Biotechnology Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran.
Department of Bioengineering, University of California, San Diego, California, USA.
Biotechnol J. 2021 Nov;16(11):e2100212. doi: 10.1002/biot.202100212. Epub 2021 Sep 2.
Recent noteworthy advances in developing high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge- and data-driven modeling methods have received much attention. Constraint-based modeling (CBM) is a knowledge-driven mathematical approach that has been widely used in fermentation analysis and optimization due to its ability to predict the cellular phenotype from genotype through high-throughput means. On the other hand, machine learning (ML) is a data-driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint-based models in a reciprocal manner when one is used as a pre-step of another. As a result, a more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters.
近年来，在开发高性能微生物和哺乳动物菌株方面的显著进展，使可持续生产具有生物经济价值的物质成为可能，如生物化合物、生物燃料和生物制药。然而，要获得工业可行的大规模生产方案，需要花费大量的时间和精力。发酵过程的稳健和合理设计需要分析和优化不同的细胞外条件和培养基成分，这些条件和成分对生长和生产力有巨大的影响。在这方面，基于知识和数据的建模方法受到了广泛关注。约束基建模（CBM）是一种知识驱动的数学方法，由于其能够通过高通量手段从基因型预测细胞表型，因此已广泛应用于发酵分析和优化中。另一方面，机器学习（ML）是一种数据驱动的统计方法，用于识别复杂生物系统和过程中的数据模式，其中缺乏表示潜在机制的知识。此外，当一种方法用作另一种方法的前序步骤时，ML 模型正在成为约束基模型的可行补充。因此，产生了更可预测的模型。本综述重点介绍了 CBM 和 ML 的独立应用，以及这两种方法的组合在分析和优化发酵参数方面的应用。
