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Effect of the population heterogeneity on growth behavior and its estimation 被引量:4

Effect of the population heterogeneity on growth behavior and its estimation
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摘要 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 N0 from heteroge-neous phases. In fact, only a certain ratio of the cells in a population is dividing at any moment during growth progress, termed as θ, and thus, ddNt not only depends on N, but also on θ. So θ 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 (N0 and θ ) into the initial value often leads to the complexity in the mathematical solution. This difficulty can be overcome by using instantaneous rates (Vinst) to express growth progress. Previous studies in our laboratory sug-gested that the Vinst 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 dis-tribution function was transformed approximately into an analytical form (0.5x ibxYi αe=20) 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. 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(t0)=N0. Therefore, these models cannot distinguish the dynamic behavior of the populations possessing the same N0 from heteroge-neous phases. In fact, only a certain ratio of the cells in a population is dividing at any moment during growth progress, termed as θ, and thus, ddNt not only depends on N, but also on θ. So θ 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 (N0 and θ ) into the initial value often leads to the complexity in the mathematical solution. This difficulty can be overcome by using instantaneous rates (Vinst) to express growth progress. Previous studies in our laboratory sug-gested that the Vinst 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 dis-tribution function was transformed approximately into an analytical form (0.5x ibxYi αe=20) 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.
出处 《Science China(Life Sciences)》 SCIE CAS 2007年第4期535-547,共13页 中国科学(生命科学英文版)
基金 Supported by the National Natural Science Foundation of China (Grant No. 30370013) National Basic Research Program of China (Grant No. 2004CB719702)
关键词 LOGISTIC model instantaneous GROWTH RATE GROWTH dynamic LAG PHASE DOUBLE time Logistic model, instantaneous growth rate, growth dynamic, lag phase, double time
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