Below is a list of Bioinformatics affiliated faculty with rotation opportunities in their labs. Nearly all faculty are potential rotation mentors, however specific rotation opportunities are listed below. These research options are available for both first year graduate students and Master’s students. If there is a particular faculty member you would like to work with who is not listed, you are encouraged to contact them to learn of future
For students interested in a lab rotation with a PIBS faculty member, a list of affiliated PIBS faculty and their respective research topics are available here:
Current Rotations Opportunities
Department of Computational Medicine and Bioinformatics (DCMB)
Alan Boyle (Computational Medicine & Bioinformatics)
We use modern genomics techniques and high-throughput experiments to explore biological systems. We can leverage wet lab and computational tools to help answer biological problems that were previously intractable. We aim to combine computational approaches with high-throughput biological assays to better understand the whole human transcriptional regulatory system. A detailed description of my lab's research interests, and a list of recent publications, can be found on the Boyle lab web site: http://BoyleLab.org.
Specific projects include variations on:
- Logic of gene regulatory control
- Machine learning tools for predicting the effect of variants on gene regulation
- Regulatory effect on gene splicing and its effect on breast cancer
- Analysis of enhancer-promoter interactions
- Genome-wide screens of enhancers, promoters, silencers, and enhancer blockers
- Study of the efficiency of the CRISPR system
Peter Freddolino (Computational Medicine & Bioinformatics/Biological Chemistry)
We are interested in combining approaches from microbial genetics, biophysics, and computational biology to understand how and why regulatory networks function, and ultimately, to improve our ability to design synthetic biological systems for specific purposes. Some specific projects include:
- Mapping and characterizing the landscape of transcriptionally activating and transcriptionally silencing contexts present on bacterial chromosomes (https://doi.org/10.1016/j.cels.2019.02.004)
- Combining high-throughput computational predictions with genetic and biochemical experiments to identify the functions of currently unannotated bacterial proteins (https://academic.oup.com/nar/article/3787871/COFACTOR-improved-protein-function-prediction-by)
- Identifying novel mechanisms through which regulatory networks enable cells to adapt to new conditions (https://elifesciences.org/articles/31867 , http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1003617)
Ivo Dinov (Computational Medicine & Bioinformatics/Nursing)
The Statistics Online Computational Resource (SOCR) develops, validates and shares resources for scientific computing, predictive big data analytics, statistical computing, and decision support. We welcome students, fellows, trainees and researchers interested in participating in SOCR projects (http://SOCR.umich.edu) and collaborating with faculty and researchers on advanced analytics, biomedical informatics, and high-throughput health information technologies.
Lana Garmire (Computational Medicine & Bioinformatics)
Looking to recruit 5 students this year once relocating to UM. The areas of interest include:
- Single-Cell Bioinformatics and Genomics
- Multi-omics and Imaging Data Integration
- Prognostic and Diagnostic Biomarker Modeling
- Computational Biology of non-coding RNAs.
For more information see: http://garmiregroup.org/
Alla Karnovsky (Computational Medicine & Bioinformatics)
Metabolomics is a rapidly developing field that uses a variety of analytical methods to detect small organic molecules and follow their changes in tissues and biofluids.
I am interested in developing new methods and tools for the analysis of metabolomics data and integrating them with other omics data. We recently developed a tool called Metscape for the analysis and visualisation of metabolomics data (http://metscape.ncibi.org/). It allows users to build and analyze networks of genes and compounds, identify enriched pathways from expression profiling data, and visualize changes in metabolomics data. As more and more metabolomics data sets become available there is great interest in more sophisticated data analysis methods. Several possible projects in this area include developing new methods for metabolite enrichment analysis, predicting metabolite changes based on transcriptome and proteome, and building disease metabolite networks.
Jeffrey Kidd (Computational Medicine & Bioinformatics)
Several projects related to genomics research including:
- structural and copy number variation in domestic dogs
- retroelement insertion polymorphism in domestic dogs
- assessment of genome assembly algorithms for determining rearrangement breakpoints
- evolution of pseudo-autosomal regions in non-human primates
- investigation of allele frequency data for disease genes in non-human primates
- inference of human demographic history from full genome sequence data
Jacob Kitzman (Computational Medicine & Bioinformatics/Human Genetics)
Several rotation projects are available involving technology development for massively parallel sequencing and high-throughput functional analysis.
