Fluxome analysis aims at the quantitative analysis of in vivo carbon fluxes in metabolic networks, i. e. intracellular activities of enzymes and pathways. It allows investigating the effects of genetic or environmental modifications and thus precisely provides a global perspective on the integrated genetic and metabolic regulation within the intact metabolic network. The experimental and computational approaches developed in this area have revealed fascinating insights into metabolic properties of various biological systems. Most of the comprehensive approaches for metabolic flux studies today involve isotopic tracer studies and GC-MS for measurement of the labeling pattern of metabolites. Initially developed and applied mainly in the field of biomedicine these GC-MS based metabolic flux approaches have been substantially extended and optimized during recent years and today display a key technology in metabolic physiology and biotechnology. In all the approaches presented here, single or multiple labeled molecules, i.e. applied tracer substrates or analyzed metabolites, play an important role. Concerning the nomenclature of such molecules labeled with stable isotopes there are no strict IUPAC guidelines. Labeled molecules are identified by the number and position(s) of heavy isotopes in their constitutive atoms. Most commonly the following definitions and terms are used by researchers in the field of metabolic flux analysis. They are exemplified for the carbon isotopes 12C and 13C, but can be generally applied for other isotope labels as well. Positional isotopomers have an exactly determined labeling pattern with a specific number of 13C atoms in specific positions of the molecule. Different positional isotopomers thus exhibit identical global isotopic composition but differ by the position of the heavy atoms in the molecule. For example, [1-13C] pyruvate and [2-13C] pyruvate are positional isotopomers with the 13C label at carbon positions C1 and C2, respectively. Generally, 2n positional isotopomers are possible for a compound with n carbons. Mass isotopomers differ only by the number of heavy atoms in their molecules, resulting in different molecular weights. However, they do not differ by the position of the label. For example, [12C3] pyruvate, [13C-12C2] pyruvate, [13C2 12C] pyruvate and [13C3] pyruvate are different mass isotopomers. Usually, the lightest atoms are not specified and these mass isotopomers are identified as pyruvate, [13C] pyruvate, [13C2] pyruvate and [13C3] pyruvate, in agreement with the International Union of Chemistry. In mass spectrometric jargon, the corresponding different mass isotopomer fractions are referred to as M0, M1, M2 and M3 or as m, m+1, m+2 and m+3. When only one element of a compound with n such atoms of this element is labeled, the number of possible mass isotopomers is (n+1). Most mass isotopomers, except m and m+n, include multiple positional isotopomers. It should be noted that the number of positional isotopomers of symmetrical molecules such as succinate is less than 2n. However, chemically symmetrical molecules may be biologically asymmetrical because of the configuration of the active site of enzymes (e.g. the citrate synthase) or metabolic channeling + at m/z 232 only contains the carbon atoms C2 and C3 of alanine due to additional loss of a CO-group including the C1 atom of alanine. Chemical ionization (CI) as alternative ionization method plays a minor role for flux analysis, but has proven valuable in selected cases such as labelling analysis of glucose aldonitrile pentaacetate in different medical flux studies  glucose as tracer substrate the labelled carbon atom is completely released as CO2 in the PPP, whereas the label is completely conserved through the different glycolytic reactions  and glycolysis, important pathways in many microorganisms. With [1-s Figure . The mast Figure  and Sacce Figure  cultivatynthesis , bidirecynthesis  or paralynthesis  and diffynthesis . In otheynthesis ,66. Hereynthesis . More deynthesis  or via Gynthesis . More coynthesis . Also otynthesis ,49,68,69ynthesis ,70, but ynthesis ,72.13C labelling of a molecule from a GC-MS measurement, the data have to be corrected for natural isotopes. Hereby, atoms of the analyte and of added derivatization residues have to be considered. Natural isotopes typically occurring in GC-MS are carbon, hydrogen, nitrogen, oxygen and silicium  and calculated  labelling data, e. g. molar fractions of mass isotopomers ratios, are normalized and the resulting relative experimental errors of the corresponding MS measurements  are used for weighting. As a result, data with relatively small error contribute to a higher extent, whereas the influence of data with a relatively high error and uncertainty has only a minor influence on the overall optimization result. Statistical analysis is of great importance in order to identify whether differences observed for intracellular flux distributions between different experiments can be really attributed to strain or condition specific differences. Such analysis can be performed by a Monte-Carlo approach including multiple parameter estimation runs with statistical variation of the experimental data [C. glutamicum (Figure By this, the differences between experimental (rtal data ,95. The m Figure . Based oThe following chapter shows the application of GC-MS to metabolic flux analysis by different examples and should illustrate the broad application potential of such approaches in different fields of research. Whereas some of the examples focus on flux quantification of only a single or a few selected reactions, others aim at the analysis of larger parts of the metabolism.E. coli, one of the most prominent model organisms. As example, different mutants were studied at miniaturized scale providing information on gene function [E. coli to the loss of key metabolic enzymes [C. glutamicum, providing important knowledge for rational strain improvement. This microorganism has been investigated with respect to the influence of different substrates in batch culture [Bacillus subtilis [Bacillus clausii [or Streptomyces noursei [Pseudomonas putida, Zymononas mobilis, Sinorhizobium meliloti, Rhodobacter sphaeroides and Lactococcus versutus [Saccharomyces cerevisiae with studies on the influence of growth rate [Pichia [Phaffia rhodozyma [Penicillium chrysogenum [Aspergillus nidulans [Aspergillus niger [GC-MS based metabolic flux analysis has been applied to various microorganisms. This includes several flux studies of function . Metabol enzymes , the fun enzymes  or growt enzymes . Additio enzymes ,98-102.  culture ,22, flux culture , the com culture ,21,103 a culture . Furthersubtilis ,104, Bac clausii or Strept noursei . In anotversutus . Among ywth rate ,27, or twth rate , several [Pichia ,88,108, hodozyma  or diffehodozyma . Flux stysogenum , Asperginidulans  or Asperus niger .Isotopic tracer experiments with GC-MS labelling analysis are utilized in the biomedical area field since many years ,13. FluxDue to high robustness, sensitivity and versatility, GC-MS based approaches will play a central role as routine technology for future analysis of metabolic fluxes in various biological systems. The use of stable isotopes in combination with GC-MS for metabolic flux analysis will establish as important method in actual fields such as functional genomics, systems biology, and pharmaceutical research for drug development ,123. The


Escherichia coli mutant strains with lower than predicted growth rates in order to identify reactions that act as flux bottlenecks in these strains. The expression of the genes corresponding to these bottleneck reactions was often found to be downregulated in the evolved strains relative to the wild-type strain. We also demonstrate the ability of the OMNI method to diagnose problems in E. coli strains engineered for metabolite overproduction that have not reached their predicted production potential. The OMNI method applied to flux data for evolved strains can be used to provide insights into mechanisms that limit the ability of microbial strains to evolve towards their predicted optimal growth phenotypes. When applied to industrial production strains, the OMNI method can also be used to suggest metabolic engineering strategies to improve byproduct secretion. In addition to these applications, the method should prove to be useful in general for reconstructing metabolic networks of ill-characterized microbial organisms based on limited amounts of experimental data.Genome-scale metabolic network models can be reconstructed for well-characterized organisms using genomic annotation and literature information. However, there are many instances in which model predictions of metabolic fluxes are not entirely consistent with experimental data, indicating that the reactions in the model do not match the active reactions in the in vivo system. We introduce a method for determining the active reactions in a genome-scale metabolic network based on a limited number of experimentally measured fluxes. This method, called optimal metabolic network identification (OMNI), allows efficient identification of the set of reactions that results in the best agreement between in silico predicted and experimentally measured flux distributions. We applied the method to intracellular flux data for evolved  Escherichia coli strains and to diagnose problems in strains designed through metabolic engineering strategies to overproduce specific desirable byproducts. The OMNI method can also be adapted to number of other settings, including identification of novel biochemical pathways in ill-characterized organisms based on limited amounts of experimental data.One of the major uses of in silico models in biology is to identify discrepancies between model predictions and experimental data and use these discrepancies to drive discovery of novel biological mechanisms. However, models