Predicting microbial relative growth in a mixed culture from growth curve data

16. CC-BY-NC 4.0 International license not peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was. Abstract 17 Fitness is not well estimated from growth curves of individual isolates in 18 monoculture. Rather, competition experiments, which measure relative growth in 19 mixed microbial cultures, must be performed to better infer relative fitness. However, 20 competition experiments require unique genotypic or phenotypic markers, and thus 21 are difficult to perform with isolates derived from a common ancestor or non-model 22 organisms. Here we describe Curveball, a new computational approach for predicting 23 relative growth of microbes in a mixed culture utilizing mono-and mixed culture 24 growth curve data. We implemented Curveball in an open-source software package 25 (http://curveball.yoavram.com) and validated the approach using growth curve and 26 competition experiments with bacteria. Curveball provides a simpler and more cost-27 effective approach to predict relative growth and infer relative fitness. Furthermore, 28 by integrating several growth phases into the fitness estimation, Curveball provides a 29 holistic approach to fitness inference from growth curve data. 30 31. CC-BY-NC 4.0 International license not peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was. 3 Growth curves are commonly used in microbiology, genetics, and evolutionary 32 biology to estimate the fitness of individual microbial isolates. Growth curves 33 describe the density of cell populations in liquid culture over a period of time and are 34 usually acquired by measuring the optical density (OD) of one or more cell 35 populations. The simplest way to infer fitness from growth curves is to estimate the 36 growth rate during the exponential growth phase by inferring the slope of the log of 37 the growth curve 1 (see example in Figure 1). Indeed, the growth rate is often used as a 38 proxy of the selection coefficient, s, which is the standard measure of relative fitness 39 in population genetics 2,3. However, exponential growth rates do not capture the 40 dynamics of other phases of a typical growth curve, such as the length of lag phase 41 and the cell density at stationary phase 4 (Figure 1). Thus, it is not surprising that 42 growth rates are often poor estimators of relative fitness 5,6. 43 44 45 Figure 1. Fitting an exponential model to growth curve data. The growth rate is calculated as …