Goals of this workshop include:
- Identify the questions, challenges, tools, and needs for microbiome studies at Ohio State University (OSU) and in the greater Columbus area.
- Stimulate interdisciplinary collaborations at OSU and in the greater Columbus area (e.g. Nationwide Children’s Hospital, Battelle).
The intended workshop participants are faculty / PIs who are laboratory scientists, mathematicians, statisticians, or metabolic modelers working on or interested in questions that involve the microbiome.
As such, preference will be given to OSU or local faculty applicants for this workshop.
From the bacteria in our guts, to microbes involved in biodegradation and crop growth, to viruses in the ocean, some of Earth’s tiniest organisms play some of the most important roles in global health, food production, and climate change. Advances in metagenomic sequencing technology including 16S, viromics, and mycobiomics - along with metabolomics, transcriptomics, and proteomics allow us to characterize these complex microbial communities and begin to understand their functions. This Big Data creates opportunities for data driven discovery and new data analytics, but Big Data also comes with challenges: Meaningful integration of multi-omic data has become increasingly critical to microbiome studies as recent work highlights the importance of community dynamics, interactions, and microbial ecology over the roles of individual microbes. For example, microbial metabolisms are now recognized to often be ‘distributed’ across consortia; viruses manipulate microbial metabolisms and population dynamics, and co-occurring fungi in most ecosystems are virtually unstudied but likely play key roles as well. Data integration techniques range from correlations to network analyses to genome-scale microbial community metabolic models that assess metabolite flux to ecosystem models that provide predictive power of which organisms drive key features of the system. Some of these techniques, like correlations, accommodate many types of –omic data but cannot account for the complex biology or ecology of a system. Other techniques, like metabolic modeling, better account for this complexity, but do not yet integrate phenotypic –omic data (i.e. metabolomics, proteomics) well. Each of these techniques has advantages and limitations and new computational tools for data integration and modeling have rapidly developed over the last 2 years. Besides data integration, Whether studying environmental, gut, or industrial microbes, the ability to accurately identify and predict the structure and function of microbial communities has far-reaching potential and paves the way for microbial engineering in bioremediation, probiotic development, and sustainable agriculture.
In this 3 day workshop, we will take a genome to phonome approach with broad perspectives provided by mathematicians, biologists, and statisticians. We will also develop interdisciplinary working subgroups to consider the questions, challenges, tools, and needs of data integration and modeling in microbiome studies. Each participant will present a short talk (5 minutes, 3 slides) highlighting his or her research, perspectives, and challenges. The goal is to help develop a broadly collaborative community of math-enabled microbiome scientists with common research goals.
This workshop is co-sponsored by the Mathematical Biosciences Institute, the National Institute of Statistical Sciences, and the Infectious Diseases Institute at Ohio State University.
Department of Mathematics / Department of Molecular Genetics
The Ohio State University
Department of Veterinary Preventive Medicine
The Ohio State University
Talks and Participants
Metabolic Mechanisms of Interaction in Microbial Communities
Jason Papin (Department of Biomedical Engineering, University of Virginia)
Abstract not provided.
Math and the Virosphere: Needs and Opportunities
Matthew Sullivan (Department of Microbiology, The Ohio State University)
Microbes are recently recognized as driving the energy and nutrient transformations that fuel Earthâ€™s ecosystems in soils, oceans and humans. Where studied, viruses appear to modulate these microbial impacts in ways ranging from mortality and nutrient recycling to complete metabolic reprogramming during infection. As environmental virology strives to get a handle on the global virosphere (the diversity of viruses in nature) clear challenges are emerging where collaboration with mathematicians will be powerfully enabling. I will present a few ripe research avenues where we (environmental virologists) could use some help from mathematicians, statisticians, theorists and modelers to better understand the nanoscale (viruses) and microscale (microbes) entities that drive Earth’s ecosystems, and human health and disease.
