Simulating heterogeneous populations using Boolean models.

Area of Science:

• Computational Biology
• Systems Biology
• Cellular Dynamics

Background:

• Rare cellular events, crucial for processes like cancer development and immune activation, are computationally challenging to simulate.
• Existing attractor analysis methods for Boolean models struggle to explicitly simulate mixed cell populations and track rare subpopulations.

Purpose of the Study:

• To develop a computational method for simulating heterogeneous cell populations that can capture rare cellular events.
• To enable direct simulation of diverse cell populations without modeling each subpopulation individually.

Main Methods:

• Utilizing Boolean network models to describe cellular states.
• Developing a population-level simulation strategy for non-interacting cells in diverse states.
• Incorporating heterogeneity in cell state and network rules (e.g., genetic alterations).

Main Results:

• Demonstrated exact simulation of non-interacting, highly heterogeneous cell populations.
• Successfully captured rarest model outcomes with no sampling error.
• Applied the method to simulate a T-cell receptor Boolean network with ~10^20 states and mutational profiles.

Conclusions:

• A novel method enables population-level simulation using Boolean models for non-interacting individuals in different states.
• This approach effectively handles populations with an overwhelming number of distinct subpopulations.
• The method advances the computational study of rare cellular events and population heterogeneity.