A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: i JN746 as a cell factory
© Nogales et al; licensee BioMed Central Ltd. 2008
Received: 05 March 2008
Accepted: 16 September 2008
Published: 16 September 2008
Pseudomonas putida is the best studied pollutant degradative bacteria and is harnessed by industrial biotechnology to synthesize fine chemicals. Since the publication of P. putida KT2440's genome, some in silico analyses of its metabolic and biotechnology capacities have been published. However, global understanding of the capabilities of P. putida KT2440 requires the construction of a metabolic model that enables the integration of classical experimental data along with genomic and high-throughput data. The constraint-based reconstruction and analysis (COBRA) approach has been successfully used to build and analyze in silico genome-scale metabolic reconstructions.
We present a genome-scale reconstruction of P. putida KT2440's metabolism, i JN746, which was constructed based on genomic, biochemical, and physiological information. This manually-curated reconstruction accounts for 746 genes, 950 reactions, and 911 metabolites. i JN746 captures biotechnologically relevant pathways, including polyhydroxyalkanoate synthesis and catabolic pathways of aromatic compounds (e.g., toluene, benzoate, phenylacetate, nicotinate), not described in other metabolic reconstructions or biochemical databases. The predictive potential of i JN746 was validated using experimental data including growth performance and gene deletion studies. Furthermore, in silico growth on toluene was found to be oxygen-limited, suggesting the existence of oxygen-efficient pathways not yet annotated in P. putida's genome. Moreover, we evaluated the production efficiency of polyhydroxyalkanoates from various carbon sources and found fatty acids as the most prominent candidates, as expected.
Here we presented the first genome-scale reconstruction of P. putida, a biotechnologically interesting all-surrounder. Taken together, this work illustrates the utility of i JN746 as i) a knowledge-base, ii) a discovery tool, and iii) an engineering platform to explore P. putida's potential in bioremediation and bioplastic production.
Pseudomonas putida is a non-pathogenic member of rRNA group I of the genus Pseudomonas that colonizes many different environments and is well known for its broad metabolic versatility and genetic plasticity [1, 2]. P. putida KT2440 is a TOL plasmid cured, spontaneous restriction deficient derivative of P. putida mt-2 [3, 4]. This strain represents the first host-vector biosafety system for cloning in gram-negative soil bacteria and hence, has been extensively used as a host for gene cloning and expression of heterologous genes [5–8]. Consequently, large efforts have been made in exploiting these capacities in a diverse range of biotechnological applications including i) bioremediation of contaminated areas [9, 10]; ii) quality improvement of fossil fuels, e.g., by desulphurization ; iii) biocatalytic production of fine chemicals [9, 12–14]; iv) production of bioplastic [15–17]; and v) as agents of plant growth promotion and plant pest control [18, 19].
Since the publication of P. putida KT2440's genome , our knowledge about this strain has significantly increased  and various "-omics" data sets have become available, such as transcriptomic [22, 23], proteomic , and fluxomic data [25, 26]. Subsequently, some in silico analyses of its metabolic and biotechnological capacities have been published [27, 28]. However, systemic understanding of metabolic and biotechnology capabilities of P. putida KT2440 requires the construction of a more comprehensive model enabling the integration of the canonical experimental data along with genomic and high-throughput data in a hierarchical and coherent fashion .
The constraint-based reconstruction and analysis (COBRA) approach is one possible modeling approach that uses stoichiometric information about biochemical transformation taking place in a target organism to construct the model. While a metabolic reconstruction is unique to the target organism one can derive many different condition-specific models from a single reconstruction. This conversion of a metabolic reconstruction of an organism into models requires the imposition of physicochemical and environmental constraints to define systems boundaries [30–32]. The conversion also includes the transformation of the reaction list into a computable, mathematical matrix format. In this so-called S matrix, where S stands for stoichiometric, the rows correspond to the network metabolites and the columns to the network reactions. The coefficients of the substrates and products of each reaction are entered in the corresponding cell of the matrix. This conversion can be done automatically (e.g., using the Matlab-based COBRA toolbox ). Once in this format, numerous mathematical tools can be used to interrogate the metabolic network properties in silico. Many of the published mathematical tools have been reviewed  and encoded in Matlab format . A large subset of these tools relies on linear programming (LP), a mathematical tool used to find a solution to an optimization problem (e.g., maximal possible growth rate of my metabolic network under a given set of environmental constraints). While LP-based tools are very helpful in studying reconstructed metabolic networks, some questions may better be addressed without having to choose an objective function. Those methods are called unbiased methods, in contrast to biased LP-based methods, because they identify all feasible flux distributions under the given set of environmental constraints rather than only the optimal distributions. The COBRA approach [30, 32] has been successfully used to build and analyze genome-scale in silico reconstructions for representatives of archaea (e.g.,Methanosarcina barkeri ), of bacteria (e.g., E. coli ; B. subtilis ; H. pylori ; M. tuberculosis [39, 40]; S. aureus [41, 42]; L. lactis ), and of eukarya (e.g., Human ). The numerous mathematical tools have been used for i) identification and filling of knowledge gaps (e.g. missing gene annotations ); ii) prediction of the outcome of adaptive evolution [46–48]; iii) design of engineered production strains ; and iv) the understanding of topological features of metabolic networks [50–53]. A recent review illustrates the variety of questions that have been addressed to E. coli's metabolic network using different biased and unbiased COBRA methods .
Here, we describe a highly detailed, genome-scale, metabolic reconstruction of Pseudomonas putida KT2440. Based on the naming convention for metabolic networks , this genome scale reconstruction was deemed i JN746, where i stands for in silico, JN are the initials of the constructor, and 746 corresponds to the number of included metabolic genes. The reconstruction was built using the COBRA approach [30, 32] and validated using flux balance analysis (FBA, ). The in silico metabolic network was further evaluated by comparing i) predicted growth rate capacities in different carbon sources and ii) predicted essential genes with experimental data from P. putida KT2440 and P. aeruginosa. Finally, we show the utility of the P. putida reconstruction to analyze its biodegradative (i.e. toluene degradation) and biotechnological (i.e. bioplastic production) capacities.
Results and discussion
Characteristics of the metabolic reconstruction of Pseudomonas putida KT2440
The metabolic reconstruction of P. putida KT2440, i JN746, was constructed based on its annotated genome sequence , primary and review publications, various genetic and biochemical databases (i.e., KEGG Database , PSEUDOCYC , and SYSTOMONAS ), and biochemical information found in Pseudomonas-specific  and biochemical textbooks.
Properties of metabolic reconstruction of P. putida KT2440
Reconstruction & Organism
Protein coding genes per genome
Genes (% of genome)
Non-gene- associated network reaction (% of network reactions)
Every network reaction was associated with confidence scores based on the available evidence for its presence in the P. putida metabolic network (Figure 1B). For instance, reactions whose enzymes have been biochemically studied in P. putida received a confidence score of 4. If physiological or genetic knockout information was available, a score of 3 was associated with the network reaction. Reactions associated with enzymes that were only annotated in P. putida's genome but had no further experimental evidence were given a confidence score of 2. Finally, during the evaluation of the network functionality (i.e. biomass precursor production) some reactions had to be added to the network for which no genetic or experimental evidence could be found. Those reactions represent modeling hypotheses, which need further experimental validation and thus received a confidence score of 1. Upon completion, the reconstruction had an overall average confidence score of 2.83. In fact, two thirds of P. putida's metabolic pathways have been very well or well studied, while only a third of the subsystems were primarily based on the genome annotation (Figure 1B). This high level of confidence is also reflected by the number of references that lead to this metabolic reconstruction. Almost 90% of the internal reactions (844) have at least one associated citation, while a total of 176 unique primary and review publications were reviewed and incorporated into this reconstruction. Subsequently, this first genome-scale reconstruction of P. putida's metabolism represents a comprehensive knowledge base summarizing and categorizing the information currently available. The content of this knowledge base will be easily accessible through the BiGG database http://BiGG.ucsd.edu.
