Plan publishable constraint-based metabolic modelling studies when the user has a broad biological or metabolic-engineering topic but no concrete dataset, organism, model, or hypothesis. Selects feasible BiGG/COBRA models, objectives, perturbations, analyses, metrics, figures, and risk controls before FBA code is generated.
Plan publishable constraint-based metabolic modelling studies when the user has a broad biological or metabolic-engineering topic but no concrete dataset, organism, model, or hypothesis. Selects feasible BiGG/COBRA models, objectives, perturbations, analyses, metrics, figures, and risk controls before FBA code is generated.
Use this skill before gsmm-builder, fba-simulator, and flux-analyzer when
the project starts from a broad prompt such as "do a metabolic flux analysis
paper" or "find a publishable idea in microbial metabolism".
The goal is to turn a vague topic into a concrete, executable, paper-shaped
study plan:
organism + model + condition + perturbation + metric + figure set + claim
This is the MFA analogue of choosing a collider process and parameter scan
before generating events.
Planning Inputs
Extract or infer the following:
Field
Examples
Biological scope
microbial metabolism, cancer metabolism, yeast fermentation, tuberculosis
Organism
E. coli, S. cerevisiae, human Recon3D, M. tuberculosis
Model source
BiGG ID, local SBML/JSON, manually constructed toy model
Objective
biomass, product secretion, ATP maintenance, dual objective
If the user provides no organism, start with one of these low-risk defaults:
Default
Model
Why
E. coli K-12
iJO1366 or core model
Fast, well curated, standard for FBA papers
S. cerevisiae
iMM904
Fermentation and product-yield studies
Human metabolism
Recon3D
Disease metabolism, but larger and harder
M. tuberculosis
iNJ661
Essentiality and drug-target hypotheses
Prefer E. coli for fully autonomous first runs because it is fast and
interpretable.
Study Archetypes
Archetype A: Knockout Strategy for Product Overproduction
Use when the topic mentions metabolic engineering, bio-production, yield, or
fermentation.
Plan:
Select a product exchange reaction, e.g. succinate, lactate, ethanol, acetate.
Run WT FBA and pFBA under a defined medium.
Screen single reaction/gene knockouts.
Rank perturbations by product secretion subject to retaining growth.
Validate top candidates with FVA and carbon-source sensitivity.
Required metrics:
WT growth rate
mutant growth fraction
product secretion flux
product yield per glucose uptake
robustness across oxygen/carbon-source bounds
Paper claim format:
Constraint-based screening predicts that perturbing <pathway> improves
<product> secretion while preserving <growth_fraction> of WT growth.
Archetype B: Nutrient-Condition Phase Map
Use when the topic mentions adaptation, nutrient limitation, aerobic/anaerobic
growth, diauxie, or environmental stress.
Plan:
Choose two exchange reactions, usually glucose and oxygen.
Generate a 2D production envelope / phenotype phase plane.
Compare secretion profiles across regimes.
Identify transitions between respiration, overflow metabolism, and no-growth
regions.
Required metrics:
growth flux_maximum
glucose uptake
oxygen uptake
major byproduct secretion fluxes
regime labels
Paper claim format:
A two-axis nutrient envelope reveals distinct feasible metabolic regimes and
predicts condition-specific secretion shifts.
Archetype C: Essentiality and Drug-Target Prioritisation
Use when the topic mentions antimicrobial targets, cancer metabolism, essential
genes, or robustness.
Plan:
Select an organism/model relevant to the disease.
Run single gene/reaction deletion.
Filter essential genes/reactions.
Remove non-specific housekeeping artifacts where possible.
Prioritise targets by subsystem, growth impact, and flux centrality.
Required metrics:
essential gene count
essential reaction count
subsystem enrichment
growth fraction after deletion
rescue condition sensitivity
Paper claim format:
FBA essentiality analysis prioritises <subsystem> as a condition-dependent
vulnerability under <medium>.
Archetype D: Method/Protocol Benchmark
Use when the topic is methodological or AutoResearchClaw asks for a benchmark.
Plan:
Compare FBA, pFBA, loopless FBA, and FVA-derived predictions.
Run across multiple models or media.
Evaluate stability of growth, secretion, and essentiality calls.
Required metrics:
runtime
solver status rate
agreement of essential genes/reactions
flux sparsity
objective consistency
Paper claim format:
A standardised COBRApy protocol improves reproducibility of metabolic
phenotype predictions across models and media.
Feasibility Gate
Before committing to a study, score candidate ideas from 1-5:
Criterion
Reject if
Model availability
no BiGG/SBML/JSON model or no clear toy model
Runtime
requires exhaustive double knockouts on large models
Interpretability
no identifiable pathway/subsystem or biological claim
Output richness
fewer than 3 meaningful figures/tables
Reproducibility
depends on undocumented proprietary data
Proceed only if total score is at least 18/25. Otherwise choose a simpler
organism, narrower product, or smaller perturbation space.
Required Study Card
Write a study_card.md before code generation:
# Metabolic Study Card## Research Question
One sentence.
## Hypothesis
One falsifiable claim.
## Model- Organism:
- Model ID / source:
- Objective reaction:
## Conditions- Medium:
- Carbon source:
- Oxygen bounds:
## Analyses- FBA:
- pFBA:
- FVA:
- Knockout screen:
- Production envelope:
## Metrics- Growth rate:
- Product flux:
- Yield:
- Essentiality:
- Robustness:
## Figures1. WT vs perturbation flux summary
2. Product yield ranking
3. Production envelope / phase map
4. Essentiality or subsystem enrichment plot
## Risks- Model curation risk:
- Solver/runtime risk:
- Biological interpretation risk:
AutoResearchClaw Guidance
When this skill is matched in AutoResearchClaw:
In hypothesis_gen, propose hypotheses tied to a named model and analysis.
In experiment_design, include a concrete model ID, objective reaction,
perturbation set, and metrics.
In code_generation, generate a self-contained COBRApy script that can run
either on a local model file or on a minimal fallback toy model if the full
model is unavailable.
In result_analysis, do not overclaim experimental validation. Phrase results
as model-based predictions.
In paper writing, explicitly state that conclusions are constraint-based
computational predictions requiring wet-lab validation.
Recommended First Autonomous Topic
If the user has no idea, start with:
Predict robust reaction knockout strategies for succinate overproduction in
E. coli using COBRApy FBA, pFBA, FVA, and oxygen/glucose production envelopes.
This topic is computationally feasible, uses a standard organism, produces
multiple figures, and has an interpretable metabolic-engineering narrative.
