Biology Reference
In-Depth Information
The fluorescence signals were analyzed with ABI PRISM 3100 genetic analyzer and
the obtained sequences compared with P. putida KT2440 genome sequence.
The pseudomonads include a diverse set of bacteria whose metabolic versatility and
genetic plasticity have enabled their survival in a broad range of environments. Many
members of this family are able to either degrade toxic compounds or to efficiently
produce high value compounds and are therefore of interest for both bioremediation
and bulk chemical production. To better understand the growth and metabolism of
these bacteria, we developed a large-scale mathematical model of the metabolism of
Pseudomonas putida , a representative of the industrially relevant pseudomonads. The
model was initially expanded and validated with substrate utilization data and carbon-
tracking data. Next, the model was used to identify key features of metabolism such as
growth yield, internal distribution of resources, and network robustness. We then used
the model to predict novel strategies for the production of precursors for bioplastics of
medical and industrial relevance. Such an integrated computational and experimental
approach can be used to study its metabolism and to explore the potential of other
industrially and environmentally important microorganisms.
Constraint-based model
Flux balance analysis
Flux variability analysis
Gene-protein-reaction relationship
Genotype-phenotype relationships
Pseudomonas putida
Conceived and designed the experiments: Jacek Puchałka, Matthew A. Oberhardt,
Kenneth N. Timmis, Jason A. Papin, and Vítor A. P. Martins dos Santos. Analyzed the
data: Jacek Puchałka and Matthew A. Oberhardt. Wrote the chapter: Jacek Puchałka,
Matthew A. Oberhardt, Jason A. Papin, and Vítor A. P. Martins dos Santos. Performed
the computational experiments: Jacek Puchałka. Developed the computational plat-
form: Miguel Godinho. Characterized the mutants and carried out wet lab experi-
ments: Agata Bielecka. Produced the mutants: Daniela Regenhardt. Contributed to
data interpretation: Kenneth N. Timmis.
We thank Victor de Lorenzo (CSIC, Madrid) and Antoine Danchin (Institute Pasteur,
Paris) for their thoughtful comments and valuable contributions to this study. We
thank Piotr Bielecki (HZI, Braunschweig) for the help in planning and experimental
Search WWH ::

Custom Search