A U-system approach for predicting metabolic behaviors and responses based on an alleged metabolic reaction network
© Sriyudthsak et al.; licensee BioMed Central Ltd. 2014
Published: 12 December 2014
Progress in systems biology offers sophisticated approaches toward a comprehensive understanding of biological systems. Yet, computational analyses are held back due to difficulties in determining suitable model parameter values from experimental data which naturally are subject to biological fluctuations. The data may also be corrupted by experimental uncertainties and sometimes do not contain all information regarding variables that cannot be measured for technical reasons.
We show here a streamlined approach for the construction of a coarse model that allows us to set up dynamic models with minimal input information. The approach uses a hybrid between a pure mass action system and a generalized mass action (GMA) system in the framework of biochemical systems theory (BST) with rate constants of 1, normal kinetic orders of 1, and -0.5 and 0.5 for inhibitory and activating effects, named Unity (U)-system. The U-system model does not necessarily fit all data well but is often sufficient for predicting metabolic behavior of metabolites which cannot be simultaneously measured, identifying inconsistencies between experimental data and the assumed underlying pathway structure, as well as predicting system responses to a modification of gene or enzyme. The U-system approach was validated with small, generic systems and implemented to model a large-scale metabolic reaction network of a higher plant, Arabidopsis. The dynamic behaviors obtained by predictive simulations agreed with actually available metabolomic time-series data, identified probable errors in the experimental datasets, and estimated probable behavior of unmeasurable metabolites in a qualitative manner. The model could also predict metabolic responses of Arabidopsis with altered network structures due to genetic modification.
The U-system approach can effectively predict metabolic behaviors and responses based on structures of an alleged metabolic reaction network. Thus, it can be a useful first-line tool of data analysis, model diagnostics and aid the design of next-step experiments.
The exploitation of logical concepts for the prediction of probable behaviors of unmeasurable data and identification of uncertainties in datasets becomes much more complicated and important if the pathway systems are large. Large-scale datasets, and in particular time series data characterizing metabolic systems, are also becoming more and more commonplace to assist the investigation. To acquire such datasets with reasonable efforts, the high-throughput analytical instruments have been developed in recent times [1–3]. Given the nature of experiments, however, it is not even always certain that all information for interested variables can be gained from experiments and not all metabolites in the large-scale pathway can be measured. It also cannot be guarantee that the replicated samples in terms of biological, technical and analytical aspects reproduce the same results, even in a qualitative sense. Two major questions thus arise. The first question is whether computational methods of systems biology which are cheap and straightforward may be able to predict or analyze such unmeasurable data with respect to system identification based on our understanding of the system. The second question is how reliable or accurate biological data are, especially if they were obtained from different cellular compartments or from different developmental stages in plants and animals.
It is therefore useful to explore to what degree it might be possible to employ the logic of the pathway to address these issues. Such an analysis seems to be feasible in principle, because the biologist executing the experiments usually has a relatively well-supported concept of the topology of the pathway system. The challenge is that even small pathways quickly become too complicated for intuitive assessments, and larger and more complicated pathway systems simply prevent us from testing assumptions or hypotheses without computational support. Such computational support of course requires mathematical models, which immediately leads to the challenge of setting up detailed, quantitatively appropriate models with a minimum of input requirements. The goal of such models is not necessarily to provide a quantitatively perfect picture of a pathway or to fit all data with great accuracy. Instead, these models should be easy to construct, robust, and merely able to identify how data within a large dataset could probably be.
Power-law models within Biochemical Systems Theory (BST; ) possess two great advantages that can be leveraged toward predicting the unmeasurable data as well as testing the consistency between the measured data and an alleged pathway structure and its regulation. First, symbolic BST models can be designed immediately for a pathway system of any complexity. These symbolic models do not include parameter values, but they do define a model's potential repertoire of responses, rather than addressing specific data fits. Second, the estimation of optimal parameter values for large systems is generally fraught with technical difficulties . This issue is greatly ameliorated for BST models, because even relatively coarse numerical settings of their parameters are often sufficient to capture the behavior of a metabolic pathway system in a semi-quantitative fashion.
