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Agent skill

Bio Systems Biology Strain Design

by GPTomics in GPTomics/bioSkills

Computes metabolic-engineering strain designs on genome-scale models with StrainDesign (OptKnock, RobustKnock, minimal cut sets, OptCouple) and cameo (heuristic knockout and FSEOF…

MITAuto-check passedResearch & Science

Install Bio Systems Biology Strain Design

skills CLI
$ npx skills add GPTomics/bioSkills --skill bio-systems-biology-strain-design -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-systems-biology-strain-design --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/systems-biology/strain-design .claude/skills/bio-systems-biology-strain-design && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
bio-systems-biology-strain-design
GitHub stars
1.2k
Used in
1 other repo
Token cost
~2.5k tokens
SKILL.md length
911 words
Files
3
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Computes metabolic-engineering strain designs on genome-scale models with StrainDesign (OptKnock, RobustKnock, minimal cut sets, OptCouple) and cameo (heuristic knockout and FSEOF…

  • Designing knockouts to overproduce a target chemical
  • SKILL.md covers Version Compatibility, The governing principle:…, Decision: which strain-design… and Growth-Coupled Knockouts with…, plus 5 more sections
  • Runs Python scripts from its folder; calls pip
  • Choosing between OptKnock and RobustKnock

What it does

Bio Systems Biology Strain Design is an agent skill from GPTomics/bioSkills. Computes metabolic-engineering strain designs on genome-scale models with StrainDesign (OptKnock, RobustKnock, minimal cut sets, OptCouple) and cameo (heuristic knockout and FSEOF over/under-expression targets), finding gene/reaction interventions that couple product formation to growth. Use when designing knockouts to overproduce a target chemical, choosing between OptKnock and RobustKnock, growth-coupling a product so evolution maintains it, computing minimal cut sets, finding amplification targets with FSEOF…

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `examples/strain_design.py` and `usage-guide.md`).

It sits in Research & Science, covering Bioinformatics. It works with Python. The repository describes itself as: a set of SKILLS.md for doing bioinformatics with agents like claude code. The licence is MIT.

When your agent uses it

  • Designing knockouts to overproduce a target chemical
  • Choosing between OptKnock and RobustKnock
  • Growth-coupling a product so evolution maintains it
  • Computing minimal cut sets

Example prompts

  • “Use the bio-systems-biology-strain-design skill to compute metabolic-engineering strain designs on genome-scale models with StrainDesign (OptKnock…”
  • “/bio-systems-biology-strain-design”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bio Systems Biology Strain Design loads about 2.5k tokens when it runs. Until then it costs about 163 tokens; SKILL.md has 911 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~163
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 911 words, ~2,504 tokens.

Download SKILL.mdSave it as .claude/skills/bio-systems-biology-strain-design/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
bio-systems-biology-strain-design
description
Computes metabolic-engineering strain designs on genome-scale models with StrainDesign (OptKnock, RobustKnock, minimal cut sets, OptCouple) and cameo (heuristic knockout and FSEOF over/under-expression targets), finding gene/reaction interventions that couple product formation to growth. Use when designing knockouts to overproduce a target chemical, choosing between OptKnock and RobustKnock, growth-coupling a product so evolution maintains it, computing minimal cut sets, finding amplification targets with FSEOF, or understanding why MILP strain design needs a strong solver and why a design is only a hypothesis.
tool_type
python
primary_tool
straindesign

Version Compatibility

Reference examples tested with: StrainDesign 1.15+, COBRApy 0.29+, Python 3.10+ (cameo optional)

Before using code patterns, verify installed versions match. If versions differ:

  • Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Note: OptKnock/RobustKnock/MCS are MILP problems and are far harder than plain FBA; StrainDesign supports GLPK/SCIP (open source) and CPLEX/Gurobi (academic, much faster and more robust for genome-scale). Set a time_limit. Reaction-based designs must be translated back to gene knockouts via GPRs.

Strain Design

"Design knockouts to make my organism overproduce a chemical" -> Search for a set of gene/reaction interventions that couples product formation to growth, so the engineered strain cannot grow well without secreting the target.

  • Python: straindesign.compute_strain_designs(model, sd_modules=[SDModule(model, OPTKNOCK, ...)]); cameo for heuristics/FSEOF

The governing principle: growth-coupling makes evolution enforce the design

The core idea of computational strain design is growth-coupling. A naive "just delete the competing pathways" design is fragile: the cell will find an alternate flux route, or evolution in the bioreactor will erode production because making product costs the cell resources. A growth-COUPLED design instead makes product secretion obligatory for growth - the cell physically cannot reach high growth without also secreting the target, so selection maintains production instead of eroding it. This is why OptKnock is a BILEVEL optimization: the inner problem is the cell maximizing its own growth, the outer problem is the engineer maximizing product AT that inner optimum. Consequences:

  • OptKnock is optimistic: it assumes the cell, among its growth-optimal states, picks the one best for the engineer. The cell need not. RobustKnock fixes this by maximizing product in the WORST-case inner optimum - a more conservative, more trustworthy design.
  • A design is a HYPOTHESIS about a model, not a strain. FBA has no regulation, no enzyme kinetics, no toxicity, no genetic stability; a computationally growth-coupled design can fail in construction or in the bioreactor. Validate with the production envelope, then in the lab.
  • MILP strain design is combinatorially hard. Bound the intervention set (max_cost), cap solutions, set a time limit, and use a strong solver (CPLEX/Gurobi for genome-scale). Reaction knockouts must be mapped back to gene deletions through the GPR to be realizable.

