In silico analysis of antibiotic-induced Clostridium difficile infection: Remediation techniques and biological adaptations

In this paper we study antibiotic-induced C. difficile infection (CDI), caused by the toxin-producing C. difficile (CD), and implement clinically-inspired simulated treatments in a computational framework that synthesizes a generalized Lotka-Volterra (gLV) model with SIR modeling techniques. The gLV model uses parameters derived from an experimental mouse model, in which the mice are administered antibiotics and subsequently dosed with CD. We numerically identify which of the experimentally measured initial conditions are vulnerable to CD colonization, then formalize the notion of CD susceptibility analytically. We simulate fecal transplantation, a clinically successful treatment for CDI, and discover that both the transplant timing and transplant donor are relevant to the the efficacy of the treatment, a result which has clinical implications. We incorporate two nongeneric yet dangerous attributes of CD into the gLV model, sporulation and antibiotic-resistant mutation, and for each identify relevant SIR techniques that describe the desired attribute. Finally, we rely on the results of our framework to analyze an experimental study of fecal transplants in mice, and are able to explain observed experimental results, validate our simulated results, and suggest model-motivated experiments.本研究围绕抗生素诱导的艰难梭菌感染（C. difficile infection, CDI）展开，该感染由产毒艰难梭菌（C. difficile, CD）引发。我们在整合广义洛特卡-沃尔泰拉（generalized Lotka-Volterra, gLV）模型与SIR建模技术的计算框架中，实现了贴合临床场景的模拟治疗方案。该gLV模型的参数源自实验小鼠模型：在此模型中，小鼠先接受抗生素给药，随后被接种艰难梭菌。我们通过数值计算甄别出实验测得的、易受艰难梭菌定植的初始条件，并从分析层面形式化定义了艰难梭菌易感性的概念。我们模拟了粪菌移植（fecal transplantation）这一临床治疗CDI的成熟手段，发现移植时机与供体均与治疗效果息息相关，该结论具备临床指导价值。我们将艰难梭菌的两种非典型却极具危险性的特性——孢子形成与抗生素耐药突变——纳入gLV模型，并分别针对这两种特性确定了适配的SIR建模方法以实现对应特性的精准描述。最终，我们依托本计算框架的分析结果，对小鼠粪菌移植实验展开研究，成功解释了观测到的实验现象，验证了模拟结果的合理性，并提出了基于模型导向的实验设计方向。