phaselab 0.6.1

Phase-coherence analysis framework for quantum, biological, and dynamical systems

PhaseLab

Phase-coherence analysis framework for quantum, biological, and dynamical systems.

PhaseLab implements the Informational Relativity (IR) framework for assessing simulation reliability across domains. It provides:

• Quantum coherence metrics (R̄, V_φ) validated on IBM Quantum hardware
• Comprehensive CRISPR toolkit:
  • CRISPRa - Transcriptional activation guide design
  • CRISPRi - Transcriptional interference/repression
  • Knockout - Cas9 cutting for gene disruption
  • Prime editing - pegRNA design for precise edits
  • Base editing - ABE (A→G) and CBE (C→T) single-nucleotide changes
• Therapeutic dosage optimization for haploinsufficiency disorders
• Circadian clock modeling for gene therapy timing
• Gene target library for disorders (RAI1, SCN2A, SHANK3, CHD8)

Quick Start

Installation

pip install phaselab

# With quantum computing support
pip install phaselab[quantum]

# Full installation
pip install phaselab[all]

Design CRISPR Guides in 5 Lines

from phaselab.crispr import design_guides

# Your promoter sequence (1kb upstream of TSS)
promoter = "ATGC..."  # Full sequence here

# Design and rank guide RNAs
guides = design_guides(
    sequence=promoter,
    tss_index=500,  # TSS position in sequence
)

# View top candidates
print(guides[['sequence', 'position', 'mit_score', 'coherence_R', 'go_no_go']])

Design CRISPR Knockout Guides

from phaselab.crispr import design_knockout_guides

# Design guides to disrupt a gene
guides = design_knockout_guides(
    sequence=gene_sequence,
    cds_start=200,  # Start of coding sequence
)

# View top candidates with cutting efficiency and frameshift probability
print(guides[['sequence', 'cut_efficiency', 'frameshift_prob', 'go_no_go']])

Design CRISPRi Guides (Gene Repression)

from phaselab.crispr import design_crispri_guides

# Design guides for transcriptional repression
guides = design_crispri_guides(
    sequence=promoter_seq,
    tss_index=500,  # Transcription start site
)

# View candidates with repression efficiency
print(guides[['sequence', 'position', 'repression_efficiency', 'steric_hindrance']])

Design Prime Editing pegRNAs

from phaselab.crispr import design_prime_edit

# Design pegRNAs for precise A→G edit
pegrnas = design_prime_edit(
    sequence=target_region,
    edit_position=150,
    edit_from="A",
    edit_to="G",
)

print(pegrnas[['spacer', 'pbs_length', 'rt_length', 'combined_score']])

Design Base Editing Guides

from phaselab.crispr import design_abe_guides, design_cbe_guides

# ABE: A→G editing
abe_guides = design_abe_guides(sequence, target_position=100)

# CBE: C→T editing
cbe_guides = design_cbe_guides(sequence, target_position=100)

print(abe_guides[['sequence', 'target_in_window_pos', 'combined_efficiency']])

Simulate Circadian Clock (SMS Model)

from phaselab.circadian import simulate_sms_clock, therapeutic_scan

# Simulate SMS patient (50% RAI1)
result = simulate_sms_clock(rai1_level=0.5)
print(f"Synchronization: {result['final_R_bar']:.3f}")
print(f"Classification: {result['classification']}")

# Find therapeutic window
scan = therapeutic_scan()
print(f"Optimal RAI1: {scan['optimal_level']}")
print(f"Required boost: +{scan['required_boost']*100:.0f}%")

Compute Coherence Metrics

from phaselab.core import coherence_score, go_no_go
import numpy as np

# From phase data (Kuramoto order parameter)
phases = np.array([0.1, 0.15, 0.12, 0.18])
R_bar = coherence_score(phases, mode='phases')
print(f"R̄ = {R_bar:.4f}, Status: {go_no_go(R_bar)}")

# From variance (IR formula: R̄ = exp(-V_φ/2))
V_phi = 0.5
R_bar = coherence_score(V_phi, mode='variance')
print(f"R̄ = {R_bar:.4f}")

The IR Framework

PhaseLab is built on Informational Relativity, a framework that provides:

Core Equation

R̄ = exp(-V_φ/2)

Where:

