The 2020 Multiscale Microbial Dynamics Modeling course was a virtual course hosted by the Environmental Molecular Sciences Laboratory (EMSL) and Pacific Northwest National Laboratory (PNNL) Subsurface Biogeochemical Research group, and featured experts from the Joint Genome Institute and KBase.
Materials from this course cover how to incorporate microbial metagenomic and environmental metabolite data from watershed ecosystems into metabolic and community modeling using computational frameworks, such as KBase and PFLOTRAN. The curriculum includes lectures and software and data analysis tutorials. All of these materials are freely accessible to the community as part of the 2020 Microbial Dynamics Summer School Organization in KBase. Create a KBase account and request to join the KBase Org to get started today!
Introduction to the WHONDRS consortium and background information on the data used during the course. Learning concepts include an introduction to metagenomics and applied metagenomics research, with a demonstration of the KBase platform used for many of the analyses.
1.2 WHONDRS IntroductionAmy Goldman & James Stegen PNNL
1.3 Metagenomics 101Kelly Wrighton Colorado State University
1.4 KBase OrientationElisha Wood-Charlson LBNL
1.5 Inferring Biogeochemical Function from Genomes (DRAM)Kayla Borton & Mike Shaffer Colorado State University
1.6 Introduction to Gene Application ExercisesKelly Wrighton Colorado State University
1.7 Connecting Genes to Function: Trait-based approachEoin Brodie, Ulas Karaoz, & Gianna Marshmann LBNL
Exploratory dive into the metabolomics of rivers. This section covers analysis pipelines and approaches to researching metabolomics, as the metabolic inputs and outputs from bacteria, and instruments that identify metabolites.
2.1 Metabolomics in River CorridorsJames Stegen & Vanessa Garayburu-Caruso PNNL
2.2 Metabolomics Analysis PipelineBob Danczak PNNL
2.3 Connecting Metabolites to FunctionHyun-Seob Song University of Nebraska, Lincoln
2.4 High-resolution Mass Spectrometry (EMSL FTICR-MS Lab)Will Kew PNNL
Applied section that demonstrates how to create models. The instructors explore the complexities of modeling microbial communities and how to develop metabolic models using KBase.
3.1 Metabolic Modeling LandscapeHyun-Seob Song University of Nebraska, Lincoln
3.2 FluxOmics Tools for Metabolic ModelingMark Borkum PNNL
3.3 Building and Using Metabolic Models in KBaseJanaka Edirisinghe ANL
3.4 Integrating Metabolomic Data in Metabolic ModelsChris Henry ANL
Additional context describing how to scale models from communities to ecosystems using reactive transport models (RTMs) to model environment dynamics. Tutorials explain how to develop RTMs and tie together the concepts of metagenomics and metabolomics for current applications to use RTMs in different riverine systems.
4.1 Opportunities for Science Advancements using WHONDRS in RTMTim Scheibe PNNL
4.2 Reactive Transport Modeling Tutorial #1Kewei Chen PNNL
4.3 ML/AI Methods for Metabolic Modeling in RTMsHyun-Seob Song University of Nebraska, Lincoln
4.4 Reactive Transport Modeling Tutorial #2Roelof Versteeg & Rebecca Rubenstein Subsurface Insights
4.5 Using R for Hydrologic DataMichelle Newcomer LBNL
4.6 Omics informed RTM in Marine and Lacustrine SystemChristof Meile University of Georgia
4.7 Omics informed RTM in Watershed SystemXingyuan Chen PNNL
4.8 Omics informed RTM in Wetland SystemsPamela Weisenhorn ANL
4.9 Opportunities for Science Advancements using WHONDRS in RTMTim Scheibe PNNL
The final section covers U.S. DOE user research facilities and their available resources.
Joint Genome Institute (JGI)Rekha Seshadri & Rex Malmstrom
Environmental Molecular Sciences Laboratory (EMSL)Will Kew PNNL
| EMSL, the Environmental Molecular Sciences Laboratory
|PNNL, Pacific Northwest National Laboratory|
|DOE, US Department of Energy|
|JGI, Joint Genome Institute|
|KBase, DOE Systems Biology Knowledgebase|
|WHONDRS, Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems|
|SBR, Subsurface Biogeochemical Research|
|ESS-DIVE, Deep Insight for Earth Science Data|