Here we capitalize on these two features of BST and propose a coarse test for the prospect of metabolic time-series data and consistency between the data and an alleged pathway diagram. The purpose is not to establish optimally fitting models but to identify how the unmeasurable data of metabolites located in a focused pathway probably behave, which time-series data within the dataset may be inconsistent with the understanding of pathway, as well as which data within a large dataset are probably reliable and which, if any, are most likely imprecise. We use a hybrid between a pure mass action system and a generalized mass action (GMA) system in the framework of BST with kinetic orders of 1, which allows for inhibitory and activating effects that are modeled with kinetic orders of -0.5 and 0.5. The rate constants are at first set to 1, but may be later subjected to order-of-magnitude adjustments, which obviously improve the model representation of the data. Once the parameter values are fixed, their effects are to some degree compensated by adjustments in metabolite concentrations according to the network structure so that we named this simplified method unity (U)-system. The diagram for U-system approach is also exhibited in Figure 1b. We begin the demonstration of the U-system approach with artificial "data" from fully known, representative models and show that coarse values for kinetic orders and rate constants retain much of the qualitative behavior of true model responses. These U-system solutions are rather coarse, but can be improved with order-of-magnitude adjustments of flux split ratios at branched points. We then demonstrate the feasibility of the method with actual data describing amino acid synthesis in Arabidopsis. This pathway system contains 351 metabolites and 441 fluxes, which are subject to various regulatory mechanisms. Apart from these, we have measurements from two different analytical methods, one comprising 268 metabolites, including 21 amino acids, and the other one accounting for 16 amino acids. As expected, although a large number of data can be measured, it is clear that not all data can be obtained. Also, none of the obtained data contain exactly the same values even in the replicated samples, mainly due to high biological variation among samples. In most cases, the measured time series are qualitatively the same, but in some cases they differ. We show that the U-system analysis estimates the probable behaviors of unmeasurable data and identifies at least some of the unreliable data. Besides, the U-system model is also applicable to predict metabolic responses when a metabolic reaction is altered.
Results and discussion
Testing of feasibility of assumptions in U-system
Obviously, a system with arbitrary parameter values of 1 is quite different from a system with diverse parameter values. The performances and characteristics of U-system approach were simply approved using a simple model of a linear pathway by comparing with the typical model constructed in Michaelis-Menten format where the enzymatic reaction velocity is usually determined by an in vitro experiment (Supplementary Information - additional file 1). The result showed that U-system model can produce qualitatively similar results to the original. Thus, a simple generic, yet representative model was used to test to what degree the U-system model can produce qualitatively comparable results.
Effect of rate constants on metabolic behaviors
Effect of kinetic orders on metabolic behaviors
Figure 2c and 2d show patterns of normalized concentrations of each metabolite over time resulting from changes in kinetic orders g13 and h34, respectively. When g13 is changed, only the magnitudes of normalized metabolite concentrations are changed but almost no difference is observed in temporal changes in time-course patterns of metabolite concentrations. Similar results are also observed for g21, g32, g41, h111, h112, h22, h33 and h44 (Supplementary Figures S5 and S6 - additional file 1). This consistency may be due to the fact that the ranges of kinetic orders are typically between −1 and 1 , so that kinetic orders do not play an overly strong role in the variation of metabolite concentrations, and the corresponding fluxes do not change much. The results for h34 (Figure 2d) and h44 (Supplementary Figure S6 - additional file 1) are different, as the trajectories noticeably elongate in the direction of the x-axis with changes in kinetic orders. According to metabolic reaction network diagram in Figure 1c, one sees that these kinetic orders are involved in the degradation of X3, so that an increase in kinetic order strongly increases the degradation flux of X3. However, this situation is somewhat unusual because of the high activation of the efflux. In this case, X4 is produced from X1 which is inhibited by X3 so that a strong activation of the efflux of X3 by X4 quite effectively slows down changes in X3. Also, X3 has a low initial value and kinetic order compared to other metabolites. Thus, the dynamics of X3 is driven by other metabolites and X3 concentration does not change much unless the other parameters vary. Furthermore, the input X1 is strongly affected by X3 which does not change much. Accordingly, other metabolites hardly change throughout the period of observed time. In general, the kinetic parameters for activation are seldom higher than kinetic parameters for the metabolite itself so that this particular scenario is rare. As a consequence, it is still able to deduce that changes of kinetic orders tend to affect the trajectories in the direction of the y-axis rather than the x-axis.