Comparison of scope and content of i JN746 with published metabolic networks
The properties of i JN746 were compared with the properties of recently published reconstructions of E. coli MG1655 (i AF1260, ), B. subtilis (i YO844 ), M. tuberculosis H37Rv (i NJ661 ), and P. aeruginosa PAO1 (i MO1056  (Table 1). We found that the percentage of included ORFs was smaller in i JN746 than in the other reconstructions. Subsequently, it can be expected that the number of metabolic functions present in P. putida is larger than currently identified in the genome annotation and literature. In fact, the number of included non-gene associated reactions was twice that of the E. coli metabolic reconstruction. Furthermore, the species knowledge index (SKI) , which relates the number of PubMed abstracts of an organism to its number of ORFs, was much lower for P. putida compared to the other reconstructions. In summary, this comparison indicates that the overall context coverage in i JN746 is comparable with other high-quality network reconstructions when the amount of available literature is considered.
A metabolic reconstruction for another representative of the Pseudomonas genera was published recently . A comparison of P. putida and P. aeruginosa metabolic reconstructions was performed (Table 1). In contrast to P. putida, P. aeruginosa is an opportunistic human pathogen and as such more information about its metabolism and physiology is available, which is directly reflected by a SKI value 7 times higher than that of P. putida (Table 1). As a consequence, a larger number of metabolic genes were included in the metabolic reconstruction (14% of P. putida's genome vs. 18% of P. aeruginosa's genome). Despite being close relatives, these two representatives have significant differences in lifestyle and metabolic capabilities. Subsequently, the two metabolic reconstructions have significant differences, emphasizing the importance of organism-specific reconstructions. For instance, the P. aeruginosa reconstruction contains pathways necessary for growth and production of common virulence factors, including alginate, rhamnolipids, phenezines, and quorum-sensing molecules , which are not present in P. putida's metabolic network. In contrast, P. aeruginosa's metabolic network does not account for pathways necessary to degrade aromatic compounds.
i JN746's metabolic versatility
Carbon sources enabling growth of i JN746 in i M9 mineral medium.
Aromatic and related compounds
Polyalcohols and glycols
No false positive carbon, nitrogen, or sulfur sources were found in i JN746, as expected, as only exchange reactions were included in the reconstruction for metabolites, which have been reported to be taken up or secreted by P. putida KT2440. In contrast, some disagreements, such as false negatives, were observed despite a good overall agreement with the in vivo data  [Additional file 1]. For example, it was reported that P. putida can use L-alanine as a carbon- and nitrogen-source  but i JN746 cannot use this compound as a carbon or nitrogen source. This disagreement could not be resolved. In contrast, i JN746 was initially unable to use choline-O-sulphate, choline, or glycine betaine as carbon- and nitrogen-sources despite experimental evidence . However, the addition of two non-gene-associated reactions, betaine-homocysteine S-methyltransferase (EC- 126.96.36.199) and dimethylglycine dehydrogenase (EC- 188.8.131.52), enabled i JN746 to use these metabolites as carbon- and nitrogen-sources through the glycine metabolism. In addition, choline-O-sulphate could also be used as sulfur source [see Additional file 1]. The two added reactions represent a hypothesis that needs further experimental verification. These examples show how discrepancies between in silico predictions and physiological properties can be used to drive new discoveries, as was shown for E. coli .
Growth on glucose
Comparison of growth performance of the in silico strain i JN746 and KT2442.
Carbon Uptake rate
O2 Uptake rate
Growth on Toluene
Aromatic compounds such as toluene or xylene are found in polluted soil. Some Pseudomonas species are known to grow on these compounds as a sole carbon source , making them interesting candidates for bioremediation of contaminated areas [9, 10]. As indicated above, P. putida KT2440 can metabolize various aromatic acids, amino acids, sugars, organic acids, fatty acids, and organo-sulfur compounds (see Table 2). More specifically, P. putida KT2440 degrades many aromatic compounds into a limited number of intermediates using a few catabolic pathways that were captured in i JN746 (Figure 2). In particular, the toluene biodegradation pathway has been extensively studied in P. putida [73–75] and its genetic regulation is well known . In this study, we assessed the capability of i JN746 to quantitatively predict aerobic growth on toluene (Table 3). The comparison showed a much lower in silico growth rate when compared to in vivo data, 0.421 versus 0.72 (60%) (Table 3). In the following, we used different mathematical tools to elucidate reasons for this significant discrepancy.
Reduced cost of toluene catabolism
Linear Programming (LP) problems have two parameters, shadow price and reduced cost, which can be used to characterize the optimal solution. While shadow prices are associated with each network metabolite, reduced costs are associated with each network reaction. The reduced cost signifies the amount by which the objective function (e.g. growth rate) would increase when the flux rate through a chosen reaction was increased by a single unit . Analyses of the reduced costs associated with uptake rates in the oxygen-limited toluene simulations identified the OUR as the only non-zero reduced cost value, 0.021 g biomass/gDW/h. This value corresponds to an increase of the OUR to 33 mmol oxygen/gDW/h to achieve the experimentally determined growth rate . At an OUR higher than 62 mmol oxygen/gDW/h oxygen is no longer a growth-limiting factor but toluene is. Note that the upper limit of 18.5 mmol oxygen/gDW/h for the OUR was taken from measurements for E. coli corresponding to the normal oxygen diffusion rate under atmospheric oxygen conditions . Mathematically, the reduced cost analysis supports the hypothesis that oxygen is the limiting factor for toluene catabolism and hence causes the reduced in silico growth rate.
Phase Plane Analysis of toluene catabolism and oxygen uptake
In order to better understand this situation and since no detailed information about OUR was found for P. putida KT2440 under toluene-dependent growth conditions, we carried out in vivo experiments to determine the OUR of P. putida KT2440 harboring the TOL plasmid (see Methods). As expected, the OUR in toluene growing cells was higher than glucose or octanoate growing cells; 20.93 compared to 15.34 and 14.88 mmol oxygen/gDW/h, respectively (Table 3). The measured OUR uptake rate for growth in toluene did not explain the high oxygen requirement of the model, but clearly indicates the importance of oxygen uptake in toluene metabolism. Also, the measured OUR was slightly higher than the E. coli value that was used for the standard in silico simulations (20.93 vs. 18.5 mmol oxygen/gDW/h). In fact, oxygen dependent growth of toluene grown cells has been described for other P. putida strains. For example, Alagappan and Cowan reported a 10× higher oxygen-half saturation of P. putida F1 grown on toluene than other aerobic organisms . Furthermore, the oxidative stress caused by toluene and other aromatic acids in the degradative process is well known [23, 80]; however, this phenomenon was found to be mainly caused by reactive oxygen species due to incomplete oxygen reduction , indicating an active oxygen metabolism under this growth condition. Oxygen-limiting growth conditions were also reported for P. putida when grown on octanoate .
Taken together, our analysis suggests that the current P. putida metabolic network is incomplete. In fact, the current information and results suggest that the network is missing one or more reactions enabling a more oxygen-efficient catabolism of toluene and other highly reduced carbon sources (e.g. other aromatic compounds or fatty acids). This analysis represents a nice example of the broad range of applications for which i JN746 can be used to evaluate the consistency of experimental data and in silico prediction. i JN746 can serve as a platform to derive hypotheses about metabolic capabilities or missing functions in the network which can be ultimately tested in the laboratory. Hence, the metabolic reconstruction can help to increase our understanding and knowledge about this biotechnologically important organism.