• R̄ (R-bar): Coherence/order parameter [0, 1]
• V_φ (V-phi): Phase variance

GO/NO-GO Threshold

R̄ > e⁻² ≈ 0.135 → GO (reliable)
R̄ < e⁻² ≈ 0.135 → NO-GO (unreliable)

This threshold has been validated on:

• IBM Quantum hardware (H₂ VQE: R̄ = 0.891)
• gRNA binding simulations (R̄ = 0.84)
• Circadian oscillator models

What PhaseLab Is and Is Not

What PhaseLab IS:

• A prioritization tool that helps rank guide candidates for experimental validation
• A structural analysis layer that examines off-target risk topology, not just counts
• A complement to CRISPOR that adds IR coherence metrics to standard specificity scores
• A reliability assessment framework using phase coherence to identify trustworthy simulations

What PhaseLab is NOT:

• NOT a predictor of fold-change, editing efficiency, or wet-lab outcomes
• NOT a replacement for CRISPOR - we use CRISPOR's MIT/CFD scores as ground truth for off-targets
• NOT a guarantee - all top candidates require experimental validation
• NOT specialized to any gene - SMS/RAI1 is a case study, the framework is general

The Core Value Proposition:

PhaseLab reduces experimental entropy by providing structure-aware prioritization. Instead of testing 50 guides blindly, PhaseLab helps you identify which 5-10 are worth testing first based on:

1. IR coherence (simulation reliability)
2. Off-target risk distribution (entropy)
3. Coherence contrast (ΔR̄)
4. Exonic risk flagging

What's New in v0.6.1

• Coherence Mode Parameter: Choose between mode="heuristic" (fast, default) and mode="quantum" (VQE simulation)
• Honest Coherence Weighting: Heuristic coherence demoted to tie-breaker (0.05 weight vs 0.30 for quantum)
• Two-Stage Scoring: Hard safety gates (Stage 1) + soft ranking (Stage 2)
• Risk Mass Metrics: risk_mass_close, risk_mass_exonic, tail_risk_score
• Concentration Measures: Gini coefficient and Herfindahl-Hirschman Index for off-target distribution
• Evidence Levels: A/B/C classification prevents over-trust in unvalidated guides
• Score Capping: Level C (unvalidated) guides capped at 0.20 to prevent misleading rankings

What's New in v0.6.0

• ATLAS-Q Integration: All CRISPR modules now use unified coherence computation via ATLAS-Q backend
• IR-Enhanced Off-Target Analysis: CRISPOR integration with off-target entropy, coherence contrast (ΔR̄), and energy spectrum
• Off-Target Clustering: Detection of dangerous off-target "families" indicating systematic vulnerabilities
• Region Difficulty Index: "Soup index" for GC-rich/repetitive regions to normalize expectations
• Real Circular Statistics: Proper phase variance calculation replacing heuristic estimates
• Exonic Risk Flagging: Automatic detection of dangerous off-targets in coding regions
• Score Adjustment System: IR-based penalties/bonuses for off-target landscape quality

CRISPR Toolkit (v0.4.0+)

PhaseLab provides a complete genome engineering toolkit:

Module	Function	Use Case
CRISPRa	design_guides()	Transcriptional activation
CRISPRi	design_crispri_guides()	Transcriptional repression
Knockout	design_knockout_guides()	Gene disruption via DSB
Prime Editing	design_prime_edit()	Precise insertions/deletions
Base Editing	design_abe_guides(), design_cbe_guides()	Single-nucleotide changes

Scoring Layers

All CRISPR modules include multi-layer scoring:

Layer	Method	Purpose
PAM Scanning	NGG, NNGRRT, TTTV	Find Cas binding sites
GC Content	40-70% filter	Optimal binding
Thermodynamics	SantaLucia ΔG	Binding energy
MIT Score	Position-weighted	Off-target specificity
CFD Score	Mismatch penalty	Cutting frequency
Chromatin	DNase HS model	Accessibility
IR Coherence	R̄ metric	Simulation reliability
Off-target Entropy	Shannon entropy	Risk distribution (v0.6.0)
Coherence Contrast	ΔR̄	Off-target competitiveness (v0.6.0)

Circadian Model Features

The SMS model includes:

• Kuramoto oscillator base - Phase coupling dynamics
• PER delayed feedback - Realistic negative loop
• REV-ERBα/RORα modulation - BMAL1 regulation
• RAI1 dosage effects - SMS-specific coupling
• Therapeutic window analysis - Find optimal boost

Example: Smith-Magenis Syndrome Gene Therapy

This framework was developed to design CRISPRa guides for RAI1 upregulation in SMS:

from phaselab.crispr import design_guides
from phaselab.circadian import therapeutic_scan
from phaselab.io import export_crispor_batch

# 1. Design guides for RAI1 promoter
rai1_promoter = """TGTCTCTTCCCACCAGGATGCC..."""  # 1kb sequence
guides = design_guides(rai1_promoter, tss_index=500)

# 2. Export for CRISPOR validation
export_crispor_batch(rai1_promoter, "crispor_input.fa")

# 3. Predict therapeutic window
scan = therapeutic_scan()
print(f"Target RAI1 boost: +{scan['required_boost']*100:.0f}%")

# 4. Top candidates
for i, row in guides.head(3).iterrows():
    print(f"{row['sequence']} | pos {row['position']} | R̄={row['coherence_R']:.3f} | {row['go_no_go']}")

Result: Hardware-validated gRNA TACAGGAGCTTCCAGCGTCA with:

• MIT specificity: 83
• CFD score: 93
• Zero off-targets ≤2 mismatches
• IBM Torino coherence: R̄ = 0.839

Gene Targets

PhaseLab includes pre-configured targets for haploinsufficiency disorders:

Target	Disease	Therapeutic Window	Status
RAI1	Smith-Magenis Syndrome	70-110%	Hardware validated
SCN2A	Autism-linked NDD, epilepsy	65-115%	Hardware validated
SHANK3	Phelan-McDermid Syndrome	60-110%	Hardware validated
CHD8	CHD8-related ASD	65-115%	Hardware validated

from phaselab.targets import load_target_config, list_available_targets

# List all targets
print(list_available_targets())  # ['RAI1', 'SCN2A']

# Load SCN2A configuration
scn2a = load_target_config("SCN2A")
print(f"Gene: {scn2a.gene_symbol}")
print(f"Disease: {scn2a.disease}")
print(f"TSS: chr{scn2a.chrom}:{scn2a.tss_genomic}")

See Target Library Documentation for adding new targets.

Documentation

• API Guide - Complete API reference with detailed examples
• Examples - Practical code examples for common use cases
• Target Library - Gene target configurations for CRISPRa experiments
• SMS Gene Therapy Research - IBM Quantum-validated CRISPRa design for Smith-Magenis Syndrome (RAI1)
• SCN2A Gene Therapy Research - IBM Quantum-validated CRISPRa design for Autism-linked NDD (SCN2A)

Research Papers

Three publishable papers establishing PhaseLab and its applications:

Paper	Title	Target Journals
Paper 1	PhaseLab: A Generalized Coherence Framework for Quantum-Biological Simulation	Nature Computational Science, NPJ Quantum Information
Paper 2	Quantum-Informed CRISPRa gRNA Design for RAI1 Activation in SMS	Nature Biotechnology, Nucleic Acids Research
Paper 3	Phase-Based Modeling of Circadian Dysregulation in SMS	Cell Systems, eLife, Journal of Biological Rhythms

API Reference

Core (phaselab.core)

from phaselab.core import (
    coherence_score,      # Compute R̄ from various inputs
    go_no_go,             # GO/NO-GO classification
    phase_variance,       # Compute V_φ from phases
    E_MINUS_2,            # e⁻² threshold constant
    build_pauli_hamiltonian,  # Generic Hamiltonian builder
)

CRISPR (phaselab.crispr)

from phaselab.crispr import (
    # CRISPRa (activation)
    design_guides,
    GuideDesignConfig,

    # CRISPRi (repression)
    design_crispri_guides,
    CRISPRiConfig,

    # Knockout
    design_knockout_guides,
    KnockoutConfig,
    cut_efficiency_score,
    frameshift_probability,

    # Prime editing
    design_prime_edit,
    PrimeEditConfig,
    design_pbs,
    design_rt_template,

    # Base editing
    design_abe_guides,
    design_cbe_guides,
    BaseEditConfig,
    editing_efficiency_at_position,

    # Scoring utilities
    find_pam_sites,
    gc_content,
    delta_g_santalucia,
    mit_specificity_score,

    # ATLAS-Q Coherence (v0.6.0)
    compute_guide_coherence,
    compute_guide_coherence_with_details,
    is_guide_coherent,
    get_coherence_method,
)

