Uncovering and resolving challenges of quantitative modeling in a simplified community of interacting cells

Abstract

Quantitative modeling is useful for predicting behaviors of a system and for rationally constructing or modifying the system. The predictive power of a model relies on accurate quantification of model parameters. Here, we illustrate challenges in parameter quantification and offer means to overcome these challenges, using a case example in which we quantitatively predict the growth rate of a cooperative community. Specifically, the community consists of two Saccharomyces cerevisiae strains, each engineered to release a metabolite required and consumed by its partner. The initial model, employing parameters measured in batch monocultures with zero or excess metabolite, failed to quantitatively predict experimental results. To resolve the model–experiment discrepancy, we chemically identified the correct exchanged metabolites, but this did not improve model performance. We then remeasured strain phenotypes in chemostats mimicking the metabolite-limited community environments, while mitigating or incorporating effects of rapid evolution. Almost all phenotypes we measured, including death rate, metabolite release rate, and the amount of metabolite consumed per cell birth, varied significantly with the metabolite environment. Once we used parameters measured in a range of community-like chemostat environments, prediction quantitatively agreed with experimental results. In summary, using a simplified community, we uncovered and devised means to resolve modeling challenges that are likely general to living systems.

Disentangling strictly self-serving mutations from win-win mutations in a mutualistic microbial community

“…We randomly isolated evolved L -H + clones from independent communities. Since the community environment was lysine-limited (Hart et al, 2019a;Waite and Shou, 2012), improved growth under lysine limitation would be self-serving. Indeed, while the ancestral strain failed to grow into micro-colonies on agar with low lysine (1.5 µM), all tested (>20) evolved clones could (Fig 2- Figure Supplement 1; Methods "Microcolony assay"), consistent with our previous findings (Hart et al, 2019a;Waite and Shou, 2012).…”

“…Since hypoxanthine is also scarce in the community (Hart et al, 2019a), a mutant L -H + cell with increased hypoxanthine release rate should allow the partner to grow faster and is thus partnerserving. We randomly chose several evolved L -H + clones, and measured their hypoxanthine release rate (Hart et al, 2019a) (Methods, "Release assay" ; Fig 3-Figure Supplement 1).…”

“…Since hypoxanthine is also scarce in the community (Hart et al, 2019a), a mutant L -H + cell with increased hypoxanthine release rate should allow the partner to grow faster and is thus partnerserving. We randomly chose several evolved L -H + clones, and measured their hypoxanthine release rate (Hart et al, 2019a) (Methods, "Release assay" ; Fig 3-Figure Supplement 1). Whereas two of the evolved clones released hypoxanthine at a similar rate as the ancestor, three increased release rates in the sense that each cell released more hypoxanthine per hour than the ancestor (Fig 3-Figure Supplement When we back-crossed evolved clones harboring DISOMY14 to the ancestral background (Methods), only meiotic segregants containing DISOMY14 showed increased hypoxanthine release rate compared to the ancestor (Fig 3 -Figure Supplement 3).…”

“…This community CoSMO (Cooperation that is Synthetic and Mutually Obligatory) (Shou et al, 2007) consists of two S. cerevisiae strains that interact via metabolite cross-feeding ( Figure 1B). L -H + requires lysine (L) and overproduces and releases the adenine precursor hypoxanthine (H) (Hart et al, 2019a). The complementary H -L + requires H, and overproduces and releases L. The two yeast strains are reproductively isolated, and thus can be regarded as two species.…”

“…CoSMO offers an ideal system for examining the evolution of mutualism, given the genetic tractability of the budding yeast and the validated phenotype quantification methods (Hart et al, 2019a).…”