One of the more common healthcare associated infection is Chronic diarrhea. This disease is caused by the bacterium Clostridium difficile which alters the normal composition of the human gut flora. The most successful therapy against this infection is the fecal microbial transplant (FMT). They displace C. difficile and contribute to gut microbiome resilience, stability and prevent further episodes of diarrhea. The microorganisms in the FMT their interactions and inner dynamics reshape the gut microbiome to a healthy state. Even though microbial interactions play a key role in the development of the disease, currently, little is known about their dynamics and properties. In this context, a Boolean network model for C. difficile infection (CDI) describing one set of possible interactions was recently presented. To further explore the space of possible microbial interactions, we propose the construction of a neutral space conformed by a set of models that differ in their interactions, but share the final community states of the gut microbiome under antibiotic perturbation and CDI. To begin with the analysis, we use the previously described Boolean network model and we demonstrate that this model is in fact a threshold Boolean network (TBN). Once the TBN model is set, we generate and use an evolutionary algorithm to explore to identify alternative TBNs. We organize the resulting TBNs into clusters that share similar dynamic behaviors. For each cluster, the associated neutral graph is constructed and the most relevant interactions are identified. Finally, we discuss how these interactions can either affect or prevent CDI.

中文翻译：

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布尔网络的生成和鲁棒性对艰难梭菌感染进行建模

与卫生保健相关的最常见感染之一是慢性腹泻。该疾病是由艰难梭菌细菌引起的，它改变了人肠道菌群的正常组成。对抗这种感染最成功的疗法是粪便微生物移植（FMT）。它们取代了艰难梭菌并有助于肠道微生物组的复原力，稳定性并防止进一步的腹泻。FMT中的微生物及其相互作用和内部动力学将肠道微生物组重塑为健康状态。尽管微生物相互作用在疾病的发展中起关键作用，但目前对其动态和特性知之甚少。在这种情况下，艰难梭菌的布尔网络模型最近提出了描述一组可能相互作用的感染（CDI）。为了进一步探索可能的微生物相互作用的空间，我们提出了一个中性空间的构建，该空间由一组模型组成，这些模型的相互作用不同，但是在抗生素扰动和CDI作用下共享肠道微生物组的最终群落状态。首先，我们使用前面描述的布尔网络模型进行分析，并证明该模型实际上是阈值布尔网络（TBN）。设置TBN模型后，我们将生成并使用一种进化算法来探索以找出替代的TBN。我们将生成的TBN组织到共享相似动态行为的集群中。对于每个聚类，将构造关联的中性图，并确定最相关的交互。最后，