Effect of random parameters on metabolic behaviors using Monte-Carlo simulation
The initial values of metabolite concentrations of X1, X2, X3, and X4 were set to 3-, 1-, 0.5- and 2-fold their steady-state values. The simulated metabolite concentrations for all cases were normalized by their steady-state values for the observation and comparison. To facilitate inspection of the calculated behaviors of the metabolite concentrations, the scales of both axes were adjusted by the maximum and minimum values of each simulation. The result shows that most Monte-Carlo simulations exhibit qualitatively comparable dynamic responses regardless of the parameter values chosen. Each metabolite concentration changes throughout the period of time and returns to its steady-state value in a similar manner. This observation implies that both models provide qualitatively comparable dynamic patterns of changes in metabolite concentrations. Furthermore, the GMA and U-system responses are surrounded by the Monte-Carlo simulation results for 1000 parameter sets.
In summary of this analysis, the U-system approach indicates that metabolic behaviors mainly depend on the network structure of a metabolic system. Unlike most of modeling methods, it does not need the process of parameter estimation in model construction, although it cannot offer actual quantitative concentrations of metabolic behaviors. This insight leads to several consequences. First, the U-system approach provides a coarse time-transient behavior of the system regardless of the requirements or dependencies of experimental data. Accordingly, it will not confront difficulties in parameter fitting, especially in a large-scale system. Second, the U-system approach is simple. Plausible parameters for inhibition and activation can be easily set into the model to grasp their possible effects, which makes it possible to design additional experiments. Third, the U-system approach is suitable for practical applications because of its simplicity and flexibility. One can simply construct a mathematical model based on only experimental facts regarding the network structure and regulation. If the U-system simulations are inconsistent with experimental data, other possible systems may be simply constructed to find out unknown candidates. As a result, an appropriate experiment may be designed to validate or refute the predictions from the simulation and to characterize biological details. Lastly, the U-system approach allows us to acquire tendencies of metabolic behaviors to assess experimental data within the context of their actual network structure.
Application to metabolic reaction network of Arabidopsis
Modified U-system approach with rough adjustments of rate constants
The mathematical model including nonlinear ordinary differential equations (ODEs) for 351 metabolites was constructed by U-system approach on the basis of only structures of metabolic reaction networks available in databases (Supplementary Model - additional file 2). Fick's laws of diffusion  was used for representing the uptake rate of lysine plus threonine from the callus culture medium to the inside of the cells from t=0, and the parameters for uptake rates were estimated using experimental data. For simulations, all ODEs representing changes of metabolite concentrations inside the cells were calculated together with equations for the supplementation throughout a period of 100 h.
Evaluation of the modified U-system approach for metabolic responses in Arabidopsis
The red lines in Figure 6 (a1 and b1) indicate that the simulated concentrations of threonine (X26) and lysine (X93) agreed with the metabolic behaviors of experimental data obtained by metabolome analysis with significant correlations (p-values < 0.005; detailed in Supplementary Model - additional file 2). However, the simulated concentration of aspartate (X78) was not consistent with metabolome data (Figure 6d1). It could be that, although the metabolic reaction network used for model construction is correct, the U-system approach might fail to predict the appropriate metabolic behavior. To assess this possibility, a specific measurement only for amino acids using another analytical instrument gas chromatography-mass spectrometry (GC-MS) was performed using the identical samples (Figure 6, middle boxes). The results from amino acid analysis seemed to provide better metabolite trajectories with lower fluctuations. With respect to the concentrations of threonine (X26), lysine (X93) and glutamate (X37), both methods yielded similar results in terms of the metabolic trajectories, although the simulations were in better agreement with the time-courses of metabolite concentrations measured by amino acid analysis. For example, lysine concentration (X93) measured by metabolome analysis (Figure 6b1) increased and maintained its level from t=24 to t=60 h before increasing again. The U-system simulation pinpointed that four data at t=24, 36, 48 and 60 h obtained by metabolome analysis may contain errors. Actually, the data at t=24 and 36 h (shown as blue dots) exhibited high standard deviations. This assumption may be indirectly supported by the results of correlation analysis. For example, the correlation between the U-system simulation and the time-series data obtained by the amino acid analysis (Figure 6b2) is 0.991, which is higher than the correlation of the value, 0.881, between the U-system simulation and the data obtained by metabolome analysis.The result indicates that if a relatively reliable metabolic reaction network diagram is available, the prediction of time-dependent changes in metabolite concentrations using the U-system is beneficial for judging the likely correctness of data measured by metabolome analysis (see, e.g., green dots in Figure 1a), which is designed to provide massive datasets of metabolite concentrations sometimes at the expense of absolute concentration accuracies for all metabolites. By contrast, if the predicted behaviors are significantly different in some of the metabolites (see, e.g., blue dots in Figure 1a), there may be an unknown metabolic pathway associated with these metabolites or an unidentified regulatory signal. Thus, one may attempt to test a possible system by modifying a pathway or regulation, which can provide a consistent result to predict an unknown candidate before performing a next experiment.
