The Master
Protocol

Institutional operational manual for Zenith v30.0. A-Z generative research pipeline.

01 / Research Journey

Step 01

Institutional Auth

Authenticate at niluslab.com. Verifies Phase IV license and establishes the PDB structural bridge connection.

Step 02

Open Simulation Lab

Navigating to the console initializes the 2.42M-cell ensemble manifold and prepares the Neural SDE update rules.

Step 03

Mode Configuration

Select OSK Partial for identity-preserving 10-year rejuvenation.

Step 04

Discovery Prompting

Input the HMVEC Rejuvenation Target into the Semantic textarea.

"Reverse biological age of HMVECs by 10 years. Restore eNOS activity. No c-MYC. Target ESI 98%."

Step 05

Initiate Discovery

Click the master CTA. Triggers discover_hybrid() and executes the Langevin dynamics loop.

Step 06

AF3 Structural Bridge

Fetch UniProt sequence via **Gene Lookup**. Run job on **alphafoldserver.com**. Enter **PAE** score to lock physical validity.

02 / Sidebar Encyclopedia

Left / A Reprogramming Factors

Select canonical cocktails or synthetic TF weights.

Left / B Transfect Population

Immediate latent state transition command.

Left / C Perturbations

Induce Tumor/SMA stress states for verification.

Left / D Genomic Knockout

Delete safety genes (TP53) to test safety margin.

Right / E Semantic Target

NLP bridge for clinical intent parsing.

Right / F Gene Lookup

Swiss-Prot bridge for sequence extraction.

Right / G AF3 Results

pLDDT/PAE manual score entry panel.

Master CTA Initiate Discovery

Unifies prediction and structural validation.

03 / Advanced Systems Biology Engine

Module I

GraphRAG & Cardiac Gene Regulatory Networks (GRN)

Resolves semantic biological queries topologically over heterogeneous graphs $G=(V,E)$ using Graph Attention message passing:

                    h_v^(k) = σ(W^(k) · CONCAT(h_v^(k-1), AGGREGATE({h_u^(k-1) : u ∈ N(v)})))
                

Module II

Zero-Shot Tabular Multi-Omics Predictor

Simulates cell state trajectories and epigenetic aging shifts using pre-trained biological transformers with self-attention:

                    Attention(Q,K,V) = softmax( (QK^T) / √d_k )V
                

Module III

High-Throughput Automation (Labcyte Echo Protocol)

Generates acoustic liquid handler transfer sheets, rounding droplet volumes to physical 2.5 nL limits, and evaluates screening assays using the Z'-factor metric:

                    Z' = 1 - 3(σ_pos + σ_neg) / |μ_pos - μ_neg|
                

Module IV

Single-Cell Quality Control (scRNA-seq QC)

Filters out low-quality or apoptotic cells from downstream processing by enforcing strict mitochondrial leak thresholds:

                    M_i = ( ∑_(g ∈ G_mito) x_i,g / ∑_(g ∈ G_all) x_i,g ) * 100  [Requirement: M_i ≤ 15%]
                

Module V

mRNA-LNP & AAV Formulation Delivery Optimizer

Optimizes lipid nanoparticle molar ratios (ionizable, cholesterol, helper, PEG) to predict encapsulation efficiency and target organ tropism:

                    EE% = β_0 + β_1*X_ion + β_2*X_chol + β_3*(P/N) + ε
                