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，它们在生物技术中有着广泛的应用，从工业和农业相关酶的生物制造到治疗学和生物制剂的制造。由于这种商业利益，有压力实现高产量的高质量的蛋白质产品的宿主细胞。然而，所有的生物系统都是为了自身的生存而优化的，而不是为了产生异源实体，因此，异源蛋白表达的过程，特别是在生产过剩的状态下，给细胞带来了巨大的负担。在高生产需求的压力下，宿主难以获得高质量的产品，即正确加工的蛋白质。这种压力触发宿主内部的反应，以应对低质量的积累，因此不受欢迎的产品。这些反应的范围从原核宿主如细菌形成包涵体到真核宿主如酵母和哺乳动物细胞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发展，并利用这些模型来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，这项研究有望为行业提供新的研究方法和工具，这将有助于他们减少细胞和工艺开发的时间，并有助于降低产品开发的成本和风险。这些好处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，因此项目的成果将使整个社会受益。为相关行业提供服务的项目所产生的知识和工具也将加强他们在全球市场上的地位，使他们在各自的业务领域保持竞争力。本研究的目的是通过建模框架建立对重组蛋白生产能力产生不利影响的哺乳动物和微生物应激机制的正式理解。它与上游生物制造的数据驱动模型相结合，将扩展模型驱动分析的预测能力。为了实现这一目标，以下目标将得到满足：-建立与上述应力机制相关的现有数值和描述性数据的知识库-对现有技术诀窍进行结构化理解，并识别在制定工作模型时产生瓶颈的缺失信息-对该机制的结构（即拓扑结构）进行假设生成，并将其转化为正式描述-构建细胞应力的结构化模型对异源蛋白生产的响应-模型实现（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）-将模型纳入现有管道以改进当前的实践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详细的参数估计技术。该研究项目与EPSRC的医疗保健技术和未来制造业的研究主题以及以下研究领域保持一致：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工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