scholarly journals Hilbert-Schmidt and Sobol sensitivity indices for static and time series Wnt signaling measurements in colorectal cancer - Part A

2015 ◽  
Author(s):  
shriprakash sinha
Keyword(s):  
Colorectal Cancer ◽  
Time Series ◽  
Sensitivity Analysis ◽  
Wnt Signaling ◽  
Signaling Pathway ◽  
Expression Profiles ◽  
Biological Knowledge ◽  
Prior Biological Knowledge ◽  
Sensitivity Indices ◽  
Higher Dimensional

AbstractEver since the accidental discovery of Wingless [Sharma R.P., Drosophila information service, 1973, 50, p 134], research in the field of Wnt signaling pathway has taken significant strides in wet lab experiments and various cancer clinical trials, augmented by recent developments in advanced computational modeling of the pathway. Information rich gene expression profiles reveal various aspects of the signaling pathway and help in studying different issues simultaneously. Hitherto, not many computational studies exist which incorporate the simultaneous study of these issues. This manuscript • explores the strength of contributing factors in the signaling pathway, • analyzes the existing causal relations among the inter/extracellular factors effecting the pathway based on prior biological knowledge and • investigates the deviations in fold changes in the recently found prevalence of psychophysical laws working in the pathway. To achieve this goal, local and global sensitivity analysis is conducted on the (non)linear responses between the factors obtained from static and time series expression profiles using the density (Hilbert-Schmidt Information Criterion) and variance (Sobol) based sensitivity indices. The results show the advantage of using density based indices over variance based indices mainly due to the former’s employment of distance measures & the kernel trick via Reproducing kernel Hilbert space (RKHS) that capture nonlinear relations among various intra/extracellular factors of the pathway in a higher dimensional space. In time series data, using these indices it is now possible to observe where in time, which factors get influenced & contribute to the pathway, as changes in concentration of the other factors are made. This synergy of prior biological knowledge, sensitivity analysis & representations in higher dimensional spaces can facilitate in time based administration of target therapeutic drugs & reveal hidden biological information within colorectal cancer samples. Code has been made available at Google drive onhttps://drive.google.com/folderview?id=0B7Kkv8wlhPU-Q2NBZGt1ZERrSVE&usp=sharing

scholarly journals A pedagogical walkthrough of computational modeling and simulation of Wnt signaling pathway using static causal models in Matlab

2014 ◽  
Author(s):  
Shriprakash Sinha
Keyword(s):  
Computational Modeling ◽  
Bayesian Network ◽  
Wnt Signaling ◽  
Signaling Pathway ◽  
Simulation Study ◽  
Wnt Signaling Pathway ◽  
Biological Knowledge ◽  
Computational Framework ◽  
Matlab Code ◽  
Prior Biological Knowledge

AbstractA tutorial introduction to computational modeling of Wnt signaling pathway in a human colorectal cancer dataset using static Bayesian network models is provided. The walkthrough might aid bio-logists/informaticians in understanding the design of computational experiments that is interleaved with exposition of the Matlab code and causal models from Bayesian Network toolbox. This is done in order to ease the understanding of beginner students and researchers in transition to computational signaling biology, who intend to work in the field of modeling of signaling pathways. The manuscript expounds the computational flow of the contents in advance article1 via code development and takes the reader in a step by step process of how • the collection and the transformation of the available biological information from literature is done, • the integration of the heterogeneous data and prior biological knowledge in the network is achieved, • conditional probability tables for nodes in biologically inspired tables are estimated, • the simulation study is designed, • the hypothesis regarding a biological phenomena is transformed into computational framework, and • results and inferences drawn using d-connectivity/separability are reported. The manuscript finally ends with a programming assignment to help the readers get hands on experience of a perturbation project. Matlab code with dataset is made available under GNU GPL v3 license at google code project on https://code.google.com/p/static-bn-for-wnt-signaling-pathwayInsight, Innovation and IntegrationSimulation study involving computational experiments dealing with Wnt signaling pathways abound in literature but often lack a pedagogical perspective that might ease the understanding of beginner students and researchers in transition who intend to work on modeling of the pathway. This paucity might happen due to restrictive policies which enforce an unwanted embargo on the sharing of important scientific knowledge. The manuscript elucidates embedding of prior biological knowledge, integration of heterogeneous information, transformation of biological hypothesis into computational framework and design of experiments in a simple manner interleaved with aspects of Bayesian Network toolbox and Matlab code so as to help readers get a feel of a project related to modeling of the pathway.