As mentioned above, the relative concentrations observed for several amino acids such as aspartate and glutamate were scattered mainly because of biological variability and possibly due to analytical limitations, and it was difficult to precisely capture the time courses of their concentrations (Figure 6c and 6d). In fact, significant fluctuations in actual metabolite concentrations were observed even in the control samples without lysine and threonine supplementation (Figure 6c3 and 6d3, black dots). However, the computational results suggested that when lysine and threonine were applied as supplements, the aspartate concentration (X78) slightly increased and then decreased to its initial concentration level or steady-state (Figure 6d1 and 2). This estimation can be supported by the previous experiment related to aspartate kinase (AK) . AK is the first committed enzyme of aspartate-family amino acid biosynthesis and regulated by the feedback inhibition by lysine and threonine (Figure 4). The flux analysis in Lemna revealed that the deregulation of AK, i.e., the increase of AK activity, caused accumulation of lysine, threonine, methionine, and AdoMet and decrease in the aspartate concentration . Thus, in our study, a reduction in the AK activity through the feedback inhibition by supplementation of lysine and threonine might lead to an initial increase in the aspartate concentration as predicted in modified U-system. The transient increases in glutamate and aspartate concentrations (X37 and X78, respectively) after lysine and threonine supplementation were also indirectly supported by the increase of proline (X50) observed both in simulation and metabolome analysis (Figure 5b). One could assume that if the simulated behaviors of dead-end metabolite concentrations (proline (X50) in this case) agree well with the experimental data, those of intermediate metabolites (glutamate (X37) and aspartate (X78) in this case) are likely to be correct. This consistency implies that the prediction of metabolic behavior by our approach is reliable even if the quality of the experimental data for some metabolites is not very high.
Prediction of dynamic behaviors of unmeasurable metabolite concentrations
The 86 out of 268 metabolite concentrations measured by metabolome analysis (purple and blue boxes in Figure 4) were included in the U-system model. The 66 out of 86 metabolite concentrations (blue boxes in Figure 4) showed unclear dynamic patterns changing throughout the period of 96 h after the supplementation of lysine and threonine (Supplementary Figures - additional file 3). One may note that these metabolite concentrations showing no significant variations do not change or change only slightly to the extent that cannot be experimentally observed due to biological fluctuations and analytical constraints. Thus, although a large number of metabolite concentrations in metabolic pathways can be simultaneously measured, the metabolic behaviors assumed from measured metabolite concentrations may sometimes not be entirely reliable. Also, the 265 out of 351 metabolites incorporated in the U-system model could not be detected by this metabolome analysis. Given a computational model, we may be able to predict the dynamic responses of metabolites that cannot be measured reliably nor detected at all. The computational results also have a potential to provide some types of theoretical validation on the accuracy of the experimental data.
