metabolic-study-planner

Metabolic Study Planner

Overview

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
Condition	aerobic, anaerobic, carbon source, nutrient limitation
Perturbation	gene knockout, reaction knockout, medium swap, oxygen sweep
Target output	growth, product yield, essential genes, secretion profile
Paper type	mechanism hypothesis, metabolic engineering strategy, benchmark, reproduction

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:

1. Select a product exchange reaction, e.g. succinate, lactate, ethanol, acetate.
2. Run WT FBA and pFBA under a defined medium.
3. Screen single reaction/gene knockouts.
4. Rank perturbations by product secretion subject to retaining growth.
5. 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:

1. Choose two exchange reactions, usually glucose and oxygen.
2. Generate a 2D production envelope / phenotype phase plane.
3. Compare secretion profiles across regimes.
4. 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:

1. Select an organism/model relevant to the disease.
2. Run single gene/reaction deletion.
3. Filter essential genes/reactions.
4. Remove non-specific housekeeping artifacts where possible.
5. 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:

1. Compare FBA, pFBA, loopless FBA, and FVA-derived predictions.
2. Run across multiple models or media.
3. 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:

## Figures
1. 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.