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Integrating Multi-Omics Data to Construct Reliable Interconnected Models of Signaling, Gene Regulatory, and Metabolic Pathways

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Computational Modeling of Signaling Networks

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2634))

Abstract

Alteration of the status of the metabolic enzymes could be a probable way to regulate metabolic reprogramming, which is a critical cellular adaptation mechanism especially for cancer cells. Coordination among biological pathways, such as gene-regulatory, signaling, and metabolic pathways is crucial for regulating metabolic adaptation. Also, incorporation of resident microbial metabolic potential in human body can influence the interplay between the microbiome and the systemic or tissue metabolic environments. Systemic framework for model-based integration of multi-omics data can ultimately improve our understanding of metabolic reprogramming at holistic level. However, the interconnectivity and novel meta-pathway regulatory mechanisms are relatively lesser explored and understood. Hence, we propose a computational protocol that utilizes multi-omics data to identify probable cross-pathway regulatory and protein-protein interaction (PPI) links connecting signaling proteins or transcription factors or miRNAs to metabolic enzymes and their metabolites using network analysis and mathematical modeling. These cross-pathway links were shown to play important roles in metabolic reprogramming in cancer scenarios.

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Authors and Affiliations

  1. Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India

    Krishna Kumar, Debaleena Bhowmik, Sapan Mandloi, Abhishake Lahiri, Nupur Biswas & Saikat Chakrabarti

  2. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India

    Debaleena Bhowmik, Abhishake Lahiri & Saikat Chakrabarti

  3. Algorithms in Bioinformatics, Institute for Bioinformatics and Medical Informatics, University of Tübingen,, Tübingen, Germany

    Anupam Gautam

  4. International Max Planck Research School “From Molecules to Organisms,” Max Planck Institute for Biology Tübingen, Tübingen, Germany

    Anupam Gautam

  5. JIS Institute of Advanced Studies and Research, JIS University, Kolkata, India

    Sandip Paul

  6. Cluster of Excellence: EXC 2124: Controlling Microbes to Fight Infection, University of Tübingen, Tübingen, Germany

    Anupam Gautam

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  1. Krishna Kumar
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  2. Debaleena Bhowmik
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  3. Sapan Mandloi
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  4. Anupam Gautam
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  5. Abhishake Lahiri
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  6. Nupur Biswas
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  7. Sandip Paul
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  8. Saikat Chakrabarti
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Correspondence to Sandip Paul or Saikat Chakrabarti .

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Editors and Affiliations

  1. Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia

    Lan K. Nguyen

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Kumar, K. et al. (2023). Integrating Multi-Omics Data to Construct Reliable Interconnected Models of Signaling, Gene Regulatory, and Metabolic Pathways. In: Nguyen, L.K. (eds) Computational Modeling of Signaling Networks. Methods in Molecular Biology, vol 2634. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3008-2_6

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  • DOI: https://doi.org/10.1007/978-1-0716-3008-2_6

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3007-5

  • Online ISBN: 978-1-0716-3008-2

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