Prediction of "unpredictable" metabolic behaviors
The modified U-system enables us to mathematically model a relatively large-scale metabolic system, and hence simulations based on the modified U-system provide predictive dynamic behaviors of metabolite concentrations which are hardly provided by existing methods. The modified U-system model for the Arabidopsis metabolic system predicted that the supplementation of lysine and threonine did not affect 4-methoxy-3-indolylmethyl GSL concentration (X261) until t=60 h (Figure 7b), nor dihydrouracil concentration (X333) in pyrimidine biosynthesis until t=40 h (Figure 7c). These results suggested that the supplementation of lysine and threonine influenced X261 and X333 in a delayed fashion due to metabolic flow passing through various reactions in long and complicated pathways (see Figure 4). The U-system predictions of the time-transient behaviors of metabolite concentrations were reasonable and this kind of metabolic relationship between distant metabolites on metabolic map cannot be examined by either statistical methods or typical mathematical modeling without delay functions. Furthermore, similar predictive simulations of several metabolite located in long and complicated pathways such as xanthine (X299) and ureidoproprionate (X344) in purine and pyrimidine biosynthesis (gray zone in Figure 4), and aminoadipate (X153), glutarate (X152), and pipecolate (X141) in lysine degradation (yellow zone in Figure 4) reasonably agreed with the experimental results (Supplementary Figures - additional file 3). The results could be an example for a large-scale system to indirectly validate the U-system approach. They also imply that predicted behaviors of unmeasurable metabolites located between detectable metabolites are probably correct, at least, qualitatively.
Qualitative predictions of consequences of gene modifications
A challenge in systems biology, in addition to the prediction of metabolic behaviors, is the prediction of metabolic responses of an altered network structure, in which, for instance, gene expression or enzyme activities are modified. To assess whether the modified U-system model is applicable for this kind of prediction, an in silico knockdown for MTO2 encoding threonine synthase gene of Arabidopsis in the biosynthetic pathway of aspartate-family amino acids (Figure 8 enlarged from Figure 4). When the flux controlled by MTO2 was 90% reduced in silico, the threonine concentration was predicted to decrease whereas the methionine concentration was predicted to increase. In overall, the 6 out of 7 amino acids or 85% prediction agreed with the original result from biological experiment using an Arabidopsis mto2 mutant . The prediction was further compared to the result from metabolome analysis . Figure 8 showed that 73% correctness from 11 metabolites showing the significant changes comparing with the wide-type whereas the metabolome and amino acid data showed only 50% agreement among 6 amino acids. The incorrect prediction might be because of the insufficient information for model input which could be considered to be an unidentified pathway. Nevertheless, changes of all unmeasurable metabolites can also be qualitatively predicted using our model. The result suggests that the U-system approach is capable of predicting metabolic levels when the system is modified, even if specific information on complicated regulatory mechanisms is not available. This information can aid the design of next-step experiments.
The U-system approach is simple, flexible and can be practically exploited to predict the coarse time-transient behavior of metabolites in a large-scale metabolic system. It is also capable of predicting metabolic behaviors when the system is modified. The U-system approach has two major characteristics: 1) the U-system model is presented in a normalized form, and 2) the U-system model constructed only using partial, currently-available metabolic pathways. Recent advances in metabolomics technology have enabled us to obtain large-scale semi-quantitative datasets for hundreds of metabolite concentrations. The first characteristic is essential to establish mathematical models for actual metabolic systems consisting of hundreds to thousands of metabolites, whose concentrations cannot be measured practically by existing methods of measurement other than metabolomics. The second characteristic is desirable for the coarse characterization of complicated systems, because metabolic responses mainly depend on the network structure, rather than parameter values, as we have shown in this study. Thus, the U-system approach is not only informatively useful for data analysis and model diagnostics but it also can aid the design of next-step experiments.
Background on Biochemical Systems Theory (BST)
1. Structure of equations
Here, X i (i=1,..., n) are dependent variables (typically metabolite concentrations), Y j (j=1,..., m) are independent variables (typically external substrates or enzyme activities), α ik (i=1,..., n; k=1,..., p) and β ik (i=1,..., n; k=1,..., q) are the non-negative rate constants for influxes and effluxes, respectively, g ijk (i=1,..., n; j=1,..., m; k=1,..., p) and h ijk (i=1,..., n; j=1,..., m; k=1,..., q) are real-valued kinetic orders associated with dependent variables in influxes and effluxes, respectively, while G ijk (i=1,..., n; j=1,..., m; k=1,..., p) and H ijk (i=1,..., n; j=1,..., m; k=1,..., q) are those associated with independent variables. n and m are the numbers of dependent and independent variables, respectively, p and q are the maximum numbers of influxes and effluxes, respectively, and t is time. Details have been documented in the literature many times, e.g. in [6, 7, 9, 21].
