02125nas a2200265   4500008004100000022001400041245009000055210006900145260000800214300000800222490000700230520122000237653002401457653002201481653001901503653003301522653004301555653002501598653002401623653002501647100002201672700003001694700002301724856011201747       2018                        eng d  a1751-884900aMethod for identification of sensitive nodes in boolean models of biological networks0 aMethod for identification of sensitive nodes in boolean models o  cFEB  a1-60 v123 a<p>Biological systems are often represented as Boolean networks and analysed to identify sensitive nodes which on perturbation disproportionately change a predefined output. There exist different kinds of perturbation methods: perturbation of function, perturbation of state and perturbation in update scheme. Nodes may have defects in interpretation of the inputs from other nodes and calculation of the node output. To simulate these defects and systematically assess their effect on the system output, two new function perturbations, referred to as not of function' and function of not', are introduced. In the former, the inputs are assumed to be correctly interpreted but the output of the update rule is perturbed; and in the latter, each input is perturbed but the correct update rule is applied. These and previously used perturbation methods were applied to two existing Boolean models, namely the human melanogenesis signalling network and the fly segment polarity network. Through mathematical simulations, it was found that these methods successfully identified nodes earlier found to be sensitive using other methods, and were also able to identify sensitive nodes which were previously unreported.</p>10abiological networks10aBoolean functions10aBoolean models10afly segment polarity network10ahuman melanogenesis signalling network10aperturbation methods10aPerturbation theory10aphysiological models1 aDnyane, Pooja, A.1 aPuntambekar, Shraddha, S.1 aGadgil, Chetan, J.  uhttps://library.ncl.res.in/content/method-identification-sensitive-nodes-boolean-models-biological-networks02806nas a2200229   4500008004100000245009000041210006900131260000800200300000700208490000700215520204000222653001902262653002402281653002502305653002302330653001802353100001802371700002502389700002302414700002402437856011502461       2020                        eng d00aNetConfer: a web application for comparative analysis of multiple biological networks0 aNetConfer a web application for comparative analysis of multiple  cMAY  a530 v183 a<p>Background Most biological experiments are inherently designed to compare changes or transitions of state between conditions of interest. The advancements in data intensive research have in particular elevated the need for resources and tools enabling comparative analysis of biological data. The complexity of biological systems and the interactions of their various components, such as genes, proteins, taxa, and metabolites, have been inferred, represented, and visualized via graph theory-based networks. Comparisons of multiple networks can help in identifying variations across different biological systems, thereby providing additional insights. However, while a number of online and stand-alone tools exist for generating, analyzing, and visualizing individual biological networks, the utility to batch process and comprehensively compare multiple networks is limited. Results Here, we present a graphical user interface (GUI)-based web application which implements multiple network comparison methodologies and presents them in the form of organized analysis workflows. Dedicated comparative visualization modules are provided to the end-users for obtaining easy to comprehend, insightful, and meaningful comparisons of various biological networks. We demonstrate the utility and power of our tool using publicly available microbial and gene expression data. Conclusion NetConfer tool is developed keeping in mind the requirements of researchers working in the field of biological data analysis with limited programming expertise. It is also expected to be useful for advanced users from biological as well as other domains (working with association networks), benefiting from provided ready-made workflows, as they allow to focus directly on the results without worrying about the implementation. While the web version allows using this application without installation and dependency requirements, a stand-alone version has also been supplemented to accommodate the offline requirement of processing large networks.</p>
10aBioinformatics10abiological networks10aInteraction networks10aNetwork comparison10avisualization1 aNagpal, Sunil1 aBaksi, Krishanu, Das1 aKuntal, Bhusan, K.1 aMande, Sharmila, S.  uhttps://library.ncl.res.in/content/netconfer-web-application-comparative-analysis-multiple-biological-networks