Desarrollo de un algoritmo y software para la inferencia de modelos de redes booleanas. Aplicación en sistemas biológicos

[ES] Los sistemas biológicos en teoría pueden ser modelados a través de sistemas de ecuaciones diferenciales. No obstante, este planteamiento es completamente inviable por la cantidad de agentes que participan en estos sistemas, lo que ocasiona un gran número de parámetros en los modelos. Por ejemplo, en una red de regulación genética (GRN) podemos encontrar más de 400 genes involucrados. Para incluir un número tan grande de parámetros, existen distintos procedimientos alternativos de representación. Como las redes booleanas, simplificaciones lógicas de sistemas de ecuaciones diferenciales. La dificultad de este enfoque radica en que mucha de la información dada en el sistema de ecuaciones original no es expresada directamente a través de las redes. Por ejemplo, la dependencia temporal de la red con respecto a sus propias variables. Como consecuencia, dado un conjunto de restricciones, existen diferentes redes que pueden representar al mismo sistema. En la práctica, el desarrollo de modelos de redes booleanas es complejo debido al hecho de que 1) el coste computacional para generar todas las potenciales funciones es elevado, y 2) se requiere una revisión por parte de expertos de cada red para validar su viabilidad desde el punto de vista biológico. En este trabajo se propone un algoritmo para estandarizar y agilizar el desarrollo de este tipo de modelos. Concretamente, redes booleanas síncronas deterministas.[EN] Biological systems, in theory, can be modelled through systems of differential equations. However, this approach is completely unfeasible due to the number of agents participating in these systems, which causes a large number of parameters in the models. For example, in a gene regulatory network (GRN), we can find more than 400 genes involved. To include such a large number of parameters, there are different representation procedures. Like the Boolean networks, logical simplifications of systems of differential equations. The difficulty in this approach is that much of the information given in the original system of equations is not directly expressed through the networks. For example, the temporal dependence of the network on its variables. As a consequence, given a set of constraints, there are different networks that can represent the same system. In practice, the development of Boolean networks models is complex because 1) the computational cost to generate all potential functions is high, and 2) an expert review of each network is required to validate its viability from the biological point of view. In this work, an algorithm is proposed to standardize and speed up the development of this type of model. Specifically, deterministic synchronous Boolean networks.Rubio Chavarría, M. (2020). Desarrollo de un algoritmo y software para la inferencia de modelos de redes booleanas. Aplicación en sistemas biológicos. http://hdl.handle.net/10251/151637TFG

Modelling Biological Systems: A New Algorithm for the Inference of Boolean Networks

[EN] Biological systems are commonly constituted by a high number of interacting agents. This great dimensionality hinders biological modelling due to the high computational cost. Therefore, new modelling methods are needed to reduce computation time while preserving the properties of the depicted systems. At this point, Boolean Networks have been revealed as a modelling tool with high expressiveness and reduced computing times. The aim of this work has been to introduce an automatic and coherent procedure to model systems through Boolean Networks. A synergy that harnesses the strengths of both approaches is obtained by combining an existing approach to managing information from biological pathways with the so-called Nested Canalising Boolean Functions (NCBF). In order to show the power of the developed method, two examples of an application with systems studied in the bibliography are provided: The epithelial-mesenchymal transition and the lac operon. Due to the fact that this method relies on directed graphs as a primary representation of the systems, its applications exceed life sciences into areas such as traffic management or machine learning, in which these graphs are the main expression of the systems handled.This paper has been supported by the Generalitat Valenciana grant AICO/2020/114Rubio-Chavarría, M.; Santamaria Navarro, C.; García Mora, MB.; Rubio Navarro, G. (2021). Modelling Biological Systems: A New Algorithm for the Inference of Boolean Networks. Mathematics. 9(4):1-22. https://doi.org/10.3390/math9040373S1229

Modelling Biological Systems: A New Algorithm for the Inference of Boolean Networks

Biological systems are commonly constituted by a high number of interacting agents. This great dimensionality hinders biological modelling due to the high computational cost. Therefore, new modelling methods are needed to reduce computation time while preserving the properties of the depicted systems. At this point, Boolean Networks have been revealed as a modelling tool with high expressiveness and reduced computing times. The aim of this work has been to introduce an automatic and coherent procedure to model systems through Boolean Networks. A synergy that harnesses the strengths of both approaches is obtained by combining an existing approach to managing information from biological pathways with the so-called Nested Canalising Boolean Functions (NCBF). In order to show the power of the developed method, two examples of an application with systems studied in the bibliography are provided: The epithelial-mesenchymal transition and the lac operon. Due to the fact that this method relies on directed graphs as a primary representation of the systems, its applications exceed life sciences into areas such as traffic management or machine learning, in which these graphs are the main expression of the systems handled

