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Such simulations rely on assumptions about the culturing environment, affecting if the culture may reach a metabolically stationary state with constant microbial concentrations. They also require assumptions on decision making by the microbes: metabolic strategies can be in the interest of individual community members or of the whole community. However, the impact of such common assumptions on community simulation results has not been investigated systematically. Here, we investigate four combinations of assumptions, elucidate how they are applied in literature, provide novel mathematical formulations for their simulation, and show how the resulting predictions differ qualitatively. Crucially, our results stress that different assumption combinations give qualitatively different predictions on microbial coexistence by differential substrate utilization. This fundamental mechanism is critically under explored in the steady state GSM literature with its strong focus on coexistence states due to crossfeeding (division of labor).<\/jats:p>","DOI":"10.1007\/978-3-030-85633-5_9","type":"book-chapter","created":{"date-parts":[[2021,9,12]],"date-time":"2021-09-12T23:04:33Z","timestamp":1631487873000},"page":"141-158","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Microbial Community Decision Making Models in Batch and Chemostat Cultures"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2403-7740","authenticated-orcid":false,"given":"Axel","family":"Theorell","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1145-891X","authenticated-orcid":false,"given":"J\u00f6rg","family":"Stelling","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,13]]},"reference":[{"key":"9_CR1","doi-asserted-by":"publisher","first-page":"3897","DOI":"10.1016\/j.csbj.2020.11.035","volume":"18","author":"\u00c1 Altamirano","year":"2020","unstructured":"Altamirano, \u00c1., Saa, P.A., Garrido, D.: Inferring composition and function of the human gut microbiome in time and space: a review of genome-scale metabolic modelling tools. 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