Development of a machine learning pipeline for assisting strain design of nonmodel yeasts

一些酵母菌株产生相对大量的油和脂肪。这些油和脂肪具有与许多药物和化学品相似的结构。这些菌株通常也能够具有高生长速率，并且可以使用废弃植物生物质来生长。这些酵母也可以进行基因工程改造，因此它们可以成为低成本生物制造过程的平台。在此之前，必须揭示其代谢机制的更多细节。该项目的总体目标是开发一种模型，可以预测使用转基因酵母的生物制造过程中的产品产量。该项目还包括高中和本科生的暑期研究计划。其中一个重点将涉及与林肯大学，一个HBCU的伙伴关系。这一伙伴关系将有助于培养一支在新兴生物经济的关键方面受过良好培训的多元化劳动力队伍：人工智能，生物信息学和合成生物学。合成生物学工具可以设计微生物以生产许多目标产品。需要多个设计-构建-测试-学习（DBTL）循环来解决由途径工程和胁迫培养条件引起的瓶颈。然而，DBTL的有效性往往在初始周期后下降，并且菌株开发可能会陷入“内卷”而没有进一步的技术突破。为了克服这一障碍，华盛顿大学/RPI团队将与桑迪亚国家实验室和太平洋西北国家实验室合作，开发一种人工智能增强的生物制造路线。首先，他们将对产油酵母文献和ABF数据库进行知识挖掘。提取的信息（包括菌株工程、发酵条件和生产指标）将被转换为结构化数据库。该数据库将用于训练机器学习（ML）模型，以预测各种生物反应器条件下工程构建体的生产率。然后，该团队将整合ML和计算菌株设计模型，以指导用于生物燃料的酵母菌株开发（例如，丁醇）和天然产物（例如，类黄酮）合成。基于模型预测，RPI团队和国家实验室将进行新型酶工程、启动子调整和CRISPRi途径修饰，以提高酵母发酵效价。实验测试将验证并进一步提高模型的适用性。该项目的目标有四个方面：（1）制定从不同来源提取和标准化生物制造信息的规则;（2）推进人工智能技术（如元学习和集成学习）用于预测非模型酵母发酵结果;（3）整合基因组规模建模、计算菌株设计和ML以改进复杂生物反应器条件下的菌株设计，以最小化DBTL循环，并降低实验性试验的成本;以及（4）工程化三种非模式酵母物种，用于可持续原料的生物生产。该项目的成功将极大地促进实验室菌株转化为工业生产者。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Some yeast strains produce relatively large amounts of oils and fats. These oils and fats have similar structures to those for many pharmaceuticals and chemicals. These strains are often also capable of high growth rates and can be grown using waste plant biomass. These yeasts can also be genetically engineered, so they could be a platform for low-cost biomanufacturing processes. Before that can be achieved, additional details of their metabolic mechanisms must be uncovered. The overall objective of this project is to develop a model that can predict the yield of product from biomanufacturing processes using genetically modified yeasts. This project also includes summer research programs for high school and undergraduate students. One point of emphasis will involve a partnership with Lincoln University, an HBCU. This partnership will help to develop a diverse workforce well-trained in key aspects of the emerging bioeconomy: artificial intelligence, bioinformatics, and synthetic biology.Synthetic biology tools can engineer microbes to produce many target products. Multiple Design-Build-Test-Learn (DBTL) cycles are required to resolve bottlenecks that resulted from both pathway engineering and stressed cultivation conditions. However, the effectiveness of DBTL often drops after initial cycles and strain development may fall into “involutions” without further technology breakthroughs. To overcome this hurdle, the Washington University/RPI team will work with Sandia National Lab and Pacific Northwest National Lab to develop an AI-enhanced biomanufacturing route. Firstly, they will perform knowledge mining of oleaginous yeast literature and the ABF data repository. The extracted information (including strain engineering, fermentation conditions, and production metrics) will be converted into a structured database. The database will be used to train machine learning (ML) models to predict productivity from engineered constructs under various bioreactor conditions. Then, the team will integrate ML and computational strain design models to guide yeast strain development for biofuels (e.g., butanol) and natural products (e.g., flavonoid) synthesis. Based on model predictions, the RPI team and the national labs will perform novel enzyme engineering, promoter tuning, and CRISPRi pathway modification to improve yeast fermentation titers. The experimental tests will validate and further improve model applicability. The objectives of this project are four fold: (1) Develop rules that extract and standardize biomanufacturing information from different sources; (2) Advance AI technology (such as meta-learning and ensemble learning) for the prediction of non-model yeast fermentation outcomes; (3) Integrate genome scale modeling, computational strain design, and ML to improve strain design under complex bioreactor conditions, to minimize DBTL cycles, and to reduce the cost for experimental trials; and (4) Engineer three non-model yeast species for bioproduction with sustainable feedstock. The successes in this project could greatly facilitate the translation of laboratory strains into industrial producers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.