One major goal of our current work is to use massively parallel mutagenesis coupled with functional screens to systematically measure the effects of all possible mutations to genes implicated in cancer and other disorders. We then use these large-scale measurements, along with lists of known pathogenic and neutral variants, to train models to prospectively classify all other alleles as to their pathogenicity. (see e.g., Kitzman et al Nature Methods 2015, or Starita et al, Genetics, 2015).
Rotation students with either computational or experimental backgrounds (or both) are welcome. Please email Jacob (firstname.lastname@example.org) or stop by (4811 Med Sci II) to learn more.
Matthias Kretzler (Computational Medicine & Bioinformatics/Internal Medicine)
The research in Dr. Kretzler’s team focuses on the analysis of molecular mechanism of glomerular failure. Using integrated biology approaches the group defines transcriptional networks in human glomerular diseases and integrates them with complex clinical data sets and other large-scale data sets. The NEPTUNE network offers the unique opportunity to analyze a prospective cohort of glomerular disease patients with high-resolution clinical and molecular phenotyping. An international multi-disciplinary research team will enable large scale data integration across the genotype-phenotype continuum of glomerular failure with carefully monitored environmental exposures, genetic predispositions, epigenetic markers, transcriptional networks, proteomic profiles, metabolic fingerprints, digital histological biopsy archive and prospective clinical disease characterization.
Opportunities for bioinformatics graduate student rotations include integrative analyzes across species along the genotype-phenotype continuum in an interdisciplinary research team.
Possible projects include work on strategies for (1) integrating systems information into genetic association analyses, (2) identifying molecular subsystems affected by multiple candidate genetic variants, and (3) identifying shared mechanisms across tissues, species, or phenotype.
Expansion of the project into a Ph.D. thesis is possible and funds for support might be available.
Background in analysis of large-scale data sets is preferred; basic concepts of molecular biology, statistics and programming are required; and ability to function and interact in a multidisciplinary team is essential.
Jun Li (Computational Medicine & Bioinformatics/Human Genetics)
Professor Li's rotation opportunities focus on aspects of quantitative analysis in statistical genetics, biomedical data science, or cancer genomics. Current open projects include: single-cell RNAseq data analysis, application of single-cell RNAseq in studying germ cell development and cancer relapse, methodological and theoretical research in clustering, mutation patterns in the human genome, multi-omics data integration involving metabolomic, proteomic, and transcriptomic data, QTL mapping in rat models of metabolomic traits and emotional traits.
Jie Liu (Computational Medicine & Bioinformatics)
Rotation positions are available in Liu Lab. The students are expected to develop novel machine learning methods for real-world bioinformatics and medical informatics problems, mentored by Dr Jie Liu. From the methodology perspective, we are interested in deep learning, representation learning, geometric (e.g., graph and manifold) data analysis, spectral methods, kernel methods, and probabilistic approaches. For applications, we are interested in dynamics of genome conformation and nuclear organization, single cell analysis, gene regulation, cellular networks, and the genetic basis of human diseases.
As future biomedical problems become more and more data-rich and data-driven, Liu Lab is committed to train next generation researchers with extensive data science experience and skills. More details about Liu Lab can be found at https://jieliu6.github.io/. Feel free to talk with me if you're interested!
Rajasree Menon (Computational Medicine & Bioinformatics)
My main research is on the role of alternative splice variants in human cancers. Experience in some programming and statistics will be an advantage.
Kayvan Najarian (Computational Medicine & Bioinformatics)
Traumatic brain injury (TBI) is a major cause of disability and death and each year around two millions TBI occur in the United States with the approximately 3% of mortality across all TBI severities. About 50% of the deaths are within the first two hours after injury. Therefore, the speed and accuracy are vital in diagnosing the TBI for which a computer-aided trauma decision making system can help reduce mortality, long-term complications, and the associated costs. Developing such a system is challenging due to the inherent noise associated with images, quality of the images, different scales and capturing orientations of the images, variation in the size, shape and location of ventricles from patient to patient, etc. A fully-automated system to identify and assess traumatic brain injury and specially localize the damage would be beneficial in guiding real-time clinical diagnosis as well as quality assurance. The proposed project intends to design a fully-automated system to utilize advanced image processing and machine learning techniques to analyze CT brain images independent of human input. Our preliminary results show the promising results of the proposed system. We also intend to integrate and combine the information in CT images with other patient data (clinical, molecular, physiological, etc) to further improve the predictions / recommendations generated by the system.