only allow for identification of the discrepancies; they do not necessarily provide any assistance in discovering what are the missing or incorrect functionalities in the model that cause these discrepancies. Herrgård et al. describe a new in silico method, optimal metabolic network identification, or OMNI, that performs this discovery process in an efficient and systematic manner for genome-scale metabolic networks. Given a preliminary metabolic network model and experimentally determined metabolic flux data, OMNI finds the changes that need to be made to the model so that its predictions match the experimental data as well as possible. Herrgård et al. apply the method to identify metabolic bottlenecks in experimentally evolved  Constraint-based models  have beeEscherichia coli or yeast there may be uncertainties about specific cofactors or reaction mechanisms used in a particular reaction in a specific organism. Finally, there may be regulatory effects that prohibit the use of all the possible metabolic reactions simultaneously under any particular growth condition.Compared to other types of modeling approaches, such as kinetic models , constraE. coli grown on acetate [E. coli on glycerol, the FBA predictions were found to be comparable with experimental data only after the strains had been adapted to the growth environment by evolving them experimentally for hundreds of generations [E. coli [In all the cases described above one would like to identify the correct active reactions to be included in the model from a larger set of possible enzymatic reactions based on comparison between model predictions and experimental data. The experimental data types that are of particular interest for the present application are growth rates, substrate uptake, and byproduct secretion rates, as well as intracellular flux distributions measured under defined conditions –10. Prev acetate  or yeast acetate , the FBAerations . However[E. coli .In cases where major discrepancies between model predictions and experimental data exist even after adaptation to a particular growth environment, methods need to be developed to systematically find minimal modifications to the metabolic network structure that improve model predictions. The changes identified in this fashion can then be validated by direct experimental techniques targeting specific novel mechanisms suggested by the computational analysis. Efficient methods for identifying parameters in small-scale kinetic models of biochemical networks have been developed –17. MethIn this paper the development and application of computational methods for optimal metabolic network identification (OMNI) based on in vivo measured growth rate, exchange flux (substrate uptake and byproduct secretion rate), and intracellular flux data is described. The model identification method uses a bilevel mixed-integer optimization strategy introduced in  to identE. coli knock-out strains evolved for 45–50 d on glucose [E. coli strain designed through metabolic engineering to overproduce lactate [The OMNI method is illustrated by using it to identify potential flux bottlenecks based on experimental data for five different  glucose ,21. For  lactate . We usedE. coli [(tpi), phosphoenolpyruvate carboxylase (ppc), and phosphoglucose isomerase (pgi) gene deletions were characterized. The growth rates of these strains were overpredicted by the E. coli iJR904 genome-scale metabolic model [FBA applied to genome-scale metabolic network models has been shown to correctly predict the physiological end points of laboratory evolution of wild-type  and metaE. coli . HoweverE. coli . In thesE. coli . In partic model  by an avpgiΔ, ppcΔ, and tpiΔ) and set the uptake/secretion rates to the mean values listed in In order to discover potential causes for the growth rate prediction discrepancies we applied the OMNI method to each of the endpoint strains. We did not include the pgiE2 strain in the analysis, since the model did not overpredict the growth rate of this strain, and hence it was not expected that the OMNI method could improve the model predictions. Because all the discrepancies between experimental data and in silico predictions were overpredictions, we assumed that these were caused by reactions in the model that for some reason could not operate at full capacity in the evolved strains. In order to apply the OMNI method to the evolved deletion strain data, we deleted in silico the reactions corresponding to the genes deleted in each parental strain  had growth rates that were 18%–32% lower than what was predicted by FBA. This strain actually evolved to the predicted optimal phenotype after 20 d, but further experimental evolution resulted in a suboptimal phenotype.The OptKnock in silico strain design methods suggests gene deletion strategies that link overproduction of a desired byproduct to biomass production through stoichiometric coupling . The idetic acid . Previoutic acid . StrainspfkA-pta strains. The physiological data for the three independently evolved strains that were used as an input to the OMNI approach are listed in pfkA-pta strains compared with the wild-type E. coli were identified  in the evolved strain were considered, most of the gene sets corresponding to the bottleneck reactions reported in tpi gene reaction.The combination of the OMNI approach applied to experimentally measured flux data together with gene expression profiling data allows us to identify potential regulatory constraints that may limit the ability of strains to evolve to achieve predicted optimal growth phenotypes. For example, in the case of the tpiE1 and tpiE2 strains, the key bottlenecks identified by OMNI were in the pentose phosphate pathway (PGL/G6PDH2r and EDA/EDD), and the expression data strongly supported the downregulation of this pathway in the evolved strains. It is not clear why the expression of the genes in the pentose phosphate pathway is lower in the evolved strain than in the wild-type strain, since allowing maximal possible flux through this pathway should be advantageous for the cell based on the model predictions. One possibility is that the cells are trying to reduce NADPH production by this pathway in order to compensate for the partial loss of ability to consume this cofactor by reactions linked to the deleted pfkA-pta deletion strain studied here was designed using the OptKnock computational strain design to maximize the production of lactate while still maintaining reasonable growth rate [pfkA-pta strains would be the PDH reaction. This conclusion is also supported by the expression profiling data that for all three independently evolved strains shows significant downregulation of the subunits of the PDH complex  [The application of the OMNI method to evolved t (MOMA) , have bet (MOMA) . With thUsing multiple datasets generated under different conditions would also avoid potential issues with over fitting the model to only one flux profile. In particular, some of the flux datasets could be used to train the model using the OMNI approach while the predictive ability of the modified models identified by OMNI could be tested against a set of independent flux datasets. In the applications to evolved strains discussed in this paper it would be possible to apply the OMNI method to multiple flux profiles from the independently evolved endpoint strains for each parental strain . However, these strains are not necessarily genetically identical, and hence it may not be appropriate to try to identify common flux bottleneck sets for both strains.E. coli strains to identify why the model predictions for growth rates of these strains were an average of 22% higher than the actual experimental measurements. The OMNI method identified specific bottleneck reactions in the iJR904 metabolic model that, when removed from the model, would reduce the discrepancies between predicted and observed fluxes significantly. Genes corresponding to these reactions were often found to be downregulated in the evolved strains relative to the wild-type strain. We also applied OMNI to strains created by metabolic engineering approaches and experimentally evolved to optimize their metabolite production potential [We have developed a method, OMNI, to identify potential changes to genome-scale metabolic models based on comparing model predictions of fluxes to experimental measurements. The method uses mixed-integer linear programming to efficiently search through the space of potential metabolic model structures. We applied the method to intracellular flux data for five experimentally evolved otential ,22,24,25v is obtained as the solution ofThe FBA approach applied to genome-scale constraint-based metabolic models can be used to make predictions of flux distributions based on linear optimization ,6. In maAll the definitions of the variables used in this section are summarized in The problem of finding the best possible metabolic reaction set to match model predictions with experimental flux data can be formulated as a bilevel optimization problem, as shown in y indicate the reactions that are included in the model and the zero elements the reactions that are excluded from the model. The bilevel optimization formulation is similar to the one used in the OptKnock computational strain design approach [E includes measured exchange fluxes that are not used in the OptKnock approach.The nonzero elements of the vector approach . The majE) are not used as part of the objective except in the case of the pfkA-pta strains; instead, the exchange fluxes are set explicitly to their measured values. This is done because at least one of the fluxes has to be bounded for the inner optimization problem to have a bounded solution. By fixing the exchange fluxes we ensure that the flux distributions satisfy at least these constraints and reduce the problem to finding a model that minimizes the discrepancy between the remaining experimentally measured