Bobrovskikh, Aleksandr (2023) Analysis of Complex Biological Systems Using Hybrid Modeling Approach. [Tesi di dottorato]
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| Tipologia del documento: | Tesi di dottorato |
|---|---|
| Lingua: | English |
| Titolo: | Analysis of Complex Biological Systems Using Hybrid Modeling Approach |
| Autori: | Autore Email Bobrovskikh, Aleksandr aleksandr.bobrovskikh@unina.it |
| Data: | 9 Ottobre 2023 |
| Numero di pagine: | 76 |
| Istituzione: | Università degli Studi di Napoli Federico II |
| Dipartimento: | Agraria |
| Dottorato: | Sustainable agricultural and forestry systems and food security |
| Ciclo di dottorato: | 35 |
| Coordinatore del Corso di dottorato: | nome email Maggio, Albino almaggio@unina.it |
| Tutor: | nome email Giannino, Francesco [non definito] |
| Data: | 9 Ottobre 2023 |
| Numero di pagine: | 76 |
| Parole chiave: | hybrid modeling; agent-based modeling; plant-soil negative feedback; xylogenesis; single-cell; plant growth |
| Settori scientifico-disciplinari del MIUR: | Area 05 - Scienze biologiche > BIO/07 - Ecologia Area 01 - Scienze matematiche e informatiche > INF/01 - Informatica Area 01 - Scienze matematiche e informatiche > MAT/08 - Analisi numerica |
| Depositato il: | 15 Ott 2023 13:46 |
| Ultima modifica: | 09 Apr 2025 13:22 |
| URI: | http://www.fedoa.unina.it/id/eprint/14994 |
Abstract
In the post-genomic era of biology, modeling of biological systems becomes especially relevant, because of obtained big data and insights of the regulation of such processes as growth and development of organisms. The biological community needs powerful approaches for identifying individual systems and their key parameters of homeostasis and functioning. One of these approaches is modeling. The main purpose of this thesis is to give a general idea of the possibilities of mathematical modeling of biological systems using an agent-based and hybrid approach with implementation on general-purpose Python language. This goal was set to demonstrate the capabilities of a general language for implementing the ecological models. This thesis is consisting of three main chapters. The first chapter (Section №2) is described the state-of-art of tools for cell-based computational modeling. Also, this chapter summarizes new opportunities for advanced plant morphogenesis models, which become possible thanks to single-cell transcriptome data. The second chapter (Section №3) is described the implementation of two-dimensional spatial model which describes plant-soil negative feedback (NF) phenomena as well as obtained simulation results. The third chapter (Section №4) is dedicated to describing the created hybrid model of multi-cellular cambial growth of conifers, which is a direct descendant of the single-cell model of xylogenesis developed by Cartenì et al., 2018. Taken together, obtained results are heterogeneous in structure and cover different areas of hybrid modeling of biological systems, as well as agent-based modeling. Python, as a general-purpose language, was suitable for developing the described models. The author notes the special need in specialized libraries suitable for hybrid framework development for effective modeling of environmental processes on the time-spatial scale.
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