Topological Characterization of Complex Systems: Using Persistent Entropy

Merelli, Emanuela; Rucco, Matteo; Sloot, Peter M. A.; Tesei, Luca

doi:10.3390/e17106872

Cited by 57 publications

(56 citation statements)

References 34 publications

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“…By offering high spatial resolution combined with long time scales as well as the unique set of capabilities, for example particle tracking and stochasticity, ABM provides a platform for molecular systems biology that could not be achieved through any other single computational method. In addition, integration of ABM with data‐driven methods, for example topological data analysis (TDA), provides further capabilities in analysis of complex molecular systems . While several studies have already demonstrated the utility of ABM in molecular systems biology, ABM is not yet commonly employed in biological simulations.…”

Section: Discussionmentioning

confidence: 99%

Agent‐Based Modeling in Molecular Systems Biology

Soheilypour

Mofrad

2018

BioEssays

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Molecular systems orchestrating the biology of the cell typically involve a complex web of interactions among various components and span a vast range of spatial and temporal scales. Computational methods have advanced our understanding of the behavior of molecular systems by enabling us to test assumptions and hypotheses, explore the effect of different parameters on the outcome, and eventually guide experiments. While several different mathematical and computational methods are developed to study molecular systems at different spatiotemporal scales, there is still a need for methods that bridge the gap between spatially-detailed and computationally-efficient approaches. In this review, we summarize the capabilities of agent-based modeling (ABM) as an emerging molecular systems biology technique that provides researchers with a new tool in exploring the dynamics of molecular systems/pathways in health and disease.

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Section: Discussionmentioning

confidence: 99%

Agent‐Based Modeling in Molecular Systems Biology

Soheilypour

Mofrad

2018

BioEssays

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“…Similar concerns are presents in the work of [24], where the authors model the dynamics of the system using the S[B] paradigm [25] via delayed discrete automaton and persistent entropy. However, where they focus on the global dynamics of the system, we aim to capture the local interactions of each components.…”

Section: Introductionmentioning

confidence: 86%

Modeling Delayed Dynamics in Biological Regulatory Networks from Time Series Data

et al. 2017

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Abstract:Background: The modeling of Biological Regulatory Networks (BRNs) relies on background knowledge, deriving either from literature and/or the analysis of biological observations. However, with the development of high-throughput data, there is a growing need for methods that automatically generate admissible models. Methods: Our research aim is to provide a logical approach to infer BRNs based on given time series data and known influences among genes. Results: We propose a new methodology for models expressed through a timed extension of the automata networks (well suited for biological systems). The main purpose is to have a resulting network as consistent as possible with the observed datasets. Conclusion: The originality of our work is three-fold: (i) identifying the sign of the interaction; (ii) the direct integration of quantitative time delays in the learning approach; and (iii) the identification of the qualitative discrete levels that lead to the systems' dynamics. We show the benefits of such an automatic approach on dynamical biological models, the DREAM4(in silico) and DREAM8 (breast cancer) datasets, popular reverse-engineering challenges, in order to discuss the precision and the computational performances of our modeling method.

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“…An additional information returned by the computation of persistent homology is the list of the generators , which are the simplices involved in the holes. Experimentally, the generators play a crucial role for the description of the data under analysis [25, 26]. …”

Section: Main Textmentioning

confidence: 99%

Topological classifier for detecting the emergence of epileptic seizures

Piangerelli

Rucco²,

Tesei

et al. 2018

BMC Res Notes

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ObjectiveAn innovative method based on topological data analysis is introduced for classifying EEG recordings of patients affected by epilepsy. We construct a topological space from a collection of EEGs signals using Persistent Homology; then, we analyse the space by Persistent entropy, a global topological feature, in order to classify healthy and epileptic signals.ResultsThe performance of the resulting one-feature-based linear topological classifier is tested by analysing the Physionet dataset. The quality of classification is evaluated in terms of the Area Under Curve (AUC) of the receiver operating characteristic curve. It is shown that the linear topological classifier has an AUC equal to while the performance of a classifier based on Sample Entropy has an AUC equal to 62.0%.

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Topological Characterization of Complex Systems: Using Persistent Entropy

Cited by 57 publications

References 34 publications

Agent‐Based Modeling in Molecular Systems Biology

Agent‐Based Modeling in Molecular Systems Biology

Modeling Delayed Dynamics in Biological Regulatory Networks from Time Series Data

Topological classifier for detecting the emergence of epileptic seizures

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