Alexey Nesvizhskii (Computational Medicine & Bioinformatics/Pathology)
Alexey Nesvizhskii Lab has rotation opportunities in proteome informatics. Projects in the lab include 1) Development of computational methods for large-scale proteomics; 2) Analysis of protein-protein interaction data; 3) Integrative analysis across multiple omics datasets (RNA-seq transcriptomics, proteomics, etc.).
Stephen Parker (Computational Medicine & Bioinformatics)
Our research group uses an integrative approach in the general fields of computational biology and functional genomics. The major goal of the lab is to generate mechanistic knowledge about how disease susceptibility is genetically encoded in the non-coding portion of the genome, with a focus on type 2 diabetes (T2D). We accomplish this through an interdisciplinary combination of molecular/cellular and computational methods – we generate multiple high-throughput data sets on the genome, epigenome, transcriptome, and proteome across the human population and diverse species and in disease-relevant tissues/cells and use computational approaches to integrate and analyze this data.
We contribute to multiple international consortia:
- FUSION (Finland-United States Investigation of NIDDM genetics)
- AMP-T2D (Accelerating Medicines Partnership for T2D)
- TOPMed (Trans-Omics for Precision Medicine)
- InsPIRE (Integrated Network for Systematic analysis of Pancreatic Islet RNA Expression)
- MoTrPAC (Molecular Transducers of Physical Activity Consortium)
Projects spanning the lab and these consortia offer many exciting rotation opportunities.
Indika Rajapakse (Computational Medicine & Bioinformatics)
I have potential projects related to:
- Dynamics of the three dimensional structure of the human genome
- The Biochronicity Program at the Defense Advanced Research Projects Agency.
If you're very creative and would like to work at the interface of mathematics and biology, please contact me, I would love to discuss potential projects.
Arvind Rao (Computational Medicine & Bioinformatics)
We use image analysis and machine learning tools to understand the relationship between the cancer-induced phenotype and genome. This work involves the use of genomic analysis techniques (NGS, ChIP, DNA methylation etc.) coupled with imaged analysis across biological scale (such as histology, radiology and 3D microscopy). We look to leverage multiple public and local data sources to help answer questions at the interface. I would be very interested to hear from you and discuss ideas for potential rotation projects.
Maureen Sartor (Computational Medicine & Bioinformatics)
We have multiple exciting rotation opportunities in our lab! The focus of my laboratory is developing bioinformatics methods and tools for the analysis and interpretation of high-throughput molecular biology data, with special focus on genome-wide regulatory and epigenomics data. The biological focus of my laboratory is cancer, in particular oral cavity and oropharyngeal cancer. Rotation and dissertation projects are available working with cancer bioinformatics data. With the great promise of immunotherapies in cancer, there is a whole new subfield of immune bioinformatics that we are involved in. Rotation projects may either be geared towards applying methods to address a biological question or helping to develop a new method.
Other projects may be available in my lab - see sartorlab.ccmb.med.umich.edu. Feel free to come talk with me if you're interested!
Josh Welch (Computational Medicine & Bioinformatics)
Our research focuses on enabling biological discovery by applying novel computational approaches to genomic data. We develop and apply algorithms, machine learning methods, and statistical models for analysis of single cell genomic data. Broadly, we seek to understand what genes define the complement of cell types and cell states within healthy tissue, how cells differentiate to their final fates, and how dysregulation of genes within specific cell types contributes to human disease.
Sample rotation topics include:
- Integrating single cell RNA-seq and singl cell epigenome data
- Deep learning methods for analysis of single cell RNA-seq data
- Comparing single cell RNA-seq data across species and tissues
- Scaling single cell RNA-seq analysis methods to datasets with millions of cells
- Predicting effects of perturbations from single cell RNA-seq data
This is just a sampling of project ideas – there is much exciting work in this area for those with a love for both computational method development and biological discovery!
