Hypothesis-to-Experiment Orchestrator (HEO)
Hypothesis-to-Experiment Orchestrator
Created on 23rd May 2025
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Hypothesis-to-Experiment Orchestrator (HEO)
Hypothesis-to-Experiment Orchestrator
The problem Hypothesis-to-Experiment Orchestrator (HEO) solves
Modern scientific research is painfully slow and siloed. Researchers must slog through thousands of papers to form a hypothesis, then spend huge amounts of time and money designing and running experiments – often only to find inconclusive results. Knowledge is scattered, wet-lab costs are high, and findings can be hard to reproduce, undermining trust. Access to cutting-edge research is mostly limited to well-funded labs, creating inequality in who can innovate.
HEO addresses critical bottlenecks in scientific research, including:
- Manual and time-consuming literature reviews.
- High costs and waste associated with reagent procurement for wet-lab protocols (currently averaging $1.4K/protocol).
- Lack of access to institutional lab resources for citizen scientists & research scholars.
- Low reproducibility accuracy in published research (69% of papers).
HEO accelerates CRISPR/Protein engineering discoveries by automating literature review, protocol design, and validation. It significantly reduces experiment costs through decentralized resource pooling and BioDAO network optimization.
App flow:
Hypothesis ⇒ Protocol ⇒ Validation ⇒ Proof ⇒ FAIR packaging ⇒ DKG query/publish
Challenges I ran into
HEO is an open-source AI agent (an ElizaOS plugin) that automates the journey from hypothesis to experiment. Give HEO a research question or hypothesis, and it does the heavy lifting: an AI literature review scours relevant publications and extracts key insights, a hypothesis is refined or generated from these insights, and then an experiment design is crafted – complete with methodology and required resources. Finally, HEO uses blockchain and cryptography to validate and preserve the experiment plan and results. The entire process is orchestrated seamlessly, so researchers can go from idea to actionable experiment in a fraction of the time.
Our challenges & how we address it:
- Model Hallucination: Ensuring the accuracy and reliability of AI-generated hypotheses and protocols.
This shall be mitigated by
a) Ensemble Validation
b) Implementing human-in-the-loop validation gates at critical stages
c) Ensuring chosen AI Model - LLM/SLM has superior benchmark performance to meet emerging needs & requirements of users - Blockchain Integration & zkSNARK complexity : Bridging off-chain AI results with on-chain verification. Working with zkSNARKs in a short time was intense, used for protocol execution and validation. We haven’t fully integrate a complex proof into the main pipeline due to time, but, the groundwork is laid. Shall be addressed through formal verification using tools like Certora.
- BioDAO Coordination Failure: Team is working to determine mitigation & contingency actions for potential BioDAO Coordination Failure.
- Regulatory Shifts: Adapting to evolving compliance requirements from bodies like the EMA and NIH.
Impact: HEO has the potential to dramatically accelerate scientific discovery. A process that once took months (from literature review to experiment prep) can now happen in days or hours, expediting the research cycle.
HEO shall incorporate a dynamic compliance engine with webhooks to stay updated on the latest guidelines with experts.
Tracks Applied (2)
Scientific Outcomes
Scientific Outcomes
Solana Foundation