Qualitative dynamics semantics for SBGN process description

Rougny, Adrien; Froidevaux, Christine; Calzone, Laurence; Paulevé, Loı̈c

doi:10.1186/s12918-016-0285-0

Cited by 7 publications

(14 citation statements)

References 53 publications

(53 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The G2 phase can also still be reached when disabling late S phase. This result is similar to the one we found earlier [39], and can be explained by the fact that the original cell cycle map does not describe any influence of (Table 4.A,B and Table 5.A,B) (b) Marker activation with markers of previous phase forced to be inactive (Table 4.C and Table 5.C) Illustration of the qualitative validations and predictions performed on the dynamical model. The precursor state is composed of the least number of entities to be active (represented in blue) so that the different processes can be activated in the future.…”

Section: Progression Of the Cell Cyclesupporting

confidence: 84%

“…All other phases but these two (early G1 and G2) could not be reached when disabling their preceding phase, suggesting that the merged model could reproduce their succession correctly. Overall, the results obtained here for the model built from the merged map are the same as those obtained with a model built from the original map of the cell cycle alone [39], showing that the addition in the model of the processes of the circadian clock does not hamper the correct reproduction of the progression of the cell cycle.…”

Section: Phase Markersupporting

confidence: 64%

“…2). The cell cycles exhibits four well-known phases (G1, S, G2, and M), for which markers had previously been defined [39]. We considered these markers in this study, and likewise divided phases G1 and S into two early and late subphases as those phases show measurable changes between their beginning and end ( Table 2).…”

Section: Validation Using Qualitative Dynamical Analysismentioning

confidence: 99%

See 2 more Smart Citations

A Detailed Map of Coupled Circadian Clock and Cell Cycle with Qualitative Dynamics Validation

Rougny

Paulevé

Teboul

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

The temporal coordination of biological processes by the circadian clock is an important mechanism, and its disruption has negative health outcomes, including cancer. Experimental and theoretical evidence suggests that the oscillators driving the circadian clock and the cell cycle are coupled through phase locking. We present a detailed and documented SBGN PD map of known mechanisms related to the regulation of the circadian clock, and its coupling with an existing cell cycle SBGN PD map which includes main interactions of the mammalian cell cycle. The coherence of the merged map has been validated with a qualitative dynamics analysis. We verified that the coupled circadian clock and cell cycle maps allow reproducing the observed sequence of phase markers. Moreover, we predicted mutations that contribute to regulating checkpoints of the two oscillators. Strikingly, our approach underlines the potential key role of the core clock protein NR1D1 in regulating cell cycle progression. We predicted that its activity influences negatively the progression of the cell cycle from phase G2 to M. It is consistent with the earlier experimental finding that pharmacological activation of NR1D1 inhibits tumour cell proliferation and shows that our approach can identify biologically relevant species in the context of large and complex networks.

show abstract

Section: Progression Of the Cell Cyclesupporting

confidence: 84%

Section: Phase Markersupporting

confidence: 64%

Section: Validation Using Qualitative Dynamical Analysismentioning

confidence: 99%

See 1 more Smart Citation

A Detailed Map of Coupled Circadian Clock and Cell Cycle with Qualitative Dynamics Validation

Rougny

Paulevé

Teboul

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Also, the output networks represent static knowledge and do not carry any dynamics semantics. Hence, they can be used as inputs for (automatic) mathematical model building under a wide variety of semantics and formalisms, such as the rule-based entity-centered semantics and ODEs 9 , 28 , 29 , plain ODEs 30 , or qualitative semantics 29 , 31 . These mathematical models can in turn be stored and exchanged using standard formats such as the BioNetGen Language format 28 for rule-based models, or the Systems Biology Markup Language format 32 for ODEs and qualitative models.…”

Section: Resultsmentioning

confidence: 99%

A logic-based method to build signaling networks and propose experimental plans

Rougny

Gloaguen

Langonné

et al. 2018

Sci Rep

Self Cite

View full text Add to dashboard Cite

With the dramatic increase of the diversity and the sheer quantity of biological data generated, the construction of comprehensive signaling networks that include precise mechanisms cannot be carried out manually anymore. In this context, we propose a logic-based method that allows building large signaling networks automatically. Our method is based on a set of expert rules that make explicit the reasoning made by biologists when interpreting experimental results coming from a wide variety of experiment types. These rules allow formulating all the conclusions that can be inferred from a set of experimental results, and thus building all the possible networks that explain these results. Moreover, given an hypothesis, our system proposes experimental plans to carry out in order to validate or invalidate it. To evaluate the performance of our method, we applied our framework to the reconstruction of the FSHR-induced and the EGFR-induced signaling networks. The FSHR is known to induce the transactivation of the EGFR, but very little is known on the resulting FSH- and EGF-dependent network. We built a single network using data underlying both networks. This leads to a new hypothesis on the activation of MEK by p38MAPK, which we validate experimentally. These preliminary results represent a first step in the demonstration of a cross-talk between these two major MAP kinases pathways.

show abstract

“…It should be noted that semantics of SBGN diagrams does not depend on the relative position of the symbols, or on colours, patterns, shades, shapes and thickness of edges [12]. [12] and [13], represents a simple example of protein phosphorylation (ERK) catalysed by an enzyme (MEK) and modulated by an inhibitor (u0126).…”

Section: Sbgn: Systems Biology Graphical Notationmentioning

confidence: 99%

SBGN2HFPN Transformation of SBGN-PD into Petri Nets Illustrated on the Glycolysis Pathway

Cherdal¹,

Mouline²,

Amghar³

2018

IJIES

View full text Add to dashboard Cite

Systems Biology Graphical Notation (SBGN) is a standard graphical language for representing biological and biochemical processes and interactions. Even that SBGN Process Description-an SBGN sub-language-facilitates biological systems representation, it allows only qualitative descriptions of metabolic pathways and does not provide quantitative analytic environment. However such descriptions are essential for behavioural analysis. This analysis can be possible using computational formalisms such as Hybrid Functional Petri Net (HFPN) which is a Petri net extension dedicated to study and verify biopathways. However, biologists use generally graphical notations such as SBGN. To address this paradox, we propose, in this paper, an SBGN-PD to HFPN transformation based on Model Driven Engineering (MDE). Furthermore, we illustrate this transformation on the Glycolysis pathway and verify the transformation resulting HFPN model by comparing it with the existing HFPN model through simulation.

show abstract

Qualitative dynamics semantics for SBGN process description

Cited by 7 publications

References 53 publications

A Detailed Map of Coupled Circadian Clock and Cell Cycle with Qualitative Dynamics Validation

A Detailed Map of Coupled Circadian Clock and Cell Cycle with Qualitative Dynamics Validation

A logic-based method to build signaling networks and propose experimental plans

SBGN2HFPN Transformation of SBGN-PD into Petri Nets Illustrated on the Glycolysis Pathway

Contact Info

Product

Resources

About