and predicted fluxes. In general, it is possible to move fluxes between sets E (constraints) and M (objective function). The fluxes in set E are assumed to be more accurately measured than those in set M so that in most applications intracellular fluxes would be included in set M and exchange fluxes in set E.In the applications described in this paper the measured exchange fluxes (set (F), and a set of reactions that can be deleted (D). For example, the set F could be the set of reactions included in the model with high confidence and D could be a set of potential reactions inferred  [S contains the stoichiometric coefficients for all reactions in both sets D and F, but only parts of this matrix would be used as constraints depending on the nonzero elements in y. In the inner optimization problem a standard FBA problem is solved for a particular metabolic network structure defined by the set of binary variables k.y The outer problem, on the other hand, treats the elements of the vector y as variables in its own optimization problem that aims to minimize the discrepancy between observed exp) ,27. The Solving the bilevel optimization problem directly using combinatorial approaches would be time consuming due to the large number of possible combinations of reactions that could be added to the model or removed from the model. Fortunately, the linear programming nature of the inner problem allows formulating the overall problem as a single mixed-integer linear program (MILP) following the approach developed in . This foThe objective function in Using this form of the objective function converts the overall problem  into a sN is the total number of previously obtained solutions. The OMNI algorithm was run with these additional constraints until no further solutions that would reduce the error between the predicted and observed flux distribution could be found.Here This MILP can be solved in most cases to optimality in a reasonable time (a few hours) using standard MILP solvers run on a single workstation. For genome-scale models the solver time can be reduced significantly by first identifying the tightest possible upper and lower bounds for each reaction using flux variability analysis . For manE. coli iJR904 genome-scale metabolic model containing 1,075 reactions catalyzed by 904 distinct enzymes [D in iw in The  enzymes  was usedpfkA-pta deletion strains was done as described in [E. coli Antisense Genome Arrays. Three biological replicates were used for each of the three evolved pfkA-pta deletion strains and six biological replicates for the wild-type strain all grown aerobically on glucose-minimal media. Probe intensity calculations and normalization was performed using the Robust Multichip Average (RMA) method [t test applied to log-transformed data. The p value cut-off for significance was determined for each strain separately using the Benjamini-Hochberg approach [p that the observed overlap between the gene sets occurs by random chance. The gene expression overlap scores reported in 10 (p). For the expression score 1 (significance-based score), the set of downregulated genes was determined by using the p value threshold determined as described above and requiring that the log2 ratio between mean evolved deletion strain and wild-type expression levels was negative . For the expression score 2 (sign-based score), the downregulated genes were determined based on the sign of the log2 ratio between the evolved knockout strain and the wild-type strain, ignoring the significance of the expression change. If the p value used to calculate either score was exactly equal to zero , the score was assigned the value 99 in The gene expression profiling for the three independently evolved ribed in  using Af) method  and diffapproach , with a approach . In ordeDataset S1(40 KB ZIP)Click here for additional data file.Protocol S1(33 KB DOC)Click here for additional data file.Table S1(30 KB XLS)Click here for additional data file.Table S2(20 KB XLS)Click here for additional data file.Table S3(1.6 MB XLS)Click here for additional data file.http://biocyc.org/ECOLI/NEW-IMAGE?type=GENE&object=b1851) accession numbers for the genes discussed in this paper are tpi (b3919), ppc (b3956), pgi (b4025), pfkA (b3916), pta (b2297), lpdA (b0116), sucA (b0726), sucB (b0727), pgl (b0767), galU (b1236), fdnG (b1474), fdnH (b1475), fdnI (b1476), edd (b1851), nuoN (b2276), nuoM (b2277), nuoL (b2278), nuoK (b2279), nuoJ (b2280), nuoI (b2281), nuoH (b2282), nuoG (b2283), nuoF (b2284), nuoE (b2285), nuoC (b2286), nuoB (b2287), nuoA (b2288), fdoI (b3892), fdoH (b3893), fdoG (b3894), fdhF (b4079), frdD (b4151), frdC (b4152), frdB (b4153), frdA (b4154), deoC (b4381), and deoB (b4383).The EcoCyc (


Rhizomucor miehei) for kinetic resolution of -β-citronellol. The acylating agent was vinyl acetate and the reaction was carried out in solvent free media.Use of enzymes in low water media is now widely used for synthesis and kinetic resolution of organic compounds. The frequently used enzyme form is the freeze-dried powders. It has been shown earlier that removal of water molecules from enzyme by rinsing with n-propanol gives preparation (PREP) which show higher activity in low water media. The present work evaluates PREP of the lipase (from R)-(+)-β-citronellyl acetate at 45% conversion (E = 42) was obtained in 4 h. The control with freeze-dried enzyme, with zero water content, gave 78% ee at 30% conversion (E = 13). FT-IR analysis showed that PREP had retained the α-helical content of the enzyme. On the other hand, freeze-dried enzyme showed considerable loss in the α-helical content.The PREP, with 0.75%  water, was indeed found to be more efficient and gave 95% conversion to the ester. Using this PREP, with no added water, 90% ee for (The results show that PREP may be a superior biocatalyst for enantioselective conversion by enzymes in low-water media. R)-β-citronellyl acetate using immobilized Rhizomucor miehei lipase, Lipozyme® RM IM. Lipozyme® RM IM is a commercially available lipase which has been frequently used for transesterification reactions [Use of enzymes in non-aqueous media for organic transformations is now a well-established approach ,2. It haeactions . Generaleactions . These aR,S)-β-citronellol by vinyl acetate. Citronellyl acetate is an important terpene ester which finds extensive applications in food, cosmetics, pharmaceutical industries [R)-β-citronellyl acetate and (S)-β-citronellyl acetate have different flavors. These workers carried out enantioselective esterification of citronellyl butyrate with somewhat limited success [R)-β-citronellol and (S)-β-citronellol have different flavors. Hence an enantiomerically pure alcohol left behind after enantioselective conversion would also be valuable. Lipases have been extensively used for enantioselective synthesis of esters from alcohols. In general, lipases show poor enantioselectivity towards primary alcohols. This is especially so for primary alcohols wherever the chiral carbon is not adjacent to the alcoholic group. Citronellol is such a primary alcohol with a "remote chiral center" [R,S)-citronellol via transesterification with hog liver carboxylesterase, high cost and the instability of the enzyme makes that process impractical. Thus, the biotransformation of citronellol to the ester is a challenging system to evaluate comparative efficiency of PREP over frequently used freeze-dried powders in enantioselective reactions.In order to evaluate the performance of PREP of the lipase for enantioselectivity, it was decided to work (instead of a model reaction) with a reaction in which both the substrate and the product are useful as pure enantiomers. The reaction was transacetylation of -β-citronellyl acetate by using 1:6 molar excess of vinyl acetate with 0.3%  water content in 8 h at 40°C. The PREP was indeed found to be more efficient. The % conversion to racemic-β-citronellyl acetate in 4 h at 40°C increased from 80% to 95% when 0.75% water (v/v) was added to the reaction medium (data not shown in the form of figure/table). Hence it was thought worthwhile to evaluate the PREP for enantioselective conversion in this case.After optimization in terms of molar ratio of vinyl acetate to racemic-R,S)-β-citronellol to (R)-citronellyl acetate with freeze dried lipase at 40°C. At 49% conversion, to (R)-citronellyl acetate, enantiomeric excess (ee) was 45% in 0.5 h. The corresponding E value was 4.Fig. R)-β-citronellyl acetate at 45% coversion (E = 42) was obtained in 4 h. Decrease in water content has been reported to improve enantioselectivity by others as well [Fig.  as well . It may  as well . A contrS)-enantioselectivity. Wang and Linko (1995) [Candida rugosa, R. japonicus, P. fluorescence and showed the (R)-enantiopreference. Recently, Sankaranarayanan et al (2003) [R)-enantiomeric excess of 93% of citronellyl acetate at 30% conversion with vinyl acetate in presence of solvent (hexane) in 24 h.Some earlier efforts have also been carried out for the kinetic resolution of racemic citronellol. Cambou and Klibanov 1984)  had trie84  had tl (2003)  have donThe improved performance of PREP in the present instance is in agreement with earlier observations that such lipase preparations show greater enantioselectivity (as compared to the lyophilized powder) in dry hexane  and ioniIn order to understand the structural basis for better performance of PREP, both the freeze-dried enzyme and the PREP were examined by FT-IR. A considerable loss in α-helix content of the untreated Lipozyme was observed after freeze-drying. The α-helical content was 21% in the case of the untreated enzyme and 5% for the freeze-dried enzyme -β-citronellyl acetate in the shortest possible time of 4 h could be obtained using PREP. For obtaining (R)-(+)-β-citronellyl acetate with the highest E of 42 again was possible by PREP under anhydrous condition. The conversion yield of the unreacted citronellol was 55% with a 70% enantiomeric excess of (S)-(-)-β-citronellol. It is not often appreciated that enzyme catalyzed reaction in organic medium tend to be slow [To sum up, high conversion of 95% for obtaining (Rhizomucor miehei lipase immobilized on macroporous anion exchange resin (Lipozyme®RM IM) was a kind gift from Dr. J.S. Rao, Novozymes, Bangalore, India. Racemic citronellol was a gift by Gogia chemical, New Delhi, India. S-(-)-citronellol and vinyl acetate  were purchased from Merck, Honenebrunn, Germany.Lipozyme was suspended in 0.5 ml of 20 mM phosphate buffer, pH 7 for 1 h at 25°C. This was frozen at -20°C and lyophilized for 24 h.The Lipozyme was suspended in 0.5 ml of 20 mM phosphate buffer, pH 7, for 1 h at 25°C. The mixture was cooled to 4°C. The supernatant was carefully pipetted out and the lipase was rinsed thrice with ice-cold n-propanol at 4°C  followed by washing with ice-cold vinyl acetate (with the same corresponding water concentrations) at 4°C.