These authors contributed equally to this work.
Mathematical models of cell factories: moving towards the core of industrial biotechnology
Version of Record online: 8 DEC 2010
© 2010 The Authors. Journal compilation © 2010 Society for Applied Microbiology and Blackwell Publishing Ltd
Volume 4, Issue 5, pages 572–584, September 2011
How to Cite
Cvijovic, M., Bordel, S. and Nielsen, J. (2011), Mathematical models of cell factories: moving towards the core of industrial biotechnology. Microbial Biotechnology, 4: 572–584. doi: 10.1111/j.1751-7915.2010.00233.x
- Issue online: 17 AUG 2011
- Version of Record online: 8 DEC 2010
- Received 2 September, 2010; accepted 11 October, 2010.
Fig. S1. Flux control coefficients of the reactions in the system on the glucose uptake by phosphotransferase system.
Fig. S2. Schematic representation of pyruvate branch in L. lactis.
Fig. S3. The OptKnock algorithm found four deletions to improve the lactic acid production in E. coli.
Fig. S4. Flow chart of an evolutionary algorithm. OptGene defines a population with a fixed number of individuals. Each individual is characterized by a ‘chromosome’, which is a list of genes labelled with ones if they are present or zeros if they are absent. The algorithm can be initialized by assigning present status to all genes or by assigning present or absent status randomly. Each individual in the population is assigned a fitness score that determines whether it will be propagated to the next generation. The fitness score is calculated using FBA, MOMA or other optimization criteria. The individuals to be propagated to the next generation are selected with a probability determined from the fitness score. The ‘chromosomes’ of the selected individuals are crossed over to generate the next population and the next iteration starts. The iterations are repeated until an individual with the desired phenotype appears.
Fig. S5. Deletions coupling succinate production to growth. After deleting the genes ser3 and ser33, the cell is forced to synthesize the serine necessary for growth from glyoxylate. Glyoxylate itself is obtained from isocitrate, generating succinate as a by-product. The deletion of sdh3 avoids the degradation of succinate, which has to be secreted from the cell. The first-generation mutant grows very slowly and needs external serine supply, but after evolution in a medium with decreasing serine concentration the cells become adapted to produce serine via glyoxilate-secreting succinate as a by-product.
Table S1. Overview of available genome-scale metabolic models of some of the industrially most exploited organisms.
Table S2. Flux control coefficients predicted by the model through acetolactate synthesis (ALS) in Lactococcus lactis.
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