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Probabilistic Critical Controllability Analysis of Protein Interaction Networks Integrating Normal Brain Ageing Gene Expression Profiles.

ABSTRACT: Recently, network controllability studies have proposed several frameworks for the control of large complex biological networks using a small number of life molecules. However, age-related changes in the brain have not been investigated from a controllability perspective. In this study, we compiled the gene expression profiles of four normal brain regions from individuals aged 20-99 years and generated dynamic probabilistic protein networks across their lifespan. We developed a new algorithm that efficiently identified critical proteins in probabilistic complex networks, in the context of a minimum dominating set controllability model. The results showed that the identified critical proteins were significantly enriched with well-known ageing genes collected from the GenAge database. In particular, the enrichment observed in replicative and premature senescence biological processes with critical proteins for male samples in the hippocampal region led to the identification of possible new ageing gene candidates.

SUBMITTER: Yamaguchi E

PROVIDER: S-EPMC8465977 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Probabilistic Critical Controllability Analysis of Protein Interaction Networks Integrating Normal Brain Ageing Gene Expression Profiles.

Yamaguchi Eimi E Akutsu Tatsuya T Nacher Jose C JC

International journal of molecular sciences 20210913 18

Recently, network controllability studies have proposed several frameworks for the control of large complex biological networks using a small number of life molecules. However, age-related changes in the brain have not been investigated from a controllability perspective. In this study, we compiled the gene expression profiles of four normal brain regions from individuals aged 20-99 years and generated dynamic probabilistic protein networks across their lifespan. We developed a new algorithm tha ...[more]

PMID: 34576052

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Project description:ObjectivePredicting how the brain can be driven to specific states by means of internal or external control requires a fundamental understanding of the relationship between neural connectivity and activity. Network control theory is a powerful tool from the physical and engineering sciences that can provide insights regarding that relationship; it formalizes the study of how the dynamics of a complex system can arise from its underlying structure of interconnected units.ApproachGiven the recent use of network control theory in neuroscience, it is now timely to offer a practical guide to methodological considerations in the controllability of structural brain networks. Here we provide a systematic overview of the framework, examine the impact of modeling choices on frequently studied control metrics, and suggest potentially useful theoretical extensions. We ground our discussions, numerical demonstrations, and theoretical advances in a dataset of high-resolution diffusion imaging with 730 diffusion directions acquired over approximately 1 h of scanning from ten healthy young adults.Main resultsFollowing a didactic introduction of the theory, we probe how a selection of modeling choices affects four common statistics: average controllability, modal controllability, minimum control energy, and optimal control energy. Next, we extend the current state-of-the-art in two ways: first, by developing an alternative measure of structural connectivity that accounts for radial propagation of activity through abutting tissue, and second, by defining a complementary metric quantifying the complexity of the energy landscape of a system. We close with specific modeling recommendations and a discussion of methodological constraints.SignificanceOur hope is that this accessible account will inspire the neuroimaging community to more fully exploit the potential of network control theory in tackling pressing questions in cognitive, developmental, and clinical neuroscience.

Dataset Information

Probabilistic Critical Controllability Analysis of Protein Interaction Networks Integrating Normal Brain Ageing Gene Expression Profiles.

Publications

Probabilistic Critical Controllability Analysis of Protein Interaction Networks Integrating Normal Brain Ageing Gene Expression Profiles.

Similar Datasets

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