Jieping Ye (Computational Medicine & Bioinformatics)
Our project aims to develop novel machine learning and data mining methods to integrate and analyze high-dimensional genomes, connectomes, and multimodal brain images to discover diagnostic and prognostic markers for human brain diseases.
Yang Zhang (Computational Medicine & Bioinformatics)
The Yang Zhang Lab provides rotation opportunities on following projects:
- Protein folding and protein structure prediction. Several of methods developed in the Zhang Lab have been ranked as the world’s best and widely used by the community (e.g., https://zhanglab.ccmb.med.umich.edu/I-TASSER/). We are particularly interested in developing new deep neural-network methods to predict residue-level contact and distance maps from co-evolution of protein sequences. It brings out the exciting opportunities to solve the protein structure prediction problem without relying on templates as traditional homology-based approach does.
- Protein design and engineering. Protein design aims to computationally designing new protein molecules beyond natural proteins that have been created from millions of years of evolution. The project is to integrate biochemistry experiments and bioinformatics approaches to re-engineer cancer and antibody proteins with the purpose of improving the fold stability and biological functionalities of the wild-type sequences.
The Zhang Lab also works on ligand-protein docking and virtual screening, genome-wide function annotation with a focus on microbial genomes, and mutation-induced human disease predictions. The projects are supported by the National Institute of General Medical Sciences (GM083107, GM084222), the National Institute of Allergy and Infectious Diseases (AI134678), and the National Science Foundation (DBI1564756).
Current Rotations Opportunities
Center for Computational Medicine and Bioinformatics (CCMB)
CCMB faculty members may serve mentors for Bioinformatics Program graduate students as well as providing research opportunities for students.
Charles Brooks (Biophysics and Chemistry)
I have multiple opportunities for rotation students in the lab. These include:
Developing and applying free energy simulation methods to drug discovery and refinement
In this project the student will work with a team on the development of novel free energy simulation methods and analysis techniques that build on both statistical mechanics and foundational methods in modern statistics. Applications will be directed toward the refinement of small molecule inhibitors against protein-based targets, which include HIV-RT, menin-MLL, small molecular transcriptional activators, as well as other protein receptors associated with benchmarking and assessing evolving computational methods.
Novel in silico drug discovery through high-throughput flexible-ligand/flexible-receptor based docking on advanced computing hardware (GPUs)
The focus of this project is in hardening and refining in silico ligand discovery methods that utilize advanced computing platforms, such as GPUs, to achieve high-throughput and scalability in docking screens. Methods development will focus on establishment of optimal protocols for flexible-ligand/flexible-receptor docking as well as the use of machine learning techniques to improving the scoring of docked poses.
Utilizing machine learning algorithms based on auto-encoder/auto-decoder technology for exploration of small molecular and multiple sequence alignment protein sequence property analysis
This project will explore the development of auto-encoder/auto-decoder (AE/AD) machine learning methods to extract and interpolate properties associated with either small molecule collections of drug-like ligands or learning to predict evolutionary trends in proteins from AE/AD based analysis of multiple sequence alignments
Sally Camper (Human Genetics)
The focus of my laboratory is identifying the molecular basis for congenital birth defects using animal models of human disease. We have three NIH grants and a March of Dimes grant supporting active projects on the development of the neuroendocrine system (pituitary-hypothalamus), auditory system, and axial skeleton. There are at least three rotation projects that are appropriate for bioinformatics students. See the PIBS rotation pages for more details.
Heather Carlson (Medical Chemistry)
The Carlson group focuses on two areas: protein flexibility in computer-based drug design and protein-ligand structural databases. We are developing improved methods for mapping flexible protein surfaces in the drug design project. In the database project, we are mining our Binding MOAD (Mother of All Databases) set to investigate protein flexibility upon binding, amino acid propensities in binding sites, methods for identifying previously unknown binding sites on protein surfaces, and other properties of protein-ligand recognition.
