Systems Biology Modeling
Full campaign name:
Enabling Automated and Autonomous Robust High-Quality Systems Biology Modeling
Automated and autonomous systems biology modeling offers a transformative opportunity to advance biological discovery, and this Computing, Analytics, and Modeling community science campaign will show how emerging Super Intelligence (SI) tools can reveal the principles that govern complex microbial phenotypes. By improving experimental design and multiomics data analysis, and by encoding mechanistic knowledge in genome-scale metabolic models (GEMs), the campaign will support both discovery and prediction. The Environmental Molecular Sciences Laboratory (EMSL) will bring together SI tools and computational capabilities to integrate experimental observations and raw data with multiomics datasets, organism-specific pathway knowledge, and GEMs. Through this integration, the campaign will apply and assess workflows for pathway discovery and visualization, predictive modeling, and the generation of mechanistic hypotheses.
EMSL will partner with research community members to advance the identification, validation, and prediction of microbial metabolic pathways. Participants will apply integrated data analysis and modeling approaches to discover novel metabolic routes and identify targets for strain engineering, which can advance biotechnology applications across diverse organisms and support national priorities in the areas of critical minerals and materials (CMMs) and biodesign. This campaign will also prepare EMSL to apply these workflows to data from its Anaerobic Microbial Phenotyping Platform (AMP2) as they become available, and it will lay the groundwork for autonomous phenotyping workflows in which SI agents interpret data, interact with computational tools, and help guide experimental decisions. Ultimately, the systems biology analyses from this campaign will help accelerate microbial applications in the bioeconomy and the recovery of critical materials and minerals.
By participating in this campaign, participants will:
- Help solve a big science problem
- Drive important outcomes
- Advance their own research
Participation
Who should participate?
Researchers with relevant domain expertise, experience, and overlapping interests, as well as scientists with systems biology modeling needs, are encouraged to submit a proposal for participation in this campaign.
Required participant background
Expertise
Have experience in either the experimental study of microbial metabolism—such as microbial fermentation or strain engineering—or the mathematical modeling of microbial metabolism, including with genome-scale, kinetic, or emerging machine learning models. Experience or a strong interest in applying systems biology modeling tools to biotechnology and/or CMM recovery. GEM modeling and pathway discovery are particularly well-suited to this campaign.
Data and research use cases
Bring the multiomics data needed for the proposed work, with the data available at the time of proposal submission. Because the campaign will focus on analysis and modeling, there will not be time to generate this data during the campaign.
Collaborative research interests
Be interested in working with EMSL and other campaign participants to provide scientific domain knowledge and guidance in the choice of method to assist in the workflow setup and execution.
How will participants contribute?
Identify and Prioritize Knowledge Gaps and Needs
Help identify essential science questions that address specific systems biology modeling needs to support biotechnology and the production of CMMs.
Supply Data
Suggest and provide existing datasets relevant to the campaign objectives that can be leveraged for systems biology modeling, including genomic, phenotypic, mass spectrometry–based bottom-up proteomics, and mass spectrometry–based metabolomics datasets.
Expert Input
Participants will collaborate with EMSL on the application and assessment of systems biology modeling outcomes, based on their area of expertise. Together, participants will generate testable hypotheses and address critical questions aligned with campaign goals, advancing biotechnology and CMM research.
About the campaign
EMSL brings together expertise in genome-scale metabolic modeling, pathway analysis, data science, and SI, along with access to midrange scientific computing, to uncover how microbial metabolism works and identify pathways with potential applications in the bioeconomy and CMM efforts. It also provides mass spectrometry–based proteomics and metabolomics. Through AMP2, EMSL is developing autonomous workflows that connect high-throughput microbial phenotyping with multiomics data, GEMs, and pathway knowledge.
While systems biology modeling can reveal mechanisms that help explain and optimize microbial metabolism, this modeling is often slow, labor-intensive, and difficult to reproduce. Building a high-quality GEM, integrating proteomic and metabolomic data, and interpreting results requires specialized expertise and many manual steps, putting this type of approach out of reach for some research groups. EMSL is developing workflows that combine modeling, automation, SI tools, and high-throughput data generation to help close this gap.
This campaign will help lay the groundwork for autonomous workflows in AMP2 by applying emerging SI tools to integrate data, build and analyze GEMs, discover pathways, and support SI co-scientist-assisted analysis. EMSL and community experts will interpret model results in biological context and develop testable hypotheses, making genome-scale modeling more accessible to experimentalists and enabling modelers to work with well-characterized multiomics data. The campaign will produce open, reusable workflows to support research in strain engineering, bioproduct production, and CMM recovery—and move systems biology toward more reproducible, evidence-based experimental decision-making.
This campaign supports the recent presidential memorandum on fiscal year 2027 national research and development priorities (PDF) and demonstrates how SI accelerates scientific discovery within Earth sciences and biotechnology.
