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Metabolic Modeling

Full Campaign Name

Enabling Automated and Autonomous Robust, High-Quality Systems Biology Modeling

Graphic featuring genetic sequencing results.

Automated and autonomous systems biology modeling presents a transformative opportunity to accelerate and enhance biological discoveries. By focusing on robust genome-scale models, the Environmental Molecular Sciences Laboratory (EMSL) aims to foster interdisciplinary collaboration between experimental and computational biology. These models are widely recognized for their ability to significantly advance the bioeconomy and the recovery of critical minerals and materials (CMMs) in combination with biotechnology. The models provide a rational framework for optimizing the performance of microbial organisms to extract critical minerals and in the production of important bioproducts.

Through this Computing, Analytics, and Modeling Community Science Campaign, EMSL is partnering with invited research community members to develop high-quality genome-scale models that accelerate microbial strain optimization by delivering testable hypotheses for metabolic engineering of the strains. Using the models to optimize the strains, researchers can improve production processes and reduce costs by improving industrial metrics such as productivity rates, yields of bioproducts, and recovery rates of CMMs.

After these models are developed, they will be made available to the research community through open calls for proposals through EMSL.

Looking ahead, EMSL will enhance its genome-scale modeling workflows alongside EMSL's new automated Anaerobic Microbial Phenotyping Platform (AMP2), thus preparing for further autonomous workflow development.

By participating in this campaign, you will:

Participation

How researchers are invited to participate

A panel of researchers (comprising both users and researchers from external organizations) have been invited and selected to participate based on their domain expertise, experience, and overlapping interests with AMP2, especially its first science campaigns.

Required participant background
  • Have expertise either in the experimental aspects of microbial metabolism (such as microbial fermentation and strain engineering techniques), or have experience in the mathematical modeling of microbial metabolism, such as genome-scale models, kinetic models, or emerging machine learning (ML) models.            
     
  • Have an interest in implementing metabolic models applied to biotechnology and/or CMM.
How will you contribute?
  • Feedback through Community Science Meeting            
    In a kick-off community science meeting, participants contributed feedback on science goals and capability development. Participants helped identify essential science questions based on the Department of Energy Office of Science's Biological and Environmental Research (BER) program priority areas that address specific metabolic modeling needs to support biotechnology and the production of CMMs, such as how to improve model predictions by integrating new emerging AI/machine learning (ML) technologies.            
     
  • Proposal Submission             
    Meeting participants have been invited to submit a short proposal to access the EMSL services presented in this campaign—specifically, in the construction or refinement of genome-scale models applied to biotechnology and CMMs. Proposals are selected based on alignment with campaign objectives and methodologies.            
     
  • Review of Models and Software          
    Regular status update meetings will be held, and these are planned to be open to other community members who express interest. Additionally, modeling work done by EMSL will be discussed with the users for final adjustments and interpretation.

About the Campaign

Researchers seeking to apply genome-scale modeling—such as simulating metabolic pathways or advancing metabolic engineering in biotechnology and CMM applications—will benefit from this campaign. These models accelerate discovery and complement EMSL's strengths in omics, high-throughput phenotyping, and autonomous systems. By providing cutting-edge modeling tools, EMSL will foster collaboration between experimental biologists and computational modelers, enabling transformative innovations. This campaign will accelerate strain optimization by delivering testable hypotheses for metabolic engineering, supporting bioeconomic and CMM goals. Simultaneously, EMSL will refine its modeling workflows in a community-oriented way, strengthening partnerships and enabling advancements in AI and autonomous systems biology.

By making its genome-scale modeling capabilities accessible to users, EMSL aims to foster greater collaboration and innovation with the community. Both biologists and modelers stand to benefit from this initiative. For instance, biological researchers with experimental capabilities but limited modeling expertise can partner with EMSL to integrate valuable modeling solutions and accelerate biological discoveries. Similarly, computational modelers could leverage EMSL's unique modeling capabilities, including ML expertise, creating a mutually beneficial collaboration.

Through this campaign, participants will contribute by providing experimental data, formulating scientific hypotheses, and collaborating with EMSL to refine genome-scale modeling workflows, integrate omics data, and identify metabolic engineering targets. Their input will help develop predictive models, advance autonomous system approaches, and generate testable hypotheses that drive innovations in biotechnology and CMM research.

EMSL staff and participants will meet on a regular basis virtually and will discuss aspects such as task progress, technical details, etc.

This campaign supports the recent presidential memo on FY 2027 national research and development priorities (PDF) and demonstrates how AI accelerates scientific discovery within Earth sciences and biotechnology.

Campaign Timeline

  • DECEMBER 2025 – KICK-OFF MEETINGS       
    Initial meetings to present the campaign's scope and to gather feedback from community experts to guide future directions in community needs to advance microbial modeling.       
     
  • JANUARY 18, 2026 – SUBMIT PROPOSALS       
    Participants will have the opportunity to submit a simplified proposal to implement or improve the predictability of genome-scale models, indicating the services from EMSL that they would like to request in order to advance their research. As different methodologies require different kinds of data, users will indicate the data available to support the requested development. Proposals will be selected based on alignment with campaign objectives and methodologies.       
     
  • JANUARY 27, 2026 – INITIATE WORK ON ACCEPTED PROPOSALS       
    Participants will be notified of the status of their proposals, and work will be initiated on accepted proposals.       
     
  • OCTOBER 2026 – COMPLETION OF PROPOSALS       
    Modeling activities will be shared with users requesting the service, and next steps (such as publications and further collaboration) will be discussed.

