LM-Merger: A workflow for merging logical models with an application to gene regulation

Abstract

Motivation: Gene regulatory network (GRN) models provide mechanistic understanding of genetic interactions that regulate gene expression and, consequently, influence cellular behavior. Dysregulated gene expression plays a critical role in disease progression and treatment response, making GRN models a promising tool for precision medicine. While researchers have built many models to describe specific subsets of gene interactions, more comprehensive models that cover a broader range of genes are challenging to build. This necessitates the development of automated approaches for merging existing models.

Results: We present LM-Merger, a workflow for semi-automatically merging logical GRN models. The workflow consists of five main steps: (a) model identification, (b) model standardization and annotation, (c) model verification, (d) model merging, and (d) model evaluation. We demonstrate the feasibility and benefit of this workflow with two pairs of published models pertaining to acute myeloid leukemia (AML). The integrated models were able to retain the predictive accuracy of the original models, while expanding coverage of the biological system. Notably, when applied to a new dataset, the integrated models outperformed the individual models in predicting patient response. This study highlights the potential of logical model merging to advance systems biology research and our understanding of complex diseases.

Availability and implementation: The workflow and accompanying tools, including modules for model standardization, automated logical model merging, and evaluation, are available at https://github.com/IlyaLab/LogicModelMerger/.

Keywords: acute myeloid leukemia; gene regulatory networks; logical models; model integration; systems biology.

Fig 1:

Overview of the logical model workflow. The flowchart illustrates the step-by-step process for merging logical models. The workflow begins with collecting eligible models from literature and databases. These models are then standardized into the SBML-qual format with added annotations. Reproducibility is verified for each model before proceeding to the merging step. The merging steps include composition of rules for overlapping nodes and non-overlapping components are directly integrated. Finally, the integrated mode is evaluated on tasks similar to their original studies and novel tasks of interest, ensuring its accuracy and applicability.

Fig 2:

The merged model of Bonzanni et al. 2011 and Krumsiek et al. 2013 and its evaluation. (A) Visualization of the merged model using BioTapestry. Lines indicate regulation relationships that point from the regulator to its targets, with arrowheads as activating and bars as repressing. (B) Steady states pattern of the merged model pair and individual models. Each row is a steady state of the Bonzanni et al. model (start with ‘B’), Krumsiek et al. model (start with ‘K’), or the merged model (Start with ‘M’). The color in the heatmap indicates that a gene is ON (red), or OFF (blue), or that the gene is not included in the model (Grey). (C) Correlation of measured frequency of expression with the modeled frequency of activation for 10 genes using the Bonzanni et al. model. (D) Correlation of measured frequency of expression with the modeled frequency of activation for the 10 genes using the merged model. (E) Correlation of measured frequency of expression with the modeled frequency of activation for 18 genes using the merged model. 8 additional genes not covered in the Bonzanni et al. model are colored in orange. Results of the ‘OR’ model are shown, for other merged models results, see Fig S1 - S2.

Fig 3:

The merged model of Palma et al. 2021 and Ikonomi et al. 2020 and its evaluation. (A) Visualization of the merged model. (B) Steady states pattern of the merged model and individual models. (C) Correlation between the average blast percentage and network scores derived from the Palma et al. model for mutation status on FLT3, NPM1 and DNMT3A. (D) Correlation between blast percentage and network scores derived from the merged model for mutation status on FLT3, NPM1 and DNMT3A. (E) Correlation between blast percentage and network scores derived from the Palma et al. model for mutation status on all available genes in the Palma et al. model. Size of node indicates number of patients for each mutation profile. (F) Correlation between blast percentage and network scores derived from the merged model for mutation status on all available genes in the merged model. Only scatterplot of the ‘AND’ model is shown, for other merged models results, see Fig S4.