Arul Chinnaiyan (Pathology)
The Michigan Center for Translational Pathology (MCTP), under the direction of Dr. Arul Chinnaiyan, employs genomic, proteomic, and bioinformatic approaches on clinical biospecimens to identify novel cancer biomarkers and therapeutic targets. The mission of the Center is to facilitate the discovery, validation, and implementation of candidate target genes/proteins in cancer diagnosis, prognosis, and therapy. The Center employs a multi-disciplinary approach, engaging talent from diverse disciplines ranging from medicine, pathology, bioinformatics, biostatistics, engineering, cytogenetics, and molecular therapeutics.
MCTP is seeking DCMB rotation students to work on the following projects:
- Comprehensive analysis of FOXA1 alterations in prostate cancer integrating whole genome data and transcriptome data. Primary paper is in press at Nature.
- Nomination of circular RNAs across various cancers that will serve as non-invasive cancer biomarkers. Follow up to Vo et al, Cell 2019.
- Defining immunogenomic signatures of cancer. Follow-up to Robinson et al, Nature 2017.
Melissa Duhaime (EEB)
The Duhaime Lab uses (meta)genomics to study the evolution and ecology of ocean viruses, their microbial hosts, and their interactions (i.e., infections) at both the community and single isolate levels. Fall and Winter rotations are available to study viruses of Lake Erie’s toxic algal blooms or Antarctica’s Southern Ocean—with focus on developing novel virus-host prediction algorithms based on both genomic and empirical data. The end goal is the incorporation of network theory to advance our understanding of these microbial predator-prey interactions and their ecosystem-level implications. Alternatively, students are encouraged to develop their own projects pertaining to viral and microbial genomics or evolutionary/ecological modeling. Please explore http://www-personal.umich.edu/~duhaimem/index.htm for more information..
Oliver He (ULAM)
Integrative bioinformatics-based vaccine design
Global public health has dramatically increased due to the successful and effective implementation of immunization programs that utilize major infectious disease vaccines. New vaccines against various infectious diseases (e.g., HIV and tuberculosis) and non-infectious diseases (e.g., cancers and allergies) are also being developed and studied. In the post-genomic era, strategies of vaccine development have progressed dramatically from traditional experiment-to-experiment approaches to bioinformatics-to-experiment approaches.
Reverse vaccinology is a cutting-edge vaccine design strategy that starts with bioinformatics analysis of microbial genomes. Dr. He's laboratory has developed Vaxign (http://www.violinet.org/vaxign/), the first web-based vaccine design system based on reverse vaccinology. This pipeline includes prediction of various features, e.g., antigen sublocation, transmembrane domains, adhesin probability, epitope binding to MHC class I and class II, sequence conservation among microbial genomes, and sequence similarities to host proteomics. Vaxign has been proven effective and efficient in vaccine target prediction in many use case applications.
Idse Heemskerk (CDB)
The goal of our lab is to understand how human pluripotent stem cells generate and interpret the chemical and physical signals that allow them to self-organize into spatial structures consisting of multiple cell types in vitro, and, by extension to the embryo, in vivo. By combining experimental and computational approaches we can answer currently intractable questions in developmental and stem cell biology.
Projects for students with a computational focus include:
- Image processing to follow signaling in thousands of individual cells as they form self-organized cell fate patterns.
- Analysis of high time resolution single cell signaling data and its relation to gene expression and cell fate.
- Mathematical modeling of differentiation and pattern formation.
Denise Kirschner (Microbiology and Immunology)
We use mathematical and computational modeling tools to study the host-pathogen interaction dynamics for the pathogen Mycobacterium tuberculosis. We are currently focusing on antibiotic treatment and vaccine development. We use many different tools and collaborate with a BSL3 non-human primate center to provide data that guides the development, testing and validation of our work.