Gene essentiality analysis in i JN746
i JN746 was used as a framework to analyze candidate essential genes in P. putida KT2440 in LB rich medium. Therefore, the network reaction(s) associated with each gene was individually "deleted" by setting the flux to 0 and optimizing for the biomass function . We wished to compare the in silico essentiality predictions with experimental data to assess the predictive potential of the model. However, no large-scale, experimental gene essentiality data are available for P. putida; the information can only be found for its phylogenetic relative P. aeruginosa PAO1 and P. aeruginosa PA14 [82, 83]. A recently published comparison between the P. putida and P. aeruginosa PAO1 genomes identified 3,143 potential orthologous pairs corresponding to 60% of P. putida's total ORFs, as well as large sections of conserved gene order (synteny) . Therefore, we decided to compare our in silico single gene deletion results with the 335 essential metabolic and non-metabolic genes of P. aeruginosa [82, 83]. About 12% (92) of the 746 metabolic genes present in i JN746 were predicted to be essential in i LB medium [see Additional file 2]. A total of 53% (48) of these predicted essential genes in i JN746 agreed with essential genes of P. aeruginosa [see Additional file 3]. More importantly, the 44 genes wrongly predicted as essential genes represent excellent targets for further refinement and expansion of the metabolism of i JN746 [see Additional file 4] as has been done for E. coli .
The disagreement between the experimental and computational results can reveal possible errors in the experimental data as well as in the reconstructed network. The disagreements might be caused by low experimental or sequence evidences, each of which would have hindered the inclusion of the information into the reconstruction. For example, the fabB gene was predicted to be only essential in i JN746; however, after carrying out a detailed search on Pseudomona's genomes using "The Pseudomonas Genome Database V2" http://www.Pseudomonas.com/ we found putative ORFs in the KT2440 and PA01 genome. These ORFs were annotated as alternative loci that could substitute a fabB deletion. Both, P. putida and P. aeruginosa have one copy of the fabB gene encoding for the 3-oxoacyl-(acyl-carrier-protein)synthase I (PP_4175 and PA1609, respectively). In addition, both strains have a copy of the fabF gene encoding for the 3-oxoacyl-(acyl-carrier-protein) synthase II (PP_1916 (40.92% identity with fabB-KT gene) and PA2965 (42.34% identity with fabB-PAO1 gene). Moreover, in the P. putida and P. aeruginosa genome, some ORFs were annotated putatively to encode for a 3-oxoacyl-(acyl-carrier-protein) synthase II (PP_3303 (35.94% identity) and PP_2780 (27.32% identity) in KT2440, and PA_1373 (36.17% identity) in PAO1 strain. These putative ORFs were not included in i JN746 due to the lack of supporting evidence for their metabolic function, but this analysis showed that i) PAO1 has an isozyme present in its genome, and ii) KT2440 is very likely to have at least one other ORF encoding this or a similar function. In a similar way, the discrepancy between in silico essentiality prediction and in vitro observation for msbA gene could be explained. The gene product of msbA encodes for a transporter of phosphatidylethanolamine, which is known to have a genetic redundancy in Pseudomonas sp. taking into account the Pseudomonas annotation present in "The Pseudomonas Genome Database V2". However, the supporting evidence for alternative ORFs was not strong enough to be included into i JN746.
Finally, 37 genes were not predicted to be essential in i JN746 but they were reported as essential genes in P. aeruginosa  [see Additional files 4 and 3]. Of these false negatives, 13 genes encode for tRNAs synthetases which are typically included into metabolic networks  but are not functionally connected to the rest of the network. Hence, this disagreement was expected. Four additional false negative predictions, namely glyA (PP_0322 or PP_0671), fold (PP_1945 or PP_2265), fabZ (PP_4174 or PP_1602), and pyrH (PP_1771 or 1593), have at least one isozyme in KT2440 which were also accounted for in i JN746. For many remaining incorrectly predicted non-essential genes, the in silico deletion had a significant effect on the growth rate, reflecting their important roles in i JN746 metabolism [see Additional file 5].
In general, many of these discrepancies suggest that metabolites enabling growth in the knock-outs might be imported from the external rich media since the exact composition of LB medium is not known [37, 38]. This observation indicates the importance of using well defined minimal media in the experimental in vivo or in vitro procedure to enable the usage of the generated data for in silico predictions and comparison.
Gene essentiality and amino acid auxotrophy
The comparison of the in silico gene essentiality and experimental P. aeruginosa data are shown under various amino acid auxotrophic conditions.
VALTA, LEUTA, ILETA
i JN746 as a cell factory
In the previous section, we used the metabolic reconstruction to assess the current knowledge of P. putida's metabolism by comparing and testing in silico predictions with physiological data. However, metabolic network reconstructions can also serve as engineering and design tools  in addition to their use for discovery purposes . Here, we investigate the poly-3-hydroxyalkanoate (PHA) production capability by the metabolic network. PHAs are a class of microbially produced polyesters that have the potential to replace conventional, petrochemically derived plastics in packaging and coating applications . The biotechnological interest originates from their biodegradability and the broad range of physical properties depending on the number of carbons and side chains present in the PHA polymers . These polymers are stored by many microorganisms under inorganic nutrient limited and carbon-excess growth conditions and are used as carbon- and energy sources under starvation conditions . The medium-side-chain PHAs (msc-PHAs) are composed of C6 to C16 3-hydroxy fatty acids and are commonly produced by fluorescent Pseudomonas. In this way, P. putida KT2440 is an excellent candidate for msc-PHA production studies, since i) the basic msc-PHA production processes in KT2440 are well known [17, 61], ii) its genome is completely sequenced, iii) KT2440 has a well known metabolic versatility (can use a large list of carbon source as PHA precursors), iv) it is a very good host-vector biosafety system for gene cloning and expression of heterologous genes and v) this strain has been used in numerous biotechnology processes including msc-PHA production.
Fatty acids resulted in the highest PHA production rate overall and when scaled per carbons (see Figure 5, and Additional file 7). In fact, fatty acids are converted into msc-PHAs quickly via β-oxidation . Experimental studies showed that the resulting msc-PHA-monomers have the same or a smaller number of carbons as the fatty acids from which they are derived [61, 85]. In contrast, in the model, higher carbon msc-PHAs could be formed since the current model formulation does not exclude simultaneous fatty acid synthesis and β-oxidation. This situation has been experimentally demonstrated using hexanoate as a msc-PHA precursor. Huijberts et al. used inhibitors of fatty acid metabolism and demonstrated that, depending on the nature of the substrate, precursors for PHA synthesis could be derived from either beta-oxidation or fatty acid biosynthesis, and interestingly, when hexanoate was used as carbon source for msc-PHA accumulation, both routes can operate simultaneously . On the other hand, the carbohydrates are converted into msc-PHA from intermediates of the fatty acid synthesis and have been shown to result primarily in C8 and C10 monomers. The model, in contrast, is able to produce the full range of msc-PHAs from carbohydrates (Figure 5). These discrepancies suggest that despite broad specificity of the Poly-(3-hydroxyalkanoate) polymerase, ranging from C6 to C16 3-hydroxy fatty acids , the PHA polymerizing enzyme system might have preferences for monomers with 8 or 10 carbon atoms, while larger and smaller monomers are incorporated less efficiently. This fact can also explain why, during growth on hexanoate, msc-PHA precursors are synthesized by elongation and de novo fatty acid synthesis pathway, resulting more preferably in the generation of C8 and C10 monomers . Such differences in specific activity could be applied as additional constraints to the model to obtain similar results as those observed experimentally.
Taken together, this example illustrates how i JN746 could be employed as a tool to identify new substrates (catechol, p-coumarate, isoleucine etc) for production of the different msc-PHA monomers or msc-PHA mixtures. Furthermore, computational tools such as OptKnock or OptStrain could help to design i) higher production strains, and/or ii) couple PHA production to growth rate. Such approaches have proven successful for other metabolic engineering designs such as lactate production in E. coli  or succinate production in M. succiniciproducens .
Here, we presented the first genome-scale reconstruction of P. putida, a biotechnologically interesting all-surrounder. i JN746 is a highly detailed reconstruction of the P. putida KT2440 metabolic network that captures the important biotechnological capabilities, such as biodegradation of aromatic compounds, of this paradigmatic bacterium. Moreover, i JN746 represents a comprehensive knowledge base summarizing and categorizing the information currently available for P. putida KT2440. This study evaluated the metabolic network content and showed some examples of how i JN746 could be used for biotechnological purposes. Taken together, our results underlined the value of i JN746 as a suitable tool to study of P. putida's metabolism and its biotechnical applications by the P. putida community.