Thermal Shock Response of Yeast Cells Characterised by Dielectrophoresis Force Measurement

Dielectrophoresis is an electric force experienced by particles subjected to non-uniform electric fields. Recently, several technologies have been developed focused on the use of dielectrophoretic force (DEP) to manipulate and detect cells. On the other hand, there is no such great development in the field of DEP-based cell discrimination methods. Despite the demand for methods to differentiate biological cell states, most DEP developed methods have been focused on differentiation through geometric parameters. The novelty of the present work relies upon the point that a DEP force cell measurement is used as a discrimination method, capable of detecting heat killed yeast cells from the alive ones. Thermal treatment is used as an example of different biological state of cells. It comes from the fact that biological properties have their reflection in the electric properties of the particle, in this case a yeast cell. To demonstrate such capability of the method, 279 heat-killed cells were measured and compared with alive cells data from the literature. For each cell, six speeds were taken at different points in its trajectory inside a variable non-uniform electric field. The electric parameters in cell wall conductivity, cell membrane conductivity, cell membrane permittivity of the yeast cell from bibliography explains the DEP experimental force measured. Finally, alive and heat-treated cells were distinguished based on that measure. Our results can be explained through the well-known damage of cell structure characteristics of heat-killed cells

Characterization of Simple and Double Yeast Cells Using Dielectrophoretic Force Measurement

[EN] Dielectrophoretic force is an electric force experienced by particles subjected to non-uniform electric fields. In recent years, plenty of dielectrophoretic force (DEP) applications have been developed. Most of these works have been centered on particle positioning and manipulation. DEP particle characterization has been left in the background. Likewise, these characterizations have studied the electric properties of particles from a qualitative point of view. This article focuses on the quantitative measurement of cells¿ dielectric force, specifically yeast cells. The measures are obtained as the results of a theoretical model and an instrumental method, both of which are developed and described in the present article, based on a dielectrophoretic chamber made of two V-shaped placed electrodes. In this study, 845 cells were measured. For each one, six speeds were taken at different points in its trajectory. Furthermore, the chamber design is repeatable, and this was the first time that measurements of dielectrophoretic force and cell velocity for double yeast cells were accomplished. To validate the results obtained in the present research, the results have been compared with the dielectric properties of yeast cells collected in the pre-existing literature.García Diego, FJ.; Rubio-Chavarría, M.; Beltrán Medina, P.; Espinos Gutierrez, FJ. (2019). Characterization of Simple and Double Yeast Cells Using Dielectrophoretic Force Measurement. Sensors. 19(17):1-17. https://doi.org/10.3390/s19173813117191

Thermal Shock Response of Yeast Cells Characterised by Dielectrophoresis Force Measurement

[EN] Dielectrophoresis is an electric force experienced by particles subjected to non-uniform electric fields. Recently, several technologies have been developed focused on the use of dielectrophoretic force (DEP) to manipulate and detect cells. On the other hand, there is no such great development in the field of DEP-based cell discrimination methods. Despite the demand for methods to differentiate biological cell states, most DEP developed methods have been focused on differentiation through geometric parameters. The novelty of the present work relies upon the point that a DEP force cell measurement is used as a discrimination method, capable of detecting heat killed yeast cells from the alive ones. Thermal treatment is used as an example of different biological state of cells. It comes from the fact that biological properties have their reflection in the electric properties of the particle, in this case a yeast cell. To demonstrate such capability of the method, 279 heat-killed cells were measured and compared with alive cells data from the literature. For each cell, six speeds were taken at different points in its trajectory inside a variable non-uniform electric field. The electric parameters in cell wall conductivity, cell membrane conductivity, cell membrane permittivity of the yeast cell from bibliography explains the DEP experimental force measured. Finally, alive and heat-treated cells were distinguished based on that measure. Our results can be explained through the well-known damage of cell structure characteristics of heat-killed cells.García Diego, FJ.; Rubio-Chavarría, M.; Beltrán Medina, P.; Espinos Gutierrez, FJ. (2019). Thermal Shock Response of Yeast Cells Characterised by Dielectrophoresis Force Measurement. Sensors. 19(23):1-14. https://doi.org/10.3390/s19235304114192