Jennifer Linderman (Chemical Engineering)
The focus of this lab is in developing mathematical and computational models that focus on the role of receptor dynamics and signaling pathways in disease. Particular project: Analysis of simulation data using machine learning tools to identify biological phenotypes relevant to the immune response to M. tuberculosis
Mats Ljungman (Radiation Oncology & Environmental Health Sciences)
We have developed a set of new techniques that we call Bru-seq that allows us to obtain very detailed molecular signatures of transcriptional and post-transcriptional regulation in cells. These techniques are based on bromouridine labeling and isolation of nascent RNA to assess genome-wide rates of transcription (Bru-seq), RNA splicing and turnover (BruChase-seq), identification of active enhancer elements (BruUV-seq) and transcription elongation rates (BruDRB-seq). We have generated a large nascent transcriptomic data set and as a newly funded ENCODE mapping center we will generate much more with great opportunities for novel bioinformatics analyses. Potential rotation projects would include:
- Assessing transcriptional and post-transcriptional regulation of cell transitions from fibroblasts into iPSCs and then into neurons
- Transcriptional and post-transcriptional regulation of cellular responses such as TGFb stimulation, heat-shock, hypoxia and DNA damage responses
- Assessing transcription ongoing in repetitive DNA sequences in the genome across multiple cell lines and treatment conditions
David Lubman (Surgery)
Our project involves database searching and processing of archival mass spectral proteomic data to identify the presence of single amino acid variants (SAAVs) which are the equivalent of genomic SNPs. We seek to identify the presence of these SAAVs in cancer stem cells, circulating tumor cells and exosome cargo. Some of the data will compare different clonal areas from different parts of a tumor to study progression of a cancer. We have also recently generated proteomic data on single cells and are interested in the heterogeneity of SAAVs and also of glycosylation of cancer cells. Bioinformatic methods will be used to search and identify these changes. Ultimately, we are interested in how these SAAVs or glycosylations affect the structure of key proteins and are involved in cancer progression.
Nambi Nallasamy (Ophthalmology and Visual Sciences)
Rotation positions are available in the Nallasamy Lab at University of Michigan's Kellogg Eye Center.
Our work focuses on the development and application of machine learning techniques for clinical problems in ophthalmology. We are currently developing a unified ophthalmology dataset for all of Kellogg Eye Center merging clinical data and imaging studies. One of our goals is to create a comprehensive database for clinical and imaging research in ophthalmology that can be queried interactively or with natural language rather than SQL queries.
Specific topics for which machine learning techniques are currently being developed and evaluated include 1) the selection of lens implant power for cataract surgery (the most commonly performed surgery in the US) based on preoperative measurements and 2) the diagnosis of cancers of the eye using specialized OCT imaging data, and 3) the diagnosis and monitoring of glaucoma through imaging studies.
Students joining the lab will be mentored by Dr. Nambi Nallasamy. Opportunities for joint mentorship between Dr. Nallasamy and Dr. Arvind Rao are also available. Those students with strong skill sets in machine learning and/or relational database design and an interest in high-impact clinical applications of machine learning are encouraged to contact Dr. Nallasamy at email@example.com.
Rudy Richardson (Toxicology)
Current areas of interest for potential research rotations:
- Computational and predictive toxicology. Applying bioinformatics, cheminformatics, QSAR, and molecular modeling (including ligand-receptor docking, inverse docking, homology modeling, and molecular dynamics simulations) to the prediction of toxic effects of chemicals.
- Predicting the structure of neuropathy target esterase (NTE, aka PNPLA6) in order to understand how chemical or genetic modification of its structure leads to neurodegeneration.
- Molecular dynamics simulations aimed at understanding allostery and changes in protein flexibility produced by ligand binding.
Brandon Ruotolo (Chemistry)
We have two main opportunities for bioinformatics rotation students:
1) We are actively developing software (in Python) for the rapid analysis of protein-based therapeutics, where the stability assessments are based on a novel gas-phase protein unfolding technology that we call collision induced unfolding (CIU). We are actively developing and improving software that enables CIU signal processing, quantitative comparisons between CIU data, and rapid classification of unknown CIU datasets using machine learning (ML). CIU data is complex and multi-dimensional, containing many features that related to protein unfolding intermediates that can built in ML-classifiers in multiple ways. We are also working with highly multiplexed CIU datatypes, as well as extracting CIU information from highly complex mixtures for the purposes of drug discovery/development and protein quantification.
2) My group is actively building methodologies that leverage mass spectrometry (MS) data to build models of proteins and protein complexes that remain refractory to other structural biology workflows. In some cases, this requires the development of novel molecular dynamics (MD) simulation capabilities that focus on refining protein structures restrained/filtered using MS data. This project stream also has space for students interested in contributing to the development of software (Python) and scripts aimed at both utilizing and integrating multiple MS data types for the construction of such 3D protein models. Example datatypes include hydrogen-deuterium exchange (HDX) MS, chemical cross-linking (CXL)-MS, and ion mobility (IM)-MS. Applications vary widely, but include a number of applications toward protein complexes associated with cancer and Alzheimer's disease.