In vivo determination of oxygen consumption and cell culture condition
P. putida KT2440 harboring the TOL plasmid was used for in vivo determination of oxygen consumption experiments. The bacterium was grown at 30°C in M9 minimal medium  with octanoate (15 mM), glucose (0.3% [wt/vol]), or toluene (6 mM) as a carbon source. Liquid cultures were agitated on a gyratory shaker operated at 250 rpm. For the OUR experiment, an overnight culture of P. putida KT2440 strain grown in each carbon source was diluted until the turbidity at 600 nm (OD600) was 0.05 in fresh M9 minimal medium with the appropriate carbon source, samples were then incubated until the culture reached a turbidity at 600 nm of 0.6 for glucose or octanoate growing cells and 0.45 in toluene growing cells. Aliquots of 2 ml were taken for OUR determination; the cells were harvested by centrifugation, washed twice and re-suspended in 1 ml of fresh medium containing the appropriate carbon source using the above concentrations. The OUR was measured by monitoring the substrate-dependent oxygen consumption rate at 30°C using an oxygen electrode (DW1 Hansa-Tech Oxygen Electrode, Hansa-Tech Oxygen Instrument Limited) in 1-ml assay mixture. Cellular dry weight (CDW) was determined using previously published methods , using at least 3 parallel 10-ml cell suspensions that were harvested by centrifugation at 15,800 × g. The pellets were washed with 0.9% NaCl and then dried at 105°C for 24 h to a constant weight using pre-dried and weighed 2-ml Eppendorf cups.
The reconstruction process was done as described previously . Briefly, the genome annotation of P. putida KT2440 was obtained from TIGR (http://cmr.tigr.org/tigr-scripts/CMR/GenomePage.cgi?org=gpp, 06/27/2007) and was used as the framework of the network reconstruction. P. putida-specific primary and review literature and books were used to retrieve information about every network reaction: i) substrate specificity, ii) coenzyme specificity, iii) reaction directionality, iv) enzyme and reaction localization, and v) g ene-p rotein-r eaction (GPR) association. Relevant references were associated with every network reaction [see Additional files 7 and 8]. Public databases such as KEGG , PSEUDOCYC , and SYSTOMONAS  were used when no literature evidence could be found for the previous reaction characteristics. Spontaneous reactions were included into the reconstruction if i) physiological evidence suggested their presence (e.g., the presence of at least the substrate or product in the reconstruction); and ii) textbooks or KEGG  suggested the existence of such reactions. Every network reaction was mass- and charge balanced assuming an intracellular pH of 7.2 [38, 55]. Note that this mass- and charge balancing also included balancing the network reactions for protons (H+), water (H2O), and various co-factors (e.g., adenosine triphosphate (ATP)). No gene-associated reactions were included when no corresponding gene was annotated in P. putida's genome but physiological or experimental data supported the presence of the biochemical transformation being part of P. putida's metabolism. Finally the reversibility was determined from primary literature data for each particular enzyme/reaction, if available. This literature search resulted in a first manually-curated reconstruction specific to P. putida's metabolism based on genome annotation and available biochemical evidence. However, this list is normally incomplete and will contain network gaps that may need to be filled depending on supporting evidence. This step requires manual effort again by searching the scientific literature for supporting information. If no P. putida-specific experimental evidence could be found for a transport reaction or biochemical transformation of a metabolite, no reaction or transporter was added to the network. Finally, the network capabilities were evaluated and compared with experimental data as described in Reed et al. . Detailed lists of the genes, proteins, and reactions are contained in the Additional file 8, and the definitions of all metabolites and their abbreviations are found in the Additional file 9.
SimPheny (Genomatica Inc., San Diego, CA) software was used for the reconstruction and gap evaluation process.
Conversion of the network reconstruction to a condition-specific model
The reconstructed metabolic network is often represented in a tabular format, listing all network reactions and metabolites in a human-readable manner along with confidence scores and comments (see Reed et al  for details). The conversion into a mathematical, or computer-readable format, can be done automatically by parsing the stoichiometric coefficients from the network reaction list (e.g. using the COBRA toolbox ). The mathematical format is called a stoichiometric matrix, or S-matrix, where the rows correspond to the network metabolites and the columns represent the network reactions. For each reaction, the stoichiometric coefficients of the substrates are listed with a minus sign in the corresponding cell of the matrix, while the product coefficients are positive numbers, by definition. The resulting size of the S-matrix is m × n, where m is the number of metabolites and n the number of network reactions. Mathematically, the S-matrix is a linear transformation of the flux vector v = (v1, v2,.., v n ) to a vector of time derivatives of the concentration vector x = (x1, x2,.., x m ) as . At steady-state, the change in concentration as a function of time is zero; hence, it follows: = 0. The set of possible flux vectors v that satisfy this equality constraint might be subject to further constraints by defining vi,min≤ v i ≤ vi,maxfor reaction i. In fact, for every irreversible network reaction i, the lower bound was defined as vi,min≥ 0 and the upper bound was defined as vi,max≥ 0.
Exchange reactions, which supply the network with nutrients or remove secretion products from the medium, were defined for all known medium components (see Additional file 9 for details). The uptake of a substrate by the network was defined by a flux rate v i < 0 and secretion of a by-product was defined to be v i > 0 for every exchange reaction i. An exchange reaction is represented in the reaction is as follows: e.g. D-glucose exchange: Ex_glc-D: 1 glc-D →. Note that this exchange reaction is unbalanced. Exchange (uptake) reactions define the presence of media components as if one would add metabolites into an in silico flask.
Finally, the application of constraints corresponding to different environmental conditions (e.g. minimal growth medium) or different genetic background (e.g. enzyme-deficient mutant) allow the transition from metabolic network reconstruction to condition-specific model. Note that the metabolic network reconstruction is unique to the target organism (and defined by its genome) while it can give rise to many different models by applying condition-specific constraints. All flux rates, v i , except biomass formation, are given in mmol/gDW/h.
It is generally assumed that the objective of living organisms is to divide and proliferate. Subsequently, many metabolic network reconstructions have a so-called biomass function, in which all known metabolic precursors of cellular biomass are gathered (e.g. amino acids, nucleotides, phospholipids, vitamins, cofactors, energetic requirements etc.) [36–39]. Since no detailed studies about P. putida's biomass composition are available, the biomass composition from E. coli [55, 93] was used as a template for i JN746's biomass function. However, data from P. putida were added, (e.g. membrane phospholipid composition ), when available. The detailed calculation of the biomass composition is provided in the Additional file 10.
in silico medium composition
Aerobic growth was modeled in two different culture media: in silico M9 minimal medium (i M9) and in silico Luria-Bertani medium (i LB) . For i M9 simulation, and according to the well described M9 minimal medium , the following external metabolites, CO2, Co2 +, Fe2 +, H+, H2O, Na2 +, Ni2 +, NH4, Pi and SO4 were allowed to enter and leave the network by setting the constraints on the corresponding exchange reactions (i) to vi,min≥ -106 mmol/gDW/h and to vi,max≤ 106 mmol/gDW/h. The uptake rate for each carbon source was constrained to vi,min≥ -10 mmol/gDW/h and vi,max≤ 0 mmol/gDW/h. The oxygen uptake rate (OUR) was limited to vi,min≥ -18.5 mmol/gDW/h (based on E. coli data ), if not noted differently. In each individual simulation, all other external metabolites were only allowed to leave the system by constraining their exchange fluxes i between vi,min≥ 0 and vi,max≥ 106 mmol/gDW/h. The i LB medium was based on the published analysis of yeast extract and tryptone provided by the corresponding manufactures, and the i LB simulations were performed according previously published methods .
