Inferring Boolean networks with perturbation from sparse gene expression data: a general model applied to the...

Area of Science:

• Computational Biology
• Systems Biology
• Bioinformatics

Background:

• Inferring genetic regulatory networks from gene expression data is a long-standing challenge due to numerous variables and limited experiments.
• Boolean networks with perturbation (BNp) offer a modeling approach for these networks.

Purpose of the Study:

• To investigate the inference of Boolean networks with perturbation (BNp) using simulated and microarray gene expression data.
• To develop a method for determining BNp models by interpreting discrete expression levels as attractor states.

Main Methods:

• Interpreting discrete gene expression levels as attractor states of the underlying genetic network.
• Utilizing sequences of attractor states to infer BNp models, considering both complete and sampled attractor sequences.
• Applying constraints of attractor state distribution and probability conservation to identify unique, multiple, or most-likely networks.

Main Results:

• A BNp can be trivially inferred when a complete sequence of attractors is known.
• When attractors are sampled, the method yields unique, multiple exact, or a most-likely network based on constraints.
• A robustness requirement is used to select a preferred network among multiple exact solutions.
• Networks are selected based on the best fit to observed attractor distributions when exact solutions are not found.

Conclusions:

• The developed algorithm effectively infers Boolean networks with perturbation (BNp) from gene expression data, even with incomplete attractor state information.
• The approach provides a robust method for modeling genetic regulatory networks, applicable to complex biological systems.
• The study successfully applied the algorithm to the interferon regulatory network in murine macrophages infected with cytomegalovirus.