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Network-Based Computational Modeling to Unravel Gene Essentiality

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Trends in Biomathematics: Modeling Epidemiological, Neuronal, and Social Dynamics (BIOMAT 2022)

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Abstract

Essential genes are reductively defined as those fundamental for an organism’s reproductive success and growth. Still, the so-called essentiality of a gene is a context-dependent dynamic attribute that can vary in different cells, tissues, or pathological conditions. Identifying essential genes at a genome-wide level is a challenging issue in primary and applied biomedical research, prominently in synthetic biology, drug targeting, and disease gene identification. Wet-lab experimental procedures designed to test whether a gene is essential or not are cost- and time-consuming, especially in the case of complex organisms such as humans. Consequently, computational approaches provide a fundamental alternative, still representing a demanding and challenging task due to the complex nature of the biological problem. Commonly explored methods are devoted to classifying nodes in protein-protein interaction networks, but they are scarcely successful, especially in the case of human genes. Node classification in graph modeling/analysis allows predicting an unknown node property based on defined node attributes. Here, we propose an overview of the different aspects of the biological background, methodologies, and applications related to identifying essential genes, with the aim to provide a small guide through the potentialities and open issues. We further present an experimental approach to examine the entire workflow, from the labeling of the nodes to the attribute choice to the learning modeling. To this extent, we exploit a tissue-specific integrated network enriched with pre-computed biological and embedding-derived topological features to develop a model through a deep learning approach.

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Notes

  1. 1.

    The UCSC TFBS Conserved Track Settings identifies motifs that are conserved across humans, mice, and rats and scores these sites based on the motif match.

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Acknowledgements

This work has been partially funded by the BiBiNet project (H35F21000430002) within POR-Lazio FESR 2014–2020. It was carried out also within the activities of the authors as members of the INdAM Research group GNCS and the ICAR-CNR INdAM Research Unit and partially supported by the INdAM research project “Computational Intelligence methods for Digital Health.” The work of Mario R. Guarracino was conducted within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE). Mario Manzo thanks Prof. Alfredo Petrosino for the guidance and supervision during the years of working together.

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Authors and Affiliations

  1. National Research Council, Institute for High-Performance Computing and Networking (ICAR), Rome, Italy

    I. Granata, M. Giordano & L. Maddalena

  2. University of Naples “L’Orientale”, ITS, Naples, Italy

    M. Manzo

  3. University of Cassino and Southern Lazio, Cassino, Italy

    M. R. Guarracino

  4. National Research University Higher School of Economics, Moscow, Russia

    M. R. Guarracino

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  1. I. Granata
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    Rubem P. Mondaini

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Granata, I., Giordano, M., Maddalena, L., Manzo, M., Guarracino, M.R. (2023). Network-Based Computational Modeling to Unravel Gene Essentiality. In: Mondaini, R.P. (eds) Trends in Biomathematics: Modeling Epidemiological, Neuronal, and Social Dynamics. BIOMAT 2022. Springer, Cham. https://doi.org/10.1007/978-3-031-33050-6_3

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