Optimization of Rifamycin B fermentation in shake flasks via a machine-learning-based approach

Rifamycin B is an important polyketide antibiotic used in the treatment of tuberculosis and leprosy. We present results on medium optimization for Rifamycin B production via a barbital insensitive mutant strain of Amycolatopsis mediterranei S699. Machine-learning approaches such as Genetic algorithm (GA), Neighborhood analysis (NA) and Decision Tree technique (DT) were explored for optimizing the medium composition. Genetic algorithm was applied as a global search algorithm while NA was used for a guided local search and to develop medium predictors. The fermentation medium for Rifamycin B consisted of nine components. A large number of distinct medium compositions Eire possible by variation of concentration of each component. This presents a large combinatorial search space. Optimization was achieved within five generations via GA as well as NA. These five generations consisted of 178 shake-flask experiments, which is a small fraction of the search space. We detected multiple optima in the form of 11 distinct medium combinations. These medium combinations provided over 600% improvement in Rifamycin B productivity. Genetic algorithm performed better in optimizing fermentation medium as compared to NA. The Decision Tree technique revealed the media-media interactions qualitatively in the form of sets of rules for medium composition that give high as well as low productivity. (C) 2004

Role of extracellular protease in nitrogen substrate management during antibiotic fermentation: a process model and experimental validation

Kinetics of extracellular protease (ECP) production has typically been studied for processes that involve protease as a product. We argue that ECP is equally important in fermentations where protease is not a product of interest. Industrial fermentations typically use complex nitrogen substrates, which are proteolytically hydrolyzed to amino acids (AA) by ECP before assimilation. However, high AA concentrations may lead to nitrogen catabolite repression (NCR) of the products such as antibiotics. Thus, ECP plays a crucial role in managing the nitrogen substrate supply thereby affecting the antibiotic productivity. Here, we have studied the induction of ECP and its effect on the antibiotic productivity for a rifamycin B overproducer strain Amycolatopsis meditterranei S699. This organism produces ECP at the level of 14 U mL(-1) in complex media, which is sufficient for hydrolysis of proteins in the media but low compared to other ECP overproducers. We find ECP secretion to be repressed by ammonia, AA, and under conditions that support high growth rate. We propose a structured kinetic model which accounts for the kinetics of ECP secretion, amino acid availability, growth, and antibiotic production. In addition to the quantity, the timing of ECP induction was critical in achieving higher rifamycin productivity. We artificially created conditions that led to delayed protease secretion, which in turn led to premature termination of batch and lower productivity. The predictive value of the model can be useful in better management of the available nitrogen supply, minimization of NCR, and in the monitoring of fermentation batches

Removal of migrated metallic prostatic stent by holmium laser

A 90-year-old male with prostatic hyperplasia with a history of ischemic heart disease and right-sided hemiplegia had undergone a Urolume stent placement because of acute urinary retention 9 months earliar. The stent had migrated into the bladder causing dysuria and a poor stream of urine. We fragmented the prostatic stent by Holmium (HO: YAG) laser followed by a laser prostatectomy. After the procedure, the patient voided satisfactorily

Model-based optimization of feeding recipe for rifamycin fermentation

Industrial fermentation processes typically use complex media and operate in fed-batch mode to minimize the effects of catabolite repression. However, model-based feeding recipes have not been reported for such processes primarily as a result of the lack of reliable process models. By using a recently published process model, we optimize the feeding recipe for rifamycin B fermentation in complex media. Experimental validation shows a twofold improvement in productivity over an optimized batch. The dynamic optimization problem was posed as a nonlinear program and solved using successive quadratic programming. The feed profiles of four substrates were parameterized to convert the problem into a finite decision space consisting of substrate feed rates and switching intervals. Several distinct recipes, each corresponding to a unique initial guess of decision variables, showed comparable productivity, implying the presence of multiple local optima. The strategy presented here can be applied for optimization of other fermentation processes for which reliable process models are available. (c) 2006 American Institute of Chemical Engineers