Biology subjects

Shriprakash Sinha

Publications and source records attributed to Shriprakash Sinha.

3 recordsLinked to original sources

bioRxiv2015

Sensitivity analysis of Wnt β-catenin based transcription complex might bolster power-logarithmic psychophysical law and reveal preserved gene gene interactions

Recently, psychophysical laws have been observed to be functional in certain factors working downstream of the Wnt pathway. This work tests the veracity of the prevalence of such laws, albeit at a coarse level, using sensitivity analysis on biologically inspired epigenetically influenced computational causal models. In this work, the variation in the effect of the predictive behaviour of the transcription complex (TRCMPLX) conditional on the evidences of gene expressions in normal/tumor samples is observed by varying the initially assigned values of conditional probability tables (cpt) for TRCMPLX. Preliminary analysis shows that the variation in predictive behaviour of TRCMPLX follows power-logarithmic psychophysical law, crudely. More recently, wet lab experiments have proved the existence of sensors that behave in a logarithmic fashion thus supporting the earlier proposed postulates based on computational sensitivity analysis of this manuscript regarding the existence of logarithmic behaviour in the signaling pathways. It also signifies the importance of systems biology approach where in silico experiments combined with in vivo/in vitro experiments have the power to explore the deeper mechanisms of a signaling pathway. Additionally, it is hypothesized that these laws are prevalent at gene-gene interaction level also. The interactions were obtained by thresholding the inferred conditional probabilities of a gene activation given the status of another gene activation. The deviation in the interactions in normal/tumor samples was similarly observed by varying the initially assigned values of conditional probability tables (cpt) for TRCMPLX. Analysis of deviation in interactions show prevalence of psychophysical laws and is reported for interaction between elements of pairs (SFRP3, MYC), (SFRP2, CD44) and (DKK1, DACT2). Based on crude static models, it is assumed that dynamic models of Bayesian networks might reveal the phenomena in a better way.

Systems Biology↗

bioRxiv2015

Reproducibility Of Parameter Learning With Missing Observations in Naive Wnt Bayesian Network Trained on Normal/Adenomas Samples and Doxycycline Treated LS174T Cell Lines

Insight, Innovation and IntegrationDoxycycline, a derivative of tetracycline, induces gene expression via reversible transcriptional activation. Levels of /3-catenin and other intra/extracellular genetic factors have been influenced in colorectal cancer cell lines, which make doxycycline a potential candidate for cancer chemotherapy. With the aim to build better computational models that show good prediction on test datasets, doxycycline treated cell lines might provide best training samples. This work tests the reproducibility of parameter learning and predictions based on the estimated parameters, using the Naive Bayesian Networks for Wnt pathway in case of missing observations for different nodes. The in silico experiments show the efficacy of causal models as one of the emerging diagnostic tools in development of targeted cancer therapy.\n\nRecent efforts in predicting Wnt signaling activation via inference methods have helped in developing diagnostic models for therapeutic drug targeting. In this manuscript the reproducibility of parameter learning with missing observations in a Bayesian Network and its effect on prediction results for Wnt signaling activation is tested, while training the networks on doxycycline treated LS174T cell lines as well as normal and adenomas samples. This is done in order to check the effectiveness of using Bayesian Network as a tool for modeling Wnt pathway when certain observations are missing. Experimental analysis suggest that prediction results are reproducible with negligible deviations. Anomalies in estimated parameters are accounted for due to the Bayesian Network model. Also, an interesting case regarding usage of hypothesis testing came up while proving the statistical significance of different design setups of the BN model which was trained on the same data. It was found that hypothesis testing may not be the correct way to check the significance between design setups for the aforementioned case, especially when the structure of the model is same. Finally, in comparison to the biologically inspired models, the naive bayesian model may give accurate results but this accuracy comes at the cost of loss of crucial biological knowledge which might help reveal hidden relations among intra/extracellular factors affecting the Wnt pathway.

Bioinformatics↗

bioRxiv2014

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

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-pathway\n\nInsight, 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.

Bioinformatics↗