scholarly journals Temporal perturbation of ERK dynamics reveals network architecture of FGF2/MAPK signaling

2019 ◽  
Vol 15 (11) ◽  
Author(s):  
Yannick Blum ◽  
Jan Mikelson ◽  
Maciej Dobrzyński ◽  
Hyunryul Ryu ◽  
Marc‐Antoine Jacques ◽  
...  
Keyword(s):  
Network Architecture ◽  
Mapk Signaling ◽  
Temporal Perturbation

scholarly journals Temporal perturbation of Erk dynamics reveals network architecture of FGF2-MAPK signaling

2019 ◽  
Author(s):  
Yannick Blum ◽  
Jan Mikelson ◽  
Maciej Dobrzyński ◽  
Hyunryul Ryu ◽  
Marc-Antoine Jacques ◽  
...  
Keyword(s):  
Cell Fate ◽  
Cell Population ◽  
Network Architecture ◽  
Population Distribution ◽  
Population Level ◽  
Mapk Signaling ◽  
Erk Signaling ◽  
Signaling Dynamics ◽  
Temporal Perturbation ◽  
Erk Activity

AbstractStimulation of PC-12 cells with epidermal (EGF) versus nerve (NGF) growth factors (GFs) biases the distribution between transient and sustained single-cell ERK activity states, and between proliferation and differentiation fates within a cell population. We report that fibroblast GF (FGF2) evokes a distinct behavior that consists of a gradually changing population distribution of transient/sustained ERK signaling states in response to increasing inputs in a dose response. Temporally-controlled GF perturbations of MAPK signaling dynamics applied using microfluidics reveals that this wider mix of ERK states emerges through the combination of an intracellular feedback, and competition of FGF2 binding to FGF receptors (FGFR) and heparan-sulfate proteoglycans (HSPGs) co-receptors. We show that the latter experimental modality is instructive for model selection using a Bayesian parameter inference. Our results provide novel insights into how different receptor tyrosine kinase (RTK) systems differentially wire the MAPK network to fine tune fate decisions at the cell population-level.Microfluidics, Erk Signaling Dynamics, Mechanistic Modelling, Parameter Estimation, Cell Fate Determination

scholarly journals FGF/MAPK signaling sets the switching threshold of a bistable circuit controlling cell fate decisions in ES cells

2015 ◽  
Author(s):  
Christian Schröter ◽  
Pau Rué ◽  
Jonathan P Mackenzie ◽  
Alfonso Martinez Arias
Keyword(s):  
Cell Fate ◽  
Network Architecture ◽  
Cell Model ◽  
Es Cells ◽  
Embryonic Stem ◽  
Transcriptional Regulators ◽  
Mapk Signaling ◽  
Threshold Concentration ◽  
Cell Fates ◽  
Cell Fate Decisions

Intracellular transcriptional regulators and extracellular signaling pathways together regulate the allocation of cell fates during development, but how their molecular activities are integrated to establish the correct proportions of cells with particular fates is not known. Here we study this question in the context of the decision between the epiblast (Epi) and the primitive endoderm (PrE) fate that occurs in the mammalian preimplantation embryo. Using an embryonic stem (ES) cell model, we discover two successive functions of FGF/MAPK signaling in this decision. First, the pathway needs to be inhibited to make the PrE-like gene expression program accessible for activation by GATA transcription factors in ES cells. In a second step, MAPK signaling levels determine the threshold concentration of GATA factors required for PrE-like differentiation, and thereby control the proportion of cells differentiating along this lineage. Our findings can be explained by a simple mutual repression circuit modulated by FGF/MAPK signaling. This may be a general network architecture to integrate the activity of signal transduction pathways and transcriptional regulators, and serve to balance proportions of cell fates in several contexts.

The Anti-Atherogenic Properties of Sesamin are Mediated via Improved Macrophage Cholesterol Efflux Through PPARγ1-LXRα and MAPK Signaling

2014 ◽  
Vol 84 (1-2) ◽  
pp. 79-91 ◽  
Author(s):  
Amin F. Majdalawieh ◽  
Hyo-Sung Ro
Keyword(s):  
Cholesterol Efflux ◽  
Transcriptional Activity ◽  
Molecular Mechanisms ◽  
Foam Cell ◽  
Mapk Signaling ◽  
Luciferase Reporter ◽  
Foam Cell Formation ◽  
Dependent Manner ◽  
Concentration Dependent Manner ◽  
Macrophage Cholesterol Efflux

Background: Foam cell formation resulting from disrupted macrophage cholesterol efflux, which is triggered by PPARγ1 and LXRα, is a hallmark of atherosclerosis. Sesamin and sesame oil exert anti-atherogenic effects in vivo. However, the exact molecular mechanisms underlying such effects are not fully understood. Aim: This study examines the potential effects of sesamin (0, 25, 50, 75, 100 μM) on PPARγ1 and LXRα expression and transcriptional activity as well as macrophage cholesterol efflux. Methods: PPARγ1 and LXRα expression and transcriptional activity are assessed by luciferase reporter assays. Macrophage cholesterol efflux is evaluated by ApoAI-specific cholesterol efflux assays. Results: The 50 μM, 75 μM, and 100 μM concentrations of sesamin up-regulated the expression of PPARγ1 (p< 0.001, p < 0.001, p < 0.001, respectively) and LXRα (p = 0.002, p < 0.001, p < 0.001, respectively) in a concentration-dependent manner. Moreover, 75 μM and 100 μM concentrations of sesamin led to 5.2-fold (p < 0.001) and 6.0-fold (p<0.001) increases in PPAR transcriptional activity and 3.9-fold (p< 0.001) and 4.2-fold (p < 0.001) increases in LXR transcriptional activity, respectively, in a concentration- and time-dependent manner via MAPK signaling. Consistently, 50 μM, 75 μM, and 100 μM concentrations of sesamin improved macrophage cholesterol efflux by 2.7-fold (p < 0.001), 4.2-fold (p < 0.001), and 4.2-fold (p < 0.001), respectively, via MAPK signaling. Conclusion: Our findings shed light on the molecular mechanism(s) underlying sesamin’s anti-atherogenic effects, which seem to be due, at least in part, to its ability to up-regulate PPARγ1 and LXRα expression and transcriptional activity, improving macrophage cholesterol efflux. We anticipate that sesamin may be used as a therapeutic agent for treating atherosclerosis.

Effect of Parathyroid Hormone on MAPK Signaling and Calcification Markers in Human Intervertebral Disk Cells

2012 ◽  
Vol 2 (1_suppl) ◽  
pp. s-0032-1319991-s-0032-1319991
Author(s):  
P. Madiraju ◽  
R. Gawri ◽  
J. Antoniou ◽  
F. Mwale
Keyword(s):  
Parathyroid Hormone ◽  
Mapk Signaling ◽  
Intervertebral Disk

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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