Dynamic Regulation of JAK-STAT Signaling Through the Prolactin Receptor Predicted by Computational Modeling
CELLULAR AND MOLECULAR BIOENGINEERING
Authors: Mortlock, Ryland D.; Georgia, Senta K.; Finley, Stacey D.
Abstract
Introduction The expansion of insulin-producing beta cells during pregnancy is critical to maintain glucose homeostasis in the face of increasing insulin resistance. Prolactin receptor (PRLR) signaling is one of the primary mediators of beta cell expansion during pregnancy, and loss of PRLR signaling results in reduced beta cell mass and gestational diabetes. Harnessing the proliferative potential of prolactin signaling to expand beta cell mass outside of the context of pregnancy requires quantitative understanding of the signaling at the molecular level. Methods A mechanistic computational model was constructed to describe prolactin-mediated JAK-STAT signaling in pancreatic beta cells. The effect of different regulatory modules was explored through ensemble modeling. A Bayesian approach for likelihood estimation was used to fit the model to experimental data from the literature. Results Including receptor upregulation, with either inhibition by SOCS proteins, receptor internalization, or both, allowed the model to match experimental results for INS-1 cells treated with prolactin. The model predicts that faster dimerization and nuclear import rates of STAT5B compared to STAT5A can explain the higher STAT5B nuclear translocation. The model was used to predict the dose response of STAT5B translocation in rat primary beta cells treated with prolactin and reveal possible strategies to modulate STAT5 signaling. Conclusions JAK-STAT signaling must be tightly controlled to obtain the biphasic response in STAT5 activation seen experimentally. Receptor up-regulation, combined with SOCS inhibition, receptor internalization, or both is required to match experimental data. Modulating reactions upstream in the signaling can enhance STAT5 activation to increase beta cell survival.
Kullback-Leibler differential entropy equation based CIMM-PDA for reliable positioning
ALEXANDRIA ENGINEERING JOURNAL
Authors: Hu, Enwen; Deng, Zhongliang; Jiang, Kun; Wu, Chengfeng
Abstract
An interactive multi-model probability data association based on Kullback-Leibler dif-ferential entropy for reliable positioning is proposed to stably track noise. When the changes in measurement noise and process noise occurs, the interactive multi-models can fuse the estimated results and by combination of probabilities in models, noise can be dynamically tracked, to improve the reliability and accuracy of positioning procedures. The simulation results show that with the increase of measurement noise, the KL-CIMM-PDA method is more stable and more adaptive to track noise than the CIMM-PDA method and compared with the IMM-UKF, the accuracy of the KL-CIMM-PDA is improved by 26.7%. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
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