Patrick Schloss (Microbiology and Immunology)
The Schloss lab develops and tests computational methods for analyzing microbial communities. The goal is to enable researchers to use data generated using next generation sequencing technology to test ecological theory with microbes and better understand how perturbations in these communities affects health. We are looking for curious and creative students interested in the interface between computational biology and microbial ecology.
- Analysis of the human microbiome:
- Heuristic clustering algorithms for clustering DNA sequences of unknown origin
- Development of metatranscriptomic analysis pipeline
- Statistical modeling of microbial communities and patient clinical data
Evan Snitkin (Microbiology and Immunology)
The Snitkin lab is interested in the application of genomics and bioinformatics approaches to study the evolution and epidemiology of hospital infections. Contact Evan to discuss specific rotation opportunities. In addition, an overview of the labs interests and past publications can be found at: http://thesnitkinlab.com
The types of projects available include:
- Mining large sets of healthcare-associated pathogen genomes to characterize the key evolutionary innovations associated with the success of pandemic lineages
- Applying phylogenetic approaches to large sets of healthcare-associated pathogen genomes to discern transmission patterns within and between healthcare facilities
- Analyzing sequencing data generated directly from patient samples to diagnose diseases of unknown infectious origin
- Mining patient health records to identify treatment protocols associated with the evolution of resistance
- Applying metabolic modeling approaches to make predictions regarding how GI pathogens colonize the host and compete with commensals
- Applying short-read sequencing approaches to characterize the genetic diversity of pathogen populations within individual patients
Chandra Sripada (Psychiatry)
My lab is uses functional neuroimaging to tackle the biomarker problem in psychiatry: the problem of constructing objective, quantitative measures for psychiatric disorders and clinically-relevant personality dimensions. Much of our recent work identifies graphical structure in “functional connectomes,” which are whole-brain maps of connectivity across thousands of brain regions. Students have opportunities to develop new analytic methods to quantify change in graphical structure over time (working with our collaborators in EECS and Statistics). We intend to use these methods to characterize aberrant change patterns in disordered populations, including adults with schizophrenia and children with attention dysfunction.
Muneesh Tewari (Biomedical Engineering/Internal Medicine)
The Tewari lab is developing and testing next generation approaches for multi-dimensional monitoring of health and early detection of diseases. This includes biomarker analyses using cell-free DNA, RNA, and other analytes in biofluids like blood and urine, as well as highly time resolved data obtained from wearable sensors and clinical data.
We are an interdisciplinary lab that collaborates with clinicians, chemists and engineers, biostatisticians, computational and data scientists and others. Dr. Tewari has an eclectic background including systems and quantitative biology, cell and molecular biology, clinical medicine (with a specialization in medical oncology), and is a technology enthusiast.
Rotation projects are available involving analysis of physiologic data from wearable sensors and other time-resolved data sources, as well as bioinformatic analysis of next generation sequencing data.
We are looking for students who are inspired, pro-active, and interested in inter-disciplinary research. A strong computational background is needed because we are not primarily a computational lab. As we are actively promoting a growth culture, an interest in self-assessment and self-development is also important to be a good fit for the lab. We offer an environment and research work that can enable training and experiences across disciplines and interaction with diverse types of collaborators.
Nils Walter (Chemistry)
We have a diverse set of projects available that use bioinformatics, computational biophysics, systems biology and kinetic modeling approaches to interpret single molecule fluorescence microscopy data that illuminate how the recently discovered, ubiquitous universe of non-protein coding RNAs drives all aspects of gene expression and regulation in eukaryotes. Applications range from basic studies of RNA catalysis and interference, pre-mRNA splicing, and mRNA translation to understanding the onset of cancer in misregulated human cell lines. Here is a brief outline of possible rotation projects:
- Use bioinformatics and clustering to analyze complex single molecule signal traces
- Use atomistic molecular dynamics and coarse-grained simulations to understand the coupling of local and global motions in RNA enzymes
- Use Monte-Carlo simulations to understand complex diffusive behaviors of single molecules and nanodevices
- Use systems biology modeling to integrate vast datasets into mechanistic models of important cellular processes
Andrzej Wierzbicki (MCDB)
Our lab is focused on understanding non-coding regions of the genomes. We study how RNA produced from non-coding sequences (non-coding RNA) controls genome activity on the chromatin level. Chromatin-level gene regulation includes DNA methylation, histone modifications, nucleosome positioning and three-dimensional organization of chromosomes. We study these processes using genomic approaches and in depth bioinformatic analysis to gain mechanistic and quantitative understanding of genome regulation. Our work may be described as an interface of RNA biology and epigenomics.