® RM IM . The reaction mixtures were incubated at varied temperatures with a constant shaking at 200 rpm. Aliquots were taken out at various time intervals and the citronellyl acetate formed was analyzed by Gas Chromatography (Agilent 6890 N chromatograph with a flame ionization detector). The capillary column used was EQUITY™-5 (30 m × .32 mm × 0.25 μm film thickness) from Supelco. N2 was used as the carrier gas at a constant pressure of 4 Kg/cm2. The column oven temperature was programmed from 150 to 250°C (at the rate of 10°C/min) with injector and detector temperatures at 240 and 250°C, respectively.Citronellol and vinyl acetate were taken in the ratio of 1:6 (mol/mol) in a screw capped vial followed by addition of different preparations of LipozymeR)-,(S)-citronellols and their acetates taken as standards. From the ee values , the enantioselectivity (E) was calculated by Chen's equation as described earlier [Reaction aliquots at different time intervals were taken and the ee values were determined by HPLC with a chiral stationary phase . The eluent was a mixture of acetonitrile and 0.1 M phosphate buffer (pH 2.5) in  ratio. Analysis was done at 210 nm with a flow rate of 0.1 ml/min. The peaks were identified by running optically pure .The spectra was recorded on Nicolet™ FT-IR 6700 spectrophotometer with Smart Miracle™ ATR accessory having zinc selenide crystal. The data collection and deconvoluation were done by using OMNIC 2.1 Software. Different formulations of Lipozyme, freeze-dried and PREP (anhydrous) were taken directly on the zinc selenide crystal plate and an average of 200 scan were taken with a resolution of 2 cm®RM IM and the secondary structure of the enzyme formulations based on FT-IR analysis are given.The FT-IR spectra of different formulations of LipozymeClick here for file


A number of algorithms for steady state analysis of metabolic networks have been developed over the years. Of these, Elementary Mode Analysis (EMA) has proven especially useful. Despite its low user-friendliness, METATOOL as a reliable high-performance implementation of the algorithm has been the instrument of choice up to now. As reported here, the analysis of metabolic networks has been improved by an editor and analyzer of metabolic flux modes. Analysis routines for expression levels and the most central, well connected metabolites and their metabolic connections are of particular interest..YANA features a platform-independent, dedicated toolbox for metabolic networks with a graphical user interface to calculate (integrating METATOOL), edit (including support for the SBML format), visualize, centralize, and compare elementary flux modes. Further, YANA calculates expected flux distributions for a given Elementary Mode (EM) activity pattern and vice versa. Moreover, a dissection algorithm, a centralization algorithm, and an average diameter routine can be used to simplify and analyze complex networks. Proteomics or gene expression data give a rough indication of some individual enzyme activities, whereas the complete flux distribution in the network is often not known. As such data are noisy, YANA features a fast evolutionary algorithm (EA) for the prediction of EM activities with minimum error, including alerts for inconsistent experimental data. We offer the possibility to include further known constraints (e.g. growth constraints) in the EA calculation process. The redox metabolism around glutathione reductase serves as an illustration example. All software and documentation are available for download at A graphical toolbox and an editor for METATOOL as well as a series of additional routines for metabolic network analyses constitute a new user-friendly software for such efforts. Metabolic networks include many enzymes. These operate together in a complex way as metabolites of one reaction may be processed (consumed or provided) by a number of different enzymes. Whereas in biochemistry textbooks such networks are often described as linear pathways or simple, separate subnetworks, real metabolic webs show an astonishing complexity regarding the number of possible routes a metabolite can take through the network.EMA is an algorithm that systematically enumerates all possibilities how enzymes can operate together without violating the steady state condition of the system (see below). Using EMA, complex networks can be analyzed in terms of contained pathways, robustness, central enzymes, medical targets, optimum yield and effector compounds, such as signaling phospholipids, with interesting applications in medicine and biotechnology .To perform a holistic network analysis, the stoichiometric and thermodynamic feasibility of all possible pathways has to be tested. We therefore assume the system to be in a steady-state, in which intermediate or internal metabolites are balanced . Their cv of enzyme coefficients, the so called flux vectors or flux distributions, which satisfy the steady-state condition ofTo find all pathways through a given network we look for all vectors N*v = 0     (1)N is the m × r stoichiometric matrix of the system with m being the number of metabolites in the system and r being the number of reactions (in eq. (4), upper case R is used). To solve such systems under consideration of additional irreversibility constraints imposed by the reactions in the system (thermodynamic feasibility), the mathematical theory of convex analysis [for all internal metabolites (stoichiometric feasibility). Here, analysis  is used   or (Elementary) Flux Modes. Both methods yield a complete description of the metabolic network in which every concrete "state" of the system can be described as a non-negative linear combination of the set of pathways or EMs returned.Elementary Mode Analysis has been successfully applied to numerous biochemical systems -8 and itMETATOOL represents an efficient implementation of the EMA algorithm and has been integrated as an analysis option in large software packages such as GEPASI . HoweverYANA offers now an integrated modeling environment with standardized data exchange capabilities. It is a platform for integrating future analysis modules and includes strategies to address one of the most important issues in current metabolic modeling, the combinatorial explosion of EMs in complex networks. Further, it allows the convenient modification editing of metabolic networks with a comfortable user interface and the possibility of performing EMA analyses using the established METATOOL algorithm. It additionally incorporates strategies to reduce network complexity by using threshold operations on the metabolites and brings a variety of visualization options for concrete flux distributions of a network. It calculates for a user-defined EM activity pattern the resulting flux distribution, and is further able to predict a valid EM activity pattern from a given flux distribution even when only few or inaccurate enzyme activity data are available from experiments.To perform pathway analysis on the network under study, YANA acts as a front-end to METATOOL and computes the Elementary Modes of a network if the following information is provided:• Metabolites used in the system, including information whether they are treated as internal or external• Enzymes / reactions involved in the network, including their substrates and products and irreversibility constraintsParsing the output from METATOOL, YANA shows a tabular overview of the calculated EMs and is able to display detailed information for each of the EMs obtained, including partaking reactions, their reaction equations, as well as the overall net reactions of the Elementary Modes.Additionally the average diameter (path length) of the EMs is displayed, an information of importance for the dissection of larger networks.To prevent combinatorial explosion of the number of EMs in well connected networks, YANA offers and implements two strategies to reduce network complexity. Both change systematically the internal / external status of the metabolites using their connectivity values as the basic criterion.In the first strategy , the netAlternatively, YANA offers the option to set all metabolites with a connectivity value below the threshold as external. In this way, only connections between the core nodes of a metabolic system are included, neglecting those on the outskirts. The resulting pathway set still holds the most important EMs, shortened and focused on the central hub metabolites .To get an estimate on the average size of the metabolic network before and after dissection the average diameter (path length) for the modes can be used.v in equation 1) is a linear combination of the obtained EMs.As described in the background section, convex analysis returns the spanning vectors of the cone that describes the solution to the steady-state equation system and thus every actual flux distribution  represent the relative flux through the respective enzyme and thus must be a combination of both the reaction