Campaign Methods

  • CONSTRUCT HIGH-QUALITY GENOME-SCALE MODELS            
    Develop or refine genome-scale models from annotated genome data, ensuring adherence to modern standards (e.g., MEMOTE > 85%). Use tools like KBase to facilitate reconstruction. Analyze metabolic capabilities by traditional methods (such as flux balance analysis and flux variability analysis), and emerging ML algorithms.            
     
  • RECONCILE GENOME-SCALE MODELS PREDICTIONS WITH DATA            
    Integrate extracellular experimental data to ensure consistency and accuracy between models and real-world observations. Tools such as GrowthMatch and Consistent Reproduction of Phenotype (CROP) will be used to harmonize experimental results with model predictions.            
     
  • CONTEXTUALIZE GENOME-SCALE MODELS WITH GENE EXPRESSION DATA            
    Incorporate transcriptomics or proteomics to refine and estimate intracellular flux distributions under specific experimental conditions for deeper biological insights. Tools such as e-Flux will be utilized to facilitate this analysis.            
     
  • IDENTIFY METABOLIC ENGINEERING TARGETS            
    Integrate multiple approaches to design strategies for enhancing titer, yield, and metabolic rates. This includes leveraging genome-scale modeling tools such as OptKnock and thermodynamic pathway analysis. Certain methodologies may also require experimental omics data, such as internal/external metabolomics and proteomics.            
     
  • ADVANCE AUTONOMOUS WORKFLOWS WITH MACHINE LEARNING            
    Utilize ML approaches to enhance genome-scale model construction, enabling more accurate predictions and advancing capabilities within autonomous workflows. For example, apply tools like CLEAN-Contact to improve enzyme function predictions, or collaborate with users to implement innovative ideas.

Solve a Big Challenge

This campaign addresses the critical scientific challenge of optimizing metabolic pathways for biotechnology advancements and CMM applications by leveraging genome-scale modeling tools. By integrating EMSL's expertise in omics data, high-throughput phenotyping, and autonomous systems, the campaign fosters collaboration between experimental biologists and computational modelers to refine predictive models and generate testable hypotheses for strain optimization and metabolic engineering. Automated workflows and advanced AI/ML approaches will enhance the accuracy and scalability of these genome-scale models, enabling transformative innovations that accelerate discoveries, optimize cellular functions, and deliver solutions to complex biological challenges aligned with BER priorities.

Expected Campaign Outcomes

  • EXPAND METABOLIC INSIGHTS FOR ORGANISMS            
    Expand metabolic insights for organisms relevant to biotechnology and CMM by leveraging genome-scale models. Use these models to identify relevant metabolic pathways linked to target substrates and products, assessing gene essentiality, and explore pathway interdependencies critical to cellular function and optimization.            
     
  • ACCELERATE STRAIN OPTIMIZATION BY DELIVERING TESTABLE HYPOTHESES            
    Accelerate strain optimization by delivering testable hypotheses for metabolic engineering, supporting bioeconomic and CMM goals. Utilize predictions from genome-scale models to guide genetic modifications aimed at enhancing titer, rate, and yield for desired products.            
     
  • CONTRIBUTE TO PEER-REVIEWED PUBLICATIONS            
    Contribute to peer-reviewed publications by transforming predicted outcomes into novel biological insights, coauthored in collaboration with EMSL users.            
     
  • FOSTER COLLABORATION ACROSS BIOTECHNOLOGY AND CMM NETWORKS            
    Promote collaborations between EMSL and the biotechnology and CMM communities to address key research gaps and accelerate biological discoveries.            
     
  • HELP POSITION EMSL AS A LEADER IN SYSTEMS BIOLOGY MODELING            
    Position EMSL as a leading collaborator in systems biology modeling within the scientific community for advancing systems biology modeling, accelerating biological discoveries, and driving innovation in predictive metabolic engineering.            
     
  • ESTABLISH A HUB OF EXPERTISE            
    Prototype workflows rely on in-house capabilities in genome-scale modeling, omics integration, and emerging ML tools. This campaign aims to strengthen EMSL's reputation as a hub of expertise in systems biology, fostering interdisciplinary approaches to solving complex biological challenges.

Advance Your Research

  • ACCELERATE YOUR SCIENCE            
    Participation in this campaign will help accelerate participants' science by enabling access to refined genome-scale models, advanced computational workflows, and AI/ML tools to generate testable hypotheses for metabolic engineering. By collaborating with EMSL's experts and leveraging integrated modeling solutions, participants can enhance their understanding of metabolic pathways, optimize strain designs, and drive innovative discoveries aligned with biotechnology and CMM priorities.            
     
  • ACCESS TO CUTTING-EDGE TECHNOLOGY            
    Participants in this campaign will have access to EMSL's cutting-edge genome-scale modeling technologies, including tools like KBase for model reconstruction, advanced AI/ML workflows to enhance predictions, and software pipelines for integrating experimental omics data such as proteomics and metabolomics. These capabilities will enable participants to refine metabolic models, contextualize gene expression data, and identify engineering targets that accelerate biological discoveries aligned with biotechnology and CMM goals.            
     
  • GAIN EXPERIENCE AND KEY KNOWLEDGE            
    Participants will gain invaluable experience with genome-scale modeling, integrating experimental omics data, and leveraging advanced AI/ML frameworks to analyze metabolic pathways and design engineering strategies. They will also acquire key knowledge on predicting flux distributions, refining metabolic models, and applying autonomous workflows to tackle challenges in biotechnology and CMM research.

Why “Community” Science Campaigns?

Each community science campaign is intended to bring together researchers with a wide variety of expertise to tackle the same strategically identified challenges that are bigger than what an individual principal investigator or a small team’s research effort can accomplish alone.

With the help of the scientific community, EMSL can tailor genome-scale modeling workflows to address the most pressing research priorities, leveraging experimental data and tackling key challenges in metabolic engineering and biotechnology advancements.

Contacts