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

项目摘要 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， 二型真菌是人类局部和全身真菌感染的重要原因，特别是 免疫功能低下的患者。它是导致医生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可植入医疗装置的表面上，并且是医院感染的主要原因。 感染.近年来，人们已经认识到，与整合到生物膜中的细菌物种的相互作用 可以影响C。白念珠菌毒力和其他特性，因此，重要的是要了解的相互作用， C.白色念珠菌与细菌物种，特别是代谢相互作用。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， 形成生物膜的能力。本项目通过基于模型的优化设计来解决这一问题， 用于控制真菌生长的细菌物种的组合物。这将通过一个 构建由细菌组成的异质生物膜的新型计算模型的组合 以及真菌物种，以及用于降维和优化的新数学工具。 该项目的成果将是更好地了解细菌和真菌之间的关系 生物膜中的物种及其治疗潜力，通过构建基于预测剂的 计算模型另一个成果将是一个数学工具，使数学模型的使用 目的是设计用于在异质生物膜中真菌生长的最佳控制。的适用性 该项目的结果远远超出了生物膜，进入了所有适合的医学和医疗保健领域。 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.