LM-Merger: a workflow for merging logical models with an application to gene regulatory network models

Area of Science:

• Systems biology
• Computational biology
• Genomics

Background:

• Gene regulatory network (GRN) models offer mechanistic insights into gene expression and cellular behavior.
• Dysregulated gene expression is crucial in disease progression and treatment response, highlighting the potential of GRN models in precision medicine.
• Developing comprehensive GRN models covering a broad range of genes is challenging, necessitating model merging approaches.

Purpose of the Study:

• To present LM-Merger, a workflow for semi-automatically merging logical GRN models.
• To demonstrate the feasibility and benefits of model merging for creating more comprehensive GRN models.
• To improve understanding of complex diseases and enhance predictive modeling for precision medicine.

Main Methods:

• The LM-Merger workflow involves five steps: model identification, standardization and annotation, verification, merging, and evaluation.
• The workflow was applied to merge pairs of published GRN models related to acute myeloid leukemia (AML).
• The integrated models were evaluated for their ability to retain predictive accuracy and expand biological system coverage.

Main Results:

• The LM-Merger workflow successfully integrated existing GRN models.
• The merged models maintained the predictive accuracy of the original models while increasing biological system coverage.
• When applied to a new dataset, the integrated models showed superior performance in predicting patient response compared to individual models.

Conclusions:

• Logical model merging, facilitated by LM-Merger, can significantly advance systems biology research and the understanding of complex diseases.
• The construction of more comprehensive GRN models through merging provides deeper insights into disease mechanisms.
• LM-Merger enhances predictive modeling capabilities, supporting the development of precision medicine applications.