Inferring Gene Regulatory Network Models from Time-Series Data Using Metaheuristics
| Autor: | Heder S. Betnardino, Alex Borges Vieira, C D Campos Luciana, J C Barbosa Helio, José Eduardo Henriques da Silva, Itamar L. de Oliveira |
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| Rok vydání: | 2020 |
| Předmět: |
0303 health sciences
State variable Computer science Continuous modelling Quantitative Biology::Molecular Networks 0206 medical engineering Ode Gene regulatory network 02 engineering and technology computer.software_genre Data modeling 03 medical and health sciences Gene expression Data mining Evolution strategy computer Metaheuristic 020602 bioinformatics 030304 developmental biology Interpretability |
| Zdroj: | CEC |
| DOI: | 10.1109/cec48606.2020.9185572 |
| Popis: | The inference of Gene Regulatory Networks (GRNs) from gene expression data is a hard and widely addressed scientific challenge with potential industrial and health-care use. Discrete and continuous models of GRNs are often used (i) to understand the process, and (ii) to predict the values of the relevant variables. Here, we propose a procedure to infer models of GRNs from data where (i) the data is binarized, (ii) a Boolean model is created using a Cartesian Genetic Programming technique, (iii) the obtained Boolean model is converted to a system of ordinary differential equations, and (iv) an Evolution Strategy defines the parameters of the continuous model. As a result, we expect to reduce the effect of noise and to improve biological interpretability. The proposed method is applied to two ODE systems that describe the circadian rhythm network dynamic, with 5 and 10 state variables. The models created by the proposed procedure are able to reproduce the behavior observed in the original data. |
| Databáze: | OpenAIRE |
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