Evolutie van metabole netwerken van micro-organismen via Quality-Diversity optimalisatie algoritmen.

Shauny
Van Hoye

In de masterthesis getiteld "Evolution and design of metabolic networks using Quality-Diversity optimization algorithms", werd een nieuw computationeel framework genaamd OptMAP ontwikkeld om strategieën te voorspellen die de groei van micro-organismen en de productie van metabolieten maximaliseren. De karakteristieken van een micro-organisme worden bepaald door zowel de genen van het micro-organisme als zijn omgevingsfactoren. De extreem grote hoeveelheid aan combinaties van mogelijke modificaties van het genoom van het micro-organisme en zijn omgevingsfactoren, maakt het op punt stellen van micro-organismen in het laboratorium zeer uitdagend. Het optimaliseren van de groei van micro-organismen en de productie van metabolieten in een laboratorium kan daarom veel tijd en middelen kosten. In silico optimalisatie van het organisme voordat het opgegroeid en getest wordt in het laboratorium kan daarom helpen om de zoekruimte, gecreëerd door de genen en de omgevingsfactoren, te exploreren.

In deze thesis worden Quality-Diversity evolutionaire algoritmen gebruikt om metabole netwerken van organismen in silico te evolueren met als doel de organismen te kunnen groeien op bepaalde substraten, specifieke metabolieten te produceren enz. We focussen ons op het gebruik van één bepaald Quality-Diversity Evolutionaire Algoritme genaamd Multi-dimensional Archive of Phenotypic (MAP)-Elites. Quality-Diversity Evolutionaire Algoritmen hebben als voordeel dat ze zoeken naar oplossingen die zowel goed presteren als divers zijn. Dit zorgt ervoor dat er een evenwicht bewaard wordt tussen exploratie om lokale minima te vermijden en convergentie tijdens het zoeken naar een set van oplossingen die verspreid is over de zoekruimte. Doorheen de thesis worden genome-scale metabole modellen gebruikt om de genen en het metabolisme van organismen in silico voor te stellen. Het MAP-Elites algoritme maakt het mogelijk om de zoekruimte intelligent te verkennen door het proces van evolutie in silico te simuleren.

Terwijl traditionele Evolutionaire Algoritmen zoeken naar één optimale oplossing, maakt OptMAP gebruik van een Quality-Diversity genaamd Multi-dimensional Archive of Phenotypic (MAP) Elites, dat zowel zoekt naar goed presterende oplossingen alsook expliciet naar diverse oplossingen (Novelty Search). Dit vermogen om verschillende sets van goed presterende en diverse oplossingen te vinden is vooral interessant in de synthetische biologie en metabolic engineering, omdat dit het mogelijk maakt om deze sets van diverse oplossingen in het laboratorium te testen om te zien welke het beste presteren in reële omstandigheden, in plaats van te hopen op een enkele optimale in silico oplossing even goed presteert in het laboratorium als in silico. Bovendien creëert OptMAP ook een archief dat de relaties tussen verschillende dimensies van de ruimte gecreëerd door de karakteristieken van de micro-organismen en het bijbehorende fitnesspotentieel in kaart te brengen om nieuwe wetenschappelijke inzichten te bieden die van belang kunnen zijn in metabolic engineering. OptMAP werd met succes gevalideerd door gen-knockouts en mediumsamenstellingen voor de productie van meerdere metabolieten in Escherichia coli te voorspellen. De resultaten van OptMAP zijn beter dan vergelijkbare bestaande frameworks om gen-knockouts en mediumsamenstellingen voor de productie van metabolieten in micro-organismen te voorspellen.

Hoewel OptMAP zijn nut heeft bewezen voor de voorspelling van gen-knockouts en mediumsamenstellingen, is er nog steeds potentieel voor uitbreiding van OptMAP voor de voorspelling van genregulatie targets, de toevoeging van heterologe routes via knock-ins en andere metabolic engineering technieken die in het laboratorium worden gebruikt, waardoor OptMAP alle nieuwe ontwikkelingen of ontdekkingen op het gebied van metabolic engineering kan bijhouden.

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Universiteit of Hogeschool
Universiteit Gent
Thesis jaar
2022
Promotor(en)
Prof. dr. Bernard De Baets, dr. ir. Michiel Stock