A Scalable Computational Approach for Simulating Complexes of Multiple Chromosomes.

Area of Science:

• Genomics
• Computational Biology
• Biophysics

Background:

• Understanding 3D genome structure is key to gene regulation.
• Hi-C experiments provide data, but theoretical models are needed for structural insights.
• Previous models focused on single chromosomes, neglecting crucial inter-chromosome interactions.

Purpose of the Study:

• To develop a computational tool for simulating multi-chromosome interactions within the nucleus.
• To enhance the MiChroM (Minimal Chromatin Model) for scalable, multi-chromosome simulations.
• To improve the accuracy of 3D genome structure predictions by incorporating inter-chromosome contacts.

Main Methods:

• Modified the MiChroM model to simulate multiple chromosomes.
• Developed a GPU-accelerated software version, Open-MiChroM, using the OpenMM Python API.
• Validated the software using GM12878 individual autosomes and simulated interactions of the four largest human chromosomes (C1-C4).

Main Results:

• Open-MiChroM successfully performed multi-chromosome simulations.
• Simulations including multiple chromosomes showed better agreement with Hi-C experimental data.
• The model accurately predicted experimentally observed inter-chromosome contacts without altering the original potential.

Conclusions:

• Multi-chromosome simulations are essential for accurate 3D genome structure modeling.
• Open-MiChroM offers a scalable platform for investigating genome organization and function.
• This approach opens avenues for higher-resolution models and more complex chromatin interaction studies.