Evaluation of MACF1-regulatory non-coding RNA as a potential therapeutic target in Colorectal Cancer

QJM ◽  
2021 ◽  
Vol 114 (Supplement_1) ◽  
Author(s):  
Rowaida Mohammed Reda M. M Aboushahba ◽  
Fayda Ibrahim Abdel Motaleb ◽  
Ahmed Abdel Aziz Abou-Zeid ◽  
Enas Samir Nabil ◽  
Dalia Abdel-Wahab Mohamed ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cell Proliferation ◽  
Wnt Signaling ◽  
Cell Line ◽  
Signaling Pathway ◽  
Therapeutic Target ◽  
Molecular Mechanisms ◽  
Wnt Signaling Pathway ◽  
Control Group ◽  
Potential Therapeutic Target

ABSTRACT Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths world-wide. There is an increasing need for the identification of novel biomarkers/targets for early diagnosis and for the development of novel chemopreventive and therapeutic agents for CRC. Recently, MACF1 gene has emerged as a potential therapeutic target in cancer as it involved in processes critical for tumor cell proliferation, invasion and metastasis. It is suggested that MACF1 may function in cancers through Wnt signaling. MiR-34a is a well-known tumor suppressor miRNA.miR-34a targets MACF1 gene as a part of the wnt signaling pathway. In this study, 40 colonic tissues were collected from CRC patients (20) and control subjects (20). miR-34a-5p was assessed by real time PCR in all study groups. The results showed highly significant decrease (P < 0.01) in miR-34a relative expression in the CRC group (median RQ 0.13) when compared to the benign group (median RQ 5.3) and the healthy control group (median RQ 19.63). miR-34a mimic and inhibitor were transfected in CaCo-2 cell line and proliferation was assessed. The transfection of the cell line with miR-34a mimic decreased cell proliferation. Our study suggests that miR-34a-5p targets MACF1 gene as a part of the wnt signaling pathway leading to the involvement in the molecular mechanisms of CRC development and progression.

scholarly journals Expression Profile and Prognostic Value of Wnt Signaling Pathway Molecules in Colorectal Cancer

Biomedicines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1331
Author(s):  
Yung-Fu Wu ◽  
Chih-Yang Wang ◽  
Wan-Chun Tang ◽  
Yu-Cheng Lee ◽  
Hoang Dang Khoa Ta ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Wnt Signaling ◽  
Signaling Pathway ◽  
Messenger Rna ◽  
Wnt Signaling Pathway ◽  
Interaction Network ◽  
Mrna Levels ◽  
Key Factor ◽  
Canonical Wnt ◽  
Upregulated Genes

Colorectal cancer (CRC) is a heterogeneous disease with changes in the genetic and epigenetic levels of various genes. The molecular assessment of CRC is gaining increasing attention, and furthermore, there is an increase in biomarker use for disease prognostication. Therefore, the identification of different gene biomarkers through messenger RNA (mRNA) abundance levels may be useful for capturing the complex effects of CRC. In this study, we demonstrate that the high mRNA levels of 10 upregulated genes (DPEP1, KRT80, FABP6, NKD2, FOXQ1, CEMIP, ETV4, TESC, FUT1, and GAS2) are observed in CRC cell lines and public CRC datasets. Moreover, we find that a high mRNA expression of DPEP1, NKD2, CEMIP, ETV4, TESC, or FUT1 is significantly correlated with a worse prognosis in CRC patients. Further investigation reveals that CTNNB1 is the key factor in the interaction of the canonical Wnt signaling pathway with 10 upregulated CRC-associated genes. In particular, we identify NKD2, FOXQ1, and CEMIP as three CTNNB1-regulated genes. Moreover, individual inhibition of the expression of three CTNNB1-regulated genes can cause the growth inhibition of CRC cells. This study reveals efficient biomarkers for the prognosis of CRC and provides a new molecular interaction network for CRC.

scholarly journals ASO Author Reflections: ATMIN Altering the WNT Signaling Pathway via PARP1 in MSI-High Colorectal Cancer

