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A compositional framework for Boolean networks.

H Alkhudhayr¹, J Steggles²

¹School of Computing, Newcastle University, Newcastle upon Tyne, UK; Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh, Saudi Arabia.

|April 26, 2019

Summary

This summary is machine-generated.

This study introduces a formal framework for composing Boolean networks, overcoming the state space explosion problem. This enables the creation and analysis of larger, more complex biological models.

Keywords:

Behaviour preservation Boolean network Interference Model composition

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Area of Science:

Systems Biology
Computational Biology
Theoretical Computer Science

Background:

Boolean networks are valuable for qualitative modeling of biological systems.
The state space explosion problem limits the scale of Boolean network models.
Analyzing large-scale biological systems with Boolean networks is challenging.

Purpose of the Study:

To develop a formal framework for composing Boolean networks.
To facilitate the construction and analysis of large-scale biological models.
To address the limitations imposed by the state space explosion problem.

Main Methods:

A compositional approach based on merging entities using a binary Boolean operator.
Formalization of behavior preservation under composition using a notion of compatibility.
Development of a trace alignment property to characterize compatibility.

Main Results:

A formal framework for composing Boolean networks is presented.
Compatibility is formalized using a trace alignment property.
An extended trace alignment property completely characterizes compatibility, considering interference.

Conclusions:

The proposed framework enables the construction and analysis of larger Boolean network models.
The developed compatibility notion and trace alignment property are key to compositional analysis.
This work advances the scalability of Boolean network modeling for complex biological systems.