Phenotypic phase-plane analysis
Phenotypic phase-plane analysis (PhPP) was carried out using SimPheny (Genomatica Inc., San Diego, CA). The underlying algorithm was described elsewhere [96, 97]. The simulation was carried out using i M9 minimal medium (as described above) and setting the bounds of toluene uptake between vi,min≥ -11.9 mmol/gDW/h (based on measurement by  and vi,max≤ 0 mmol/gDW/h; and of oxygen between vi,min≥ -160 mmol/gDW/h and vi,max≤ 0 mmol/gDW/h. The step size was chosen to be 35.
Reduced cost is a parameter of linear programming (LP) problems which is associated with each network reaction (v i ) and represents the amount by which the objective function (e.g. growth rate) could be increased when the flux rate through this reaction was increased by a single unit . Reduced cost is often used to analyze the obtained optimal solution and evaluate alternate solutions from the original solution . In this study, we analyzed the reduced costs associated with uptake reactions to identify candidate reactions through which an increased flux would result in a higher growth rate (under the chosen simulation condition). The growth condition was i M9 medium with toluene as carbon source. The constraints were set as described above and linear programming was employed to solve the optimization problem (maximizing growth).
Gene essentiality and auxotrophy
In order to determine the effect of a single gene deletion, all the reactions associated with each gene in i JN746 were individually "deleted" by setting the flux to 0 and optimizing for the biomass function . A lethal deletion was defined if no positive flux value for the biomass function could be obtained for the given mutant in silico strain and medium. The simulations were performed using i) i LB rich medium for general gene essentially experiment and ii) glucose-iM9 minimal medium for auxotrophy experiments (See above). The glucose uptake rate was fixed to vi,min= vi,max= -6.3 mmol/gDW/h in the latter study. OUR was set to be vi,min≥ -18.5 mmol/gDW/h in both cases.
The msc-PHA production from each possible carbon source (Table 2) in i M9 medium was determined by setting the growth rate to vgrowth,min= vgrowth,max0.2 gDW/gDW/h. The lower bound of each carbon uptake reaction was set to vi,min≥ -10 mmol/gDW/h and the upper bound was set to be vi,max≤ 0 mmol/gDW/h. The lower bound of the oxygen uptake rate was set to vi,min≥ -20 mmol/gDW/h for all simulations. In i JN746, six types of msc-PHAs are defined as well as msc-PHA compounds consisting of four different carbon chains [see Figure 5 and Additional file 7]. The corresponding demand functions were used as objective functions independently for the optimization problem. The resulting msc-PHA production rates were scaled by the number of carbons of the corresponding carbon sources to facilitate a yield comparison.
All computational simulations were performed using Matlab (The MathWorks Inc., Natick, MA) if not stated otherwise. TomLab (Tomlab Optimization Inc., San Diego, CA) was used as linear programming solver. Optimization formulations and the gene deletion studies employed the Matlab-based COBRA toolbox .
We thank J.R. Luque-Ortega for help in the oxygen uptake experiments. We thank M. Abrahams, M. Mo, and S. Burning for critical reading of the manuscript. JN is grateful to T. Conrad for your help during the San Diego stay and E. Díaz and M.A. Prieto for their valuable help and suggestion during the metabolic reconstruction. JN is the recipients of an I3P predoctoral Fellowship from the Consejo Superior de Investigaciones Científicas (CSIC) and JN stay in San Diego was supported by a short term I3P fellowship.
- Clarke P, Richmond MH: Genetics and Biochemistry of Pseudomonas. 1975, New York, USA: John Wiley & SonsGoogle Scholar
- Clarke P: The metabolic versatility of pseudomonads. Antonie Van Leeuwenhoek. 1982, 48 (2): 105-130. 10.1007/BF00405197PubMedGoogle Scholar
- Franklin FC, Bagdasarian M, Bagdasarian MM, Timmis K: Molecular and functional analysis of the TOL plasmid pWWO from Pseudomonas putida and cloning of genes for the entire regulated aromatic ring meta cleavage pathway. Proc Natl Acad Sci USA. 1981, 78 (12): 7458-7462. 10.1073/pnas.78.12.7458PubMed CentralPubMedGoogle Scholar
- Bayley SA, Duggleby CJ, Worsey MJ, Williams PA, Hardy KG, Broda aP: Two modes of loss of the Tol function from Pseudomonas putida mt-2. Mol Gen Genet. 1977, 154 (2): 203-204. 10.1007/BF00330838PubMedGoogle Scholar
- Mermod N, Harayama S, Timmis K: New route to bacterial production of indigo. Bio/Technology. 1986, 4: 321-324. 10.1038/nbt0486-321.Google Scholar
- Ramos J, Wasserfallen A, Rose K, Timmis K: Redesigning metabolic routes: manipulation of TOL plasmid pathway for catabolism of alkylbenzoates. Science. 1987, 235 (4788): 593-596. 10.1126/science.3468623PubMedGoogle Scholar
- Cases I, de Lorenzo V: Expression systems and physiological control of promoter activity in bacteria. Curr Opin Microbiol. 1998, 1 (3): 303-310. 10.1016/S1369-5274(98)80034-9PubMedGoogle Scholar
- Gilbert ES, Walker AW, Keasling J: A constructed microbial consortium for biodegradation of the organophosphorus insecticide parathion. Appl Microbiol Biotechnol. 2003, 61: 77-81.PubMedGoogle Scholar
- Timmis KN, Steffan RJ, Unterman R: Designing microorganisms for the treatment of toxic wastes. Annu Rev Microbiol. 1994, 48: 525-557. 10.1146/annurev.mi.48.100194.002521PubMedGoogle Scholar
- Dejonghe W, Boon N, Seghers D, Top EM, Verstraete W: Bioaugmentation of soils by increasing microbial richness: missing links. Environ Microbiol. 2001, 3 (10): 649-657. 10.1046/j.1462-2920.2001.00236.xPubMedGoogle Scholar
- Galán B, Díaz E, García JL: Enhancing desulphurization by engineering a flavin reductase-encoding gene cassette in recombinant biocatalysts. Environ Microbiol. 2000, 2 (6): 687-669. 10.1046/j.1462-2920.2000.00151.xPubMedGoogle Scholar
- Zeyer J, Lehrbach PR, Timmis KN: Use of cloned genes of Pseudomonas TOL plasmid to effect biotransformation of benzoates to cis-dihydrodiols and catechols by Escherichia coli cells. Appl Environ Microbiol. 1985, 50 (6): 1409-1413.PubMed CentralPubMedGoogle Scholar
- Wubbolts MG, Timmis KN: Biotransformation of substituted benzoates to the corresponding cis-diols by an engineered strain of Pseudomonas oleovorans producing the TOL plasmid-specified enzyme toluate-1, 2-dioxygenase. Appl Environ Microbiol. 1990, 56 (2): 569-571.PubMed CentralPubMedGoogle Scholar
- Schmid A, Dordick JS, Hauer B, Kiener A, Wubbolts M, Witholt B: Industrial biocatalysis today and tomorrow. Nature. 2001, 409 (6817): 258-268. 10.1038/35051736PubMedGoogle Scholar
