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Minucci SB, Heise RL, Reynolds AM. Agent-based vs. equation-based multi-scale modeling for macrophage polarization. PLoS One 2024; 19:e0270779. [PMID: 38271449 PMCID: PMC10810539 DOI: 10.1371/journal.pone.0270779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 09/29/2023] [Indexed: 01/27/2024] Open
Abstract
Macrophages show high plasticity and result in heterogenic subpopulations or polarized states identified by specific cellular markers. These immune cells are typically characterized as pro-inflammatory, or classically activated M1, and anti-inflammatory, or alternatively activated M2. However, a more precise definition places them along a spectrum of activation where they may exhibit a number of pro- or anti-inflammatory roles. To understand M1-M2 dynamics in the context of a localized response and explore the results of different mathematical modeling approaches based on the same biology, we utilized two different modeling techniques, ordinary differential equation (ODE) modeling and agent-based modeling (ABM), to simulate the spectrum of macrophage activation to general pro- and anti-inflammatory stimuli on an individual and multi-cell level. The ODE model includes two hallmark pro- and anti-inflammatory signaling pathways and the ABM incorporates similar M1-M2 dynamics but in a spatio-temporal platform. Both models link molecular signaling with cellular-level dynamics. We then performed simulations with various initial conditions to replicate different experimental setups. Similar results were observed in both models after tuning to a common calibrating experiment. Comparing the two models' results sheds light on the important features of each modeling approach. When more data is available these features can be considered when choosing techniques to best fit the needs of the modeler and application.
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Affiliation(s)
- Sarah B. Minucci
- Department of Mathematics & Applied Mathematics, Virginia Commonwealth University, Richmond, VA, United States of America
| | - Rebecca L. Heise
- Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA, United States of America
| | - Angela M. Reynolds
- Department of Mathematics & Applied Mathematics, Virginia Commonwealth University, Richmond, VA, United States of America
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Frank ASJ, Larripa K, Ryu H, Röblitz S. Macrophage phenotype transitions in a stochastic gene-regulatory network model. J Theor Biol 2023; 575:111634. [PMID: 37839584 DOI: 10.1016/j.jtbi.2023.111634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/11/2023] [Accepted: 10/05/2023] [Indexed: 10/17/2023]
Abstract
Polarization is the process by which a macrophage cell commits to a phenotype based on external signal stimulation. To know how this process is affected by random fluctuations and events within a cell is of utmost importance to better understand the underlying dynamics and predict possible phenotype transitions. For this purpose, we develop a stochastic modeling approach for the macrophage polarization process. We classify phenotype states using the Robust Perron Cluster Analysis and quantify transition pathways and probabilities by applying Transition Path Theory. Depending on the model parameters, we identify four bistable and one tristable phenotype configuration. We find that bistable transitions are fast but their states less robust. In contrast, phenotype transitions in the tristable situation have a comparatively long time duration, which reflects the robustness of the states. The results indicate parallels in the overall transition behavior of macrophage cells with other heterogeneous and plastic cell types, such as cancer cells. Our approach allows for a probabilistic interpretation of macrophage phenotype transitions and biological inference on phenotype robustness. In general, the methodology can easily be adapted to other systems where random state switches are known to occur.
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Affiliation(s)
| | - Kamila Larripa
- Department of Mathematics, California State Polytechnic University Humboldt, Arcata, CA, USA.
| | - Hwayeon Ryu
- Department of Mathematics and Statistics, Elon University, Elon, NC, USA.
| | - Susanna Röblitz
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
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3
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Qi H, Yin YS, Yin ZY, Li X, Shuai JW. Mitochondrial outer membrane permeabilization and inner membrane permeabilization in regulating apoptosis and inflammation. J Theor Biol 2023; 571:111558. [PMID: 37327862 DOI: 10.1016/j.jtbi.2023.111558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 06/05/2023] [Indexed: 06/18/2023]
Abstract
Recent studies delineate an intimate crosstalk between apoptosis and inflammation. However, the dynamic mechanism linking them by mitochondrial membrane permeabilization remains elusive. Here, we construct a mathematical model consisting of four functional modules. Bifurcation analysis reveals that bistability stems from Bcl-2 family member interaction and time series shows that the time difference between Cyt c and mtDNA release is around 30 min, which are consistent with previous works. The model predicts that Bax aggregation kinetic determines cells to undergo apoptosis or inflammation, and that modulating the inhibitory effect of caspase 3 on IFN-β production allows the concurrent occurrence of apoptosis and inflammation. This work provides a theoretical framework for exploring the mechanism of mitochondrial membrane permeabilization in controlling cell fate.
