Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India.
Biotechnol Bioeng. 2010 Jan 1;105(1):109-20. doi: 10.1002/bit.22505.
Actinomycetes, the soil borne bacteria which exhibit filamentous growth, are known for their ability to produce a variety of secondary metabolites including antibiotics. Industrial scale production of such antibiotics is typically carried out in a multi-substrate medium where the product formation may experience catabolite repression by one or more of the substrates. Availability of reliable process models is a key bottleneck in optimization of such processes. Here we present a structured kinetic model to describe the growth, substrate uptake and product formation for the glycopeptide antibiotic producer strain Amycolatopsis balhimycina DSM5908. The model is based on the premise that the organism is an optimal strategist and that the various metabolic pathways are regulated via key rate limiting enzymes. Further, the model accounts for substrate inhibition and catabolite repression. The model is also able to predict key phenomena such as simultaneous uptake of glucose and glycerol but with different specific uptake rates, and inhibition of glycopeptide production by high intracellular phosphate levels. The model is successfully applied to both production and seed medium with varying compositions and hence has good predictive ability over a variety of operating conditions. The model parameters are estimated via a well-designed experimental plan. Adequacy of the proposed model was established via checking the model sensitivity to its parameters and confidence interval calculations. The model may have applications in optimizing seed transfer, medium composition, and feeding strategy for maximizing production.
放线菌是一种具有丝状生长特征的土壤细菌,以能够产生多种次级代谢产物而闻名,包括抗生素。此类抗生素的工业规模生产通常在多底物培养基中进行,其中产物形成可能会受到一种或多种底物的分解代谢物抑制。可靠的过程模型的可用性是此类过程优化的关键瓶颈。在这里,我们提出了一个结构化的动力学模型来描述糖肽抗生素产生菌株 Amycolatopsis balhimycina DSM5908 的生长、底物摄取和产物形成。该模型基于以下前提:该生物体是一个最优策略者,并且各种代谢途径通过关键限速酶进行调节。此外,该模型考虑了底物抑制和分解代谢物抑制。该模型还能够预测关键现象,如同时摄取葡萄糖和甘油,但具有不同的特定摄取率,以及高细胞内磷酸盐水平抑制糖肽的产生。该模型成功地应用于具有不同组成的生产和种子培养基,因此在各种操作条件下具有良好的预测能力。通过精心设计的实验计划来估计模型参数。通过检查模型对其参数的敏感性和置信区间计算来确定所提出模型的充分性。该模型可用于优化种子转移、培养基组成和进料策略,以实现最大产量。