Biomedical subjects

Juergen Hahn

Publications and source records attributed to Juergen Hahn.

3 recordsLinked to original sources

PubMed2006

Modeling regulatory mechanisms in IL-6 signal transduction in hepatocytes.

Cytokines like interleukin-6 (IL-6) play an important role in triggering the acute phase response of the body to injury or inflammation. Signaling by IL-6 involves two pathways: Janus-associated kinases (JAK) and signal transducers and activators of transcription (STAT 3) are activated in the first pathway while the second pathway involves the activation of mitogen-activated protein kinases (MAPK). While it is recognized that both pathways play a major role in IL-6 signal transduction, a majority of studies have focused on signaling through either one of the pathways. However, simultaneous signaling through both JAK/STAT and MAPK pathways is still poorly understood. In this work, a mathematical model has been developed that integrates signaling through both the JAK/STAT and the MAPK pathway. The presented model is used to analyze the effect of three molecules that are involved in the regulation of IL-6 signaling-SHP-2 (domain containing tyrosine phosphatase 2), SOCS3 (suppressor of cytokine signaling 3), and a STAT3 nuclear phosphatase (PP2)-on the dynamics of IL-6 signal transduction in hepatocytes. The obtained results suggest that interactions between SHP-2 and SOCS3 influence signaling through the JAK/STAT and the MAPK pathways. It is shown that SHP-2 and SOCS3 do not just regulate the pathway that they are known to be associated with, (SHP-2 with MAPK and SOCS3 with JAK/STAT), but also have a strong effect on the other pathway. Several simulations with SOCS3, SHP-2, and PP2 knockout cells, that is, where the signaling pathway is unable to produce these proteins, have been performed to characterize the effect of these regulatory proteins on IL-6 signal transduction in hepatocytes.

Hepatocytes↗

PubMed2005

Sensor fault diagnosis for nonlinear processes with parametric uncertainties.

This paper addresses the problem of detecting, discriminating, and reconstructing sensor faults for nonlinear systems with known model structure but uncertainty in the parameters of the process. The convenience of the proposed technique lies in the fact that historical operational data and/or a priori fault information is not required to achieve accurate fault reconstruction except for fixed, short intervals. The overall fault diagnosis algorithm is composed of a series of nonlinear estimators, which estimates parameter and a fault isolation and identification filter. Parameter estimation and fault reconstruction cannot be performed accurately since faults and parametric uncertainty interact with each other. Therefore, these two tasks are performed at different time scales, where the fault diagnosis takes place at a more frequent rate than the parameter estimation. It is shown that the fault can be reconstructed under some realistic assumptions and the performance of the proposed methodology is evaluated on a simulated chemical process exhibiting nonlinear dynamic behavior.

Algorithms↗

PubMed2003

Fault detection and classification in chemical processes based on neural networks with feature extraction.

Feed forward neural networks are investigated here for fault diagnosis in chemical processes, especially batch processes. The use of the neural model prediction error as the residual for fault diagnosis of sensor and component is analyzed. To reduce the training time required for the neural process model, an input feature extraction process for the neural model is implemented. An additional radial basis function neural classifier is developed to isolate faults from the residual generated, and results are presented to demonstrate the satisfactory detection and isolation of faults using this approach.

Algorithms↗