Data integration in logic-based models of biological mechanisms

Hall, Benjamin A.; Niarakis, Anna

doi:10.1016/j.coisb.2021.100386

Cited by 12 publications

(8 citation statements)

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“…To support this adoption, the CoLoMoTo interactive notebook [ 56 ] is a community-driven effort to improve reproducibility and reusability within the subdomain of logical models and software tools. Combined with suggestions for data retrieval [ 57 ], data integration [ 58 ] and proper annotation [ 1 ] it provides a fairly complete suggestion for best practices for building logical models in biology.…”

Section: Interoperability Through Standards’ Implementation In Tools ...mentioning

confidence: 99%

Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology

Niarakis

Waltemath

Glazier

et al. 2022

Briefings in Bioinformatics

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Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here, we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field.

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Section: Interoperability Through Standards’ Implementation In Tools ...mentioning

confidence: 99%

Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology

Niarakis

Waltemath

Glazier

et al. 2022

Briefings in Bioinformatics

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“…Furthermore, intercellular interactions must be clearly defined, mapped, and described to provide a comprehensive view of the cellular interplay in RA. Lastly, our maps can serve as a basis for constructing executable disease networks (Singh et al, 2018;Aghamiri et al, 2020;Hall and Niarakis, 2021;Miagoux et al, 2021;Niarakis and Helikar, 2021), allowing for in-silico simulations, hypotheses formation and predictions.…”

Section: Perspectivesmentioning

confidence: 99%

A Mechanistic Cellular Atlas of the Rheumatic Joint

Zerrouk

Aghakhani

Singh³

et al. 2022

Front. Syst. Biol.

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Rheumatoid Arthritis (RA) is an autoimmune disease of unknown aetiology involving complex interactions between environmental and genetic factors. Its pathogenesis is suspected to arise from intricate interplays between signalling, gene regulation and metabolism, leading to synovial inflammation, bone erosion and cartilage destruction in the patients’ joints. In addition, the resident synoviocytes of macrophage and fibroblast types can interact with innate and adaptive immune cells and contribute to the disease’s debilitating symptoms. Therefore, a detailed, mechanistic mapping of the molecular pathways and cellular crosstalks is essential to understand the complex biological processes and different disease manifestations. In this regard, we present the RA-Atlas, an SBGN-standardized, interactive, manually curated representation of existing knowledge related to the onset and progression of RA. This state-of-the-art RA-Atlas includes an updated version of the global RA-map covering relevant metabolic pathways and cell-specific molecular interaction maps for CD4+ Th1 cells, fibroblasts, and M1 and M2 macrophages. The molecular interaction maps were built using information extracted from published literature and pathway databases and enriched using omic data. The RA-Atlas is freely accessible on the webserver MINERVA (https://ramap.uni.lu/minerva/), allowing easy navigation using semantic zoom, cell-specific or experimental data overlay, gene set enrichment analysis, pathway export or drug query.

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“…Discrete-state network modeling characterizes regulatory interactions in networks with Boolean rules or gates that define signal flow through the network . These network models are part of a larger class of executable models, which can be simulated to investigate the behavior of a dynamic system, in this case biological signaling networks .…”

Section: Introductionmentioning

confidence: 99%

“…Discrete-state network modeling characterizes regulatory interactions in networks with Boolean rules or gates that define signal flow through the network. 9 These network models are part of a larger class of executable models, which can be simulated to investigate the behavior of a dynamic system, in this case biological signaling networks. 10 Discrete-state network models can be simulated either synchronously or asynchronously to identify limit cycles or attractors that correspond to network-specific states/phenotypes.…”

Section: Introductionmentioning

confidence: 99%

Executable Network Models of Integrated Multiomics Data

et al. 2023

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Multiomics profiling provides a holistic picture of a condition being examined and captures the complexity of signaling events, beginning from the original cause (environmental or genetic), to downstream functional changes at multiple molecular layers. Pathway enrichment analysis has been used with multiomics data sets to characterize signaling mechanisms. However, technical and biological variability between these layered data limit an integrative computational analyses. We present a Boolean network-based method, multiomics Boolean Omics Network Invariant-Time Analysis (mBONITA), to integrate omics data sets that quantify multiple molecular layers. mBONITA utilizes prior knowledge networks to perform topologybased pathway analysis. In addition, mBONITA identifies genes that are consistently modulated across molecular measurements by combining observed fold-changes and variance, with a measure of node (i.e., gene or protein) influence over signaling, and a measure of the strength of evidence for that gene across data sets. We used mBONITA to integrate multiomics data sets from RAMOS B cells treated with the immunosuppressant drug cyclosporine A under varying O 2 tensions to identify pathways involved in hypoxia-mediated chemotaxis. We compare mBONITA's performance with 6 other pathway analysis methods designed for multiomics data and show that mBONITA identifies a set of pathways with evidence of modulation across all omics layers. mBONITA is freely available at https://github.com/Thakar-Lab/mBONITA.

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Data integration in logic-based models of biological mechanisms

Cited by 12 publications

References 50 publications

Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology

Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology

A Mechanistic Cellular Atlas of the Rheumatic Joint

Executable Network Models of Integrated Multiomics Data

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