Systems modelling of microbial communities using in vitro and computational approaches

了解和预测微生物群落对环境变化的反应对于开发基于微生物组的应用至关重要。在许多领域，能够控制微生物群的组成和活动将是有益的，包括生物技术、生物修复和动物/人类营养。以前的研究表明，通过了解微生物组的组成，我们可以预测它对特定干预措施的反应。微生物群落的体外模型为高通量研究分子对微生物群落的影响提供了有用的系统。此前已经表明，在体外微生物群可以实现在动物模型中复制的发现。这些方法还能够以经济高效的方式提供大量信息，这些信息对于用机器学习对微生物群落进行建模是必要的。为了研究微生物群，科学家们对细菌群落的基因组进行了测序。这提供了大量必须仔细解释的DNA序列。在大多数研究中，研究人员量化了细菌分类起源的丰度，并确定了细菌基因组中编码的基因的代谢功能。然而，来自肠道微生物组的基因中只有35%到45%与实际功能有关。这往往限制了对微生物组相关研究的分析和解释，并忽视了微生物群落广泛的功能和生态可能性。在这项研究计划中，我们的目标是创建一个框架的基础，有针对性地调节微生物群落。为此，我们将解决两个关键方面，在我们可以故意调节微生物群以获得特定效果之前，需要解决这两个方面。首先，我们将设计一种新的方式来表示微生物群，这种方式允许有效地考虑所有基因和细菌物种，包括那些功能未知的基因和细菌。我们将使用机器学习来发现数据中有意义的模式，这些模式可能会被当前基于细菌量化和基因功能图谱的方法所忽视。其次，我们将使用机器学习来预测微生物组对特定条件的反应，并确定最佳干预序列，以获得所需的微生物组效果。我们将使用存在三种微量矿物质的粪便样本的体外培养作为模型，开发新的方法来实现有针对性的微生物组调控。利用机器学习来模拟微生物对其化学环境的反应，将使人们能够更深入地了解微生物与其环境之间的相互作用。总体而言，我们的研究计划将提供调节微生物群的方法，这些方法将适用于许多领域，包括生物技术和为人和动物提供个性化营养。该项目还将为利用机器学习优化微生物群落的研究设计提供具体的指导方针。

Understanding and predicting how microbial communities react to changes in their environment is critical for the development of microbiome-based applications. There are many fields where being able to control the composition and activity of microbiomes would be beneficial, including biotechnology, bioremediation and animal/human nutrition. Previous studies suggest that by knowing the composition of a microbiome, we could predict its response to specific interventions. In vitro models of microbial communities provide useful systems for the high-throughput study of the impact of molecules on microbiomes. It has been shown previously that in vitro microbiomes allowed discoveries that were replicated in animal models. These approaches are also able to cost-effectively provide a large quantity of information that is necessary to model microbial communities with machine learning. To study the microbiome, scientists sequence the genomes of bacterial communities. This provides large quantities of DNA sequences that must be carefully interpreted. In most studies, researchers quantify the abundance of the taxonomical origin of bacteria and determine the metabolic functions of the genes encoded in bacterial genomes. However, only 35% to 45% of genes from the gut microbiome can be associated with actual functions. This often limits the analysis and interpretation of microbiome-related studies and overlooks the extensive functional and ecological possibilities of microbial communities. In this research program, we aim to create the basis of a framework for the targeted modulation of microbial communities. To do so, we will address two critical aspects that need to be resolved before we can deliberately modulate microbiomes to get specific effects. First, we will devise new ways to represent microbiomes in a manner that allows to efficiently consider all the genes and bacterial species, including those with unknown functions. We will use machine learning to discover meaningful patterns in the data that may be overlooked using current methods based on bacteria quantification and gene function profiling. Second, we will use machine learning to predict the response of the microbiome to specific conditions and to determine the best sequence of interventions to obtain a desired microbiome effect. We will use in vitro culture of stool samples in presence of three trace minerals as a model to develop new methods to make possible targeted microbiome modulation. The use of machine learning to model the response of microbiomes to their chemical environment will permit a deeper understanding of the interplay between microorganisms and their environment. Overall, our research program will provide methods to modulate microbiomes that will be applicable to many fields, including biotechnology and personalized nutrition for both humans and animals. This project will also provide specific guidelines to design studies to optimize microbial communities using machine learning.