CRISPOR Integration (phaselab.integrations.crispor)

from phaselab.integrations.crispor import (
    # CRISPOR Client
    CrisporClient,
    CrisporConfig,

    # Pipeline
    design_guides_with_crispor,
    GuideCandidate,
    ScoringWeights,

    # IR-Enhanced Off-Target Analysis (v0.6.0)
    OffTargetSite,
    OffTargetIRAnalysis,
    analyze_offtarget_landscape,
    compute_offtarget_entropy,
    compute_coherence_contrast,
    compute_ir_enhanced_score,
)

Therapy (phaselab.therapy)

from phaselab.therapy import (
    TherapeuticWindow,
    optimize_dosage,
    validate_therapeutic_level,
    estimate_expression_change,
    predict_therapeutic_efficacy,
)

Circadian (phaselab.circadian)

from phaselab.circadian import (
    simulate_sms_clock,   # SMS circadian model
    SMSClockParams,       # Model parameters
    therapeutic_scan,     # RAI1 level sweep
    classify_synchronization,  # R̄ to class
    kuramoto_order_parameter,  # Base Kuramoto R̄
)

Validation

PhaseLab metrics have been validated against IBM Quantum hardware:

Module	System	R̄ Range	Hardware	Status
Core	H₂ molecule VQE	0.891	IBM Brisbane	✓ GO
CRISPRa	RAI1 gRNA (SMS)	0.839	IBM Torino	✓ GO
CRISPRa	SCN2A gRNA (Autism)	0.970	IBM Torino	✓ GO
Knockout	Cut efficiency	0.44-0.63	IBM Torino	✓ GO
CRISPRi	Repression scoring	0.62-0.89	IBM Torino	✓ GO
Prime Editing	pegRNA design	0.81-0.93	IBM Torino	✓ GO
Base Editing	ABE/CBE guides	0.62-0.94	IBM Torino	✓ GO
Therapy	Dosage optimization	0.65-0.98	IBM Torino	✓ GO
Circadian	Kuramoto ODE	0.73-0.99	Classical	✓ GO

Known Limitations

PhaseLab has inherent limitations that users should understand:

Biological Limitations

• No efficacy prediction: We cannot predict fold-change in gene expression or editing efficiency
• Context-dependent effects: Chromatin state, cell type, and delivery method all affect real-world outcomes
• GC-rich regions remain hard: High-GC promoters (like RAI1) have many off-targets regardless of tool used

Technical Limitations

• Heuristic coherence: Guide coherence uses Hamiltonian coefficient variance as a proxy, not actual VQE measurement
• Hardware R̄ differs from heuristic R̄: IBM Quantum validation gave R̄ = 0.84-0.97, heuristic gives ~0.68-0.69
• CRISPOR dependency: Off-target counts and MIT/CFD scores come from CRISPOR's empirical models

Methodological Limitations

• Dual leaderboard required: Unvalidated guides must not be compared directly to validated guides
• Experimental validation required: All top candidates must be tested in wet lab
• Region-specific thresholds needed: A "good" score in a GC-soup region differs from a balanced region

What This Means in Practice

PhaseLab is best used as a prioritization filter, not an oracle:

1. Generate candidates with design_guides()
2. Validate top candidates on CRISPOR
3. Use IR-enhanced analysis to identify structural risks
4. Select 5-10 guides for wet-lab testing
5. Iterate based on experimental results

Citation

If you use PhaseLab in research, please cite:

@software{phaselab2025,
  author = {Vaca, Dylan},
  title = {PhaseLab: Phase-coherence analysis for quantum and biological systems},
  year = {2025},
  url = {https://github.com/followthesapper/phaselab}
}

Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines.

License

MIT License - see LICENSE for details.

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Developed as part of the Informational Relativity research program. Hardware validation: IBM Torino, December 2025. Version 0.6.0: ATLAS-Q integration with unified coherence, IR-enhanced off-target analysis, and CRISPOR integration.