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Metabolic Study Planner

OrganizationPopular
aiming-lab
metabolic-study-planner

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.

Overview

Publisheraiming-lab
RepositoryAutoResearchClaw
Skill namemetabolic-study-planner
Stars
14.4K
Forks
1.7K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by aiming-lab on GitHub. Read the source before you install it.

Installation

Install the Metabolic Study Planner AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git /tmp/AutoResearchClaw
mkdir -p .claude/skills
cp -r /tmp/AutoResearchClaw/external/agents/Biology-Agent/skills/metabolic-study-planner .claude/skills/metabolic-study-planner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Metabolic Study Planner in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Metabolic Study Planner on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Metabolic Study Planner is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

text
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:

FieldExamples
Biological scopemicrobial metabolism, cancer metabolism, yeast fermentation, tuberculosis
OrganismE. coli, S. cerevisiae, human Recon3D, M. tuberculosis
Model sourceBiGG ID, local SBML/JSON, manually constructed toy model
Objectivebiomass, product secretion, ATP maintenance, dual objective
Conditionaerobic, anaerobic, carbon source, nutrient limitation
Perturbationgene knockout, reaction knockout, medium swap, oxygen sweep
Target outputgrowth, product yield, essential genes, secretion profile
Paper typemechanism hypothesis, metabolic engineering strategy, benchmark, reproduction

If the user provides no organism, start with one of these low-risk defaults:

DefaultModelWhy
E. coli K-12iJO1366 or core modelFast, well curated, standard for FBA papers
S. cerevisiaeiMM904Fermentation and product-yield studies
Human metabolismRecon3DDisease metabolism, but larger and harder
M. tuberculosisiNJ661Essentiality 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:

CriterionReject if
Model availabilityno BiGG/SBML/JSON model or no clear toy model
Runtimerequires exhaustive double knockouts on large models
Interpretabilityno identifiable pathway/subsystem or biological claim
Output richnessfewer than 3 meaningful figures/tables
Reproducibilitydepends 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:

markdown
# 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:

text
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.

Frequently asked questions

What does the Metabolic Study Planner AI skill do?

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.

Why use Metabolic Study Planner on TypingMind?

Because you install it once and use it with any model. Metabolic Study Planner is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Metabolic Study Planner in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aiming-lab/AutoResearchClaw/tree/main/external/agents/Biology-Agent/skills/metabolic-study-planner. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Metabolic Study Planner?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Metabolic Study Planner?

As many as you like. As long as a model supports skills, you can use Metabolic Study Planner with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Metabolic Study Planner AI skill free?

Yes. It is published on GitHub by aiming-lab under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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