Investigation of IL-6 and IL-10 signalling via mathematical modelling

Moya, Colby; Huang, Zuyi; Cheng, Pingyan; Jayaraman, Arul; Hahn, Juergen

doi:10.1049/iet-syb.2009.0060

Cited by 28 publications

(69 citation statements)

References 34 publications

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“…A characteristic feature of these models is that they describe the dynamics of signaling initiated by a single pro-inflammatory cytokine. Moya et al [29] developed a mathematical model to represent interactions between IL-6 (pro-inflammatory) and IL-10 (anti-inflammatory) in hepatocytes when both of these cytokines were used as stimuli to the cells. The current work describes an interplay between de novo synthesized pro-inflammatory (TNF-α) and anti-inflammatory (IL-10) cytokines in macrophages exposed to LPS (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

“…The anti-inflammatory (IL-10) module is adapted from the IL-6 and IL-10 model by Moya et al [29]. Only the ODEs involved in IL-10 signaling through the IL-10 receptor (as mentioned in Moya et al [29]) are included in the anti-inflammatory module of our current model to formulate the feedback effects of IL-10 on its own production (through positive feedback regulation of STAT3) and TNF-α production (through negative feedback regulation of NF-κB).…”

mentioning

confidence: 99%

“…Only the ODEs involved in IL-10 signaling through the IL-10 receptor (as mentioned in Moya et al [29]) are included in the anti-inflammatory module of our current model to formulate the feedback effects of IL-10 on its own production (through positive feedback regulation of STAT3) and TNF-α production (through negative feedback regulation of NF-κB). Biochemical reactions, as described in Moya et al, for STAT3 phosphorylation, dimerization and nuclear translocation to initiate transcription are retained in our current model.…”

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confidence: 99%

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Mathematical Modeling of Pro- and Anti-Inflammatory Signaling in Macrophages

et al. 2014

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Inflammation is a beneficial mechanism that is usually triggered by injury or infection and is designed to return the body to homeostasis. However, uncontrolled or sustained inflammation can be deleterious and has been shown to be involved in the etiology of several diseases, including inflammatory bowel disorder and asthma. Therefore, effective anti-inflammatory signaling is important in the maintenance of homeostasis in the body. However, the inter-play between pro-and anti-inflammatory signaling is not fully understood. In the present study, we develop a mathematical model to describe integrated pro-and anti-inflammatory signaling in macrophages. The model incorporates the feedback effects of de novo synthesized pro-inflammatory (tumor necrosis factor α; TNF-α) and anti-inflammatory (interleukin-10; IL-10) cytokines on the activation of the transcription factor nuclear factor κB (NF-κB) under continuous lipopolysaccharide (LPS) stimulation (mimicking bacterial infection). In the model, IL-10 upregulates its own production (positive feedback) and also downregulates TNF-α production through NF-κB (negative feedback). In addition, TNF-α upregulates its own production through NF-κB (positive feedback). Eight model parameters are selected for estimation involving sensitivity analysis and clustering techniques. We validate the mathematical model predictions by measuring phosphorylated OPEN ACCESSProcesses 2015, 3 2 NF-κB, de novo synthesized TNF-α and IL-10 in RAW 264.7 macrophages exposed to LPS. This integrated model represents a first step towards modeling the interaction between proand anti-inflammatory signaling.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Mathematical Modeling of Pro- and Anti-Inflammatory Signaling in Macrophages

et al. 2014

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“…Interleukin-6 (IL-6) is a cytokine involved in a variety of inflammatory processes [1][2][3][4]. Understanding the signaling pathways associated with extracellular IL-6 excitation is important for elucidating and modulating the biological response to inflammation.…”

Section: Introductionmentioning

confidence: 99%

“…An IL-6 signaling model (Appendix A) was previously developed in [1][2][3][4]. Originally, a detailed model containing 77 ordinary differential equations (ODEs) and 128 parameters was derived, followed by a model simplification procedure to decrease the number of ODEs and parameters to 13 and 19, respectively.…”

Section: Introductionmentioning

confidence: 99%

Optimal Experimental Design for Parameter Estimation of an IL-6 Signaling Model

Sinkoe

Hahn

2017

Processes

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IL-6 signaling plays an important role in inflammatory processes in the body. While a number of models for IL-6 signaling are available, the parameters associated with these models vary from case to case as they are non-trivial to determine. In this study, optimal experimental design is utilized to reduce the parameter uncertainty of an IL-6 signaling model consisting of ordinary differential equations, thereby increasing the accuracy of the estimated parameter values and, potentially, the model itself. The D-optimality criterion, operating on the Fisher information matrix and, separately, on a sensitivity matrix computed from the Morris method, was used as the objective function for the optimal experimental design problem. Optimal input functions for model parameter estimation were identified by solving the optimal experimental design problem, and the resulting input functions were shown to significantly decrease parameter uncertainty in simulated experiments. Interestingly, the determined optimal input functions took on the shape of PRBS signals even though there were no restrictions on their nature. Future work should corroborate these findings by applying the determined optimal experimental design on a real experiment.

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Regularization of inverse problems to determine transcription factor profiles from fluorescent reporter systems

Bansal¹,

Chu²,

Laird³

et al. 2012

AIChE Journal

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Signal transduction pathways are characterized by complex biochemical reactions which involve a large number of proteins. The availability and quality of experimental data pose challenges for identifying the role of individual proteins in these pathways. To address this issue, this article formulates and solves an inverse problem to determine the dynamics of transcription factors from fluorescence intensity measurements of green fluorescent protein (GFP) reporter systems. In the presented approach, a model describing transcription and translation of GFP is discretized and concentrations of transcription factor are estimated at discrete time points. Unlike previous studies, this approach has no restrictions with regard to a particular shape of the profiles. However, the resulting inverse problem is ill‐conditioned and requires the use of regularization techniques. Two regularization methods—truncated singular value decomposition and Tikhonov regularization—are investigated in this work and the characteristics of the results obtained are discussed in detail. © 2012 American Institute of Chemical Engineers AIChE J, 2012

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Investigation of IL-6 and IL-10 signalling via mathematical modelling

Cited by 28 publications

References 34 publications

Mathematical Modeling of Pro- and Anti-Inflammatory Signaling in Macrophages

Mathematical Modeling of Pro- and Anti-Inflammatory Signaling in Macrophages

Optimal Experimental Design for Parameter Estimation of an IL-6 Signaling Model

Regularization of inverse problems to determine transcription factor profiles from fluorescent reporter systems

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