Graduate students rotating in the lab would have the opportunity to work in one of the two following projects:
- Resolve the molecular mechanisms used by non-coding RNA to control chromatin structure. This project, funded by a recently awarded NIH grant, involves studying nucleosome positioning and higher level chromatin organization in various mutant backgrounds defective in non-coding RNA production and processing. This project involves establishment and application of a bioinformatic toolset suitable for high quality analysis of genome-wide nucleosome positioning data.
- Test if structures of non-coding RNA affect their functions and if these structures are actively modulated during RNA-mediated processes. This project, funded by an NSF grant, involves establishment of a toolset for genome-wide structural assays of non-coding RNA structures.
Qiong Yang (Biophysics)
The Yang Lab studies biological oscillations and self-organization processes in cell-free systems and early zebrafish embryos. The local interactions in the form of mechanical and biochemical signals allow individual molecules and cells to generate collective spatiotemporal patterns. To pin down the physical mechanisms behind these processes, we integrate mathematical modeling, live-cell and super-resolution imaging, and microfluidics to study both water-in-oil droplet-based artificial cells and live embryos, and connect the understandings across the molecular, cellular, and embryonic levels. We look forward to recruiting students with a quantitative background and a strong interest in interdisciplinary research. The position is immediately available and the starting date is flexible. How to apply: Interested candidates should send a brief description of your interest and CV to Email: firstname.lastname@example.org. Dr. Qiong Yang.
Below are two current major research efforts in lab:
- Emergent phenomena in biological systems
Investigating how self-organization behaviors, such as mitotic trigger waves in artificial cell-free systems and somite pattern formation in embryos, arise from complex interactive networks of cells and molecules through biochemical signals and mechanical forces.
- Method development for quantitative biology
Development of an integrated interdisciplinary approach to study biological clock design, function, and coordination, e.g. statistical techniques to identify network motifs for clock function, combination of modeling, fluorescence imaging, and microfluidics, to quantitatively manipulate and analyze the oscillatory processes in artificial mitotic cells, live tissues and zebrafish embryos.
Jianzhi George Zhang (EEB)
Rotation options for evolutionary genetics, genomics and systems biology are available in my lab. They can be either experimental or computational. The experimental work is typically done in budding yeast. Current experimental and computational projects focus on position effects on gene expression level and noise, genome organization and 3D chromosome conformation, recombination rate variation, epistasis, pleiotropy, phenotypic plasticity, fitness landscape, and post-transcriptional modification, all in the context of evolution. I also encourage students to develop their own projects in the general area of evolutionary genetics/genomics. More information found at www.umich.edu/~zhanglab/index.html.
Xiang Zhou (Biostatistics)
Rotation opportunity is currently available in Dr. Xiang Zhou's lab in the Department of Biostatistics. The Zhou Lab is focused on developing statistical and computational methods to address interesting biological problems in genetic and genomic studies. These studies often involve large-scale and high-dimensional data sets, and examples include genome-wide association studies and functional genomic sequencing studies (RNAseq, ChIPseq etc.). Through developing novel analytic methods to extract important information from these data, we hope to advance our understanding of the genetic basis of phenotypic variation for various quantitative traits and complex diseases. Please refer to the website (www.zhoulab.org) for more details. Current research projects include developing statistical methods for association tests with multiple correlated phenotypes, for integrating functional genomic studies with genome-wide association studies, for phenotype/risk prediction, for epistasis contribution and heritability estimation, and Bayesian methods for big data sets. Please contact Dr. Xiang Zhou (email@example.com) if you are interested.