Whole cell modelling for bacteria E.coli

系统和合成生物学领域目前面临的最大挑战之一是提高跨应用程序设计和生产设计基因组的能力。为了实现这一点，特别需要更好的细胞数学模型。这些模型被命名为全细胞计算模型(WCMS)，其目的是设计它们以使所有基因和分子的功能都被考虑在内。应对这一挑战将极大地有助于更好地了解不同的疾病和创造个性化的治疗方法。WCMS将使我们能够直接从基因型别预测表型，从而研究细胞在暴露于不同环境因素后的变化。这个项目将专注于改进最近开发的针对大肠杆菌的WCM[1]。这一模型是由斯坦福大学Covert实验室的成员设计的，尽管代表着细胞工程和设计方面的突破，但仍有一些挑战需要解决。目前，这个模型只考虑了细胞中一半的基因。如果没有一个包括细胞所有功能的WCM，就很难准确预测在抗生素或其他外部刺激存在的情况下的生长，也很难模拟没有实施的基因敲除。该项目试图通过实施基于人工智能(AI)技术的创建和改进WCM的新方法来解决这一挑战。与数学模型相比，这些模型的优点是更容易建立原型，特别是结合了常微分方程组、随机建模和几何分析等不同技术的模型(如当前的WCMS)。此外，该项目的另一个重要目标是解开基因和细胞表型之间的联系。无监督和深度学习等技术的结合将使我们能够回答已经建模的基因的这样的问题，例如：多个基因敲除/注入如何影响细胞的生长和新陈代谢？社区在设计WCM时面临的另一个重大挑战是根据实验数据验证数学模型的能力。这个项目将通过为生物过程推导以数据为中心的子模型(例如使用粒子群优化技术)来解决这个问题，这些过程提供了容易访问实验数据的方法，例如RNA表达。系统/合成生物学和人工智能的交叉是一个具有很大潜力的新研究领域。由于在过去的几十年里从社区收集了大量的数据，对于开始探索人工智能在系统生物学中的应用来说，WCM对于细菌E.Coli项目特别感兴趣。这个项目属于EPSRC合成生物学研究领域。这个项目的合作者是：Lucia Marucci博士(主要主管)，Claire Grierson教授(主要主管)，Thomas Gorochowski博士(联合主管，皇家学会大学生物科学学院研究员)，WePang博士(联合主管，[1]来自斯坦福大学的Covert实验室小组。[1]Macklin，Derek&Ahn-Horst，Travis&Bray，Heejo&Ruggero，Nicholas&Carrera，Jille&Mason，John&Sun，Gwanggyu&Agmon，Eran&Defelice，Mialy&Maayan，Inbal&Lane，Keara&Spangler，Ryan&Gillie，Taryn&Paull，Morgan&Akhter，Sajia&Bray，Samuel&Weaver，Daniel&Keseler，Ingrid&Karp，Peter&Covert，Markus。(2020)。通过机械模拟同时交叉评估不同种类的大肠杆菌数据集。科学(纽约，纽约州)。369.10.1126/Science.aav3751

One of the greatest challenges that the fields of systems and synthetic biology are facing at the moment is the ability to make the engineering and production of designed genomes across applications more efficient. In order to achieve this, there is a particular need for better mathematical models of cells. These models have taken the name of whole-cell computational models (WCMs), and the aim is to design them such that the function of all genes and molecules are taken into account. Addressing this challenge would contribute greatly to a better understanding of different diseases and the creation of personalised treatments. WCMs will allow us to predict phenotypes directly from genotypes and therefore investigate changes in the cell after being exposed to different environmental factors.This project will focus on improving the recently developed WCM for bacteria E. coli [1]. This model was designed by the members of the Covert lab from the University of Stanford and despite representing a breakthrough in cell engineering and design, there are still some challenges that need to be addressed. Currently, this model is taking into account only half of the genes of the cell. Without a WCM that includes all functionalities of the cell, it is hard to accurately predict the growth in the presence of antibiotics or other external stimulus, and to simulate gene knockouts for genes that are not implemented. This project attempts to solve this challenge by implementing novel ways in which WCMs are created and improved, based on artificial intelligence (AI) technologies. These models have the advantage of being easier to prototype compared to a mathematical model, especially one that is combining different techniques such as ordinary differential equations, stochastic modelling and geometric analysis (like the current WCMs).In addition, another important aim of this project is to unravel the link between the genotype and the phenotype of the cells. A combination of techniques such as unsupervised and deep learning will allow us to answer questions such as 'How do multiple gene knock-out/in affect the growth and metabolism of the cell?', for the genes that have already been modelled. Another big challenge that the community is facing when designing WCM is the ability to validate the mathematical models against the experimental data. This project will start to address this issue by deriving data-centric sub-models (using for example particle swarm optimisation techniques), for the biological processes that provide easy access to experimental data, such as RNA expression.The intersection of systems/synthetic biology and AI is a new area of research that has a lot of potential. The WCM for bacteria E.coli project is particularly interesting for starting to explore the applications of AI in systems biology because of the large amount of the data that has been gathered from the community during the past decades.This project falls within the EPSRC Synthetic Biology research area.The collaborators of this project are: Dr. Lucia Marucci (main supervisor), Prof. Claire Grierson (main supervisor), Dr. Thomas Gorochowski (co-supervisor, Royal Society University Research Fellow in the School of Biological Sciences), Dr. Wei Pang (co-supervisor, Associate Professor in Computer Science at Heriot-Watt University and an Honorary Senior Lecturer at Aberdeen University) and the Covert lab group from the University of Stanford.[1] Macklin, Derek & Ahn-Horst, Travis & Choi, Heejo & Ruggero, Nicholas & Carrera, Javier & Mason, John & Sun, Gwanggyu & Agmon, Eran & Defelice, Mialy & Maayan, Inbal & Lane, Keara & Spangler, Ryan & Gillies, Taryn & Paull, Morgan & Akhter, Sajia & Bray, Samuel & Weaver, Daniel & Keseler, Ingrid & Karp, Peter & Covert, Markus. (2020). Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation. Science (New York, N.Y.). 369. 10.1126/science.aav3751