Working Paper Review Version 1 This version is not peer-reviewed

Computational Biology and Machine Learning Approaches to Study Mechanistic Microbiomehost Interactions

Version 1 : Received: 6 September 2020 / Approved: 7 September 2020 / Online: 7 September 2020 (14:50:42 CEST)

How to cite: Sudhakar, P.; Machiels, K.; Vermeire, S. Computational Biology and Machine Learning Approaches to Study Mechanistic Microbiomehost Interactions. Preprints 2020, 2020090167 Sudhakar, P.; Machiels, K.; Vermeire, S. Computational Biology and Machine Learning Approaches to Study Mechanistic Microbiomehost Interactions. Preprints 2020, 2020090167

Abstract

The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on inflammation, nutrition, and homeostasis. Although driven by a complex combination of intrinsic and extrinsic factors, many chronic diseases such as diabetes, cancer, Inflammatory Bowel Disease among others are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites etc have recently been elucidated, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to pragmatic limitations with respect to parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a systems and community level. In the past decade, various computational biology and machine learning methodologies and approaches have been developed with an aim to fill these existing gaps. Due to the agnostic nature of the tools, they have been applied in various disease contexts to analyze and infer the interactions between the microbiome and host molecular components, and in the case of a few selected tools, on downstream host processes. Generally, most of the tools are enabled by frameworks to statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic microbiome-host interactions and their potential benefit for basic and clinical research. These could include but are not limited to the development of activity and mechanism based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

Subject Areas

Computational methods; machine learning; microbiome-host interactions

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