AliKishk

FluxForge is a disease-agnostic, Model Context Protocol (MCP) server that fuses primary literature, regulatory/PPI networks, and genome-scale metabolic models into an executable CRISPR gene-dependency and drug-target discovery pipeline. Its core design principle: the biological decision gates live in code as a state machine, so the orchestrating LLM is physically prevented from proposing a downstream target when an upstream biological gate has failed — integrity is enforced by the harness, not by prompt instructions. It exposes 18 typed tools over stdio + HTTP, with provenance quarantine, input guardrails, and pluggable LLM backends, usable from any AI app (Claude Desktop, ChatGPT, Cursor). What we found (rigorously, permutation-controlled across 16 systems vs real CRISPR/Tn-seq/DepMap truth): - Naively bolting literature-extracted regulatory networks onto a metabolic model adds no CRISPR signal that survives a permutation null — because generic transcription-factor networks target non-metabolic genes (a target-space mismatch). - The decisive result: re-seeding literature extraction onto the model's own metabolic gene set rescues the null. On glioma (astrocytoma + glioblastoma) and melanoma, this flipped a null layer to a significant lift — ROC Δ +0.04 to +0.06, delta-permutation p = 0.005, n_pos ≥ 50 — with glioblastoma matching or exceeding a human-curated network. We productized this as a metabolic_target_focus capability in the extraction tool. Why it matters: it reframes literature-integration for systems biology from "decorate predictions" to "reshape the model," and it demonstrates that where you aim an LLM extractor matters more than extraction quality. Just as important, we held ourselves to honest standards — every claimed lift is permutation-controlled with an n_pos floor, nulls are reported as nulls, and we corrected our own earlier overclaims. We also caveat the recall-bias risk in LLM-recalled networks and show an independent human-curated network reproduces the same rescue.

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