Modelling cellular stress during recombinant protein production for improving upstream biomanufacturing processes

重组蛋白是由生物体非内源性表达的，在生物技术中有着广泛的应用，从工业和农业相关的酶的生物制造到治疗和生物制品的制造。由于这种商业利益，宿主细胞面临着获得高质量蛋白质产品的压力。然而，所有的生物系统都是为了自身的生存而优化的，而不是为了生产异源实体而优化的，因此，异源蛋白的表达过程，特别是在生产过剩的状态下，给细胞造成了巨大的负担。在高产量需求的压力下，宿主很难获得高质量的产品，即正确加工的蛋白质。这种压力会触发宿主内部的反应，以应对低质量产品的积累，因此也就是不受欢迎的产品。这些反应从细菌等原核宿主形成包涵体，到酵母和哺乳动物细胞等真核宿主诱导未折叠蛋白反应。这项研究将促进这种应激反应的定量模型的开发，并将这些模型用于改进和优化重组蛋白生产的上游生物制造过程。在这一领域内，这项研究有望为该行业配备新的研究方法和工具，这将帮助他们减少细胞和工艺开发的时间，并帮助降低产品开发的成本和风险。惠益与生物制药和生物催化活动直接相关，它们是可持续发展的重要贡献者，因此该项目的成果将造福于整个社会。该项目提供给相关行业的知识和工具也将加强它们在全球市场上的地位，使它们在各自的业务领域保持竞争力。本研究的目的是通过一个模型框架，正式了解哺乳动物和微生物的应激机制，这些应激机制对重组蛋白的生产能力产生不利影响。它与上游生物制造的数据驱动模型的结合将扩大模型驱动分析的预测能力。为了实现这一目标，将实现以下目标：-填充与上述应激机制有关的现有数值和描述性数据的知识库-发展对现有技术的结构化理解并识别在制定工作模型时造成瓶颈的缺失信息-关于该机制的结构(即拓扑)的假设生成以转换为正式描述-响应异源蛋白质生产的结构化细胞应激模型的构建-模型实施(数学+计算框架)-测试和改进模型的预测/描述能力(通过模拟或编程)-纳入在现有管道中建立模型以改进目前的做法将根据知识库中积累的数据的类型和性质，根据需要探索处理和建模数据的新方法。预计将不得不采用随机模型和复杂的参数估计技术。这项研究项目符合EPSRC的医疗保健技术和未来制造业的研究主题，并与以下研究领域相一致：生物信息学、制造技术、数学生物学和运筹学。它也与EPSRC目前在数字制造和可持续产业方面的研究重点保持一致。目前没有公司或合作者参与该项目，如果未来有任何合作，此信息将被更新

Recombinant proteins are expressed non-endogenously by an organism, and they have wide applications in biotechnology ranging from the biomanufacture of industrially and agriculturally relevant enzymes to therapeutics and the manufacture of biologics. Due to this commercial interest, there is pressure to achieve high yields of high quality protein product by the host cells. However, all biological systems are optimised for their own survival, and not for the production of a heterologous entity, and therefore, the process of heterologous protein expression, particularly in a state of overproduction, creates a huge burden on the cell. The host has difficulty to achieve a high quality product, i.e. a correctly processed protein, under the stress imposed by high production demand. This stress triggers responses within the host to cope with the accumulation of the low quality, and therefore undesirable, product. These responses range from the formation of inclusion bodies by prokaryotic hosts such as bacteria, to the induction of unfolded protein response by eukaryotic hosts such as yeast and mammalian cells. This research will facilitate the development of quantitative models of this stress response and the utilisation of these models in the improvement and optimisation of upstream biomanufacturing processes of recombinant protein production. Within this domain, the research is expected to equip the industry with new research methods and tools, which will help them reduce time in cell and process development and assist the alleviation of costs and risks of product development. The benefits are directly associated with biopharmaceutical and biocatalysis activities, substantial contributors of sustainability, thus the outcomes of the project will benefit the society as a whole. The knowledge and the tools resulting from the project being offered to the service of relevant industries will also strengthen their position in the global markets allowing them to remain competitive in their respective fields of operation.The aim of this research is to develop a formal understanding mammalian and microbial stress mechanisms that have an adverse impact on recombinant protein production capacity via a modelling framework. Its incorporation with data-driven models of upstream biomanufacturing will expand the predictive capability of model-driven analysis. In order to achieve this goal, the following objectives will be met:- Population of a knowledge-base of existing numerical and descriptive data pertaining the stress mechanisms described above- Development of a structured understanding of the existing know-how and identification of missing information creating bottlenecks in formulating a working model- Hypothesis generation on the structure (i.e. the topology) of this mechanism to translate into a formal description- Construction of a structured model of cellular stress in response to heterologous protein production- Model implementation (mathematical + computational framework)- Testing and improving the predictive/descriptive capacity of the model (via simulations or programming) - Incorporation of the model into existing pipelines to improve current practiceNovel approaches in handling and modelling the data will be explored as needed based on the type and the nature of the data accumulating in the knowledge-base. It is expected that stochastic models and elaborate parameter estimation techniques will have to be employed.This research project aligns with EPSRC's research themes of healthcare technologies and manufacturing the future, and with the following research areas: Biological informatics, manufacturing technologies, mathematical biology, and operational research. It also aligns with EPSRC's current research priorities in Digital Manufacturing and Sustainable Industries.No companies or collaborators are currently involved in the project, in the event of any future collaborations, this information will be updated