Decision: which strain-design method

GoalMethodTrade-off
Growth-coupled knockouts, optimisticOptKnock (Burgard 2003)bilevel; assumes the cell cooperates at its growth optimum
Growth-coupled knockouts, conservativeRobustKnock (Tepper & Shlomi 2010)guarantees product in the worst-case inner optimum; harder
Guaranteed intervention sets, enumerate all minimalMinimal Cut Sets (von Kamp & Klamt 2014)strong guarantees; enumerates smallest intervention sets
Strong growth-coupling (obligatory)OptCouplemaximizes the growth-coupling potential directly
Over/under-EXPRESSION targets, not just knockoutsFSEOF (Choi 2010) / cameoscans fluxes that rise with enforced product; amplification targets
Heuristic/evolutionary search when MILP is intractableOptGene / cameofast approximate designs; no optimality guarantee

Prefer RobustKnock or MCS over plain OptKnock when the design must be trustworthy; OptKnock's optimism is a well-known way to overstate a design.

Growth-Coupled Knockouts with StrainDesign (OptKnock)

Goal: Find a small set of reaction knockouts that couples secretion of a target product to growth.

Approach: Build an OptKnock SDModule with the cell's growth as the inner objective and product secretion as the outer objective, plus a minimum-growth constraint so the design keeps the strain viable, then call compute_strain_designs with an intervention budget and solver. Translate the returned reaction knockouts back to gene deletions via the GPR.

python
import cobra
import straindesign as sd

model = cobra.io.load_model('textbook')
biomass = 'Biomass_Ecoli_core'   # the model's actual biomass reaction id (verify per model)

optknock = sd.SDModule(
    model, sd.OPTKNOCK,
    inner_objective=biomass,            # the cell maximizes growth
    outer_objective='EX_ac_e',          # the engineer maximizes acetate secretion
    constraints=[f'{biomass} >= 0.3'],  # keep the strain viable
)

solutions = sd.compute_strain_designs(
    model, sd_modules=[optknock],
    max_cost=3,          # at most 3 interventions
    max_solutions=3,
    solver='glpk',       # use 'cplex'/'gurobi' for genome-scale models
    time_limit=120,
)
# solutions.reaction_sd is a list of intervention dicts {reaction_id: marker}; a knockout is
# marked -1.0 (not 0). Verify this marker for the installed StrainDesign version -- a wrong marker
# silently yields empty designs. For a knockout-only OptKnock module every entry is a knockout.
for design in solutions.reaction_sd:
    print('knockouts:', [rid for rid, mark in design.items() if mark == -1.0])
Show full SKILL.md (342 more words)Show less

Verify Growth-Coupling with the Production Envelope

python
from cobra.flux_analysis import production_envelope

# A genuinely growth-coupled design shows a NONZERO minimum product flux across the growth range:
# the strain cannot grow without secreting product. Apply the design's knockouts, then:
env = production_envelope(model, reactions=['EX_ac_e'])   # objective defaults to biomass
# Inspect the lower bound of product at each growth level; if it can be zero at max growth, the
# coupling is weak (the OptKnock-optimism problem) -- consider RobustKnock. See flux-balance-analysis.

Over-Expression Targets (FSEOF, cameo)

python
# Knockouts are not the only lever. FSEOF (flux scanning with enforced objective flux) finds
# reactions whose flux RISES as product formation is enforced -- candidate amplification/over-
# expression targets. cameo implements FSEOF and heuristic (evolutionary) design search:
#   from cameo.strain_design import OptGene           # heuristic knockout search
#   from cameo.strain_design.deterministic import FSEOF
# Use FSEOF/over-expression when the bottleneck is low flux through an existing pathway rather than
# a competing drain that a knockout would remove.

