Graph learning methods for engieering mammalian promoters in bioproduction

mRNA COVID-19疫苗有效地防止了大流行期间的住院和死亡。在哺乳动物细胞系中可以实现有价值的疫苗和生物治疗剂的生物生产，但难以开发稳健的、可预测的和可持续的表达。因此，设计增强的启动子和基因电路是一个关键的战略工业目标。该项目旨在探索大规模转录组数据集的结构特性，并通过图形表示学习来优化哺乳动物细胞中的工程启动子。将使用伦敦生物基金会提供的自动DNA组装和分析在哺乳动物细胞系中进行演示。作为该项目的一部分，将使用多个公开可用的转录组数据库，包括EPD [2]，DEE 2统一转录组数据库[7]和SRA [5]来构建异质图，其中每个节点代表一组DNA序列（即启动子和基因）;边缘类型由两个节点共享的生物背景决定。初始节点表示可以利用内部的DNA序列结构来生成，构建异构网络有两个目标：首先，我们将使用图神经网络（GNNs）[3，4，8]来学习网络中每个节点的表示，它将节点映射到嵌入空间以反映图结构。通过这种方式，细胞和环境背景将被嵌入到学习的节点表示中;然后我们可以使用得到的节点嵌入来预测启动子活性（即基因表达的速度和数量）。启动子活性预测任务可以与表征学习过程整合，也可以使用单独的预测因子来解决。该项目的第二阶段将结合联合收割机的实验结果，以迭代的方式改进GNN。训练的模型将选择50-100个在一系列条件下具有功能并且具有期望性能的启动子作为合成启动子设计的起点。在与伦敦生物基金会的合作中，将在瞬时转染中测试它们的特性，以消除由于拷贝数和物理环境的差异而导致的行为变化。具有验证行为的启动子将被GNNs算法用作模块化构建块，以构建一组具有设计行为的50个合成启动子。构建性能将与GNNs生成的预测结果进行比较，并用于改进模型性能。总体而言，该项目将利用GNNs的表达能力和大规模转录组数据集来开发一个广泛适用的基因表达问题分析框架，并应用于预测上下文启动子行为和工程新的哺乳动物启动子。此外，可以通过应用符号回归[1]和GNN-explainer [6]来实现学习模型的可解释性。

mRNA COVID-19 vaccines have effectively prevented hospitalization and deathduring the pandamic. Bioproduction of valuable vaccines and biotherapeuticsin mammalian cell lines can be achieved, but it is difficult to develop robust,predictable, and sustainable expression. The design of enhanced mammalianpromoters and genetic circuits is therefore a key strategic industrial target. Thisproject aims to explore the structural properties of large-scale transcriptomicdatasets with graph representation learning to optimise engineered promoters inmammalian cells. Demonstration will be performed in mammalian cell lines us-ing automated DNA assembly and analytics available at the London Biofoundry.As part of this project, multiple publicly available transcriptomic datasetswill be used, including the EPD [2], DEE2 uniform transcriptomic database[7], and SRA [5] for constructing heterogeneous graphs, in which each noderepresents a set of DNA sequences (i.e. promoters and genes); edge types aredetermined by the biological context shared by two nodes. The initial noderepresentation may be generated using the internal DNA sequence structure.With constructed heterogeneous network, there are two objectives: Firstly,we will use Graph neural networks (GNNs) [3, 4, 8] to learn the representationof each node in the network, which maps the nodes into embedding space toreflect the graph structure. In this way, the cellular and environmental con-text will be embedded into the learnt node representations; and then we canuse the resulting node embedding to predict the promoter activity (i.e. thespeed and quantity of gene expression). Promoter activity prediction task maybe integrated with the representation learning process or solved using separatepredictors. The second stage of the project will combine results from exper-iments to iteratively refine the GNNs. The trained model will select a baseset of 50-100 promoters functional over a range of conditions and with desiredperformance to be used as starting point for synthetic promoter design. In col-laboration with the London Biofoundry, their characteristics will be tested intransient transfection to eliminate changes in behaviour due to differences incopy number and physical context. Promoters with verified behaviour will beused by the GNNs algorithm as modular building blocks to build a set of 50synthetic promoters with designed behaviour. Construct performance will becompared against predictions generated by the GNNs and used to refine modelperformance.Overall, the project will leverage the expression power of GNNs and thelarge-scale transcriptomic datasets to develop a broadly applicable frameworkfor gene expression problem analysis, with the application in predicting con-textual promoter behaviours and engineering new mammalian promoters. Inaddition, the interpretability of learnt model can be achieved by applying sym-bolic regression [1] and GNN-explainer [6].