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@davewalker5 davewalker5 released this 06 Jul 17:26
0.3.0
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Relaxed Molecular Clock Engine

This release introduces the Relaxed Molecular Clock Engine, extending the project beyond the idealised strict molecular clock to model lineage-specific evolutionary rates.

While the existing Strict Clock Engine remains unchanged, the new simulator provides an independent implementation of a simple autocorrelated relaxed molecular clock, allowing different branches of the phylogenetic tree to evolve at different mutation rates while preserving a complete known evolutionary history.

Highlights

  • Added a new Relaxed Molecular Clock Engine
  • Independent implementation alongside the existing Strict Clock Engine
  • Support for lineage-specific mutation rates
  • Autocorrelated rate inheritance between parent and descendant lineages
  • New JSON configuration format for relaxed clock simulations
  • Generation of synthetic datasets in FASTA, Newick and JSON formats
  • Complete recording of lineage rates, mutation history and simulation metadata
  • New workflow documentation describing the relaxed clock simulation process
  • Expanded project documentation covering relaxed molecular clock concepts

Overview

The Relaxed Molecular Clock Engine follows the same overall simulation workflow as the Strict Clock Engine:

  • Generate an ancestral DNA sequence
  • Construct a rooted phylogenetic tree
  • Calibrate an ultrametric time tree
  • Assign lineage-specific mutation rates
  • Simulate sequence evolution
  • Export complete simulation datasets

By separating the two simulation engines, the project provides a clear comparison between strict and relaxed molecular clock assumptions while keeping each implementation focused, readable and reproducible.

Looking Ahead

With both strict and relaxed molecular clock simulation engines now in place, the project is well positioned to move on to phylogenetic reconstruction algorithms.

Future releases will explore methods such as UPGMA and other distance-based tree reconstruction techniques, using the simulated datasets as known ground truth for algorithm validation.

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