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Updated: Jul 13, 2026

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression
Published on: April 18, 2021
Effect of the population heterogeneity on growth behavior and its estimation
HuaiQiang Zhang1, LiLi Lu, XueLan Yan
1State Key Laboratory of Microbial Technology, Shandong University, Jinan 250100, China.
Abstract:
Different types of the Logistic model are constructed based on a simple assumption that the microbial populations are all composed of homogeneous members and consequently, the condition of design for the initial value of these models has to be rather limited in the case of N(t(0))=N(0). Therefore, these models cannot distinguish the dynamic behavior of the populations possessing the same N(0) from heterogeneous phases. In fact, only a certain ratio of the cells in a population is dividing at any moment during growth progress, termed as theta, and thus, dN/dt not only depends on N, but also on theta. So theta is a necessary element for the condition design of the initial value. Unfortunately, this idea has long been neglected in widely used growth models. However, combining together the two factors (N(0) and theta) into the initial value often leads to the complexity in the mathematical solution. This difficulty can be overcome by using instantaneous rates (V(inst)) to express growth progress. Previous studies in our laboratory suggested that the V(inst) curve of the bacterial populations all showed a Guassian function shape and thus, the different growth phases can be reasonably distinguished. In the present study, the Gaussian distribution function was transformed approximately into an analytical form (Y(i)=alpha(e)[-0.5(x(i)-x(0)/b)(2)]) that can be conveniently used to evaluate the growth parameters and in this way the intrinsic growth behavior of a bacterial species growing in heterogeneous phases can be estimated. In addition, a new method has been proposed, in this case, the lag period and the double time for a bacterial population can also be reasonably evaluated. This approach proposed could thus be expected to reveal important insight of bacterial population growth. Some aspects in modeling population growth are also discussed.
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