1,721,101 research outputs found
A Loop Grammar to Understand the roles of miRNAs in the Tumor Cell
Full text linkA miRNA is a small non-coding RNA molecule that regulates gene expression. Current studies showed that miRNAs may function both as oncogenes and as tumor suppressors, but not revealed the precise conditions that cause miRNAs to alter gene expression of the cancer cells. In this study, we introduce a context-free grammar, Loop Grammar, that formalizes the primary and secondary structure as a composition of loops, corresponding to concatenation or nesting of hairpins. We also formalize the concatenation and nesting on fatgraphs, oriented surfaces with boundary, and we define a Surface Loop Grammar, whose algebraic expressions uniquely identify such surfaces associated to given RNA structures. The Loop Grammar has been used to model tumor and healthy miRNAs of the mir-515 family, and we observed that the mutations of elements of primary structure involved in loops formation changed the secondary structure of tumor miRNAs. The Surface Loop Grammar is useful to classify RNA structures in terms of loops and relations among them.
References:
1) Peng, Y., Croce, C. M. The role of MicroRNAs in human cancer. Signal transduction and targeted
therapy, 2016, 1, 15004.
2) Penner, R.C., Knudsen, M., Wiuf, C., Andersen, J.E., Fatgraph models of proteins. Communications on Pure and Applied Mathematics, 2010, 63(10), 1249–1297
3) Quadrini, M., Culmone, R., Merelli, E.: Topological Classification of RNA Structures via Intersection Graph. In: International Conference on Theory and Practice of Natural Computing, Springer, 2017, 203–215
4) Quadrini, M., Merelli, E.: Loop-loop interaction metrics on RNA secondary structures with pseudoknotsth International Conference on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018 3, 2018
Epileptic seizures can be anticipated by geometric-topological entropy analysis
Full text linkEpilepsy is a complex brain disorder characterized by an hypersynchronous activity of neural ensemble in the brain. Nowadays electroencephalography (EEG) is the golden stan- dard for studying, monitoring and diagnosing epilepsy. Signals (time series), recorded by EEG, represent a description of the dynamics of the brain. Epilepsy is an emergent behavior given by a phase transition between a non-epileptic state (pre-ictal state) and an epileptic one (ictal state) of the neural hypergraph [1-2]. Traditional linear techniques applied to EEG show some limitation to identify these transitions while the non-linear ones seem to be more promising. The understanding of the underlying mechanisms of ictogenesis and propagation requires a suitable formal method to compute the model that supports the anticipation of ictal states. Recently, Topological Data Analysis and topological entropy [3-4], the so-called persistent entropy, are proven to be encouraging for distinguishing healthy from unhealthy patients by showing numerical evidence of the occurrence of phase transitions. We extend the previous work by providing a theoretical justification, based on statistical indexes (skewness and kurtosis), persistent entropy and topological invariants (Betti numbers), of the preliminary numerical results which describe the occurrence of a phase transition; moreover, we also intend to investigate the role of geometric entropy in quantifying the complexity of the networks since a change of complexity is also an indicator of a phase transition [5].
References
1. Varela F.J.; Naturalizing Phenomenology: Issues in Contemporary Phenomenology and Cognitive Science Edited by Jean, Petitot, Francisco J. Varela, Bernard Pachoud abd Jean-Michel Roy Stanford University Press, Stanford Chapter 9, pp.266-329
2. Piangerelli M.; Merelli E.; RNN-based Model for Self-adaptive Systems - The Emer- gence of Epilepsy in the Human Brain. IJCCI (NCTA).2014: 356-361
3. Merelli E.; Piangerelli M.; Rucco M.; Toller D.; A topological approach for multivariate time series characterization: the epileptic brain.2015
4. Rucco M.; Castiglione F.; Merelli E.; Pettini M.; Characterization of idiotypic immune network through Persistent Entropy. In Proc. Complex2015
5. Franzosi R.; Felice D.; Mancini M.; Pettini M.; A geometric entropy detecting the Erdös-Rényi phase transition. EPL.201
10th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS) MARCH 15–16, 2017 | HOBOKEN, NEW JERSEY, UNITED STATES
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