Control of heterogeneous microbial communities using model-based multi-objective optimization

项目总结 该项目解决了一个重要的生物医学问题：如何控制由白色念珠菌形成的生物膜， 是人类局部和全身真菌感染的重要原因，尤其是二相性真菌 免疫功能受损的病人。它是导致就诊的所有阴道感染的85%-95%的原因。C。 白色念珠菌生物膜也形成在植入式医疗器械的表面，是医院内感染的主要原因。 感染。近年来，人们已经认识到，与细菌物种的相互作用整合到生物膜中 可以影响白念珠菌的毒力和其他特性，因此了解 白念珠菌与细菌的种类，特别是代谢的相互作用。然后，下一步是理解和， 最终，控制不同微生物物种的不同组成如何影响它们的代谢状态和 它们形成生物膜的能力。本项目通过基于模型的最优设计来解决这一问题 控制真菌生长的细菌种类的组成。这将通过一个 细菌非均相生物膜新型计算模型的构建 以及真菌种类，以及用于降维和优化的新的数学工具。 该项目的成果将是更好地了解细菌和真菌之间的关系 通过构建基于预测剂的生物膜中的物种及其治疗潜力 计算模型。另一个结果将是一种数学工具，它能够使用数学模型 目的是设计异质生物膜中真菌生长的最佳控制措施。该协议的适用性 这一项目的成果远远超出了生物膜的范围，扩展到了医学和保健的所有领域 基于代理的建模，例如对人类微生物组的研究。

PROJECT SUMMARY The project addresses an important biomedical problem: how to control biofilms formed by Candida albicans, a dimorphic fungus that is an important cause of both topical and systemic fungal infection in humans, in particular immunocompromised patients. It is responsible for 85-95% of all vaginal infections resulting in doctor visits. C. albicans biofilms also form on the surface of implantable medical devices, and are a major cause of nosocomial infections. In recent years, it has been recognized that interactions with bacterial species integrated into biofilms can affect C. albicans virulence and other properties, It is therefore important to understand the interactions of C. albicans with bacterial species, in particular metabolic interactions. The next step then is to understand and, ultimately, control how varying compositions of the different microbial species affect their metabolic state and their ability to form biofilms. This project approaches the problem through model-based design of optimal compositions of the bacterial species for control of fungal growth. This will be accomplished through a combination of the construction of a novel computational model of a heterogeneous biofilm consisting of bacterial as well as fungal species, and novel mathematical tools for dimension reduction and optimization. The outcome of the project will be a better understanding of the relationship between bacterial and fungal species in a biofilm and its therapeutic potential through the construction of a predictive agent-based computational model. Another outcome will be a mathematical tool that enables the use of mathematical models for the purpose of designing optimal controls for fungal growth in heterogeneous biofilms. The applicability of the results of this project extends far beyond biofilms into all areas of medicine and healthcare that are amenable to agent-based modeling, such as studies of the human microbiome.