Common Errors

SymptomCauseFix
compute_strain_designs never finishesMILP is hard and GLPK is slow on genome-scaleset time_limit, lower max_cost, use CPLEX/Gurobi
Design gives zero product when builtOptKnock optimism: the cell chose a different growth-optimal stateuse RobustKnock, or check the production envelope's lower bound
Constraint parser rejects the biomass idwrong reaction id string for this modellook up the actual objective reaction id (linear_reaction_coefficients)
Design not realizable in the labreaction knockouts have no clean gene mapping, or hit an essential genetranslate reaction KOs to gene KOs via GPR; exclude essential genes
Predicted overproduction never materializesFBA has no regulation/kinetics/toxicity/stabilitytreat the design as a hypothesis; validate the envelope, then in vivo
No feasible design foundgrowth constraint too tight or product infeasible on the mediumrelax the minimum-growth constraint; confirm the product can be made on the medium
  • systems-biology/flux-balance-analysis - Production envelope and the FBA/medium foundation
  • systems-biology/gene-essentiality - Avoid designing knockouts of essential genes; GPR mapping
  • systems-biology/model-curation - A curated model is a prerequisite for a trustworthy design
  • systems-biology/context-specific-models - Constrain the chassis to a condition before designing
  • metabolomics/pathway-mapping - Interpret the affected pathways of a design

References

  • Burgard AP, Pharkya P, Maranas CD. 2003. OptKnock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng 84(6):647-657.
  • Schneider P, Bekiaris PS, von Kamp A, Klamt S. 2022. StrainDesign: a comprehensive Python package for computational design of metabolic networks. Bioinformatics 38(21):4981-4983.
  • Tepper N, Shlomi T. 2010. Predicting metabolic engineering knockout strategies for chemical production: accounting for competing pathways. Bioinformatics 26(4):536-543. (RobustKnock)
  • von Kamp A, Klamt S. 2014. Enumeration of smallest intervention strategies in genome-scale metabolic networks. PLoS Comput Biol 10(1):e1003378. (minimal cut sets)
  • Choi HS, Lee SY, Kim TY, Woo HM. 2010. In silico identification of gene amplification targets for improvement of lycopene production. Appl Environ Microbiol 76(10):3097-3105. (FSEOF)
  • Cardoso JGR, Jensen K, Lieven C, et al. 2018. Cameo: a Python library for computer-aided metabolic engineering and optimization of cell factories. ACS Synth Biol 7(4):1163-1166.

© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in systems-biology/strain-design of GPTomics/bioSkills.

  • SKILL.md
  • examples/strain_design.py
  • usage-guide.md

Open the folder on GitHubat commit d91ed3d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in GPTomics/bioSkills, which our catalogue first saw on October 7, 2026.

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Works with

Categories

Questions about Bio Systems Biology Strain Design

What does Bio Systems Biology Strain Design do?

Computes metabolic-engineering strain designs on genome-scale models with StrainDesign (OptKnock, RobustKnock, minimal cut sets, OptCouple) and cameo (heuristic knockout and FSEOF…. Bio Systems Biology Strain Design is an agent skill from GPTomics/bioSkills. Computes metabolic-engineering strain designs on genome-scale models with StrainDesign (OptKnock, RobustKnock, minimal cut sets, OptCouple) and cameo (heuristic knockout and FSEOF over/under-expression targets), finding gene/reaction interventions that couple product formation to growth.

When should I use Bio Systems Biology Strain Design?

Bio Systems Biology Strain Design fits situations like: designing knockouts to overproduce a target chemical; choosing between OptKnock and RobustKnock; growth-coupling a product so evolution maintains it; computing minimal cut sets.

How do I install Bio Systems Biology Strain Design in Claude Code?

Run `npx skills add GPTomics/bioSkills --skill bio-systems-biology-strain-design -a claude-code`. Or copy the skill folder (systems-biology/strain-design in GPTomics/bioSkills) into .claude/skills/bio-systems-biology-strain-design in your project. Claude Code loads it when a task matches its description.

How do I install Bio Systems Biology Strain Design in Codex?

Run `npx skills add GPTomics/bioSkills --skill bio-systems-biology-strain-design -a codex`. Or copy the skill folder (systems-biology/strain-design in GPTomics/bioSkills) into .agents/skills/bio-systems-biology-strain-design in your project. Codex loads it when a task matches its description.

Can I use Bio Systems Biology Strain Design in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GPTomics/bioSkills --skill bio-systems-biology-strain-design -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bio-systems-biology-strain-design, .gemini/skills/bio-systems-biology-strain-design, .github/skills/bio-systems-biology-strain-design and .opencode/skills/bio-systems-biology-strain-design in your project.

What does Bio Systems Biology Strain Design need to run?

Going by SKILL.md and its folder, Bio Systems Biology Strain Design needs Python for the scripts in its folder and the command-line tools its instructions call (pip). Our summary lists: Python 3.

Does Bio Systems Biology Strain Design access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Bio Systems Biology Strain Design safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Bio Systems Biology Strain Design use?

Bio Systems Biology Strain Design is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bio Systems Biology Strain Design use?

About 2.5k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Bio Systems Biology Strain Design?

Skills that share tags, products or a category with Bio Systems Biology Strain Design: Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars), Singlecell Qc (xuzhougeng/wisp-science, 1k stars) and Trackplot (ygidtu/trackplot, 109 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bio Systems Biology Strain Design?

GPTomics (a GitHub organization) maintains it in GPTomics/bioSkills, which has 1,218 GitHub stars. The repository holds 559 skills in this directory. The repository was last updated on August 15, 2026.

Source: GPTomics/bioSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.