ProjektEinfluss biotischer Resistenz auf mikrobielle Invasionen
Grunddaten
Titel:
Einfluss biotischer Resistenz auf mikrobielle Invasionen
Laufzeit:
01.09.2024 bis 31.08.2027
Abstract / Kurz- beschreibung:
Microbes are true experts in dispersal, because of their small size they can reach even most remote habitats. This culminated in the idea that “everything is everywhere, but, the environment selects” because microbes were assumed as not being limited in their dispersal. Accordingly, microbial invasion was not regarded as an important phenomenon until recently and therefore only barely studied. But, advancements in next generation sequencing showed that also for microbes dispersal is limited and thus invasion into new territory is an important challenge for them as well. This situation leaves us with a fundamental lack of knowledge about microbial invasion processes, especially their temporal dynamics.
Microbes are usually not invading empty space, but are confronted with native species that often make establishment of the invaders difficult – a property called biotic resistance. Such biotic resistance can for example protect us from diseases by hindering the invasion of pathogens. There has been some research on the origin of biotic resistance, but essentially no studies investigated how the invasion dynamics are altered by this phenomenon. However, understanding this temporal invasion dynamics is essential because they decide if an invasion is successful or not.
In this work we want to combine lab experiments with mathematical modeling to explore how biotic resistance impacts microbial invasions, in particular, the invasion of pathogens into native communities.
For that purpose, we will establish a microbial model system in which we can easily vary the dispersal rate and strength of biotic resistance. Thus, we can systematically study the impact of those properties on invasion success. We will use the obtained data to develop a theoretical framework that predicts invasion dynamics and success based on the interactions between the invader and native microbes. Finally, we will transfer the obtained knowledge to predict the invasion dynamics of a pathogen entering a complex community of gut microbes.
Microbes are usually not invading empty space, but are confronted with native species that often make establishment of the invaders difficult – a property called biotic resistance. Such biotic resistance can for example protect us from diseases by hindering the invasion of pathogens. There has been some research on the origin of biotic resistance, but essentially no studies investigated how the invasion dynamics are altered by this phenomenon. However, understanding this temporal invasion dynamics is essential because they decide if an invasion is successful or not.
In this work we want to combine lab experiments with mathematical modeling to explore how biotic resistance impacts microbial invasions, in particular, the invasion of pathogens into native communities.
For that purpose, we will establish a microbial model system in which we can easily vary the dispersal rate and strength of biotic resistance. Thus, we can systematically study the impact of those properties on invasion success. We will use the obtained data to develop a theoretical framework that predicts invasion dynamics and success based on the interactions between the invader and native microbes. Finally, we will transfer the obtained knowledge to predict the invasion dynamics of a pathogen entering a complex community of gut microbes.
Beteiligte Mitarbeiter/innen
Leiter/innen
Interfakultäres Institut für Mikrobiologie und Infektionsmedizin (IMIT)
Interfakultäre Institute
Interfakultäre Institute
Lokale Einrichtungen
Exzellenzcluster: Kontrolle von Mikroorganismen zur Bekämpfung von Infektionen (CMFI)
Zentren oder interfakultäre wissenschaftliche Einrichtungen
Universität Tübingen
Universität Tübingen
Interfakultäres Institut für Mikrobiologie und Infektionsmedizin (IMIT)
Interfakultäre Institute
Universität Tübingen
Universität Tübingen
Geldgeber
Bonn, Nordrhein-Westfalen, Deutschland