Deriving a Boolean dynamics to reveal macrophage activation with in vitro temporal cytokine expression profiles

Abstract

BackgroundMacrophages show versatile functions in innate immunity, infectious diseases, and progression of cancers and cardiovascular diseases. These versatile functions of macrophages are conducted by different macrophage phenotypes classified as classically activated macrophages and alternatively activated macrophages due to different stimuli in the complex in vivo cytokine environment. Dissecting the regulation of macrophage activations will have a significant impact on disease progression and therapeutic strategy. Mathematical modeling of macrophage activation can improve the understanding of this biological process through quantitative analysis and provide guidance to facilitate future experimental design. However, few results have been reported for a complete model of macrophage activation patterns.ResultsWe globally searched and reviewed literature for macrophage activation from PubMed databases and screened the published experimental results. Temporal in vitro macrophage cytokine expression profiles from published results were selected to establish Boolean network models for macrophage activation patterns in response to three different stimuli. A combination of modeling methods including clustering, binarization, linear programming (LP), Boolean function determination, and semi-tensor product was applied to establish Boolean networks to quantify three macrophage activation patterns. The structure of the networks was confirmed based on protein-protein-interaction databases, pathway databases, and published experimental results. Computational predictions of the network evolution were compared against real experimental results to validate the effectiveness of the Boolean network models.ConclusionThree macrophage activation core evolution maps were established based on the Boolean networks using Matlab. Cytokine signatures of macrophage activation patterns were identified, providing a possible determination of macrophage activations using extracellular cytokine measurements.

Insights on TAM Formation from a Boolean Model of Macrophage Polarization Based on In Vitro Studies

“…This could be explained by the important regulatory mechanisms that lead from expression of a gene to the appearance of a protein on the cell membrane (which could be typically detected by FACS), on which most cell type descriptions in immunology are based. Nevertheless, we think that the integration of data in such a model would greatly improve both the usefulness of this model and our understanding of the different phenotypes and we will consider this approach in further work [29,79,80]. For example, Ramirez et al [29] employed transcriptomic time courses to arrive at Boolean models of macrophages in different polarization states, showcasing the potential of integrating experimental data in our mostly literature-based approach.…”

“…An important computational model of macrophage polarization was able to detect 3 different M2 subgroups of macrophages, as a result of various combinations of proand anti-inflammatory extra-cellular signals [27], using exclusively literature-based knowledge of the intra-cellular regulatory interactions and pathways involved in the polarization process. In a more recent work, Ramirez et al [29] used temporal expression profiles of in vitro macrophage cytokines to infer logical models of macrophage polarization (M1 and 3 subcategories of M2: M2a, M2b and M2c) in the presence of different stimuli. Nevertheless, many important questions remain to be explored regarding the polarization states, especially in a tumor setting.…”

“…More specifically, it is important to identify the pathways involved in TAM formation and to understand to what extent the macrophage plasticity facilitates this process inside a tumor. On the other hand, despite the wealth of quantitative information from bulk and single-cell sequencing datasets, the inference of regulatory networks based on experimental data remains a difficult challenge, with most approaches proposing a combination of both literature-and data-driven methods [29][30][31][32].…”

A data-driven computational model enables integrative and mechanistic characterization of dynamic macrophage polarization

“…Following this idea, several systems-level computational models with distinctive features have been formulated to investigate this complex polarization process at the cell level. Palma et al and Ramirez et al both used a semi-mechanistic approach based on Boolean gene regulatory networks to identify macrophage activation patterns (Ramirez et al, 2019;Palma et al, 2018). Two more models by Rex et al and Liu et al both employed logic-based modeling and ordinary differential equations complemented by calibration against selected experimental datasets of M1-M2 marker profiles to simulate macrophage marker expression signatures in response to exogenous stimuli .…”

“…and Ramirez et al. both used a semi-mechanistic approach based on Boolean gene regulatory networks to identify macrophage activation patterns ( Ramirez et al., 2019 ; Palma et al., 2018 ). Two more models by Rex et al.…”