Spatial biology of Ising-like synthetic genetic networks
| dc.contributor.advisor | Federici, Fernán | |
| dc.contributor.advisor | Keymer, Juan E. | |
| dc.contributor.author | Simpson Alfaro, Kevin Matías | |
| dc.contributor.other | Pontificia Universidad Católica de Chile. Facultad de Ciencias Biológicas | |
| dc.date.accessioned | 2021-07-15T14:02:21Z | |
| dc.date.available | 2021-07-15 10:02 | |
| dc.date.issued | 2021 | |
| dc.date.updated | 2021-07-13T14:04:37Z | |
| dc.description | Tesis (Doctor en Ciencias con mención en Genética Molecular y Microbiología)--Pontificia Universidad Católica de Chile, 2021 | |
| dc.description.abstract | Understanding how spatially-correlated cellular states emerge from the local interaction of gene network dynamics is a fundamental challenge in biology. Short and long-range correlations and anti-correlations in gene expression can be found in spatially-distributed cellular systems such as eukaryotic tissues and microbial communities. However, the study of gene spatial correlations emerging from cell-cell coupling in natural systems is difficult since complex interactions are the norm. An alternative is to generate synthetic genetic networks (SGNs) that capture essential features of cell-cell interactions and reveal their influence in the emergence of cellular state patterns. Here, we combine synthetic biology, theoretical modelling and computational simulations to study the emergence of macroscopic gene correlations and address possible mechanisms for multi-scale self-organization of gene states in bacteria. We applied the Ising model as a theoretical framework to study the self-organization of spatially-correlated gene expression in two-state SGNs that are coupled by short-range chemical signals in E. coli. Inspired by the Ising model, we name these SGNs ferromagnetic or anti-ferromagnetic depending if they stabilize the same or the opposite state in neighboring cells. As predicted by our simulations that combine the two-dimensional Ising model with the Contact Process lattice model of cell population dynamics, these SGNs allowed the self-organization of spatial patterns of short and long-scale cellular state domains in bacterial colonies, where the size of the domains depends on the type of interaction, ferromagnetic or anti-ferromagnetic. The emergence of spatial correlations showed to be independent of the cell shape and the underpinning mechanical forces. The similarity found between ferromagnetic colonies and simulated ferromagnetic populations suggest these colonies are near the critical point of phase transition, implying that far regions in the colony are correlated. This work provides resources and a general scope theoretical framework that explain how both short and long-range correlations (and anti-correlations) are able to self-organize from locally-interacting networks. These results on multi-scale organization of gene network states shed light onto the study of pattern formation in developmental biology and microbial ecology, as well as provide a theoretical framework for the engineering of spatially-arranged cell systems. | |
| dc.description.version | 2022-01-02 | |
| dc.format.extent | xii, 113 páginas | |
| dc.fuente.origen | Autoarchivo | |
| dc.identifier.uri | https://repositorio.uc.cl/handle/11534/60990 | |
| dc.information.autoruc | Facultad de Ciencias Biológicas ; Federici, Fernán ; 0000-0001-9200-5383 ; 123357 | |
| dc.information.autoruc | Facultad de Ciencias Biológicas ; Keymer, Juan E. ; 0000-0001-6566-3778 ; 93580 | |
| dc.information.autoruc | Facultad de Ciencias Biológicas ; Simpson Alfaro, Kevin Matías ; S/I ; 1031199 | |
| dc.language.iso | en | |
| dc.nota.acceso | Contenido completo | |
| dc.rights | acceso abierto | |
| dc.subject.ddc | 572.86 | |
| dc.subject.dewey | Biología | es_ES |
| dc.subject.other | Biología celular - Investigaciones - Chile | es_ES |
| dc.subject.other | Biología sintética - Investigaciones - Chile | es_ES |
| dc.title | Spatial biology of Ising-like synthetic genetic networks | es_ES |
| dc.type | tesis doctoral | |
| sipa.codpersvinculados | 123357 | |
| sipa.codpersvinculados | 93580 | |
| sipa.codpersvinculados | 1031199 |