Algorithmic approaches to clonal reconstruction in heterogeneous cell populations

Area of Science:

• Computational Biology
• Evolutionary Biology
• Genomics

Background:

• Reconstructing clonal haplotypes and evolutionary history is crucial in microbial and cancer biology.
• The clonal theory of evolution provides a framework for modeling clone evolution.

Purpose of the Study:

• To review the theoretical framework and assumptions of clonal reconstruction.
• To formally define the clonal reconstruction problem, its complexity, and solution space.
• To categorize existing methods based on input data and computational formulation.

Main Methods:

• Review of theoretical frameworks and assumptions in clonal reconstruction.
• Categorization of methods by input data (space-resolved vs. time-resolved) and computational approach (combinatorial vs. probabilistic).
• Discussion of complementary data sources like single-cell and whole-genome sequencing.

Main Results:

• Various methods exist for inferring clonal phylogeny from observed data.
• Input data types significantly reduce the solution space for clonal reconstruction.
• Existing algorithms and tools for clonal reconstruction are reviewed and categorized.

Conclusions:

• Most clonal inference algorithms are tailored for tumor evolution, with less focus on unicellular genomes.
• Open problems include the lack of benchmark datasets and performance comparisons for available tools.
• Further development is needed for robust clonal reconstruction in diverse biological systems.