Integrated modeling and experimental approach for determining transcription factor profiles from fluorescent reporter data

Huang, Zuyi; Senocak, Fatih; Jayaraman, Arul; Hahn, Juergen

doi:10.1186/1752-0509-2-64

Cited by 21 publications

(46 citation statements)

References 14 publications

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“…In addition, the state variables x and parameters p are restricted within certain ranges, as shown in Equations (6) and (7), determined by the underlying biology and prior knowledge based on mathematical models developed by Lipniacki et al [26], Huang et al [25] and Rangamani et al [27]. The simulated output vector y (Equation (5)), which is validated by experimental data, includes the intracellular ratio of phosphorylated NF-κB to total NF-κB (relative to the control) and the concentration of the cytokines, TNF-α and IL-10, in the cell culture supernatant.…”

Section: Parameter Selection and Estimationmentioning

confidence: 99%

“…These include a model for the IL-6 signal transduction pathway by Singh et al [24], the TNF-α signaling pathway by Huang et al [25], Lipniacki et al [26], Rangamani et al [27] and Hoffmann et al [28]. A characteristic feature of these models is that they describe the dynamics of signaling initiated by a single pro-inflammatory cytokine.…”

Section: Introductionmentioning

confidence: 99%

“…The inflammatory (TNF-α) module is adapted from Huang et al [25] and Lipniacki et al [26]. While these models use TNF-α as the input, our model describes LPS (input)-induced signaling through TLR4 (LPS receptor), which leads to TNF-α production.…”

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confidence: 99%

“…Some values of the parameters (Table 2) and initial concentrations of proteins (Table 3) are adapted from the TNF-α signaling models by Huang et al [25], Lipniacki et al [26], Rangamani et al [27], Hoffmann et al [28] and the IL-6 and IL-10 model by Moya et al [29]. The previously developed models consisted of 37 differential equations and 60 parameters for the TNF-α model by Huang et al and 68 differential equations and 118 parameters for the IL-6 and IL-10 model by Moya et al [29] Among the proteins included in these models, very few are quantifiable by experimental methods, making parameter estimation difficult.…”

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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: Parameter Selection and Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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

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“…These measurements were chosen because of the availability of a fluorescent reporter system for measuring the concentrations of these two proteins [2,34]. To design an optimal experiment for minimizing parameter uncertainty, experiments were considered in which the concentrations of these two proteins are measured in a time series every 45 min for 22 h using a fluorescent reporter system.…”

Section: Formulation Of the Optimal Experimental Design Problem For Tmentioning

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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Integrated modeling and experimental approach for determining transcription factor profiles from fluorescent reporter data

Cited by 21 publications

References 14 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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