Modeling and analyses of complex systems using network theory have been an object of study for a long time. They have caught attention in many disciplines such as sociology, epidemiology, ecology, psychology, biology, biomedicine, and other fields. Network theory is especially an efficient tool to model biological networks such as gene co-expression networks, protein-protein interaction networks, or pathways. Considering the enhanced resolutions of complex real-world systems, the interest has been directed to multilayered networks. However, despite this surge of recent attention, the use of the multilayer framework in the biological field is still in its youth. In this paper, we review the different aspects and terminologies of multilayered networks. We also briefly discuss the variant applications of the multilayer framework, and finally, we give an overview of various existing applications of the multilayer model in network biology.
Supplementary data are available at Bioinformatics online.
The modelling of complex biological networks such as pathways has been a necessity for scientists over the last decades. The study of these networks also imposes a need to investigate different aspects of nodes or edges within the networks, or other biomedical knowledge related to it. Our aim is to provide a generic modelling framework to integrate multiple pathway types and further knowledge sources influencing these networks. This framework is defined by a multi-layered model allowing automatic network transformations and documentation. By providing a tool that generates this model, we aim to facilitate the data integration, boost the reproducibility and increase the interoperability between different sources and databases in the field of pathways. We present mully R package that allows the user to create, modify and visualize graphs with multi-layers. The package is implemented with features to specifically handle multilayered graphs.
This work opens a new path to fight parasites by targeting host molecular functions by repurposing available and approved drugs. We created a novel approach to identify key proteins involved in any biological process by combining gene regulatory networks and expression profiles.
Biological pathway data integration has become a topic of interest in the past years. This interest originates essentially from the continuously increasing size of existing prior knowledge as well as from the many challenges scientists face when studying biological pathways. Multipath is a framework that aims at helping re-trace the use of specific pathway knowledge in specific publications, and easing the data integration of multiple pathway types and further influencing knowledge sources. Multipath thus helps scientists to increase the reproducibility of their code and analysis by allowing the integration of numerous data sources and documentation of their integration steps while doing so. In this paper, we present the package Multipath, and we describe how it can be used for data integration and tracking pathway modifications. We present a multilayer model built from the Wnt Pathway as a demonstration.
Personalized medicine, i.e. a medicine focused on the individual and proactive in nature, promises an improved health care by customizing the treatment according to patient needs [3]. The methods to analyze data, model knowledge and store interpretable results vary widely. A common approach is to use networks for modelling and organizing this information.Network theory has been used for many years in the modelling and analysis of complex systems, as epidemiology, biology and biomedicine [1]. As the data evolves and becomes more heterogeneous and complex, monoplex networks become an oversimplification of the corresponding systems [3]. This imposes a need to go beyond traditional networks into a richer framework capable of hosting objects and relations of different scales [4], called Multilayered Network.These complex networks have contributed in many contexts and fields [1], and they are very applicable in the investigation of biological networks [2].In order to enrich this investigation, we aim to implement a multilayer framework that can be applicable in various domains, especially in the field of pathway modelling.Our idea is to integrate pathways and their related knowledge into a multilayer model, where each layer represents one of their elements. The model offers a feature we call "Selective Inclusion of Knowledge", as well as a collection of related knowledge into a single graph, like diseases and drugs. The main layers are mapped to the entities of the pathways and the additional knowledge, for instance, a convenient model would have 3 layers respectively representing proteins, drugs and diseases. The model imports knowledge from multiple sources like the Reactome database, PharmGKB, DrugBank, OMIM and other public sources.The submitted poster will give an overview of the various models of multilayered networks, then it will describe the model we are building, and the workflow of implementing it into R as well as the future plan. The workflow consists of multiple R packages, of which we present the first implemented package, mully [5], that provides the multilayer layout. The data import and the integration will be done by another package to be implemented, Multipath.
I also gratefully acknowledge the help of my supervisor, Prof. Dr. Frank Kramer, for sharing this whole journey with me, all the way, and step by step. Prof. Kramer has helped me academically and personally. He showed me how to look at life from a new perspective. He also helped me change many aspects of my personality and thus became my idol, and I always look up to him, always being generous, kind, thoughtful, and above all successful, optimistic, always spreading positivity among his students and institute members, which was the greatest help in my work. He kept encouraging me to accomplish more, and believe in myself. I consider myself the luckiest student to have had him as my supervisor and mentor. God bless him and his family.I would also like to thank my other supervisors, Prof. Dr. Edgar Wingender, and Prof. Dr. Stephan Waack who were of great help, by giving me very perceptive and valuable remarks and comments respectively in the field of Bioinformatics and Mathematics. It is also essential to mention that Prof. Wingender was my ticket to Germany. He granted me the opportunity to prepare my Master's thesis at his department, and he recommended me to Prof. Kramer, by saying "Sie ist sehr rührig und recht smart", which translates to "She is very eager and really smart". I thank you whole-heartedly.In addition, I thank my lab members, old and new, in Göttingen and Augsburg, for all the help they have given me, especially Florian Auer, who accompanied me in my whole journey and answered all of my questions unconditionally.Finally, yet importantly, I would like to thank my parents and siblings, for allowing and helping me to go beyond our cultural limits, sending me, by myself, abroad to make my dreams come true, supporting me on my busy days, and encouraging me on my very lonely ones. I thank them for their life-long love and support, and I dedicate my work to them, and my lately passing grandfather.Many thanks to all. Without your support, this work would not be.
Personalized medicine, i.e. a medicine focused on the individual and proactive in nature, promises an improved health care by customizing the treatment according to patient needs [3]. The methods to analyze data, model knowledge and store interpretable results vary widely. A common approach is to use networks for modelling and organizing this information.Network theory has been used for many years in the modelling and analysis of complex systems, as epidemiology, biology and biomedicine [1]. As the data evolves and becomes more heterogeneous and complex, monoplex networks become an oversimplification of the corresponding systems [3]. This imposes a need to go beyond traditional networks into a richer framework capable of hosting objects and relations of different scales [4], called Multilayered Network.These complex networks have contributed in many contexts and fields [1], and they are very applicable in the investigation of biological networks [2].In order to enrich this investigation, we aim to implement a multilayer framework that can be applicable in various domains, especially in the field of pathway modelling.Our idea is to integrate pathways and their related knowledge into a multilayer model, where each layer represents one of their elements. The model offers a feature we call "Selective Inclusion of Knowledge", as well as a collection of related knowledge into a single graph, like diseases and drugs. The main layers are mapped to the entities of the pathways and the additional knowledge, for instance, a convenient model would have 3 layers respectively representing proteins, drugs and diseases. The model imports knowledge from multiple sources like the Reactome database, PharmGKB, DrugBank, OMIM and other public sources.The submitted poster will give an overview of the various models of multilayered networks, then it will describe the model we are building, and the workflow of implementing it into R as well as the future plan. The workflow consists of multiple R packages, of which we present the first implemented package, mully [5], that provides the multilayer layout. The data import and the integration will be done by another package to be implemented, Multipath.
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