Problem Statement

Every hour, scientists post questions on r/bioinformatics. Most of these questions are poorly-posed. Most questions aren’t even about bioinformatics - they’re about basic scientific thinking. While the desire to learn bioinformatics is commendable, many scientists approach it backwards: instead of starting with a biological question that requires computational analysis, they start with a desire to use -omics approaches and bioinformatics tools and retrofit a question to match. This leads to confusion and inefficient learning paths – especially because most bioinformatics tools are dilapidated (“please intall Python 2.7”).
The first step towards properly using bioinformatics is understanding whether and which computational approaches are actually needed for the scientific question at hand.

We want an agent that goes into Reddit (or similar) forums and helps steer the ship towards the optimal outcome.

Key Requirements

• Must capture Brenner’s distinctive voice and wit while maintaining scientific rigor
• Should identify core scientific issues in posts (mostly unclear hypotheses or that potential responders have been given little to no valuable context to help)
  • is the broader scientific context clear?
  • what are the hypotheses of the poster?
  • does it seem like a bioinformatics expert has enough information to read the post and help them? is there a clear exposition of why they need help from strangers on r/bioinformatics?
  • what were the expected results?
  • if applicable: have they run any checks for self-consistency in the data (i.e. in silico controls)?
  • have they done any amount of literature searching or using generally available resources?
• Generate responses that make sure the scientist has elucidated why they are using bioinformatics, prioritize biological meaning over technical details of implementing certain tools
• Must maintain conversation history to avoid contradicting previous advice
• Should recognize when a question needs human intervention
• Should recognize if a post actually is well-posed and potentially not comment or give some Brenner-like quip or encouragement

Background

Context

Sydney Brenner was a master at cutting through confusion. He could spot a poorly designed experiment from a mile away. And he’d tell you about it - with humor, with history, and always with helpful advice.

Similar Work

• GPT-Agent Reddit Bots: Several examples of LLM-powered bots monitoring subreddits
• Historical Figure Impersonation: Projects like “Einstein Bot” for physics education
• Scientific Writing Assistants: Tools that help researchers clarify their methods

Key Challenges

• Capturing Brenner’s voice without caricature
• Maintaining scientific accuracy
• Avoiding repetitive or formulaic responses
• Knowing when to defer to human experts or not comment at all

Success Criteria

• Bot can maintain consistent Brenner-like persona across multiple interactions
• Responses consistently guide users toward better experimental design
• Users engage with bot’s suggestions (measured by response rates)
• Community feedback indicates helpful scientific guidance
• No instances of harmful or misleading scientific advice

Resources

Papers

• Brenner’s Nobel lecture: “Nature’s Gift to Science”
• Brenner’s collected letters and correspondence
• Key papers on C. elegans development (for authentic references)
• Literature on scientific mentorship and education (e.g. Uri Alon’s communications)

Datasets

• Archive of r/bioinformatics posts and responses
• Collection of Brenner’s writings and interviews
• Examples of good/bad experimental design or good/bad forum questions
• Curated set of molecular biology anecdotes and history (to reduce hallucination)
• Curated set of reddit posts and successful human-surpervised LLM responses

Notes for AI Agents

Development Considerations

• start with a strong foundation in Brenner’s writing style and scientific philosophy
• implement conversation memory to maintain context
• safeguards against giving dangerous or unethical advice
• build in graceful degradation when confidence is low

Potential Pitfalls

• over-automation leading to generic responses
• misidentifying technical issues as scientific ones (or vice versa)
• missing serious underlying issues that need human attention
• becoming too focused on humor or voice at the expense of helpful advice

Changelog

• 2025-01-26: Initial draft
• 2025-02-04 Tweaked Language of Spec
• [Future updates will go here]

Note: This is a living document. Future AI agents are encouraged to update and improve it based on implementation experience and community feedback.