Notions of similarity for systems biology models

Henkel, Ron; Hoehndorf, Robert; Kacprowski, Tim; Knüpfer, Christian; Liebermeister, Wolfram; Waltemath, Dagmar

doi:10.1093/bib/bbw146

Cited by 8 publications

(14 citation statements)

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“…The presented workflow can be used to test hypotheses about reoccurring patterns in domain-specific model sets. It can furthermore help to calculate structure-based similarities of model (see [43] for a discussion of possible measures). Based on the calculated similarities, models can then be classified.…”

Section: Discussionmentioning

confidence: 99%

“…The consideratoin of patterns will further enable search for models that share similar structures, improve the mapping of similar models onto each other [45], and lead to recommender systems that support the modeling process. In addition with with already existing similarity measures [43], this work will impact the reuse and reproducibility of scientific modeling results. A number of approaches exist to compare models based on the encoding format, the XML tags, or semantic annotations.…”

Section: Discussionmentioning

confidence: 99%

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Finding patterns in biochemical reaction networks

Henkel¹,

Lambusch²,

Sandkuhl³

et al. 2016

Preprint

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Background: Computational models in biology encode molecular and cell biological processes. These models often can be represented as biochemical reaction networks. Studying such networks, one is mostly interested in systems that share similar reactions and mechanisms. Typical goals of an investigation include understanding of the parts of a model, identification of reoccurring patterns, and recognition of biologically relevant motifs. The large number and size of available models, however, require automated methods to support researchers in achieving their goals. Specifically for the problem of finding patterns in large networks only partial solutions exist. Results:We propose a workflow that identifies frequent structural patterns in biochemical reaction networks encoded in the Systems Biology Markup Language. The workflow utilises a subgraph mining algorithm to detect frequent network patterns. Once patterns are identified, the textual pattern description can automatically be converted into a graphical representation. Furthermore, information about the distribution of patterns among the selected set of models can be retrieved. The workflow was validated with 575 models from the curated branch of BioModels. In this paper, we highlight interesting and frequent structural patterns. Further, we provide exemplary patterns that incorporate terms from the Systems Biology Ontology. Our workflow can be applied to a custom set of models or to models already existing in our graph database MaSyMoS. Conclusions:The occurrences of frequent patterns may give insight into the encoding of central biological processes, evaluate postulated biological motifs, or serve as a similarity measure for models that share common structures.Availability: https://github.com/FabienneL/BioNet-Mining

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Finding patterns in biochemical reaction networks

Henkel¹,

Lambusch²,

Sandkuhl³

et al. 2016

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Annotations have contributed to the successful reuse and exploration of models and data in tasks such as comparison 27,28 , interpretation 29 , retrieval [30][31][32] , integration [33][34][35][36][37] , simulation 38 , translation between formats 29,37,[39][40][41][42] (see also http://sbfc.sourceforge.net/mediawiki/index.php/SBML2BioPAX), and visualization 37,[43][44][45] . Semantic annotations are also a key component for model-driven design of synthetic biological systems where they are used in model composition tasks when constructing optimum biological systems built from models 41,[45][46][47][48] .…”

Section: Semantic Annotations and Their Utilitymentioning

confidence: 99%

“…Researchers cannot easily search across multiple repositories to find models of interest, and when a researcher does retrieve a relevant model, they must determine whether it (or part of it) can be repurposed for use in their modeling work. Similarity measures and pattern-matching algorithms can help identify relevant modeling components 28,49 , but existing proposals for cross-repository search and retrieval 32 are mostly theoretical and not yet applicable in practice.…”

Section: Challenges In Model Reuse and Integrationmentioning

confidence: 99%

“…While semantic annotations cannot yet capture the purpose for which a model was built or modified, they can make the biological content of a model explicit, and therefore help researchers decide whether or not to repurpose it. Semantic annotations can also be used to quantify the similarity between model elements 27,28,[50][51][52][53][54] , helping to ensure that the user is presented with the most relevant models following a search. Other types of annotations, such as those that capture provenance information [55][56][57][58] , can also help researchers determine which models best meet their research needs and make comparisons between different model versions 59 .…”

Section: Challenges In Model Reuse and Integrationmentioning

confidence: 99%

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Harmonizing semantic annotations for computational models in biology

Neal

König

Nickerson

et al. 2018

Preprint

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Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition, and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current semantic annotation practices among the COmputational Modeling in BIology NEtwork (COMBINE) community and provide a set of recommendations for building a consensus approach to semantic annotation.All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

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Network Features and Dynamical Landscape of Naive and Primed Pluripotency

Pfeuty

Kress

Pain

2018

Biophysical Journal

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Although the broad and unique differentiation potential of pluripotent stem cells relies on a complex transcriptional network centered around Oct4, Sox2, and Nanog, two well-distinct pluripotent states, called "naive" and "primed", have been described in vitro and markedly differ in their developmental potential, their expression profiles, their signaling requirements, and their reciprocal conversion. Aiming to determine the key features that segregate and coordinate these two states, data-driven optimization of network models is performed to identify relevant parameter regimes and reduce network complexity to its core structure. Decision dynamics of optimized networks is characterized by signal-dependent multistability and strongly asymmetric transitions among naive, primed, and nonpluripotent states. Further model perturbation and reduction approaches reveal that such a dynamical landscape of pluripotency involves a functional partitioning of the regulatory network. Specifically, two overlapping positive feedback modules, Klf4/Esrrb/Nanog and Oct4/Nanog, stabilize the naive or the primed state, respectively. In turn, their incoherent feedforward and negative feedback coupling mediated by the Erk/Gsk3 module is critical for robust segregation and sequential progression between naive and primed states before irreversible exit from pluripotency.

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Notions of similarity for systems biology models

Cited by 8 publications

References 0 publications

Finding patterns in biochemical reaction networks

Finding patterns in biochemical reaction networks

Harmonizing semantic annotations for computational models in biology

Network Features and Dynamical Landscape of Naive and Primed Pluripotency

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