@article {46188,
	title = {Web-gLV: a web based platform for lotka-volterra based modeling and simulation of microbial populations},
	journal = {Frontiers in Microbiology},
	volume = {10},
	year = {2019},
	month = {FEB },
	pages = {288},
	type = {Article},
	abstract = {<p>The affordability of high throughput DNA sequencing has allowed us to explore the dynamics of microbial populations in various ecosystems. Mathematical modeling and simulation of such microbiome time series data can help in getting better understanding of bacterial communities. In this paper; we present Web-gLV- a GUI based interactive platform for generalized Lotka-Volterra (gLV) based modeling and simulation of microbial populations. The tool can be used to generate the mathematical models with automatic estimation of parameters and use them to predict future trajectories using numerical simulations. We also demonstrate the utility of our tool on few publicly available datasets. The case studies demonstrate the ease with which the current tool can be used by biologists to model bacterial populations and simulate their dynamics to get biological insights. We expect Web-gLV to be a valuable contribution in the field of ecological modeling and metagenomic systems biology.</p>
},
	keywords = {lotka-volterra, microbial population, Microbiome, Modeling, numerical-simulation, time-series, visualization, web-server},
	issn = {1664-302X},
	doi = {10.3389/fmicb.2019.00288},
	author = {Kuntal, Bhusan K. and Gadgil, Chetan and Mande, Sharmila S.}
}
@article {46852,
	title = {NetConfer: a web application for comparative analysis of multiple biological networks},
	journal = {BMC Biology},
	volume = {18},
	year = {2020},
	month = {MAY},
	pages = {53},
	type = {Article},
	abstract = {<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>
},
	keywords = {Bioinformatics, biological networks, Interaction networks, Network comparison, visualization},
	doi = {10.1186/s12915-020-00781-9},
	author = {Nagpal, Sunil and Baksi, Krishanu Das and Kuntal, Bhusan K. and Mande, Sharmila S.}
}
@article {47336,
	title = {Time: a web application for obtaining insights into microbial ecology using longitudinal microbiome data (vol 9, 36, 2018)},
	journal = {Frontiers in Microbiology},
	volume = {11},
	year = {2020},
	month = {NOV},
	pages = {605295},
	type = {Correction},
	keywords = {clustering, community state, Granger causality algorithm, Microbiome, time series, visualization, Web server},
	issn = {1664-302X},
	doi = {10.3389/fmicb.2020.605295},
	author = {Baksi, Krishanu D. and Kuntal, Bhusan K. and Mande, Sharmila S.}
}
@article {47112,
	title = {Web-gLV: a web based platform for lotka-volterra based modeling and simulation of microbial populations (vol 10, 288, 2019)},
	journal = {Frontiers in Microbiology},
	volume = {11},
	year = {2021},
	month = {JAN },
	pages = {605308},
	type = {Correction},
	keywords = {lotka-volterra, microbial population, Microbiome, Modeling, numerical-simulation, time-series, visualization, web-server},
	issn = {1664-302X},
	doi = {10.3389/fmicb.2020.605308},
	author = {Kuntal, Bhusan K. and Gadgil, Chetan and Mande, Sharmila S.}
}
