Montagna, Sara
(2011)
Multi-level models and infrastructures for simulating biological system development, [Dissertation thesis], Alma Mater Studiorum Università di Bologna.
Dottorato di ricerca in
Ingegneria elettronica, informatica e delle telecomunicazioni, 23 Ciclo. DOI 10.6092/unibo/amsdottorato/3415.
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Abstract
The hierarchical organisation of biological systems plays a crucial role in the pattern formation of gene expression resulting from the morphogenetic processes, where autonomous internal dynamics of cells, as well as cell-to-cell interactions through membranes, are responsible for the emergent peculiar structures of the individual phenotype. Being able to reproduce the systems dynamics at different levels of such a hierarchy might be very useful for studying such a complex phenomenon of self-organisation. The idea is to model the phenomenon in terms of a large and dynamic network of compartments, where the interplay between inter-compartment
and intra-compartment events determines the emergent behaviour resulting in the formation of spatial patterns.
According to these premises the thesis proposes a review of the different approaches already developed in modelling developmental biology problems, as well as the main models and infrastructures available in literature for modelling biological systems, analysing their capabilities in tackling multi-compartment / multi-level models.
The thesis then introduces a practical framework, MS-BioNET, for modelling and simulating these scenarios exploiting the potential of multi-level dynamics. This is based on (i) a computational model featuring networks of compartments and an enhanced model of chemical reaction addressing molecule transfer, (ii) a logic-oriented language to flexibly specify complex simulation scenarios, and (iii) a simulation engine based on the many-species/many-channels optimised version of Gillespie’s direct method.
The thesis finally proposes the adoption of the agent-based model as an approach capable of capture multi-level dynamics.
To overcome the problem of parameter tuning in the model, the simulators are supplied with a module for parameter optimisation. The task is defined as an optimisation problem over the parameter space in which the objective function to be minimised is the distance between the output of the simulator and a target one. The problem is tackled with a metaheuristic algorithm.
As an example of application of the MS-BioNET framework and of the agent-based model, a model of the first stages of Drosophila Melanogaster development is realised. The model goal is to generate the early spatial pattern of gap gene expression. The correctness of the models
is shown comparing the simulation results with real data of gene expression with spatial and temporal resolution, acquired in free on-line sources.
Abstract
The hierarchical organisation of biological systems plays a crucial role in the pattern formation of gene expression resulting from the morphogenetic processes, where autonomous internal dynamics of cells, as well as cell-to-cell interactions through membranes, are responsible for the emergent peculiar structures of the individual phenotype. Being able to reproduce the systems dynamics at different levels of such a hierarchy might be very useful for studying such a complex phenomenon of self-organisation. The idea is to model the phenomenon in terms of a large and dynamic network of compartments, where the interplay between inter-compartment
and intra-compartment events determines the emergent behaviour resulting in the formation of spatial patterns.
According to these premises the thesis proposes a review of the different approaches already developed in modelling developmental biology problems, as well as the main models and infrastructures available in literature for modelling biological systems, analysing their capabilities in tackling multi-compartment / multi-level models.
The thesis then introduces a practical framework, MS-BioNET, for modelling and simulating these scenarios exploiting the potential of multi-level dynamics. This is based on (i) a computational model featuring networks of compartments and an enhanced model of chemical reaction addressing molecule transfer, (ii) a logic-oriented language to flexibly specify complex simulation scenarios, and (iii) a simulation engine based on the many-species/many-channels optimised version of Gillespie’s direct method.
The thesis finally proposes the adoption of the agent-based model as an approach capable of capture multi-level dynamics.
To overcome the problem of parameter tuning in the model, the simulators are supplied with a module for parameter optimisation. The task is defined as an optimisation problem over the parameter space in which the objective function to be minimised is the distance between the output of the simulator and a target one. The problem is tackled with a metaheuristic algorithm.
As an example of application of the MS-BioNET framework and of the agent-based model, a model of the first stages of Drosophila Melanogaster development is realised. The model goal is to generate the early spatial pattern of gap gene expression. The correctness of the models
is shown comparing the simulation results with real data of gene expression with spatial and temporal resolution, acquired in free on-line sources.
Tipologia del documento
Tesi di dottorato
Autore
Montagna, Sara
Supervisore
Co-supervisore
Dottorato di ricerca
Scuola di dottorato
Scienze e ingegneria dell'informazione
Ciclo
23
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
morphogenesis developmental biology self-organisation computational biology multi-level models formal methods agent-based model stochastic simulation meta- heuristic parameter optimisation
URN:NBN
DOI
10.6092/unibo/amsdottorato/3415
Data di discussione
28 Aprile 2011
URI
Altri metadati
Tipologia del documento
Tesi di dottorato
Autore
Montagna, Sara
Supervisore
Co-supervisore
Dottorato di ricerca
Scuola di dottorato
Scienze e ingegneria dell'informazione
Ciclo
23
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
morphogenesis developmental biology self-organisation computational biology multi-level models formal methods agent-based model stochastic simulation meta- heuristic parameter optimisation
URN:NBN
DOI
10.6092/unibo/amsdottorato/3415
Data di discussione
28 Aprile 2011
URI
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