Machine Learning Algorithms for Actionable Knowledge Discovery in Synthetic Biology

合成生物学应用工程原理来设计自然界中不存在的生物系统，以便在特定的有机体内实现所需的特性。这种方法具有很大的社会价值，因为它可以用于生产高价值的材料，如精细化学品、药品、生物修复、生物燃料等。然而，无法预测生物系统的行为在很大程度上阻碍了生物工程应用的进展。虽然领域知识无法预测基因类型变化对表型的影响，但机器学习技术的发展和组学技术产生的大量数据使这一点成为可能。因此，该项目设想了创新的计算方法，以发现可操作的知识，这些知识可以提供给合成生物学实验并在工业上开发。它的好处有两个：(1)从组学信息中推断出有意义的生物学发现。(2)能够从高通量数据集中提取高级信息的新型机器学习模型。更具体地说，主要的生物任务是识别特定生物状态的生物标记物，通常与风险相关，并构建生物网络，其节点表示基因、蛋白质、代谢物和表示复杂关系的边，这些关系可以是功能上的或调节的。例如，这个项目的第一部分是研究细菌是如何调整它们的转运能力以适应不同的环境刺激的。在合成生物学中，细菌经常被用作设计遗传电路的有机体。这一点非常重要，因为细菌在不同的条件下生长时几乎总是经历各种压力，这可能会影响它们自己的生长以及所需的特性。据我们所知，以前的研究没有表征支持有机体在不同条件下可能表现出的不同生物状态的基因变化，也没有探索过缓解压力的补偿性遗传电路。这项研究将了解细菌在各种条件下生长的表型格局，识别对一般应激条件做出反应的基因(即生物标记物)，通过观察这些生物标记物的基因表达(即基因指纹)来预测细胞状态，并最终回答细菌如何以生物网络的形式调整其转录产物以适应不同条件的问题。这项工作可以扩展到任何类似的问题，用于不同的目的，而相同的例程集可以被复制。将设计一种数据挖掘技术的自动流水线，以从高噪声、高维组学数据中提取所需的信息。作为一种完全数据驱动的研究，作为对领域知识的详细机械理解的补充，统计测试和无监督学习算法(如差分表达式分析、降维和聚类方法)将首先用于有效地处理数据维度和提取感兴趣的数据模式，然后在此基础上进行监督学习。生物标志物的识别将通过设计嵌入分类器的特征选择方法来实现，这些方法对小样本数据具有健壮性。虽然生物网络可以更灵活，但研究最广泛的是联合网络，在这种网络中，实体只知道以某种方式在功能上联系在一起。我们的目标是通过利用机器学习模型的结构和各种表示来描述生物过程，最好是以概率的方式，从而超越单纯的关联到因果关系。

Synthetic biology applies engineering principles to design biological systems that do not exist in the natural world so as to achieve desired properties within a given organism. This approach is of great value to society since it can be used to produce high-value materials, such as fine chemicals, pharmaceuticals, bio-remediation, bio-fuels, etc. However, the inability to predict the behaviour of biological systems largely hinders progress in bioengineering applications. While domain knowledge fails to predict the effect of genotypes changes on phenotype, the development of machine learning techniques and tremendous amounts of data generated by omics technologies have made this possible. This project thus envisions innovative computational methods to discover actionable knowledge that can be fed into synthetic biology experiments and exploit in industry. The benefits are two-folds: (1) Meaningful biological findings deduced from omics information. (2) Novel machine learning model capable of extracting high-level information from high throughput dataset.More specifically, the main biological tasks are identifying biomarkers for a particular biological state, typically related to the risk, and constructing the biological network whose nodes representing gene, proteins, metabolites and edges indicating complex relations which can be functional or regulatory. For example, the first part of this project is to look at how bacteria, which are often used as the organism to design genetic circuits in synthetic biology, adjust their transciptomics to adapt to different environmental stimuli. This is very important as bacteria almost always experience a diverse range of stresses while growing in different conditions which may affect their own growth as well as the desired properties. To our knowledge no previous research has characterised genetic changes underpinning different biological states an organism may exhibit in various conditions, nor compensatory genetic circuit to relieve the stresses has been explored. This research will learn the phenotypical landscape of bacteria growing in various conditions, identify the genes responding to general stress conditions (i.e. the biomarkers), predict the cell state by looking at the gene expressions of these biomarkers (i.e. the gene fingerprint) and ultimately answer the question of how bacteria adjust their transcriptomics to adapt to different conditions in the form of a biological network. This work can be extended to any similar questions for different purpose while the same set of routines may be replicated.An automatic pipeline of data mining techniques will be designed to extract desired information from heavy noise, high dimension omics data. As a totally data-driven research to complement with detailed mechanistic understanding in domain knowledge, statistical tests and unsupervised learning algorithms such as differential expression analysis, dimension reduction and clustering methods will first be applied to effectively tackling the data dimensionality and extract interesting data patterns, based on which supervised learnings are followed. Biomarker identification will be achieved by devising feature selection methods embedded with classier that are robust to small sample size data. While biological networks can be much more flexible, the most widely studied ones are association networks where entities are only known to be functionally connected in some way. We aim to go beyond the mere association to causation by exploiting the structure and various representations of machine learning models being used to describe the biological processes, preferably in a probabilistic way.