactions predicted by high-throughput approaches by integrating them with a metabolic network (Ref: Chandrasekaran and Price, PloS Computational Biology 2013)

– ASTRIX (Analyzing Subsets of Transcriptional Regulators Influencing eXpression) uses gene expression data to identify regulatory interactions between transcription factors and their target genes. (Ref: Chandrasekaran et al. PNAS 2011)

– PROM (Probabilistic Regulation of Metabolism) enables the quantitative integration of regulatory and metabolic networks to build genome-scale integrated metabolic–regulatory models (Ref: Chandrasekaran and Price, PNAS 2010)

 

Research Overview: We develop computational algorithms that integrate omics measurements to create detailed genome-scale models of cellular networks. Some clinical applications of our algorithms include finding metabolic vulnerabilities in pathogens (M. tuberculosis) using PROM, and designing multi combination therapeutics for reducing antibiotic resistance using INDIGO.

Research Overview: We develop computational algorithms that integrate omics measurements to create detailed genome-scale models of cellular networks. Some clinical applications of our algorithms include finding metabolic vulnerabilities in pathogens (M. tuberculosis) using PROM, and designing multi combination therapeutics for reducing antibiotic resistance using INDIGO.

Sebastian Zoellner

By |

Sebastian Zöllner is a Professor of Biostatistics. He also holds an appointment in the Department of Psychiatry. Dr. Zöllner joined the University of Michigan after a postdoctoral fellowship in the Department of Human Genetics at the University of Chicago. His research effort is divided between generating new methods in statistical genetics and analyzing data. The general thrust of his work is problems from human genetics, evolutionary biology and statistical population biology.

Gilbert S. Omenn

By |

Gilbert Omenn, MD, PhD, is Professor of Computational Medicine & Bioinformatics with appointments in Human Genetics, Molecular Medicine & Genetics in the Medical School and Professor of Public Health in the School of Public Health and the Harold T. Shapiro Distinguished University Professor at the University of Michigan, Ann Arbor.

Doctor Omenn’s current research interests are focused on cancer proteomics, splice isoforms as potential biomarkers and therapeutic tar- gets, and isoform-level and single-cell functional networks of transcripts and proteins. He chairs the global Human Proteome Project of the Human Proteome Organization.

Qiang Zhu

By |

Dr. Zhu’s group conducts research on various topics, ranging from foundational methodologies to challenging applications, in data science. In particular, the group has been investigating the fundamental issues and techniques for supporting various types of queries (including range queries, box queries, k-NN queries, and hybrid queries) on large datasets in a non-ordered discrete data space. A number of novel indexing and searching techniques that utilize the unique characteristics of an NDDS are developed. The group has also been studying the issues and techniques for storing and searching large scale k-mer datasets for various genome sequence analysis applications in bioinformatics. A virtual approximate store approach to supporting repetitive big data in genome sequence analyses and several new sequence analysis techniques are suggested. In addition, the group has been researching the challenges and methods for processing and optimizing a new type of so-called progressive queries that are formulated on the fly by a user in multiple steps. Such queries are widely used in many application domains including e-commerce, social media, business intelligence, and decision support. The other research topics that have been studied by the group include streaming data processing, self-management database, spatio-temporal data indexing, data privacy, Web information management, and vehicle drive-through wireless services.