Incorporating Prior Knowledge with the with_priors Clause¶
Introduction¶
The with_priors clause represents an advanced extension of GeneForgeLang that allows incorporating prior knowledge and statistical information into generative design and experimental optimization processes. This functionality enables the use of historical data, domain knowledge, and probabilistic constraints to significantly improve the quality and efficiency of artificial intelligence algorithms.
Inspired by Bayesian methodologies and learning with prior information, the with_priors clause allows:
- Incorporating results from previous experiments as prior information
- Defining probability distributions over parameter spaces
- Specifying constraints based on domain knowledge
- Accelerating convergence of optimization algorithms
- Improving the quality of generated designs through informed biases
This capability is especially valuable in genomics, where decades of research have generated a vast body of knowledge that can guide new experiments and designs more efficiently.
Fundamental Concepts¶
Prior Knowledge in Biology¶
In the context of GeneForgeLang, "priors" can take multiple forms:
- Parametric Distributions: Knowledge about typical ranges of experimental parameters
- Structural Constraints: Known limitations in the biological design space
- Historical Results: Data from previous experiments that inform success probabilities
- Biological Symmetries: Known invariances in biological systems
- Functional Relationships: Known dependencies between experimental variables
Integration with Main Blocks¶
The with_priors clause can be associated with:
designblocks: To guide the generation of biological entitiesoptimizeblocks: To accelerate search in parameter spacesanalyzeblocks: To incorporate knowledge in statistical analyses
Clause Structure¶
General Syntax¶
main_block:
# ... block configuration ...
with_priors:
distributions:
parameter1: distribution_type(parameters)
parameter2: distribution_type(parameters)
constraints:
- constraint_expression1
- constraint_expression2
historical_data:
source: path_or_reference
weight: relative_weight
symmetries:
- symmetry_type(parameters)
domain_knowledge:
rules:
- rule1
- rule2
Main Components¶
1. distributions - Prior Distributions¶
Specifies probability distributions for parameters in the search or design space:
with_priors:
distributions:
temperature: normal(37.0, 2.0) # Mean 37°C, std 2°C
concentration: lognormal(50, 1.5) # LogNormal for concentrations
ph_value: beta(7.4, 0.5) # Beta distribution for pH
success_rate: uniform(0.6, 0.9) # Uniform for success rates
Supported Distributions:
- normal(mean, std): Normal distribution
- lognormal(mean, std): Log-normal for positive values
- beta(alpha, beta): Beta for values in [0,1]
- gamma(shape, scale): Gamma for positive values
- uniform(min, max): Uniform in interval
- exponential(lambda): Exponential for waiting times
2. constraints - Probabilistic Constraints¶
Defines constraints based on domain knowledge:
with_priors:
constraints:
- P(binding_affinity > 0.8 | structure_type='alpha_helix') > 0.7
- correlation(gc_content, stability) > 0.3
- mutual_info(promoter_strength, expression_level) > 2.0
3. historical_data - Historical Data¶
Incorporates results from previous experiments:
with_priors:
historical_data:
source: "experiments_database.csv"
columns:
input: ["temp", "conc", "ph"]
output: "efficiency"
weight: 0.8 # Relative weight vs new data
relevance_filter:
target_gene: "TP53" # Only relevant experiments
date_range: "2020-2024"
4. symmetries - Biological Symmetries¶
Specifies known invariances of the system:
with_priors:
symmetries:
- rotational_symmetry(protein_complex, 4) # 4x rotational symmetry
- mirror_symmetry(dna_palindrome) # Palindrome in DNA
- translational_invariance(sequence_motif) # Positional invariance
5. domain_knowledge - Domain Rules¶
Incorporates heuristics and known rules:
with_priors:
domain_knowledge:
rules:
- "IF gc_content > 0.7 THEN stability += 0.2"
- "IF has_motif('TATA') THEN promoter_activity *= 1.5"
- "AVOID stop_codons IN coding_sequences"
confidence_weights:
literature_backed: 0.9 # Rules with literature support
expert_opinion: 0.7 # Expert opinions
heuristic: 0.5 # General heuristics
Usage Examples¶
1. Protein Design with Structural Prior Information¶
design:
entity: ProteinSequence
model: ProteinGeneratorVAE
objective:
maximize: binding_affinity
target: ACE2_receptor
count: 50
output: informed_designs
with_priors:
distributions:
# Length distribution based on known proteins
sequence_length: normal(150, 25)
# Hydrophobicity distribution typical
hydrophobicity: beta(0.4, 0.3)
historical_data:
source: "pdb_ace2_binders.json"
relevance_score: > 0.8
weight: 0.7
domain_knowledge:
rules:
- "IF has_motif('RGD') THEN binding_score += 0.3"
- "IF secondary_structure='beta_sheet' THEN stability += 0.2"
- "AVOID aggregation_prone_regions"
symmetries:
- binding_site_symmetry(ACE2_interface, bilateral)
2. CRISPR Optimization with Historical Data¶
optimize:
search_space:
guide_concentration: range(10, 100)
temperature: range(25, 42)
incubation_time: choice([2, 4, 6, 8])
strategy:
name: BayesianOptimization
objective:
maximize: editing_efficiency
budget:
max_experiments: 40
run:
experiment:
tool: CRISPR_editor
params:
guide_conc: ${guide_concentration}
temp: ${temperature}
incubation: ${incubation_time}
output: editing_results
with_priors:
distributions:
# Based on previous successful experiments
guide_concentration: normal(50, 15)
temperature: normal(37, 3)
historical_data:
source: "crispr_optimization_history.csv"
columns:
input: ["guide_conc", "temp", "incubation"]
output: "efficiency"
weight: 0.6
domain_knowledge:
rules:
- "IF temp > 40 THEN efficiency *= 0.8" # High temp reduces efficiency
- "IF guide_conc < 20 THEN off_target_risk += 0.3"
Advanced Features¶
Hierarchical Priors¶
For complex scenarios with hierarchical knowledge:
with_priors:
hierarchical:
organism_level:
temperature: normal(37, 2)
ph: normal(7.4, 0.2)
tissue_level:
if: "${tissue_type} == 'liver'"
then:
temperature: normal(37.5, 1.5)
ph: normal(7.2, 0.1)
Time-Varying Priors¶
For scenarios where prior knowledge changes over time:
with_priors:
time_varying:
initial_phase:
distribution: uniform(25, 42)
duration: 10 experiments
adaptive_phase:
distribution: normal(37, 3)
update_frequency: 5 experiments
Integration with Other Blocks¶
The with_priors clause seamlessly integrates with other GFL workflow components:
design: Inform generative design with prior knowledgeoptimize: Guide optimization algorithms with informed priorsanalyze: Enhance analysis with domain knowledgesimulate: Improve simulation accuracy with prior information
This integration enables sophisticated workflows that combine prior knowledge with generative design, experimental optimization, and analysis.
Performance Considerations¶
When using the with_priors clause, consider:
- Computational Overhead: Prior incorporation may add computational complexity
- Data Quality: Ensure historical data is relevant and accurate
- Prior Weighting: Balance prior knowledge with new experimental evidence
- Validation: Always validate that priors improve rather than bias results
- Storage Requirements: Historical data and prior distributions may require significant storage
Best Practices¶
- Start Simple: Begin with basic priors and gradually increase complexity
- Validate Priors: Ensure prior knowledge is accurate and relevant
- Weight Appropriately: Balance prior knowledge with new evidence
- Monitor Impact: Track whether priors improve workflow performance
- Update Regularly: Refresh priors as new knowledge becomes available
The with_priors clause represents a powerful capability for incorporating domain expertise into AI-driven biological workflows, enabling researchers to leverage accumulated knowledge for more effective scientific discovery.