2021 ◽  
Author(s):  
Yue-Ju Li ◽  
Cheng-Ning Yang ◽  
Yen-Ping Kuo ◽  
Wei-Ting Lai ◽  
Tai-Sheng Wu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Wnt Signaling ◽  
Signaling Pathway ◽  
Wnt Signaling Pathway

Hsa-miR-942 fingerprint in colorectal cancer through Wnt signaling pathway

Gene ◽  
2019 ◽  
Vol 712 ◽  
pp. 143958 ◽  
Author(s):  
Ali Fasihi ◽  
Bahram M. Soltani ◽  
Zahra Sadat Ranjbaran ◽  
Sajedeh Bahonar ◽  
Romina Norouzi ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Wnt Signaling ◽  
Signaling Pathway ◽  
Wnt Signaling Pathway

scholarly journals Technical note: Analytical sensitivity analysis and uncertainty estimation of baseflow index calculated by a two-component hydrograph separation method with conductivity as a tracer

2019 ◽  
Vol 23 (2) ◽  
pp. 1103-1112 ◽  
Author(s):  
Weifei Yang ◽  
Changlai Xiao ◽  
Xiujuan Liang
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Measurement Errors ◽  
Uncertainty Estimation ◽  
Separation Method ◽  
Analytical Sensitivity ◽  
Hydrograph Separation ◽  
Time Step ◽  
Sensitivity Indices ◽  
Two Component

Abstract. The two-component hydrograph separation method with conductivity as a tracer is favored by hydrologists owing to its low cost and easy application. This study analyzes the sensitivity of the baseflow index (BFI, long-term ratio of baseflow to streamflow) calculated using this method to errors or uncertainties in two parameters (BFC, the conductivity of baseflow, and ROC, the conductivity of surface runoff) and two variables (yk, streamflow, and SCk, specific conductance of streamflow, where k is the time step) and then estimates the uncertainty in BFI. The analysis shows that for time series longer than 365 days, random measurement errors in yk or SCk will cancel each other out, and their influence on BFI can be neglected. An uncertainty estimation method of BFI is derived on the basis of the sensitivity analysis. Representative sensitivity indices (the ratio of the relative error in BFI to that of BFC or ROC) and BFI′ uncertainties are determined by applying the resulting equations to 24 watersheds in the US. These dimensionless sensitivity indices can well express the propagation of errors or uncertainties in BFC or ROC into BFI. The results indicate that BFI is more sensitive to BFC, and the conductivity two-component hydrograph separation method may be more suitable for the long time series in a small watershed. When the mutual offset of the measurement errors in conductivity and streamflow is considered, the uncertainty in BFI is reduced by half.

MODELING NONLINEAR GENE REGULATORY NETWORKS FROM TIME SERIES GENE EXPRESSION DATA

2008 ◽  
Vol 06 (05) ◽  
pp. 961-979 ◽  
Author(s):  
ANDRÉ FUJITA ◽  
JOÃO RICARDO SATO ◽  
HUMBERTO MIGUEL GARAY-MALPARTIDA ◽  
MARI CLEIDE SOGAYAR ◽  
CARLOS EDUARDO FERREIRA ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
A Priori ◽  
Molecular Networks ◽  
Biological Knowledge ◽  
Vector Autoregressive ◽  
Gene Regulatory

In cells, molecular networks such as gene regulatory networks are the basis of biological complexity. Therefore, gene regulatory networks have become the core of research in systems biology. Understanding the processes underlying the several extracellular regulators, signal transduction, protein–protein interactions, and differential gene expression processes requires detailed molecular description of the protein and gene networks involved. To understand better these complex molecular networks and to infer new regulatory associations, we propose a statistical method based on vector autoregressive models and Granger causality to estimate nonlinear gene regulatory networks from time series microarray data. Most of the models available in the literature assume linearity in the inference of gene connections; moreover, these models do not infer directionality in these connections. Thus, a priori biological knowledge is required. However, in pathological cases, no a priori biological information is available. To overcome these problems, we present the nonlinear vector autoregressive (NVAR) model. We have applied the NVAR model to estimate nonlinear gene regulatory networks based entirely on gene expression profiles obtained from DNA microarray experiments. We show the results obtained by NVAR through several simulations and by the construction of three actual gene regulatory networks (p53, NF-κB, and c-Myc) for HeLa cells.

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