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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 biologists. Systems biology models are widely recognized for their ability to significantly advance the recovery of critical minerals and materials (CMMs) in combination with biotechnology. These 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 the metabolic engineering of the strains. Using the models to optimize the strains, researchers can enhance 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 as EMSL resources.

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

By participating in this campaign, you will:

  • Help solve a big science problem 
     
  • Drive important outcomes 
     
  • Advance your own research

Participation

How researchers have been invited to participate

  • A panel of researchers was invited to participate in the initial community science meeting based on their domain expertise, experience, and overlapping interests with AMP2. After this meeting to identify high-priority campaign objectives, a call for proposals was opened and shared with the community science meeting attendees.

Required participant background

  • Expertise either in the experimental aspects of microbial metabolism (such as microbial fermentation and strain engineering techniques) or in the mathematical modeling of microbial metabolism, such as genome-scale models, kinetic models, or emerging machine learning (ML) models.

  • Experience or interest in implementing metabolic models applied to biotechnology and/or CMM recovery.

How will you contribute?

  • Identify and Prioritize Knowledge Gaps and Needs

    Participants help identify essential science questions that address specific metabolic modeling needs to support biotechnology and the production of CMMs.

  • Supply Data

    Participants suggest and provide existing datasets that can be leveraged for metabolic modeling relevant to the campaign objectives.

  • Feedback Through Community Science Meetings

    Attendees collaborate with EMSL to refine genome-scale modeling workflows, integrate omics data, and identify metabolic engineering targets.

  • Expert Input

    Campaign participants work with EMSL to develop predictive models, advance autonomous system approaches, and generate testable hypotheses that drive innovations in biotechnology and CMM research.

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. This campaign will accelerate strain optimization by delivering testable hypotheses for metabolic engineering, supporting biotechnology and CMM goals. Simultaneously, EMSL will refine its modeling workflows in a community-oriented way, strengthening partnerships and enabling advancements in artificial intelligence (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, while computational modelers can leverage EMSL’s unique modeling capabilities, including ML expertise, creating a mutually beneficial collaboration.

This campaign supports the recent presidential memorandum on fiscal year 2027 national research and development priorities and demonstrates how AI accelerates scientific discovery within Earth sciences and biotechnology.

Campaign Timeline

OCTOBER 2025 – CAMPAIGN TOPICS AND DESCRIPTIONS DRAFTED

  • Identify community science campaign topics aimed at solving a significant scientific challenge or filling current gaps in knowledge.

NOVEMBER 2025 – POTENTIAL CAMPAIGN PARTICIPANTS IDENTIFIED

  • Strategically identify researchers with ideal domain expertise and experience to invite participation in the upcoming community science meeting.

DECEMBER 2025 – COMMUNITY SCIENCE MEETING

  • Host a community science meeting to guide future directions in community needs to advance microbial modeling.

DECEMBER 2025 – CALL FOR PROPOSALS

  • Invite community science meeting attendees to submit proposals for projects that implement or improve the predictability of genome-scale models. As different methodologies require different kinds of data, users are required to indicate the data available to support the requested development.

JANUARY 2026 – CAMPAIGN PROPOSAL DEADLINE

  • Deadline for invited campaign participants to submit proposals for projects contributing to overall campaign goals.

JANUARY 2026 – INITIATE WORK ON ACCEPTED PROPOSALS

  • Work begins on accepted proposals for projects contributing to overall campaign goals.

FEBRUARY–SEPTEMBER 2026 – APPLY METABOLIC MODELING WORKFLOWS

  • Apply the refined metabolic modeling workflows to address key science questions prioritized by campaign participants.

SEPTEMBER 2026 – COMPLETE CAMPAIGN

  • Complete the campaign and share modeling activities with users requesting the service.

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 MODEL 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.

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 the Biological and Environmental Research (BER) program’s priorities.

Expected Campaign Outcomes

EXPAND METABOLIC INSIGHTS FOR ORGANISMS

  • Use genome-scale 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

  • Utilize predictions from genome-scale models to guide genetic modifications aimed at enhancing titer, rate, and yield for desired products.

INCREASED 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.

Advance Your Research

  • Accelerate your science
  • Access cutting-edge technology
  • Gain experience and key knowledge
  • Co-author scientific publications

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.

By partnering with the scientific community, EMSL can tailor the genome-scale modeling workflows that EMSL stewards to address the most pressing research priorities, leveraging experimental data and tackling key challenges in metabolic engineering and biotechnology advancements.

Contacts