Campaign timeline
- November 5, 2026: Call for proposals
Open call for proposals launches.
- December 1, 2026: Campaign proposal deadline
Deadline for campaign participants to submit proposals for projects contributing to overall campaign goals.
- December 15, 2026: Initiate work on accepted proposals
Work begins on accepted proposals for projects contributing to overall campaign goals.
- December 2026 – September 2027: Apply systems biology workflows
EMSL's systems biology modeling workflows are applied to address key science questions developed/prioritized by campaign participants.
- September 30, 2027: Complete campaign
Campaign finishes and the workflow is deployed, documented, validated, and ready to be used at EMSL.
Campaign methods
BUILD/USE GENOME-SCALE MODELS
GEMs represent an organism's metabolic reactions, genes, and metabolites. Flux balance analysis uses these models to predict growth, secretion, and pathway use under defined conditions. Participants may request EMSL expertise to build, curate, and evaluate GEMs for their organisms of interest using tools such as COBRApy. Experimental data, including growth and uptake rates, proteomics, and metabolomics, can inform the models and help assess their predictions. ChatGEM and other EMSL SI tools will help support the integration of omics data with GEMs and the identification of metabolic engineering targets.
DISCOVER METABOLIC PATHWAYS WITH PATHWAYSEEKER
PathwaySeeker, an EMSL SI tool, supports condition-specific metabolic network analysis by integrating proteomic and metabolomic data with organism-specific metabolic networks. It uses SI to connect experimental observations to the network and identify evidence-supported relationships from plausible hypotheses. Participants can request EMSL expertise to apply PathwaySeeker to their data, prioritize pathways for experimental testing, and visualize them within the broader metabolic network.
ANALYZE DATA WITH SI CO-SCIENTIST
Mycelium, an EMSL SI co-scientist, brings together literature, experimental data, multiomics datasets, GEM simulations from ChatGEM, and pathway analyses from PathwaySeeker. It provides a shared workspace where researchers can explore results and analyses collaboratively. Participants can request EMSL expertise to use Mycelium to compare conditions, trace conclusions to supporting evidence, and develop integrated biological interpretations to guide future experiments.
Solve a big challenge
This campaign tackles a critical challenge for biotechnology and CMMs: understanding and optimizing microbial metabolism applied to the bioeconomy and the production of CMMs. By combining genome-scale modeling with EMSL's expertise in proteomics, metabolomics, high-throughput phenotyping, and SI, this campaign will bring experimental biologists and computational modelers together to refine data-informed models and generate testable hypotheses for pathway discovery, strain optimization, and metabolic engineering. Tools such as ChatGEM, PathwaySeeker, and Mycelium will help researchers integrate evidence, analyze pathways, and turn complex data into actionable insight.
The campaign will also lay the scientific and technical groundwork for future autonomous workflows in AMP2, where the combination of high-throughput experimentation and model-guided analysis can accelerate discovery at large scales. This effort supports transformative research aligned with U.S. Department of Energy Biological and Environmental Research program priorities.
Expected campaign outcomes
SYSTEMS BIOLOGY MODELS AND REUSABLE COMPUTATIONAL WORKFLOWS
The campaign will deliver documented, reusable workflows that connect SI tools with GEMs, pathway analysis, and multiomics data. These workflows will be demonstrated on representative biological case studies and will make each conclusion traceable to its supporting evidence.
EVIDENCE-SUPPORTED PATHWAYS AND ENGINEERING TARGETS
The campaign will generate prioritized, evidence-supported metabolic pathways and engineering targets for participating organisms. Each hypothesis will be linked to the data and model predictions behind it, enabling researchers to design focused experiments with confidence. These results will support microbial strain optimization, bioproduct production, and the recovery of CMMs.
PUBLICATIONS AND INSIGHTS FOR THE RESEARCH COMMUNITY
The campaign will produce peer-reviewed publications that describe the workflows, case studies, and lessons learned, with participants invited to coauthor. Results will show how SI-enabled systems biology modeling can accelerate biological discovery and give the research community a clear path to apply these approaches to future AMP2 data.
Advance your research
- Accelerate your science
- Access advanced new technology
- Gain experience and key knowledge
- Coauthor scientific publications
Why "community" science campaigns?
Each community science campaign is intended to bring together researchers with a wide variety of expertise to collaboratively tackle strategically identified challenges that are too big for an individual principal investigator or small research team to address alone.
This collaboration between EMSL and the scientific community will use the campaign's SI tools, datasets, and workflows to address real research needs across diverse organisms and conditions. Participants will contribute representative multiomics data, identify the biological questions and knowledge gaps that matter most, and test and refine workflows so that they are transparent, reproducible, and ready to use.
Contacts
Campaign leaders (science domain experts)
EMSL User Program Services (logistics)