velocity of the enzyme  and the amount of protein available.By assigning an activity value in percent to each EM, except for a scalar factor, every flux distribution possible for the system can be reached. These valid flux distributions, or flux vectors YANA offers the possibility to compute flux distributions both in absolute (ignoring reaction directions) and relative values. This is done by iterating over all calculated modes, and summing the absolute (eq. 2) or relative (eq. 3) flux coefficients of each enzyme multiplied with the activity of the EM.The resulting flux distribution is visualized and presented to the user either in form of graphical bar and pie charts Figure  or in taCalculating flux distributions from a given theoretical set of EM activities is important e.g. to estimate the relevance of an enzyme in a metabolic network , but it To obtain flux distributions of a living cell, one could either measure metabolite fluxes directly or estimate fluxes from protein quantification and enzyme turnover rates. Protein amounts will, in practice, be measured either by proteomics or gene expression. For the latter, an estimate from a comprehensive RNA and protein expression analysis in yeast indicates that for each mRNA copy on average there are 4000 molecules of synthesized protein found , with inTo find the resulting EM activities from the estimated or observed flux distributions, there are, in general, many solutions possible. One could first choose a certain preferred flux mode, adjust its activity, and try to fit the observed flux distribution as accurately as possible. Next, select the next mode and so on. As the modes can be given by different preference schemes, it is absolutely possible that several schemes will fit the distribution equally well.E. coli . In addition, metabolic webs have been shown to grow selectively around central "hub" metabolites to favor short metabolic paths [To find a rational and compact criterion for mode selection, here, we have chosen to first select the modes which are the shortest. It has already been shown in an earlier work  that theic paths ,23.C) and target enzyme activities (ET) which is to be minimized and, with R being the number of reactions in the system:For calculating EM activities from observed or estimated flux distributions, there are analytical treatments possible based on criteria other than pathway length . HoweverTo achieve a fast and robust convergent solution for this error criterion, an evolutionary algorithm (EA) was successfully applied here. The algorithm starts to calculate flux distributions, even if only one enzyme activity or very few are known. A Pareto-optimal solution  for suchM. This depends on the number of features F taken into account and the number of iterations I already run through, thus introducing a cool-down factor to the mutation probability scaling logarithmically with the number of time steps.The algorithm uses a randomly initialized population of 100 individuals with a per feature mutation probability R to take part in the recombination process that depends upon its rank r in the population and the population size P.Furthermore, recombination between individuals is achieved by uniform crossover, randomly selecting one of the individuals as a parent for the feature in question. Selection pressure is induced by calculating a rank-based fitness from the square deviation of each individual to the target distribution thus giving each individual a probability The evolutionary algorithm routine allows the inclusion of further fitness parameters and helps to fit enzyme activities including these additional constraints. For example, these could be (i) correlations between enzyme expression levels (or just ratios) according to gene-chip experimental results or (ii) constraints based on biochemical data and knowledge on enzyme activities; (iii) metabolite constraints, for instance production of certain amino acids has to be above a certain threshold , (iv) genetic constraints (certain enzyme genes are known to have modified expression or enzymatic activities), (v) necessary activity or certain levels for specific enzyme pools . The fitness function is implemented in such a way, that further positive or negative functions can easily be added with desired weights to the total fitness by the user. Also in that case, the evolutionary algorithm searches for the best possible solution describing the enzyme fluxes with minimum error according to observed enzyme expression data, while including these additional constraints.In each refinement step, the flux mode fitting routine implemented in YANA selects the shortest modes. If two modes are equal, it picks the better connected inside the network, counting all metabolites according to the reaction they participate in.For an overview of EA performance, see Results section c).For the development of the YANA software package Borland JBuilder 2005 was used in combination with EJ-technologies real-time profiling suite JProfiler. The profiling of the software and the evolutionary algorithm in particular was carried out on a dual Intel Xeon 3.06 GHz CPU with Hyper Threading and 8 GB of RAM. For development and testing, a standard PC with a single 1.7 GHz Pentium 4 processor was used.The YANA program flow includes the initial editing of a metabolic network in terms of enzymes and their respective substrates and products or, alternatively, loading networks from files in the METATOOL or SBML format.Subsequently, elementary mode analysis is performed by YANA using the provided precompiled METATOOL binaries. It then parses the resulting output file and presents the arising elementary modes to the user, giving the possibility to retrieve detailed information about a specific elementary mode including partaking enzymes, net reactions, and the stoichiometry involved.Once the analysis is performed, flux distributions can be visualized in several presentation formats simulating either a virtual spot intensity on a gel, or displaying comparative enzyme copy numbers in a virtual cell. From there on, the user can enter a target flux distribution and YANA tries to adjust the elementary mode activities to approximate the entered flux distribution using the EA.In any part of the program, diagrams and tables can be printed and exported to graphic files in the portable network graphics (PNG) format or into text files using comma separated values (CSV) for easy import in e.g. Microsoft Excel.To gain the advantage of platform independence, YANA was implemented using the Java SDK 1.5 and we provide, with our download precompiled METATOOL, binaries for both Windows and UNIX systems. The graphical user interface is based on the SWING Java framework, making strict use of the model-view-controller (MVC) paradigm.To further add to the usability of the program, support for the Systems Biology Markup Language  (SBML Le(i) YANA analyses metabolic networks using EMA:The required input for YANA (and the integrated METATOOL software) to perform an EMA is the set of enzymes and metabolites in the network under study. Their specific reactions and reversibility can be obtained from textbooks and databases. Metabolites must be defined as internal or external according to available biochemical knowledge. Kinetic data, RNA or protein expression data are not required for this part of the analysis.(ii) For the calculation of flux distributions, the user has to choose as input the activities of the different EMs. If there is no information on this available, YANA assumes all EMs to be equally active. For accurate predictions of enzyme activities experimental data on flux ratios is helpful.(iii) To analyze how the predicted elementary modes from step 1 fit expression data, some experimental data on protein or gene expression are required. For most accurate predictions in this step, kinetic data on enzyme activities, on translation speed, protein stability and transcription are required. However, YANA needs, as minimum information for predictions, only the estimated activity levels of some of the enzymes involved. Nevertheless, it calculates an optimal solution, mapping the information on enzyme activity available to a predicted EM activity pattern.(i) All pathways in the network are calculated, a list of EMs is given, both as enzyme cascades as well as the overall reactions of the elementary modes with educts and products.(ii) Calculation of specific flux distributions and visualization in form of pie and bar charts and in tabular form.