@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 {46654,
	title = {EviMass: a literature evidence-based miner for human microbial associations (vol 10, 849, 2019)},
	journal = {Frontiers in Genetics},
	volume = {11},
	year = {2020},
	month = {NOV },
	pages = {614051},
	type = {Correction},
	keywords = {Human Disease, literature mining, microbial association, Microbiome, Web server},
	doi = {10.3389/fgene.2020.614051},
	author = {Srivastava, Divyanshu and Baksi, Krishanu D. 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.}
}
@article {49910,
	title = {Microbiome knowledge graph as a tool to understand bacteria-host associations},
	journal = {Archives of Microbiology},
	volume = {207},
	year = {2025},
	month = {AUG},
	pages = {222},
	type = {Article},
	abstract = {<p>
	Gut bacteria are well known to significantly influence human health and physiology. Knowledge Graph (KG) can effectively integrate the heterogenous factors modulating gut bacteria-host associations. Limited studies describe the construction and application of KGs capturing these associations for domain experts. This work outlines a methodology for constructing microbiome-centric KG and demonstrates how it enhances conventional microbiome data analysis workflows. Towards construction and deployment of this domain centric KG, methodologies involved in collection of data, selecting relevant entities and relationships, and preprocessing them are discussed. Key relevant entities include bacteria, host genetic and immune factors, chemicals and diseases. The KG construction in both RDF (Resource Description Framework) and LPG (Labeled Property Graph) models are demonstrated. Comparison of the querying techniques in both these models and applications of the KG using biologically relevant case studies are also presented. Overall, the work is intended to provide domain experts with a complete protocol for construction of a microbiome-centric KG starting from entity selection and schema design to utilizing the KG for microbiome data analysis and hypothesis generation.</p>
},
	keywords = {Bacteria-host association, Bioinformatics, Knowledge graph, Knowledge graph question answering, Microbiome},
	issn = {0302-8933},
	doi = {10.1007/s00203-025-04413-0},
	author = {Pudavar, Anand Eruvessi and Baksi, Krishanu Das and Pokhrel, Vatsala and Kuntal, Bhusan K.}
}