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Affiliation(s)
- Hong Qi
- Complex Systems Research Center, Shanxi University, Taiyuan, China; Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China.
| | - Yu-Song Yin
- Complex Systems Research Center, Shanxi University, Taiyuan, China
| | - Zhi-Yong Yin
- School of Mathematics and Statistics, Guangxi Normal University, Guilin, China
| | - Xiang Li
- Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China
| | - Jian-Wei Shuai
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China
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Avila-Ponce de León U, Vázquez-Jiménez A, Padilla-Longoria P, Resendis-Antonio O. Uncoding the interdependency of tumor microenvironment and macrophage polarization: insights from a continuous network approach. Front Immunol 2023; 14:1150890. [PMID: 37283734 PMCID: PMC10240616 DOI: 10.3389/fimmu.2023.1150890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 05/10/2023] [Indexed: 06/08/2023] Open
Abstract
The balance between pro- and anti-inflammatory immune system responses is crucial to preventing complex diseases like cancer. Macrophages are essential immune cells that contribute to this balance constrained by the local signaling profile of the tumor microenvironment. To understand how pro- and anti-inflammatory unbalance emerges in cancer, we developed a theoretical analysis of macrophage differentiation that is derived from activated monocytes circulating in the blood. Once recruited to the site of inflammation, monocytes can be polarized based on the specific interleukins and chemokines in the microenvironment. To quantify this process, we used a previous regulatory network reconstructed by our group and transformed Boolean Network attractors of macrophage polarization to an ODE scheme, it enables us to quantify the activation of their genes in a continuous fashion. The transformation was developed using the interaction rules with a fuzzy logic approach. By implementing this approach, we analyzed different aspects that cannot be visualized in the Boolean setting. For example, this approach allows us to explore the dynamic behavior at different concentrations of cytokines and transcription factors in the microenvironment. One important aspect to assess is the evaluation of the transitions between phenotypes, some of them characterized by an abrupt or a gradual transition depending on specific concentrations of exogenous cytokines in the tumor microenvironment. For instance, IL-10 can induce a hybrid state that transits between an M2c and an M2b macrophage. Interferon- γ can induce a hybrid between M1 and M1a macrophage. We further demonstrated the plasticity of macrophages based on a combination of cytokines and the existence of hybrid phenotypes or partial polarization. This mathematical model allows us to unravel the patterns of macrophage differentiation based on the competition of expression of transcriptional factors. Finally, we survey how macrophages may respond to a continuously changing immunological response in a tumor microenvironment.
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Affiliation(s)
- Ugo Avila-Ponce de León
- Programa de Doctorado en Ciencias Biológicas, Universidad Nacional Autónoma de Mexico, Ciudad de Mexico, Mexico
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de Mexico, Mexico
| | - Aarón Vázquez-Jiménez
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de Mexico, Mexico
| | - Pablo Padilla-Longoria
- Institute for Applied Mathematics (IIMAS), Universidad Nacional Autónoma de Mexico, Ciudad de Mexico, Mexico
| | - Osbaldo Resendis-Antonio
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de Mexico, Mexico
- Coordinación de la Investigación Científica - Red de Apoyo a la Investigación, Universidad Nacional Autónoma de México (UNAM), Ciudad de Mexico, Mexico
- Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de Mexico, Ciudad de Mexico, Mexico
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Geiß C, Salas E, Guevara-Coto J, Régnier-Vigouroux A, Mora-Rodríguez RA. Multistability in Macrophage Activation Pathways and Metabolic Implications. Cells 2022; 11:cells11030404. [PMID: 35159214 PMCID: PMC8834178 DOI: 10.3390/cells11030404] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/18/2022] [Accepted: 01/22/2022] [Indexed: 12/22/2022] Open
Abstract
Macrophages are innate immune cells with a dynamic range of reversible activation states including the classical pro-inflammatory (M1) and alternative anti-inflammatory (M2) states. Deciphering how macrophages regulate their transition from one state to the other is key for a deeper understanding of inflammatory diseases and relevant therapies. Common regulatory motifs reported for macrophage transitions, such as positive or double-negative feedback loops, exhibit a switchlike behavior, suggesting the bistability of the system. In this review, we explore the evidence for multistability (including bistability) in macrophage activation pathways at four molecular levels. First, a decision-making module in signal transduction includes mutual inhibitory interactions between M1 (STAT1, NF-KB/p50-p65) and M2 (STAT3, NF-KB/p50-p50) signaling pathways. Second, a switchlike behavior at the gene expression level includes complex network motifs of transcription factors and miRNAs. Third, these changes impact metabolic gene expression, leading to switches in energy production, NADPH and ROS production, TCA cycle functionality, biosynthesis, and nitrogen metabolism. Fourth, metabolic changes are monitored by metabolic sensors coupled to AMPK and mTOR activity to provide stability by maintaining signals promoting M1 or M2 activation. In conclusion, we identify bistability hubs as promising therapeutic targets for reverting or blocking macrophage transitions through modulation of the metabolic environment.
