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使设计基因组的工程和生产在应用中更有效的能力。为了实现这一点，特别需要更好的细胞数学模型。这些模型被称为全细胞计算模型（WCM），其目的是设计它们，以便考虑所有基因和分子的功能。应对这一挑战将大大有助于更好地了解不同的疾病和创造个性化的治疗方法。WCM将使我们能够直接从基因型预测表型，从而研究暴露于不同环境因素后细胞的变化。coli [1]。该模型由斯坦福大学Covert实验室的成员设计，尽管代表了细胞工程和设计的突破，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。如果没有包括细胞所有功能的WCM，就很难准确预测抗生素或其他外部刺激存在下的生长，也很难模拟未实施的基因的基因敲除。该项目试图通过实施基于人工智能（AI）技术创建和改进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、随机建模和几何分析等不同技术（如当前的WCM）的模型。此外，该项目的另一个重要目标是解开细胞基因型和表型之间的联系。无监督和深度学习等技术的结合将使我们能够回答诸如“多个基因敲除/敲入如何影响细胞的生长和代谢？”，对于已经被建模的基因。社区在设计WCM时面临的另一个巨大挑战是根据实验数据验证数学模型的能力。该项目将开始通过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，为生物过程提供方便的实验数据，例如RNA表达。系统/合成生物学和人工智能的交叉是一个新的研究领域，具有很大的潜力。WCM for bacteria E.coli项目对于开始探索人工智能在系统生物学中的应用特别有趣，因为在过去的几十年里从社区收集了大量的数据。该项目福尔斯EPSRC合成生物学研究领域。该项目的合作者是：Lucia Marucci博士（主要主管），教授克莱尔格里尔森（主要主管），托马斯戈罗霍夫斯基博士（联席导师、皇家学会大学生物科学学院研究员）庞伟博士（共同导师，赫瑞瓦特大学计算机科学副教授和阿伯丁大学荣誉高级讲师）和来自斯坦福大学的Covert实验室小组。[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 &吉利斯，Taryn & Paull，Morgan & Akhter，Sajia & Bray，Samuel & Weaver，丹尼尔& Keseler，Ingrid & Karp，Peter & Covert，Markus.（2020年）。同时交叉评价异质性E。大肠杆菌数据集通过机械模拟。Science（纽约，N.Y.）。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