gaucho

gaucho infers clonal relationships and reconstructs clonal architectures in heterogeneous populations, such as cancer sequencing samples, by modeling evolutionary processes with genetic algorithms.

Key Features:

• Genetic Algorithms: Employs genetic algorithms to search solution space for optimal clonal configurations.
• Evolutionary Modeling: Models evolutionary processes to represent clone emergence and divergence within a sample.
• Fitness-based Optimization: Uses fitness criteria to evaluate and optimize candidate clonal structures.
• Clonal Relationship Analysis: Dissects relationships between clones to reveal tumor heterogeneity and diversity.

Scientific Applications:

• Cancer Research: Reconstructs tumor clonal architectures to inform studies of tumor heterogeneity, clonal evolution, and potential treatment resistance.
• Population Genetics: Analyzes genetic diversity and clonal relationships within any heterogeneous population dataset.

Methodology:

Applies genetic algorithms that iteratively improve solutions by modeling selection, crossover, and mutation and optimizing clonal architectures based on fitness criteria.