Bardini Roberta, Di Carlo Stefano, Politano Gianfranco, Benso Alfredo
Politecnico di Torino, Control and Computer Engineering Department, Corso Duca degli Abruzzi 24, Torino, 10129, Italy.
BMC Syst Biol. 2018 Nov 22;12(Suppl 6):108. doi: 10.1186/s12918-018-0627-1.
The unregulated use of antibiotics not only in clinical practice but also in farm animals breeding is causing a unprecedented growth of antibiotic resistant bacterial strains. This problem can be analyzed at different levels, from the antibiotic resistance spreading dynamics at the host population level down to the molecular mechanisms at the bacteria level. In fact, antibiotic administration policies and practices affect the societal system where individuals developing resistance interact with each other and with the environment. Each individual can be seen as a meta-organism together with its associated microbiota, which proves to have a prominent role in the resistance spreading dynamics. Eventually, in each microbiota, bacterial population dynamics and vertical or horizontal gene transfer events activate cellular and molecular mechanisms for resistance spreading that can also be possible targets for its prevention.
In this work we show how to use the Nets-Within-Nets formalism to model the dynamics between different antibiotic administration protocols and antibiotic resistance, both at the individuals population and at the single microbiota level. Three application examples are presented to show the flexibility of this approach in integrating heterogeneous information in the same model, a fundamental property when creating computational models complex biological systems. Simulations allow to explicitly take into account timing and stochastic events.
This work demonstrates how the NWN formalism can be used to efficiently model antibiotic resistance population dynamics at different levels of detail. The proposed modeling approach not only provides a valuable tool for investigating causal, quantitative relations between different events and mechanisms, but can be also used as a valid support for decision making processes and protocol development.
抗生素的无节制使用，不仅在临床实践中存在，在农场动物养殖中也如此，这正导致抗生素耐药菌株以前所未有的速度增长。这个问题可以在不同层面进行分析，从宿主群体层面的抗生素耐药性传播动态，到细菌层面的分子机制。事实上，抗生素给药政策和做法会影响社会系统，在这个系统中，产生耐药性的个体相互之间以及与环境相互作用。每个个体与其相关的微生物群一起可被视为一个超级生物体，事实证明微生物群在耐药性传播动态中起着重要作用。最终，在每个微生物群中，细菌种群动态以及垂直或水平基因转移事件会激活耐药性传播的细胞和分子机制，而这些机制也可能成为预防耐药性的靶点。
在这项工作中，我们展示了如何使用网中网形式体系，在个体群体和单个微生物群层面，对不同抗生素给药方案与抗生素耐药性之间的动态关系进行建模。给出了三个应用示例，以展示这种方法在将异质信息整合到同一模型中的灵活性，这是创建复杂生物系统计算模型时的一个基本特性。模拟能够明确考虑时间和随机事件。
这项工作证明了网中网形式体系可用于在不同详细程度上有效地对抗生素耐药性种群动态进行建模。所提出的建模方法不仅为研究不同事件和机制之间的因果定量关系提供了一个有价值的工具，还可作为决策过程和方案制定的有效支持。