(iii) A specific EM activity pattern best fitting the user given flux distribution with preferably high activities on short EMs.The YANA main screen is divided into two parts. On the left hand side, the user is able to enter the metabolites involved in the network with information about whether they are considered internal or external. On the right hand side, the enzymes are defined using the metabolites entered earlier. User actions are usually invoked using the toolbar at the top of the YANA main window. By clicking the analysis button, elementary mode analysis is performed, showing the results in a new frame in a tabular form. YANA gives the user detailed information about the elementary flux modes, their irreversibility constraints, partaking enzymes and so on. The user has the possibility to adjust the activity of each elementary mode on a percent scale using the slider incorporated into the table. By clicking the diagram button, YANA calculates the enzyme activity pattern using the activity vector entered before and displays the results either in a diagram or table format. The flux calculation button brings up a new screen on which the user can enter the target flux distribution for use with the evolutionary algorithm. All table data in YANA can be exported into text files using comma separated values; for convenience the target flux vector can be imported the same way. For further and more detailed information see the readme file that comes with the software package and which is also available via online help.To demonstrate the YANA package we use a running example Figure  of the hUnder stationary conditions, this system yields a total of 134 EMs. From these, 46 include glutathione reductase, 117 involve energy consumption (ATP), whereas 128 involve redox reactions. The complete listing of these modes is given in supplementary material see .To illustrate the complexity reduction power of the YANA strategies we performed a series of EMAs with rising threshold values using both threshold types.Using a threshold of 7 , only membrane phosphorylation is placed in a separate sub-network. A threshold of 5 splits the system into seven sub-networks with instructive specific biochemical functions and flux modes: (i) a well connected sub-network includes salvage pathway, pentose phosphate cycle, NOS, SOD and redox protection by uric acid and GSH; other sub-networks are (ii) GSH synthesis, (iii) GSH degradation and GSH protection of protein groups, (iv) membrane phosphorylation (as with threshold 7), (vi) lower glycolysis (trioses), (vii) adenylate kinase. Threshold 3 splits these sub-networks further into a total of 18 components, e.g. the well connected sub-network (i) is now put into its single pathways as named above.The complex system of 134 elementary modes is first reduced to a 87-mode system . Only 24 modes remain if the threshold is set to the best connected metabolite, the currency metabolite ATP. The very short diameter obtained shows that this analysis zooms in on hub metabolites  and wellTable In the example, GR as a central enzyme of the network has an activity of 399. Besides this, the most active enzymes are: GAPDH (598), PGM (598), LDH (598), PGK (560), PK (598) and EN (598), as a parts of glycolysis, and the enzymes G6PD (576); PGLase (576) and GL6PDH (576), as components of the oxidative part of the pentose phosphate pathway. For the obtained flux distribution, we notice a tight connection between glycolysis and the glutathione reductase metabolism. The main pathways of glycolysis and PPP supply energy and reduction equivalents for strong redox protection provided by the glutathione reductase network. In contrast, several other enzymes are downregulated, in particular, those which use uric acid as an antioxidant as well as catalase.The program also quickly calculates and visualizes flux distributions for any other chosen EM activities as given in Table Using our illustration example, we givea) the results for the situation where only the enzyme fluxes for glycolytic enzymes are all set to 100  and all others are known to be at zero.b) The same as before, but all other fluxes are unknown or simply have not been measured (the enzyme activity is then set to -1 in order to indicate lack of knowledge).Situation a) reveals a flux distribution in which, after upregulation of glycolytic enzymes, the three enzymes forming the oxidative part of PPP are also highly active. In addition, glutathione reductase (GR), NO synthase (NOS) and TrxRI (thioredoxin reductase) are upregulated as well, showing that a major part of the metabolite flux uses the path from glycolysis via oxidative PPP to redox protection enzymes. Not connected to glycolysis at all, and thus set to zero activity, are again the use of uric acid as an antioxidant and catalase.Situation b) – a scenario where the measured data are similar but more incomplete – gives similar results, underlining that glycolysis or its side-products are important for many reactions in this network. For this case, uric acid as anti-oxidant and catalase are predicted not to be used. Detailed results for both situations are given in supplementary material. see .For comparison, experimental data on the activity of glutathione reductase and the connected redox network have been reported by Krauth-Siegel et al. (1996)  and othePlasmodium falciparum in its various developmental stages [Transcriptome analyses have been reported for antioxidant proteins of the malaria parasite l stages . The othl stages .The still sparse and incomplete data support the scenarios discussed here, in particular regarding the high activity of glutathione reductase modes as well as the importance of energy providing reactions. However, a detailed kinetic and experimental metabolic flux analysis of the whole system has not yet been achieved.The convergence criterion for the EA was to achieve a sum-of-squares error of less then 5% of the best evolved flux distribution to the target flux distribution. Regarding measurement or experimental errors and constraints, the user is alerted in case measurements are incompatible with the calculated theoretical flux distribution but also about which data are responsible for maximizing the difference between observed and calculated flux distribution.In Table Using the example network above, with a threshold of 8, more than 50% convergence could be reached after 100 iterations (22 seconds).After its conceptual description , ElementThe current software package allows user-friendly post-processing of the METATOOL output. In particular visualization of the modes, editing metabolites and reactions, and graphical comparisons of enzymes and their involvement in reactions of the metabolic network are available for the user. YANA is a stand-alone visualization tool with its focus on user intervention, the quick comparison of results and thorough data exchange capabilities. In contrast, there are a number of more complex and integrated packages available such as GEPASI ,35 whichFor addressing the major problem of combinatorial explosion of the number of EMs in complex networks, YANA implements a decomposition method proposed earlier . In thisMetabolic fluxes are difficult to measure. YANA offers a specific approach to correlate metabolic fluxes with EM activities. Alternative algorithms for such an effort have been proposed ,22. The The calculated EM activity pattern should additionally satisfy metabolite restrictions, as well as growth or genetic considerations on the enzyme or metabolite profile. Further constraints, which might be considered, are, for example, expression constraints dependent on promoter structure, RNA stability or protein stability. In spite of this flexibility, the evolutionary strategy converges swiftly to a solution. The great advantage of this is that we have both robust optimization and already take into account that there is noise, and that no perfect solution is possible. If desired, more criteria could be added with ease to the EA.YANA adds a compact, user-friendly software package to the analysis of metabolic webs, offering several new implementations for typical challenges in such analyses including modeling of expression data. The results illustrate the application for a central redox network around glutathione reductase. Further developments will consider additional regulatory constraints profiting from the evolutionary strategy applied as well as a graphical editor for the metabolic networks including dedicated algorithms for the automatic layout of the graphs..All software and documentation are available for download at The package requires at least Java Runtime Environment (JRE) Version 1.5.0 and the following libraries, which are included in the download bundle and can be found in the /lib subdirectory:• GenJava-CSV • Jakarta Common Collections 3.1 • JFreeChart 0.9.21 • JigCell Modelbuilder • JMat 5.0 • Mosfet Liquid L&F • Noia KDE 1.00 (© Carles Carbonell Bernado)All libraries are licensed under either GNU General Public License (GPL) , Lesser A 1.4 GHz CPU and 256 MB RAM are recommended for running the YANA software package. Installation requires at least 30 MB of hard disk space. YANA is supposed to run on any 32-bit Windows or Linux platform.• EMA – Elementary Mode Analysis• EM – Elementary Mode, also known as Elementary Flux Mode or Flux Mode• EA – Evolutionary AlgorithmAll authors read and approved the ms and made critical comments, adding to the final version presented here. In addition they contributedRS: Architecture and implementation, graphical design, design of user interface.PM: Tested and wrote an early implementation of the software and simplification routine.AVK: Tested YANA, Metatool expertise, compatibility with Metatool.BE: Provided theoretical insights and chemistry knowledge.RHS: Provided experimental insights and discussion points.SS: Biophysical knowledge, expertise in flux balance analysis, metabolic modelling and interpretation of obtained modes as well as for algorithm strategy.TD: Concept; plan for the software and strategy, lead and guided the study.Metabolic network around GR reductase and flux distribution examples (Microsoft Excel 2003): The file contains the complete metabolic network used for elementary mode analysis including the metabolites, reactions / enzymes and elementary modes. Additionally, two flux distributions for upregulated glycolysis are given in the file, as discussed in the main section of the article.Click here for file


