POPULATION BIOLOGY AND GENETICS OF ANTIMICROBIAL ACTION

描述：本提案的总体目标是开始分析 抗生素耐药性的进化动力学，以评估 扭转人类抗生素耐药性增加的战略 病原体 这一提议有三个部分是合乎逻辑的 连接，但在模型和实验系统中是分开的。 的 首先是体外抗性的种群动态，其次是 体内动力学，第三个涉及建模的流行病学， 阻力 第一部分将研究细菌模型的现实性 增长（第18页上的公式2）其中m（A）是描述如何 浓度A的抗微生物剂影响生长。 一个可能 m（A）的函数是（n+1）f（A），其中f（A）是被杀死的细胞的分数 每个细胞周期 抗生素浓度对生长的影响 将使用分批培养进行研究。 该模型适用于恒化器 培养（方程式5，第20页）。 恒化器培养物被设计成使得 有大量的耐药细胞，只有一小部分 敏感的细胞。 当加入抗生素时， 细胞会减少，但恒化器的环境， 由大多数人决定，不会改变。 假设在 函数f（A）将从分批培养中确定，然后这将 可以用来预测恒化器中的生长速率。 抗生素抗性 研究的是染色体--由突变引起的抗性， 通过质粒转移。 在与竞争对手竞争时， 将确定敏感性，并在需要时将其纳入模型。 第二部分研究了免疫反应和抗菌剂的作用 对小鼠细菌生长的作用。 免疫反应分为两种 部分--非特异性和抗原特异性免疫反应。 这些将 可以通过使用SCID小鼠来区分，SCID小鼠仅具有非特异性和 同基因对照小鼠，两者都有。 细菌的生长速度 种群（L. 单核细胞增多症）将在小鼠的脾脏中监测。 将测定两种类型小鼠的生长速率， 抗生素的剂量。 这里抗生素的浓度是不同的 比第一部分的变化。 在第一部分中，浓度是 细菌所经历的浓度;在这一部分中， 浓度是注射到小鼠中的浓度。 模型中 在本节中，抗生素对生长速率的影响被假定为 在生长速率和抗生素浓度之间有相互作用， m（r，A）。 第三部分涉及图7所示模型的开发。 这 该模型的性能将使用的结果和数据进行研究 从其他两个部分。 特别是，安妮塔将看到， S.似乎有一个阈值约40%耐药的肺炎， 解释一下

DESCRIPTION: The general goal of this proposal is to start the analysis of the evolutionary dynamics of antibiotic resistance in order to evaluate strategies for reversing the increase in antibiotic resistance among human pathogens. There are three parts to this proposal that are logically connected, but are separate in both models and experimental systems. The first is the population dynamics of resistance in vitro, the second, the dynamics in vivo, and the third involves modeling the epidemiology of resistance. The first part will investigate the reality of the model for bacterial growth (equation 2 on page 18) where m(A) is the function that describes how an antimicrobial agent at concentration A effects growth. One possible function of m(A) is (n+1)f(A) where f(A) is the fraction of cells killed each cell cycle. The effect of the concentration of antibiotic on growth will be investigated using batch culture. The model is adapted to chemostat culture (equation 5, page 20). The chemostat culture is designed such that there is a large population of resistant cells, with only a small fraction of sensitive cells. When the antibiotic is added, the number of sensitive cells will decline, but the environment of the chemostat, which is determined by the majority population, will not change. It is assumed that the function f(A) will be determined from batch culture and this will then be used to predict the growth rate in chemostats. The antibiotic resistance studied is chromosomal--resistance arising from mutation rather than transfer via a plasmid. The cost of this resistance when competing with the sensitive will be determined and included in the model if required. The second part studies the effect of the immune response and antimicrobial action on bacterial growth in mice. The immune response is divided into two parts--the non-specific and antigen-specific immune responses. These will be distinguished by using SCID mice, which have only the non-specific and isogenic control mice which have both. The growth rate of the bacterial population (L. monocytogenes) will be monitored in the spleen of the mouse. This growth rate will be determined for the two types of mice with various dosing of antibiotics. Here the concentration of antibiotics is a different variable than in the first part. In the first part, the concentration is the concentration experienced by the bacteria; in this part the concentration is the concentration injected into the mouse. In the model for this section, the effect of antibiotics on growth rate is assumed to have an interaction between growth rate and antibiotic concentration, m(r,A). The third part involves development of a model as given in figure 7. This properties of this model will be investigated using the results and data from the other two sections. In particular, Anita will see if the data from S. pneumoniae where there seems to be a threshold of about 40% resistant can be explained.