- Olivera ER, Carnicero D, Jodra R, Minambres B, Garcia B, Abraham GA, Gallardo A, Roman JS, Garcia JL, Naharro G, et al: Genetically engineered Pseudomonas: a factory of new bioplastics with broad applications. Environmental Microbiology. 2001, 3 (10): 612-618. 10.1046/j.1462-2920.2001.00224.xPubMedGoogle Scholar
- Ouyang SP, Luo RC, Chen SS, Liu Q, Chung A, Wu Q, Chen GQ: Production of Polyhydroxyalkanoates with High 3-Hydroxydodecanoate Monomer Content by fadB and fadA Knockout Mutant of Pseudomonas putida KT2442. Biomacromolecules. 2007, 8 (8): 2504-2511. 10.1021/bm0702307PubMedGoogle Scholar
- Huijberts GN, Eggink G, de Waard P, Huisman GW, Witholt B: Pseudomonas putida KT2442 cultivated on glucose accumulates poly(3-hydroxyalkanoates) consisting of saturated and unsaturated monomers. Appl Environ Microbiol. 1992, 58 (2): 536-544.PubMed CentralPubMedGoogle Scholar
- O'Sullivan DJ, O'Gara F: Traits of fluorescent Pseudomonas spp. involved in suppression of plant root pathogens. Microbiol Rev. 1992, 56 (4): 662-676.PubMed CentralPubMedGoogle Scholar
- Walsh UF, Morrissey JP, O'Gara F: Pseudomonas for biocontrol of phytopathogens: from functional genomics to commercial exploitation. Curr Opin Biotechnol. 2001, 12 (3): 289-295. 10.1016/S0958-1669(00)00212-3PubMedGoogle Scholar
- Nelson KE, Weinel C, Paulsen IT, Dodson RJ, Hilbert H, Martins dos Santos VAP, Fouts DE, Gill SR, Pop M, Holmes M, et al: Complete genome sequence and comparative analysis of the metabolically versatile Pseudomonas putida KT2440. Environmental Microbiology. 2002, 4 (12): 799-808. 10.1046/j.1462-2920.2002.00366.xPubMedGoogle Scholar
- Ramos JL: Pseudomonas. 2004, New York Kluwer: Academic/Plenum PublishersGoogle Scholar
- Yuste L, Hervas AB, Canosa I, Tobes R, Jimenez JI, Nogales J, Perez-Perez MM, Santero E, Diaz E, Ramos J-L, et al: Growth phase-dependent expression of the Pseudomonas putida KT2440 transcriptional machinery analysed with a genome-wide DNA microarray. Environmental Microbiology. 2006, 8 (1): 165-177. 10.1111/j.1462-2920.2005.00890.xPubMedGoogle Scholar
- Dominguez-Cuevas P, Gonzalez-Pastor J-E, Marques S, Ramos J-L, de Lorenzo V: Transcriptional Tradeoff between Metabolic and Stress-response Programs in Pseudomonas putida KT2440 Cells Exposed to Toluene. J Biol Chem. 2006, 281 (17): 11981-11991. 10.1074/jbc.M509848200PubMedGoogle Scholar
- Kim Hwan Young, Sung-Ho Cho Kun, Young Jin Yun, Kyung-Hoon Kim, Shin Jong Kwon, YSI Kim: Analysis of aromatic catabolic pathways in Pseudomonas putida KT 2440 using a combined proteomic approach: 2-DE/MS and cleavable isotope-coded affinity tag analysis. PROTEOMICS. 2006, 6 (4): 1301-1318. 10.1002/pmic.200500329PubMedGoogle Scholar
- del Castillo T, Ramos JL, Rodriguez-Herva JJ, Fuhrer T, Sauer U, Duque E: Convergent Peripheral Pathways Catalyze Initial Glucose Catabolism in Pseudomonas putida: Genomic and Flux Analysis. J Bacteriol. 2007, 189 (14): 5142-5152. 10.1128/JB.00203-07PubMed CentralPubMedGoogle Scholar
- del Castillo T, Ramos JL: Simultaneous Catabolite Repression between Glucose and Toluene Metabolism in Pseudomonas putida Is Channeled through Different Signaling Pathways. J Bacteriol. 2007, 189 (18): 6602-6610. 10.1128/JB.00679-07PubMed CentralPubMedGoogle Scholar
- Jimenez JI, Minambres B, Garcia JL, Diaz E: Genomic analysis of the aromatic catabolic pathways from Pseudomonas putida KT2440. Environmental Microbiology. 2002, 4 (12): 824-841. 10.1046/j.1462-2920.2002.00370.xPubMedGoogle Scholar
- dos Santos VAPM, Heim S, Moore ERB, Stratz M, Timmis KN: Insights into the genomic basis of niche specificity of Pseudomonas putida KT2440. Environmental Microbiology. 2004, 6 (12): 1264-1286. 10.1111/j.1462-2920.2004.00734.xPubMedGoogle Scholar
- Palsson BØ: In silico biotechnology. Era of reconstruction and interrogation. Curr Opin Biotechnol. 2004, 15 (1): 50-51. 10.1016/j.copbio.2004.01.006PubMedGoogle Scholar
- Reed JL, Famili I, Thiele I, Palsson BO: Towards multidimensional genome annotation. Nat Rev Genet. 2006, 7 (2): 130-141. 10.1038/nrg1769PubMedGoogle Scholar
- Palsson BO: Two-dimensional annotation of genomes. Nat Biotechnol. 2004, 22 (10): 1218-1219. 10.1038/nbt1004-1218PubMedGoogle Scholar
- Price ND, Reed JL, Palsson BO: Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Micro. 2004, 2 (11): 886-897. 10.1038/nrmicro1023.Google Scholar
- Becker SA, Feist AM, Mo ML, Hannum G, Palsson BO, Herrgard MJ: Quantitative Prediction of Cellular Metabolism with Constraint-based Models: The COBRA Toolbox. Nat Protoc. 2007, 2 (3): 727-738. 10.1038/nprot.2007.99PubMedGoogle Scholar
- Price ND, Reed JL, Palsson BO: Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol. 2004, 2 (11): 886-897. 10.1038/nrmicro1023PubMedGoogle Scholar
- Feist AM, Scholten JCM, Palsson BO, Brockman FJ, Ideker T: Modeling methanogenesis with a genome-scale metabolic reconstruction of Methanosarcina barkeri. Mol Syst Biol. 2006, 2:Google Scholar
- Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Broadbelt LJ, Hatzimanikatis V, Palsson BO: A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol. 2007, 3:Google Scholar
- Oh Y-K, Palsson BO, Park SM, Schilling CH, Mahadevan R: Genome-scale Reconstruction of Metabolic Network in Bacillus subtilis Based on High-throughput Phenotyping and Gene Essentiality Data. J Biol Chem. 2007, 282 (39): 28791-28799. 10.1074/jbc.M703759200PubMedGoogle Scholar
- Thiele I, Vo TD, Price ND, Palsson B: An Expanded Metabolic Reconstruction of Helicobacter pylori (i IT341 GSM/GPR): An in silico genome-scale characterization of single and double deletion mutants. J Bacteriol. 2005, 187 (16): 5818-5830. 10.1128/JB.187.16.5818-5830.2005PubMed CentralPubMedGoogle Scholar
- Jamshidi N, Palsson B: Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets. BMC Systems Biology. 2007, 1 (1): 26- 10.1186/1752-0509-1-26PubMed CentralPubMedGoogle Scholar
- Beste DJ, Hooper T, Stewart G, Bonde B, Avignone-Rossa C, Bushell ME, Wheeler P, Klamt S, Kierzek AM, McFadden J: GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosis metabolism. Genome Biol. 2007, 8 (5): R89- 10.1186/gb-2007-8-5-r89PubMed CentralPubMedGoogle Scholar
- Becker SA, Palsson BO: Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation. BMC Microbiol. 2005, 5 (1): 8- 10.1186/1471-2180-5-8PubMed CentralPubMedGoogle Scholar
- Heinemann M, Kummel A, Ruinatscha R, Panke S: In silico genome-scale reconstruction and validation of the Staphylococcus aureus metabolic network. Biotechnol Bioeng. 2005, 92 (7): 850-864. 10.1002/bit.20663PubMedGoogle Scholar
- Oliveira AP, Nielsen J, Forster J: Modeling Lactococcus lactis using a genome-scale flux model. BMC Microbiol. 2005, 5: 39- 10.1186/1471-2180-5-39PubMed CentralPubMedGoogle Scholar