Escherichia coli. During adaptation to sequential use of glucose and acetate, these bacteria differentiate into two ecotypes that differ in their growth profiles. The “slow-switcher” exhibits a long lag when switching to growth on acetate after depletion of glucose, whereas the “fast-switcher” exhibits a short switching lag. We show that the short switching time in the fast-switcher is associated with a failure to down-regulate potentially costly acetate metabolism during growth on glucose. While growing on glucose, the fast-switcher expresses malate synthase A (aceB), a critical gene for acetate metabolism that fails to be properly down-regulated because of a transposon insertion in one of its regulators. Swapping the mutant regulatory allele with the ancestral allele indicated that the transposon is in part responsible for the observed differentiation between ecological types. Our results provide a rare example of a mechanistic integration of diversifying processes at the genetic, physiological, and ecological levels.While there has been much recent focus on the ecological causes of adaptive diversification, we know less about the genetic nature of the trade-offs in resource use that create and maintain stable, diversified ecotypes. Here we show how a regulatory genetic change can contribute to sympatric diversification caused by differential resource use and maintained by negative frequency-dependent selection in  E. coli strain on a mixture of two resources, we found sympatric diversification into two descendant strains. This diversification occurs in a shared, well-mixed environment and is caused by competition for resources. We show that 1) the diversified strains use physiological pathways differently to consume the resources, 2) this physiological difference is caused by differences in the expression levels of genes controlling metabolism, and 3) this difference in gene expression is influenced by genetic differences in regulatory genes. Our paper thus contributes to an integrative understanding of sympatric diversification in E. coli at the genetic, physiological, and ecological levels.Understanding the origin of diversity is a fundamental problem in evolutionary biology. The past decade has seen a shift in our understanding of speciation, away from considering geographical isolation as the main cause and towards elucidating how ecological interactions can drive diversification in populations that occupy a single and contiguous spatial area, a process called sympatric diversification. By culturing bacteria over many generations it is possible to observe processes of diversification in real time. This paper characterizes diversification caused by ecological interactions in bacteria at the physiological and genetic level. Propagating a single ancestral  Adaptive diversification describes the splitting of an ancestral lineage into two derived groups due to frequency-dependent ecological interactions . During Pseudomonas fluorescens can readily diversify into three morphological types from a common ancestor . Th. ThacetyaceB for the ancestral and derived strains in medium containing acetate as the sole carbon source, and contrasted this with expression in media containing glucose. When grown on acetate alone, aceB levels were high and equivalent for the ancestral, SS, and FS strains  gene, a negative regulator of aceBAK [IS1 genetic element that terminates the iclR transcript when it is two-thirds complete. To determine whether this mutation was prevalent in the FS population in addition to our focal FS strain, we screened nine subsequent isolated FS and SS strains for this IS1iclR allele: eight of nine FS strains carried the mutant allele, but neither of the SS strains, nor the ancestor, carried this insertion. We additionally PCR screened FS genotypes from two similarly evolved populations and recovered only alleles of ancestral size at this locus. Indeed, we would not expect the exact same mutation  to occur at the same site in two independently evolved populations, as it is likely that there are many different genetic mechanisms by which regulation of acetate metabolism can be altered.The enhanced f aceBAK ,22, contIS1 insertion in the iclR gene affected acetate use in the FS, we substituted the ancestral AnciclR allele into the FS genetic background and then estimated growth profile characteristics of the genetically modified strain. Inserting the ancestral iclR allele resulted in FS strains that had a significantly longer lag when switching from glucose to acetate use  allele did not significantly affect colony morphology or growth rates on glucose or acetate (unpublished data). This clearly indicates that this single mutation is not sufficient to cause all of the resource use changes between FS and the ancestor, or between FS and SS. In particular, iclR does not act alone to cause the critical trade-off in performance that enables coexistence between the FS and SS strains.  The observed differences in the growth rates and the colony morphology between the various strains must therefore result from additional modifications to metabolism. Nevertheless, our data show that a genetic change in the regulation of genes controlling carbohydrate metabolism has contributed substantially to the differentiation of coexisting ecotypes in E. coli populations.Although the  strains C. FurtheE. coli, gram-positive Bacillus subtilis, and halophilic archea Haloferax volcanii [E. coli, as evolutionary changes in the acetate utilization are correlated with pathogenicity in both closely related species such as Shigella [Yersinia spp [Our results not only confirm that regulatory changes can provide a mechanism for rapid evolutionary change ,6–8,23, volcanii . The impShigella  and lessinia spp . Our curE. coli B strain , and acetate concentrations were determined using a UV-method Acetic Acid kit . Manufacturer-supplied standards and non-inoculated media samples were used as controls.Three independent cultures of each of FS, SS, and Anc were sampled throughout log growth in DM50:50 to determine glucose and acetate concentrations. For each sample, we removed approximately 1 ml of culture, centrifuged to pellet the cells, and stored the supernatant at −80 °C. After all samples were obtained, we estimated the glucose concentrations in the supernatant using a Glucose (HK) Assay kit  evolved under identical conditions. PCR was conducted in 25 μl volumes . Primers 5′-TCGAAAATACACGCTGCAAG and 5′-TTCCACTTTGCTGCTCACAC amplified from 217 bp upstream of the IclR start site through 83% of the gene. The template for each reaction was sampled from a single colony grown for 24 h (for SS and Anc) or 48 h (for FS). Reaction conditions were as follows: 95 °C for 10 min, 30 cycles amplification , and final extension period (72 °C for 5 min). PCR products were size separated in 2.0% agarose to assay for the slower migration of the IS1iclR allele relative to the wild type.Colonies were PCR assayed for the presence of the acs, aceBAK, and iclR and for the iclR gene, we sequenced strains 33A, 33K, and Anc. Additional sequence data were collected for acs from one additional FS and SS from population 33 as well as two FS and two SS from two additional populations. Sequencing was performed by the University of British Columbia's Nucleic Acid and Protein Service (NAPS), using the ABI PRISM Big Dye V.3.1 sequencing kit . Primers were from Treves et al. [acs regulatory region), 5′-GCTGGCGTAGTCACGGTAAT and 5′-ATCGGTTGTTGTTGCCTGTT (aceB regulatory region), or as for PCR and qPCR.For the regulatory regions of s et al.  (acs reghttp://www.ambion.com). RNAs were prepared using RNeasy  or Ribopure (Ambion) kits, following the manufacturers' protocols. Remaining genomic DNA was removed using DNAfree (Ambion). cDNAs were generated using the TaqMan kit (Applied Biosystems), primed with random hexamers.Three independent samples each of 33K, 33A, and Anc were prepared from early log growth in DM-glucose, DM-acetate, and DM50:50. Cultures were collected on ice, pelleted, and resuspended in RNAlater  and 5′-GCTGATACCGCCCAAGAGTTC and 5′-CAGGATGTGATGAGCCGAC (23SrRNA-4). Controls lacking reverse transcriptase were included for each sample to detect genomic DNA contamination and primer dimerization. As well, dissociation curves were used to confirm a single product. Standard curves were prepared for each primer set using five serial 5-fold dilutions of Anc genomic DNA template.Reactions were carried out in duplicate in a 7000 SDS (ABI) using SYBR green master mix (ABI) according to the manufacturers protocol and primers designed with Primer Express 2.0 (ABI). Primers used were: 5′-TGGCGTGGTGAGGCAAT and 5′-GGAAGAAATAGAGCGCAAAATCA aceB template to the quantity of cDNA 23SrRNA-4 template, chosen as a control because of its unchanging, high expression in both glucose and acetate growth conditions [RNA abundance was quantified by normalizing the quantity of cDNA nditions .IS1iclR to Anc and AnciclR into FS using a suicide plasmid mediated technique [We transferred echnique  but subsechnique . Controlhttp://www.mtxlsi.com). Bacteria were inoculated into assay wells containing 250 μl aliquots of DM supplemented with 0.0002% thiamine HCl, 0.1% MgSO4, and one of three resource combinations: 410 mg/L glucose, 410 mg/L acetate, or 41 mg/L glucose and 369 mg/L acetate (DM10:90). Cultures were grown for 24 h at 37 °C with continuous shaking and measured for wide band optical density every 10 min. Five technical replicates of each sample were averaged after anomalous growth curves were removed. We used C++ code provided by J. Tyerman to estimate three growth curve parameters: the maximum growth rates on glucose and acetate and switching time between resources, defined as the time elapsed between the end of growth on glucose and the maximum growth rate on acetate. Briefly, the code linearizes the data over a sliding window of nine time units to establish the changes in slope of the optical density over a 24-h growth period. The first critical point where the slope changes from positive to zero (or negative) defines the switching point. We calculate the glucose growth rate as the maximum slope between time 0 and the switching point, the acetate growth rate as the maximum slope between the switching point and the time of maximum optical density, and the switching lag as the difference in time units between the switching point and the maximum growth rate on acetate. Analyses of variance (ANOVAs) were calculated for these three parameter estimates for the FS, SS, and Anc strains and for the four genetically modified strains, AncAnc),(iclR AncIS1),(iclR FSAnc),(iclR and FSIS1), accession numbers for the E. coli strains discussed in this paper are acetyl-CoA synthetase (EB1417), malate synthetase A (EB0022), isocitrate lyase (EB0021), isocitrate dehydrogenase kinase/phosphatase (EB0025), and isocitrate lyase repressor (EB0486).The EchoBASE (


Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication.A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet.This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer. A key issue in functional genomics is to understand how genetic or epigenetic perturbations to cellular machinery result in disease. Related to this is the question of how to perturb the system so that it functions in a desirable way. The first issue is the concern of hypothesis generation and the second is drug target discovery. Much of the work in these areas is now done by human experts who mentally integrate experimental data reported in the literature and perhaps statistical summaries of data from a variety of sources. Unfortunately, the number of papers published each year on any specific disease pathway continues to grow rapidly, making it difficult for specialists to incorporate all new results into their mental model of a pathway. Furthermore, the amount of data becoming available through new measurement technologies is far greater than the human brain can adequately synthesize. A principal goal of systems biology is to devise computer algorithms to help with the task of integrating expert knowledge, largely gleaned through published results, with high throughput data sources to create new hypotheses and putative drug targets. A particularly vexing problem is that the amount of data available about any pathway is usually incomplete. The human mind is fortunately very good at reasoning with incomplete knowledge, which enables researchers to create new hypotheses even when only incomplete information is available. Systems biology may benefit from emulating human reasoning: less detailed models that can incorporate many sources of incomplete information may yield more useful output .Many engineering system designs today use descriptive models rather than mathematical models derived from mathematical formulas, such as differential equations. This is often achieved using a modeling approach based on fuzzy logic. Fuzzy logic is a computational method for formulating and transferring human expert knowledge to computational models. It provides a flexible tool for modeling the relationship between input and output information and is distinguished by its robustness with respect to noise and variations in system parameters -4. This Fuzzy logic has become ubiquitous in modern control systems engineering, including medical applications -9. It haany nonlinear function as precisely as desired, give appropriate data  for simplicity in illustrating the dynamics of particular motifs. However, the dynamic range of Shh is defined to be  in this model. This was set to illustrate that variables can be assigned values in Feedback motifs are common in biological systems -43. NegaCells have a remarkable ability to orchestrate precise sequences of events using imprecise components. An important mechanism for accomplishing this feat is to filter out some signals and respond to others. A feedforward motif can filter the effects of transient signals while allowing sustained signals to activate downstream components . Figure +/- mice. Approximately 14–20% of heterozygous Ptc+/- mice develop medulloblastoma tumors, whereas these tumors are rare in Ptc+/+ mice , then 0.0316 = sqrt(0.001) is fuzzified by linearly combining logarithms of the fuzzy set values. Log(0.0316) = -1.5 = 0.5 log(10-2) + 0.5 log(101) = 0.5 very low + 0.5 low. Logarithmic scales are useful when concentrations vary over a large range and changes in biochemical activity require concentration changes over many orders of magnitude. The rate at which the actual  concentration changes in each reaction is still determined by the reaction rate. We emphasize again that the reaction rules are still defined in terms of the effect of fuzzy values  on the reaction rate. The fuzzy set definitions, whether linear or logarithmic, provide meaning to the linguistic fuzzy sets in the context of particular reactions.For wider dynamic range, a logarithmic scale has also been implemented. As illustrated in figure The reaction network in figure Node Shh 0.1 log 0.001 0.1Node NmycPNode Ptc1 0.5Node SmoNode Gli1The keyword 'Node' identifies this as a node definition. Initial values 0.0 by default if not given explicitly. Similarly, the domain or universe of discourse is  if not given. In the above, Shh has an initial value of 0.1 and a domain of  on a logarithmic scale. This was done primarily for illustration, since in this simulation Shh was set to a constant value and maintained. The initial value for Ptc1 is 0.5; all others have initial value 0.0. The meaning of "low" or "high" for each variable is context dependent and is defined for each reaction that a variable participates in.The model has 8 reactions, which are essentially fuzzy inference engines that map the fuzzy input variables to a fuzzy output rate. The network of reactions is illustrated in figure Reaction Smo_production 0.002   pro Smo   inh Ptc1 5 2 1 1 0 0Reaction Smo_decay 0.002   sub SmoThe keyword 'Reaction' defines a reaction definition. The reaction name follows the keyword. The number after the reaction name is the reaction rate constant. If not given the default value is 1.0. The second reaction shown here, Smo_decay, has a single reactant, Smo, which is a substrate. The 'sub' keyword implies the following default values:Reaction Smo_decay 0.002   sub Smo 0 1 2 3 4 5 stoi -1 centroids 0.0 0.2 0.4 0.6 0.8 1.0The first six numbers that follow 'sub Smo' are the rules that define how the reactant Smo contributes to the rate of this reaction. The six integers are the output rate for each fuzzy level of Smo. These are a shorthand for the linguistic rules: "when Smo is zero, the rate is 0; when Smo is very low, the rate is 1 or very low; when Smo is low, the rate is 2 or low; when Smo is medium the rate is 3 or medium; when Smo is high, the rate is 4 or high; when Smo is highest, the rate is 5 or highest. If more information is available to give more accurate rules or centroids, they can be written here. More likely, these values will be set by automatic optimization methods when high throughput data is used to refine models that are initially built manually by experts. We have implemented a simple genetic algorithm that manipulates the rules and centroids. Incorporation of procedures for specific data sources remain for future research, but the model is designed for these enhancements.The Smo_production reaction fires on every time step. The default rules for a product, Smo in this case, are 5 5 5 5 5 0. This means that the output rate is 5 or "highest" when the Smo concentration is zero through high (0 to 4). If Smo reaches the highest concentration, 5, it cannot go higher because that would exceed its defined limit. The rate goes to zero at that point. The inhibitor, Ptc1, has rules 5 2 1 1 0 0, which means that when Ptc1 concentration is high or highest, the rate is zero. Similarly, when Ptc1 is zero, the rate is 5 (unaffected), when Ptc1 is very low, the rate tends to low (2), when Ptc1 is low or medium, the rate is very low (1).The actual reaction rate is computed as follows: the real concentration of each participating reactant is fuzzified. The rate is a multi-dimensional function of the number of reactants. Following the usual Standard Additive Model, the rate is found by adding fractional membership of each variable to find the total contribution:If on a particular time step Smo is 0.55 = 0.25 low and 0.75 medium, while Ptc1 is 0.4 high and 0.6 highest, then equation (1) becomesHA + 0.75 * 0.4 * HA + 0.25 * 0.6 * HA + 0.75 * 0.6 * HA0.25 * 0.4 * = 0.1 * 1.7 + 0.3 * 0 + 0.15 * 0 + 0.45 * 0= 0.17All rates are computed on a 0 to 1 scale, and then scaled by the rate coefficient. The final rate for this reaction step is thus 3.4e-4. The value of Smo on the next time step will be 0.55034.The list of variables and the definition of all reactions is the primary input to define a Bionet model. Start and end times for the simulation and the number of time steps are also needed as input. At each time step, all reactions take place and variables are updated. Bionet simulations are similar to explicit time-stepping schemes in numerical approximations to differential equations. Bionet implements the Standard Additive Model or SAM for each fuzzy reaction . This immax must hold for the fastest reaction, where xmax is either the difference between the highest and next-to-highest centroid for any variable that is a product substrate in that reaction, or xmax is the difference between the next-to-lowest and lowest centroid for any substrate in that reaction. For all simulations in this paper, the time step was 1 minute. Most reaction rates were on the order of 10-3 minutes, but the cyclin decay rates were faster (1.0 min-1 for cyclin D).Time limits and time step size must be specified as for any dynamic model. This method uses explicit time stepping, so the step size is controlled by the fastest reaction. The condition rate*Δt < ΔxReaction order is asynchronous to prevent any bias in the simulation. The order of reactions is randomly chosen at every time step. Furthermore, if desired, a probability of firing may be assigned to a reaction. In this case, reactions will occur with some probability on each of the time steps. Models that include stochastic reactions can represent stochastic processes in cells, such as stochastic gene expression  or a proSimulation software, source code and input files containing rules for all examples discussed in this paper are available for download on the Bionet web site.Project name: BionetProject home page: Operating systems: Platform independent; GUI version for MS-Windows only.Programming language: java (Java 2 runtime environment)License: Use of this software is governed by the Gnu General Public License .