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Affiliation(s)
- Carsten Geiß
- Institute for Developmental Biology and Neurobiology (IDN), Johannes Gutenberg University, 55128 Mainz, Germany;
- Correspondence: (C.G.); (R.A.M.-R.)
| | - Elvira Salas
- Department of Biochemistry, Faculty of Medicine, Campus Rodrigo Facio, University of Costa Rica, San José 11501-2060, Costa Rica;
| | - Jose Guevara-Coto
- Department of Computer Sciences and Informatics (ECCI), Faculty of Engineering, Campus Rodrigo Facio, University of Costa Rica, San José 11501-2060, Costa Rica;
- Research Center for Information and Communication Technologies (CITIC), Campus Rodrigo Facio, University of Costa Rica, San José 11501-2060, Costa Rica
| | - Anne Régnier-Vigouroux
- Institute for Developmental Biology and Neurobiology (IDN), Johannes Gutenberg University, 55128 Mainz, Germany;
| | - Rodrigo A. Mora-Rodríguez
- Institute for Developmental Biology and Neurobiology (IDN), Johannes Gutenberg University, 55128 Mainz, Germany;
- Research Center on Surgery and Cancer (CICICA), Campus Rodrigo Facio, University of Costa Rica, San José 11501-2060, Costa Rica
- Research Center for Tropical Diseases (CIET), Lab of Tumor Chemosensitivity (LQT), Faculty of Microbiology, Campus Rodrigo Facio, University of Costa Rica, San José 11501-2060, Costa Rica
- Correspondence: (C.G.); (R.A.M.-R.)
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Mathematical modeling of ventilator-induced lung inflammation. J Theor Biol 2021; 526:110738. [PMID: 33930440 DOI: 10.1016/j.jtbi.2021.110738] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 04/20/2021] [Accepted: 04/22/2021] [Indexed: 12/13/2022]
Abstract
Despite the benefits of mechanical ventilators, prolonged or misuse of ventilators may lead to ventilation-associated/ventilation-induced lung injury (VILI). Lung insults, such as respiratory infections and lung injuries, can damage the pulmonary epithelium, with the most severe cases needing mechanical ventilation for effective breathing and survival. Damaged epithelial cells within the alveoli trigger a local immune response. A key immune cell is the macrophage, which can differentiate into a spectrum of phenotypes ranging from pro- to anti-inflammatory. To gain a greater understanding of the mechanisms of the immune response to VILI and post-ventilation outcomes, we developed a mathematical model of interactions between the immune system and site of damage while accounting for macrophage phenotype. Through Latin hypercube sampling we generated a collection of parameter sets that are associated with a numerical steady state. We then simulated ventilation-induced damage using these steady state values as the initial conditions in order to evaluate how baseline immune state and lung health affect outcomes. We used a variety of methods to analyze the resulting parameter sets, transients, and outcomes, including a random forest decision tree algorithm and parameter sensitivity with eFAST. Analysis shows that parameters and properties of transients related to epithelial repair and M1 activation are important factors. Using the results of this analysis, we hypothesized interventions and used these treatment strategies to modulate the response to ventilation for particular parameters sets.
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