Covering a Broad Dynamic Range: Information Processing at the Erythropoietin Receptor

Becker, Verena; Schilling, Marcel; Bachmann, Julie; Baumann, Ute; Raue, Andreas; Maiwald, Thomas; Timmer, Jens; Klingmüller, Ursula

doi:10.1126/science.1184913

Cited by 153 publications

(204 citation statements)

References 20 publications

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“…The sensitive parameters might be useful for further investigation of the biological mechanisms. For example, local sensitivity analysis has recently been employed to identify the critical signalling steps that are responsible for the linear detection of the Epo ligand concentrations [73] and the ultrasensitivity of signalling responses in TGF-b pathway [74]. Therefore the precision of model prediction from local sensitivity analysis is dependent on the identifiability of model parameters.…”

Section: Sensitivity and Identifiability Of Biological Modelsmentioning

confidence: 99%

Sensitivity analysis approaches applied to systems biology models

2011

IET Systems Biology

317

263

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With the rising application of systems biology, sensitivity analysis methods have been widely applied to study the biological systems, including metabolic networks, signalling pathways and genetic circuits. Sensitivity analysis can provide valuable insights about how robust the biological responses are with respect to the changes of biological parameters and which model inputs are the key factors that affect the model outputs. In addition, sensitivity analysis is valuable for guiding experimental analysis, model reduction and parameter estimation. Local and global sensitivity analysis approaches are the two types of sensitivity analysis that are commonly applied in systems biology. Local sensitivity analysis is a classic method that studies the impact of small perturbations on the model outputs. On the other hand, global sensitivity analysis approaches have been applied to understand how the model outputs are affected by large variations of the model input parameters. In this review, the author introduces the basic concepts of sensitivity analysis approaches applied to systems biology models. Moreover, the author discusses the advantages and disadvantages of different sensitivity analysis methods, how to choose a proper sensitivity analysis approach, the available sensitivity analysis tools for systems biology models and the caveats in the interpretation of sensitivity analysis results.

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Section: Sensitivity and Identifiability Of Biological Modelsmentioning

confidence: 99%

Sensitivity analysis approaches applied to systems biology models

2011

IET Systems Biology

317

263

View full text Add to dashboard Cite

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“…Statistical analysis of mechanistic models describing biological systems plays a vital role in furthering our understanding of the underlying mechanisms driving observed behaviour [4,19,30,48]. Both frequentist and Figure 11.…”

Section: Discussionmentioning

confidence: 99%

“…The initial condition of EpoR may also be found in the experiment of Becker et al [30]. The initial condition of Epo is assumed to be known, and the other four initial conditions are set to zero.…”

Section: Methodsmentioning

confidence: 99%

“…In addition, we find that such a model based on mass action kinetics also induces a highly correlated, asymmetric posterior distribution that is challenging to sample from, as shown in figure 10, particularly as the parameter values are of differing orders of magnitude. Finally, model predictions are made with very low uncertainty ( figure 11); however, it has been noted [30] that a careful initial analysis of the model is necessary to achieve this structural and practical identifiability.…”

Section: Cell Signalling Modelmentioning

confidence: 99%

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Statistical analysis of nonlinear dynamical systems using differential geometric sampling methods

Calderhead

Girolami

2011

Interface Focus.

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Mechanistic models based on systems of nonlinear differential equations can help provide a quantitative understanding of complex physical or biological phenomena. The use of such models to describe nonlinear interactions in molecular biology has a long history; however, it is only recently that advances in computing have allowed these models to be set within a statistical framework, further increasing their usefulness and binding modelling and experimental approaches more tightly together. A probabilistic approach to modelling allows us to quantify uncertainty in both the model parameters and the model predictions, as well as in the model hypotheses themselves. In this paper, the Bayesian approach to statistical inference is adopted and we examine the significant challenges that arise when performing inference over nonlinear ordinary differential equation models describing cell signalling pathways and enzymatic circadian control; in particular, we address the difficulties arising owing to strong nonlinear correlation structures, high dimensionality and non-identifiability of parameters. We demonstrate how recently introduced differential geometric Markov chain Monte Carlo methodology alleviates many of these issues by making proposals based on local sensitivity information, which ultimately allows us to perform effective statistical analysis. Along the way, we highlight the deep link between the sensitivity analysis of such dynamic system models and the underlying Riemannian geometry of the induced posterior probability distributions.

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“…This time-integral represents the cumulative activation of the receptor over time and depends on the duration and peak of activation. The area under the activation curve is a good measure of a transient response and has proven useful to study signaling cascades, 21,22 reaction-diffusion systems, 23 cell surface receptors, 20 and was also found to link ERK activation with DNA synthesis. 24 To study the input-output relationship of cellular signaling models, simulations are typically used to obtain sensitivities of outputs on the different model components.…”

Section: Exact Computation Of the Integrated Response In Signalling Nmentioning

confidence: 99%

The EGFR demonstrates linear signal transmission

et al. 2014

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Cells sense information encoded in extracellular ligand concentrations and process it using intracellular signalling cascades. Using mathematical modelling and high-throughput imaging of individual cells, we studied how a transient extracellular growth factor signal is sensed by the epidermal growth factor receptor system, processed by downstream signalling, and transmitted to the nucleus. We found that transient epidermal growth factor signals are linearly translated into an activated epidermal growth factor receptor integrated over time. This allows us to generate a simplified model of receptor signaling where the receptor acts as a perfect sensor of extracellular information, while the nonlinear input-output relationship of EGF-EGFR triggered signalling is a consequence of the downstream MAPK cascade alone. Insight, innovation, integrationWe derived an analytical formula from nonlinear models of receptor signalling that have been used to model the signalling of the epidermal growth factor receptor (EGFR). The formula predicted that the cumulative EGFR signalling is linearly dependent on the ligand concentration outside the cell. Quantitative imaging of HeLa cells suggests that the cumulative EGFR signalling indeed depends linearly on the EGF concentration. Our mathematical approach therefore allows a simplified view on complex networks of receptor dynamics and describes the EGFR as a linear sensor of extracellular information encoded in ligand concentrations.

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Covering a Broad Dynamic Range: Information Processing at the Erythropoietin Receptor

Cited by 153 publications

References 20 publications

Sensitivity analysis approaches applied to systems biology models

Sensitivity analysis approaches applied to systems biology models

Statistical analysis of nonlinear dynamical systems using differential geometric sampling methods

The EGFR demonstrates linear signal transmission

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