- Duarte NC, Becker SA, Jamshidi N, Thiele I, Mo ML, Vo TD, Srivas R, Palsson BO: Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proceedings of the National Academy of Sciences. 2007, 104 (6): 1777-1782. 10.1073/pnas.0610772104.Google Scholar
- Reed JL, Patel TR, Chen KH, Joyce AR, Applebee MK, Herring CD, Bui OT, Knight EM, Fong SS, Palsson BO: Systems approach to refining genome annotation. Proceedings of the National Academy of Sciences. 2006, 103 (46): 17480-17484. 10.1073/pnas.0603364103.Google Scholar
- Ibarra RU, Edwards JS, Palsson BO: Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature. 2002, 420 (6912): 186-189. 10.1038/nature01149PubMedGoogle Scholar
- Joyce AR, Fong SS, Palsson BO: Adaptive Evolution of E. coli on Either Lactate or Glycerol Leads to Convergent, Generalist Phenotypes. International E Coli Alliance Second Annual Meeting: 2004; Banff, Alberta. 2004Google Scholar
- Fong SS, Palsson BO: Metabolic gene deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes. Nature Genetics. 2004, 36 (10): 1056-1058. 10.1038/ng1432PubMedGoogle Scholar
- Park JH, Lee KH, Kim TY, Lee SY: Metabolic engineering of Escherichia coli for the production of L-valine based on transcriptome analysis and in silico gene knockout simulation. Proceedings of the National Academy of Sciences. 2007, 104 (19): 7797-7802. 10.1073/pnas.0702609104.Google Scholar
- Thiele I, Price ND, Vo TD, Palsson BO: Candidate metabolic network states in human mitochondria: Impact of diabetes, ischemia, and diet. J Biol Chem. 2005, 280 (12): 11683-11695. 10.1074/jbc.M409072200PubMedGoogle Scholar
- Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabasi AL: Hierarchical organization of modularity in metabolic networks. Science. 2002, 297 (5586): 1551-1555. 10.1126/science.1073374PubMedGoogle Scholar
- Barabasi AL, Oltvai ZN: Network biology: understanding the cell's functional organization. Nature reviews. 2004, 5 (2): 101-113. 10.1038/nrg1272PubMedGoogle Scholar
- Almaas E, Kovacs B, Vicsek T, Oltvai ZN, Barabasi AL: Global organization of metabolic fluxes in the bacterium Escherichia coli. Nature. 2004, 427 (6977): 839-843. 10.1038/nature02289PubMedGoogle Scholar
- Feist AM, Palsson BO: Metabolic Flux Balancing: Basic concepts, Scientific and Practical Use – 13 Years Later. Nat Biotechnol. 2008, 26 (6): 659-667. 10.1038/nbt1401PubMed CentralPubMedGoogle Scholar
- Reed JL, Vo TD, Schilling CH, Palsson BO: An expanded genome-scale model of Escherichia coli K-12 (i JR904 GSM/GPR). Genome Biology. 2003, 4 (9): R54.51-R54.52. 10.1186/gb-2003-4-9-r54.Google Scholar
- Varma A, Palsson BO: Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl Environ Microbiol. 1994, 60 (10): 3724-3731.PubMed CentralPubMedGoogle Scholar
- Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, Katayama T, Araki M, Hirakawa M: From genomics to chemical genomics: new developments in KEGG. Nucl Acids Res. 2006, 34 (suppl_1): D354-357. 10.1093/nar/gkj102.PubMed CentralPubMedGoogle Scholar
- Romero P, Karp P: PseudoCyc, A Pathway-Genome Database for Pseudomonas aeruginosa. Journal of Molecular Microbiology and Biotechnology. 2003, 5 (4): 230-239. 10.1159/000071075.PubMedGoogle Scholar
- Choi C, Munch R, Leupold S, Klein J, Siegel I, Thielen B, Benkert B, Kucklick M, Schobert M, Barthelmes J, et al: SYSTOMONAS – an integrated database for systems biology analysis of Pseudomonas. Nucl Acids Res. 2007, 35 (suppl_1): D533-537. 10.1093/nar/gkl823.PubMed CentralPubMedGoogle Scholar
- Revelles O, Wittich R-M, Ramos JL: Identification of the Initial Steps in D-Lysine Catabolism in Pseudomonas putida. J Bacteriol. 2007, 189 (7): 2787-2792. 10.1128/JB.01538-06PubMed CentralPubMedGoogle Scholar
- Huijberts GN, de Rijk TC, de Waard P, Eggink G: 13C nuclear magnetic resonance studies of Pseudomonas putida fatty acid metabolic routes involved in poly(3-hydroxyalkanoate) synthesis. J Bacteriol. 1994, 176 (6): 1661-1666.PubMed CentralPubMedGoogle Scholar
- Hazer B, Steinbüchel A: Increased diversification of polyhydroxyalkanoates by modification reactions for industrial and medical applications. Appl Microbiol Biotechnol. 2007, 74 (1): 1-12. 10.1007/s00253-006-0732-8PubMedGoogle Scholar
- Madison LL, Huisman GW: Metabolic Engineering of Poly(3-Hydroxyalkanoates): From DNA to Plastic. Microbiol Mol Biol Rev. 1999, 63 (1): 21-53.PubMed CentralPubMedGoogle Scholar
- Oberhardt MA, Puchalka J, Fryer KE, Martins dos Santos VAP, Papin JA: Genome-Scale Metabolic Network Analysis of the Opportunistic Pathogen Pseudomonas aeruginosa PAO1. J Bacteriol. 2008, 190 (8): 2790-2803. 10.1128/JB.01583-07PubMed CentralPubMedGoogle Scholar
- Janssen P, Goldovsky L, Kunin V, Darzentas N, Ouzounis CA: Genome coverage, literally speaking. The challenge of annotating 200 genomes with 4 million publications. EMBO Rep. 2005, 6 (5): 397-399. 10.1038/sj.embor.7400412PubMed CentralPubMedGoogle Scholar
- Ryan PR, Delhaize E, Jones DL: Function and mechanism Of Organic anion exudation from plant roots. Annual Review of Plant Physiology and Plant Molecular Biology. 2001, 52 (1): 527-560. 10.1146/annurev.arplant.52.1.527.PubMedGoogle Scholar
- Espinosa-Urgel M, Ramos J-L: Expression of a Pseudomonas putida Aminotransferase Involved in Lysine Catabolism Is Induced in the Rhizosphere. Appl Environ Microbiol. 2001, 67 (11): 5219-5224. 10.1128/AEM.67.11.5219-5224.2001PubMed CentralPubMedGoogle Scholar
- Stanier RY, Palleroni N, Doudoroff M: The aerobic pseudomonads: a taxonomic study. J Gen Microbiol. 1966, 43 (2): 159-271.PubMedGoogle Scholar
- Galvao TC, de Lorenzo V, Canovas D: Uncoupling of choline-O-sulphate utilization from osmoprotection in Pseudomonas putida. Molecular Microbiology. 2006, 62 (6): 1643-1654. 10.1111/j.1365-2958.2006.05488.xPubMedGoogle Scholar
- Vicente M, Canovas JL: Glucolysis in Pseudomonas putida: Physiological Role of Alternative Routes from the Analysis of Defective Mutants. J Bacteriol. 1973, 116 (2): 908-914.PubMed CentralPubMedGoogle Scholar
- Reed JL, Famili I, Thiele I, Palsson BO: Towards multidimensional genome annotation. Nat Rev Genet. 2006, 7 (2): 130-141. 10.1038/nrg1769PubMedGoogle Scholar
- Worsey MJ, Williams PA: Metabolism of toluene and xylenes by Pseudomonas (putida (arvilla) mt-2: evidence for a new function of the TOL plasmid. J Bacteriol. 1975, 124 (1): 7-13.PubMed CentralPubMedGoogle Scholar
- Assinder SJ, PA W: The TOL plasmids: determinants of the catabolism of toluene and the xylenes. Adv Microb Physiol. 1990, 31 (1–69):PubMedGoogle Scholar
- Harayama S, Rekik M, Wubbolts M, Rose K, Leppik RA, Timmis KN: Characterization of five genes in the upper-pathway operon of TOL plasmid pWW0 from Pseudomonas putida and identification of the gene products. J Bacteriol. 1989, 171 (9): 5048-5055.PubMed CentralPubMedGoogle Scholar