Metabolites, Germs and People: Eavesdropping on Human-Associated Microbial Communities
Katrine Whiteson (Molecular Bio and Biochem, UC Irvine)
Persistent and unique microbial communities impart the majority of genetic and metabolic diversity in humans, and their composition and activity are important indicators of health and disease. The Whiteson lab uses culture-independent metagenomics, metabolomics, and ecological statistics along with hypothesis driven, reductionist microbiology to answer questions about how bacteria and viruses affect human health. We and others find that the most important source of variance in both microbiome and metabolome data is the individual the sample was taken from, making longitudinal samples where a person’s own sample can act as the baseline an important approach. Several recent research projects using metabolomics and sequencing will be presented from healthy humans and Cystic Fibrosis patients, with the hope of brainstorming analytical approaches to relate longitudinal microbiome and metabolomics data.
|Besma Abbaouiemail@example.com||Food Science & Technology, The Ohio State University|
|Khaled Altabtbaeifirstname.lastname@example.org||Biosciences, The Ohio State University|
|Baha Alzalgemail@example.com||Mathematics, The University of Jordan|
|Matt Andersonfirstname.lastname@example.org||Microbiology Admin, The Ohio State University|
|Mike Baileyemail@example.com||Pediatrics, The Ohio State University|
|Rick Ballwegfirstname.lastname@example.org||Systems Biology and Physiology Graduate Program, University of Cincinnati|
|Clifford Beallemail@example.com||Center for Gene Therapy, The Ohio State University|
|Johannes Bjorkfirstname.lastname@example.org||Department of Biological Sciences, University of Notre Dame|
|Ben Bolducemail@example.com||Microbiology, The Ohio State University|
|Angela Chukwufirstname.lastname@example.org||Department of Statistics, University of Ibadan|
|Jing Cui||Jing.Cui@agri.ohio.gov||Animal Disease Diagnostic Laboratory, Ohio Department of Agriculture|
|Karen Dannemilleremail@example.com||Civil, Envir & Geod Eng, The Ohio State University|
|Jayajit Dasfirstname.lastname@example.org||Battelle Ctr. for Mathematical Medicine/ Dept. of Pediatrics, The Ohio State University|
|Adriana Dawesemail@example.com||Department of Mathematics / Department of Molecular Genetics, The Ohio State University|
|Rebecca Garabedfirstname.lastname@example.org||Veterinary Preventive Medicine, The Ohio State University|
|Mostafa Ghanememail@example.com||Veterinary Preventive Medicine, The Ohio State University|
|Ming Gongfirstname.lastname@example.org||Electrical Engineering, University of Dayton|
|Vanessa Haleemail@example.com||Department of Veterinary Preventive Medicine, The Ohio State University|
|Jia (John) Kangfirstname.lastname@example.org||Biostatistics, Merck Research Laboratories|
|Kristina Kigerlemail@example.com||Neuroscience, The Ohio State University|
|Chris Lauber||Christian.Lauber@nationwidechildrens.org||Institute for Genomic Medicine, Nationwide Children's Hospital|
|Amy Mackosfirstname.lastname@example.org||College of Nursing, The Ohio State University|
|Katherine Mifflinemail@example.com||Neuroscience, The Ohio State University|
|Chiranjit Mukherjeefirstname.lastname@example.org||Biomedical Sciences, The Ohio State University|
|Samuel Oyamakinemail@example.com||Statistics, Centre for Environment, Renewable Natural Resources Management, Research and Development, (CENRAD)|
|Jason Papinfirstname.lastname@example.org||Department of Biomedical Engineering, University of Virginia|
|Kimberly Rothemail@example.com||Mathematics, Juniata College|
|Denise Russi Rodriguesfirstname.lastname@example.org||OARDC Animal Sciences, The Ohio State University|
|Zakee Sabreeemail@example.com||EEOB, The Ohio State University|
|Sarah Shortfirstname.lastname@example.org||Entomology, The Ohio State University|
|Jalal Khalid Siddiquiemail@example.com||SBS-Biomedical Informatics, The Ohio State University|
|Chi Songfirstname.lastname@example.org||COPH-Division of Biostatistics, The Ohio State University|
|Ivan Sudakovemail@example.com||Physics, University of Dayton|
|Matthew Sullivanfirstname.lastname@example.org||Department of Microbiology, The Ohio State University|
|Christine Sunemail@example.com||Microbiology Admin, The Ohio State University|
|Vasily Tokarevfirstname.lastname@example.org||Biomedical Sciences, Juniata College|
|Katrine Whitesonemail@example.com||Molecular Bio and Biochem, UC Irvine|
|Jenessa Winstonfirstname.lastname@example.org||PHP, North Carolina State University - College of Veterinary Medicine|
|Yan Zhangemail@example.com||Animal Disease Diagnostic Laboratory, Ohio Department of Agriculture|