Synthetic Data Foundations Enable Modular Analysis

Generate structured inputs for four biological layers using fixed parameters for reproducibility. For gene regulatory networks, create a 14x14 weight matrix W with edge_prob=0.20 (uniform -1.5 to 1.5 weights, excluding self-loops), simulate 80-step expression X via X_t = sigmoid(X_ @ W + N(0,0.08)). Protein features (40 proteins, 10D normals) include families (5 classes) and localization (4 classes); PPI dataset from 780 pairs uses cosine sim, family/local same flags, abs diff/elementwise product feats, latent score=1.4sim +1.0fam_same +0.8loc_same +0.15hidden_proj yielding sigmoid prob labels. Metabolic net has 7 reactions/metabolites (e.g., R3_TCA: 1.0 biomass, 2.4 ATP yield, 1.4 O2 need) with substrate_costs. Cell signaling ODEs (T=220, dt=0.05, ligand=1.2) model receptor/kinase/TF/phosphatase dynamics via rates like dR=1.6lig(1-R)-0.9*R, clipping 0,1 (phos to 1.5).

These functions produce analyzable outputs: GRN yields ~20-30 true edges; PPI ~10-15% positives; met balances biomass/ATP vs constraints; signaling reaches peaks (e.g., receptor 0.8 at t10-20).

Specialized Agents Extract Key Metrics and Rankings

GeneRegulatoryNetworkAgent infers edges from |corrcoef(X.T)|>0.35 (yielding ~15-25 associations vs true edges), builds DiGraph for top-5 hubs/sinks by out/in-degree (e.g., G5 out_deg=4), ranks most_dynamic by var(X:,g) (top often >0.05). ProteinInteractionPredictionAgent splits PPI rows, scales feats, fits LogisticRegression(max_iter=1000), reports test ROC-AUC/AP (~0.85-0.90 on held-out), ranks top-10 pairs by pred_prob (e.g., P12-P28:0.92). MetabolicOptimizationAgent runs 8000 random Dirichlet(ones(6))U(1.5,5) fluxes, penalizes O2>3.5/sub>4.2 by 6(excess), scores 2.2biomass+0.6ATP (best ~5-7, e.g., R3_TCA flux=2.1 dominant). CellSignalingSimulationAgent computes max/peak_time for receptor/kinase/TF (~0.75/0.85/0.65 at t=15/25/35), final states.

Agents return dict summaries with exact counts (e.g., 14 genes, 196 pairs, 0.124 pos rate), top lists, preserving floats rounded to 4dec for downstream use—enables quick ranking without retraining.

Workflow Integration, Visualization, and LLM Synthesis

Execute agents sequentially on generated data, aggregate AgentResult list, print JSON summaries/tables (e.g., dynamic genes G7 var=0.0824), plot weight matrices (imshow coolwarm), expression trajectories (6 lines), signaling curves (4 components), met trace (converging to best), networks (spring_layout, green/red edges >0.4 |W|, PPI widths=2+4*prob). Save artifact JSON.

PrincipalInvestigatorAgent prompts GPT-4o-mini (temp=0.4) with agent summaries to generate report: Executive Summary, Key Findings (per-agent), Cross-System Interpretation (e.g., dynamic hubs link to PPI clusters driving met flux/signaling amplification), Wet-Lab Hypotheses, Limitations (synthetic data), Extensions (real omics). Prompt enforces concise science, no fabrication—yields coherent story tying regulation to metabolism/signaling via interactions, runnable in Colab for rapid prototyping.

Trade-offs: Synthetic data ignores real priors (extend with omics); random met opt crude vs LP solvers; correlation inference misses causality (add Granger); scales to 100s genes/proteins but LLMs add latency/cost (~$0.01/run).