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. A crown jewel of any scientific investigation is to make accurate and quantitative predictions based on mechanistic understanding of a system. Although quantitative prediction has been the norm and the expectation in physical sciences, living systems are notoriously difficult to predict quantitatively. One of the major challenges is obtaining model parameters. Choosing model parameters to fit data often results in a model that can explain the fitted data but not predict new data. When modeling cells, “guessing” phenotype parameters or “borrowing” parameters from a different genetic background can be highly problematic. In addition, phenotype parameters can vary significantly over time, as cell physiology changes with changing environments, or as cells evolve rapidly. Thus, although parameters are assumed to be constant in most models, this is a far cry from reality and may interfere with quantitative prediction. Here, using a simple engineered yeast community as our case example, we demonstrate that quantitative modeling is possible, but only after overcoming multiple difficulties in parameter measurements. Our approach should be generalizable for modeling communities of interacting cells for which genetic or chemical information of interaction mechanisms is available.

Many pathogens can cause cancer, but cancer itself does not normally act as an infectious agent. However, transmissible cancers have been found in a few cases in nature: in Tasmanian devils, dogs, and several bivalve species. The transmissible cancers in dogs and devils are known to spread through direct physical contact, but the exact route of transmission of bivalve transmissible neoplasia (BTN) has not yet been confirmed. It has been hypothesized that cancer cells from bivalves could be released by diseased animals and spread through the water column to infect/engraft into other animals. To test the feasibility of this proposed mechanism of transmission, we tested the ability of BTN cells from the soft-shell clam (Mya arenaria BTN, or MarBTN) to survive in artificial seawater. We found that MarBTN cells are highly sensitive to salinity, with acute toxicity at salinity levels lower than those found in the native marine environment. BTN cells also survive longer at lower temperatures, with 50% of cells surviving greater than 12 days in seawater at 10 °C, and more than 19 days at 4 °C. With one clam donor, living cells were observed for more than eight weeks at 4 °C. We also used qPCR of environmental DNA (eDNA) to detect the presence of MarBTN-specific DNA in the environment. We observed release of MarBTN-specific DNA into the water of laboratory aquaria containing highly MarBTN-diseased clams, and we detected MarBTN-specific DNA in seawater samples collected from MarBTN-endemic areas in Maine, although the copy numbers detected in environmental samples were much lower than those found in aquaria. Overall, these data show that MarBTN cells can survive well in seawater, and they are released into the water by diseased animals. These findings support the hypothesis that BTN is spread from animal-to-animal by free cells through seawater.

Background: Microbes live in dynamic environments where nutrient concentrations fluctuate. Quantifying fitness in terms of birth rate and death rate in a wide range of environments is critical for understanding microbial evolution and ecology.Methods: Here, using high‐throughput time‐lapse microscopy, we have quantified how Saccharomyces cerevisiae mutants incapable of synthesizing an essential metabolite (auxotrophs) grow or die in various concentrations of the required metabolite. We establish that cells normally expressing fluorescent proteins lose fluorescence upon death and that the total fluorescence in an imaging frame is proportional to the number of live cells even when cells form multiple layers. We validate our microscopy approach of measuring birth and death rates using flow cytometry, cell counting, and chemostat culturing.Results: For lysine‐requiring cells, very low concentrations of lysine are not detectably consumed and do not support cell birth, but delay the onset of death phase and reduce the death rate compared to no lysine. In contrast, in low hypoxanthine, hypoxanthine‐requiring cells can produce new cells, yet also die faster than in the absence of hypoxanthine. For both strains, birth rates under various metabolite concentrations are better described by the sigmoidal‐shaped Moser model than the well‐known Monod model, while death rates can vary with metabolite concentration and time.Conclusions: Our work reveals how time‐lapse microscopy can be used to discover non‐intuitive microbial birth and death dynamics and to quantify growth rates in many environments.

In eukaryotes, conserved mechanisms ensure that cell growth is coordinated with nutrient availability. Overactive growth during nutrient limitation (“nutrient–growth dysregulation”) can lead to rapid cell death. Here, we demonstrate that cells can adapt to nutrient–growth dysregulation by evolving major metabolic defects. Specifically, when yeast lysine-auxotrophic mutant lys⁻ encountered lysine limitation, an evolutionarily novel stress, cells suffered nutrient–growth dysregulation. A subpopulation repeatedly evolved to lose the ability to synthesize organosulfurs (lys⁻orgS⁻). Organosulfurs, mainly reduced glutathione (GSH) and GSH conjugates, were released by lys⁻ cells during lysine limitation when growth was dysregulated, but not during glucose limitation when growth was regulated. Limiting organosulfurs conferred a frequency-dependent fitness advantage to lys⁻orgS⁻ by eliciting a proper slow growth program, including autophagy. Thus, nutrient–growth dysregulation is associated with rapid organosulfur release, which enables the selection of organosulfur auxotrophy to better tune cell growth to the metabolic environment. We speculate that evolutionarily novel stresses can trigger atypical release of certain metabolites, setting the stage for the evolution of new ecological interactions.

Cooperation, paying a cost to benefit others, is widespread. Cooperation can be promoted by pleiotropic ‘win-win’ mutations which directly benefit self (self-serving) and partner (partner-serving). Previously, we showed that partner-serving should be defined as increased benefit supply rate per intake benefit. Here, we report that win-win mutations can rapidly evolve even under conditions unfavorable for cooperation. Specifically, in a well-mixed environment we evolved engineered yeast cooperative communities where two strains exchanged costly metabolites, lysine and hypoxanthine. Among cells that consumed lysine and released hypoxanthine, ecm21 mutations repeatedly arose. ecm21 is self-serving, improving self’s growth rate in limiting lysine. ecm21 is also partner-serving, increasing hypoxanthine release rate per lysine consumption and the steady state growth rate of partner and of community. ecm21 also arose in monocultures evolving in lysine-limited chemostats. Thus, even without any history of cooperation or pressure to maintain cooperation, pleiotropic win-win mutations may readily evolve to promote cooperation.

Mutualisms can be promoted by pleiotropic win-win mutations which directly benefit self (self-serving) and partner (partner-serving). Intuitively, partner-serving phenotype could be quantified as an individual’s benefit supply rate to partners. Here, we demonstrate the inadequacy of this thinking, and propose an alternative. Specifically, we evolved well-mixed mutualistic communities where two engineered yeast strains exchanged essential metabolites lysine and hypoxanthine. Among cells that consumed lysine and released hypoxanthine, a chromosome duplication mutation seemed win-win: it improved cell’s affinity for lysine (self-serving), and increased hypoxanthine release rate per cell (partner-serving). However, increased release rate was due to increased cell size accompanied by increased lysine utilization per birth. Consequently, total hypoxanthine release rate per lysine utilization (defined as ‘exchange ratio’) remained unchanged. Indeed, this mutation did not increase the steady state growth rate of partner, and is thus solely self-serving during long-term growth. By extension, reduced benefit production rate by an individual may not imply cheating.