- Harayama S, Rekik M: The meta cleavage operon of TOL degradative plasmid pWW0 comprises 13 genes. Mol Gen Genet. 1990, 221 (1): 113-120. 10.1007/BF00280375PubMedGoogle Scholar
- Ramos JL, Marques S, Timmis KN: Transcriptional control of the pseudomonas tol plasmid catabolic operons is achieved through an interplay of host factors and plasmid-encoded regulators. Annual Review of Microbiology. 1997, 51 (1): 341-373. 10.1146/annurev.micro.51.1.341PubMedGoogle Scholar
- Ramakrishna R, Edwards JS, McCulloch A, Palsson BO: Flux-balance analysis of mitochondrial energy metabolism: consequences of systemic stoichiometric constraints. American journal of physiology. 2001, 280 (3): R695-704.PubMedGoogle Scholar
- Fischer E, Zamboni N, Sauer U: High-throughput metabolic flux analysis based on gas chromatography-mass spectrometry derived 13C constraints. Anal Biochem. 2004, 325 (2): 308-316. 10.1016/j.ab.2003.10.036PubMedGoogle Scholar
- Alagappan G, Cowan RM: Effect of temperature and dissolved oxygen on the growth kinetics of Pseudomonas putida F1 growing on benzene and toluen. Chemosphere. 2004, 54 (8): 1255-1265. 10.1016/j.chemosphere.2003.09.013PubMedGoogle Scholar
- Denef VJ, Klappenbach JA, Patrauchan MA, Florizone C, Rodrigues JLM, Tsoi TV, Verstraete W, Eltis LD, Tiedje JM: Genetic and Genomic Insights into the Role of Benzoate-Catabolic Pathway Redundancy in Burkholderia xenovorans LB400. Appl Environ Microbiol. 2006, 72 (1): 585-595. 10.1128/AEM.72.1.585-595.2006PubMed CentralPubMedGoogle Scholar
- Fridovich I: Superoxide radicals, superoxide dismutases and the aerobic lifestyle. Photochem Photobiol. 1978, 28 (4–5): 733-741. 10.1111/j.1751-1097.1978.tb07009.xPubMedGoogle Scholar
- Jacobs MA, Alwood A, Thaipisuttikul I, Spencer D, Haugen E, Ernst S, Will O, Kaul R, Raymond C, Levy R, et al: Comprehensive transposon mutant library of Pseudomonas aeruginosa. Proceedings of the National Academy of Sciences. 2003, 100 (24): 14339-14344. 10.1073/pnas.2036282100.Google Scholar
- Liberati NT, Urbach JM, Miyata S, Lee DG, Drenkard E, Wu G, Villanueva J, Wei T, Ausubel FM: An ordered, nonredundant library of Pseudomonas aeruginosa strain PA14 transposon insertion mutants. Proceedings of the National Academy of Sciences. 2006, 103 (8): 2833-2838. 10.1073/pnas.0511100103.Google Scholar
- Ward PG, de Roo G, O'Connor KE: Accumulation of Polyhydroxyalkanoate from Styrene and Phenylacetic Acid by Pseudomonas putida CA-3. Appl Environ Microbiol. 2005, 71 (4): 2046-2052. 10.1128/AEM.71.4.2046-2052.2005PubMed CentralPubMedGoogle Scholar
- Timm A, Steinbuchel A: Formation of polyesters consisting of medium-chain-length 3-hydroxyalkanoic acids from gluconate by Pseudomonas aeruginosa and other fluorescent pseudomonads. Appl Environ Microbiol. 1990, 56 (11): 3360-3367.PubMed CentralPubMedGoogle Scholar
- Burgard AP, Pharkya P, Maranas CD: Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng. 2003, 84 (6): 647-657. 10.1002/bit.10803PubMedGoogle Scholar
- Pharkya P, Burgard AP, Maranas CD: OptStrain: a computational framework for redesign of microbial production systems. Genome Res. 2004, 14 (11): 2367-2376. 10.1101/gr.2872004PubMed CentralPubMedGoogle Scholar
- Hua Q, Joyce AR, Fong SS, Palsson BO: Metabolic analysis of adaptive evolution for in silico designed lactate-producing strains. Biotechnol Bioeng. 2006Google Scholar
- Lee SY, Lee DY, Kim TY: Systems biotechnology for strain improvement. Trends Biotechnol. 2005, 23 (7): 349-358. 10.1016/j.tibtech.2005.05.003PubMedGoogle Scholar
- Abril MA, Michan C, Timmis KN, Ramos JL: Regulator and enzyme specificities of the TOL plasmid-encoded upper pathway for degradation of aromatic hydrocarbons and expansion of the substrate range of the pathway. J Bacteriol. 1989, 171 (12): 6782-6790.PubMed CentralPubMedGoogle Scholar
- Fuhrer T, Fischer E, Sauer U: Experimental Identification and Quantification of Glucose Metabolism in Seven Bacterial Species. J Bacteriol. 2005, 187 (5): 1581-1590. 10.1128/JB.187.5.1581-1590.2005PubMed CentralPubMedGoogle Scholar
- Becker SA, Feist AM, Mo ML, Hannum G, Palsson BO, Herrgard MJ: Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protocols. 2007, 2 (3): 727-738. 10.1038/nprot.2007.99.PubMedGoogle Scholar
- Neidhardt FC, Ingraham JL, Schaechter M: Physiology of the bacterial cell: a molecular approach. 1990, Sunderland, Mass.: Sinauer AssociatesGoogle Scholar
- Pinkart HC, White DC: Lipids of pseudomonas. 111-138. Pseudomonas. Plenum Press
- Edwards JS, Ibarra RU, Palsson B: In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol. 2001, 19 (2): 125-130. 10.1038/84379PubMedGoogle Scholar
- Schilling CH, Edwards JS, Letscher D, Palsson BO: Combining pathway analysis with flux balance analysis for the comprehensive study of metabolic systems. Biotechnol Bioeng. 2000, 71 (4): 286-306. 10.1002/1097-0290(2000)71:4<286::AID-BIT1018>3.0.CO;2-RPubMedGoogle Scholar
- Edwards JS, Ibarra RU, Palsson BO: In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol. 2001, 19: 125-130. 10.1038/84379PubMedGoogle Scholar
- Riley M, Abe T, Arnaud MB, Berlyn MK, Blattner FR, Chaudhuri RR, Glasner JD, Horiuchi T, Keseler IM, Kosuge T, et al: Escherichia coli K-12: a cooperatively developed annotation snapshot-2005. Nucleic Acids Res. 2006, 34 (1): 1-9. 10.1093/nar/gkj405PubMed CentralPubMedGoogle Scholar
- Nogales J, Canales A, Jimenez-Barbero J, Garcia JL, Diaz E: Molecular Characterization of the Gallate Dioxygenase from Pseudomonas putida KT2440: The prototype of a new subgroup of extradiol dioxygenases. J Biol Chem. 2005, 280 (42): 35382-35390. 10.1074/jbc.M502585200PubMedGoogle Scholar
- Fan CL, Miller DL, Rodwell VW: Metabolism of Basic Amino Acids in Pseudomonas putida. Transport of lysine, ornithine, and arginine. J Biol Chem. 1972, 247 (8): 2283-2288.PubMedGoogle Scholar
- Vilchez S, Molina L, Ramos C, Ramos JL: Proline Catabolism by Pseudomonas putida: Cloning, Characterization, and Expression of the put Genes in the Presence of Root Exudates. J Bacteriol. 2000, 182 (1): 91-99.PubMed CentralPubMedGoogle Scholar
- Haywood GW, Anderson AJ, Ewing DF, Dawes EA: Accumulation of a Polyhydroxyalkanoate Containing Primarily 3-Hydroxydecanoate from Simple Carbohydrate Substrates by Pseudomonas sp. Strain NCIMB 40135. Appl Environ Microbiol. 1990, 56 (11): 3354-3359.PubMed CentralPubMedGoogle Scholar
- Huisman GW, de Leeuw O, Eggink G, Witholt B: Synthesis of poly-3-hydroxyalkanoates is a common feature of fluorescent pseudomonads. Appl Environ Microbiol. 1989, 55 (8): 1949-1954.PubMed CentralPubMedGoogle Scholar
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