2. Validity and accuracy of BST for modeling biological systems
Since the power-law format in BST appears to be quite restrictive, the accuracy of different model variants within BST has been assessed many times. As Taylor approximations, the models are mathematically guaranteed to be correct at an operating point of choice, but their ranges of validity were also tested against results from more traditional formulations like the Michaelis-Menten formalism. These comparisons demonstrated sufficient accuracy of BST models over quite large ranges in variation of the involved variables [22–25]. Applications of BST models were also demonstrated with various large-scale systems, including the TCA cycle , ethanol fermentation , purine metabolism , and sphingolipid metabolism .
Meaning of parameters and U-system approach
Model parameters of pathway systems are usually estimated from kinetic information or from metabolic time series data. In Eq. (5), the familiar kinetic parameters involved in Michaelis-Menten kinetics (maximum reaction rate Vmax, Michaelis constant KM, etc.) are not explicitly visible, but they are instead distributed to the rate constants and kinetic orders. The BST parameters have their own meaning either as turn-over rates or as the specific effect that a variable has on the flux term in which it is involved. Thus, if a kinetic order is negative, the variable with which it is associated has a negative (inhibitory) effect on the flux term. This effect is given exclusively by the one appropriate kinetic order, thereby reducing the number of kinetic parameters in the system, which can be much higher in traditional rate functions. Still, the simplified BST parameters are able to capture time-transient behaviors of metabolite concentrations and other characteristics of metabolic reaction systems very well.
A consequence of the direct meaning of kinetic orders in BST is that it is possible to set up differential equations in symbolic notation, solely based on the structure of a metabolic pathway and its regulatory structure, whereas deep knowledge of their regulatory mechanisms is not needed. Furthermore, the kinetic parameters in Eq. (5) can be estimated directly from time-series data of metabolite concentrations, and subsequently convey important information about the system characteristics. In some cases, traditional kinetic parameters from the literature, such as Vmax, can be converted into BST parameters, but this is seldom possible on the basis of metabolome data, which are often presented as relative rather than absolute concentrations.
It was recently shown  that a coarse grid of kinetic orders is sufficient to fit time developments in pathway systems with surprisingly high accuracy. Expanding on this idea, we here fix all kinetic orders to 1, 0.5 or -0.5 to represent enzyme kinetics, positive or negative effects of metabolites, respectively. In other words, we use a hybrid between a pure mass action system and a GMA system, which allows for inhibitory and activating effects that are modeled with kinetic orders of -0.5 and 0.5. The rate constants are at first also set to 1, but may be later subjected to order-of-magnitude adjustments, which obviously improve the model representation of the data. Thus, at branched points, the equations are adjusted based on stoichiometry. For example, in a branched reaction of the type shown in Figure 1c the X1 is converted into X2 and X4, so that the equation is dX1/dt = VinX1 - VoutX1-1 - VoutX1-2. Since most of the parameters are set at unity in the proposed approach, we name this system as Unity-system or U-system.
Fixing the parameters values is to some degree compensated by adjustments in metabolite concentrations. In other words, if the kinetic orders are forced to be 1, +0.5, or -0.5, a data fit will lead to adjusted metabolite concentrations.
Monte-Carlo simulations were employed to test in an unbiased manner whether the qualitative behavior of a model is consistent with the U-system representation. Most Monte-Carlo simulations were calculated for 1,000 loops. The parameter values were generated randomly from uniform distributions. The ranges for rate constants and kinetic orders were between 0.5 and 20, and ±0.2 and ±0.8, respectively.
Differential equations were solved using automatic switching for stiff and non-stiff problems (LSODA)  using the ODE integration package from SciPy
Details of the Arabidopsis system
Arabidopsis callus culture
Arabidopsis thaliana liquid callus culture derived from accession Col-0 was prepared as described in Murota et al.  with slight modifications. For callus induction, minced seedlings were incubated in RM28 medium under constant light. The medium was changed every 6 days. For a metabolic perturbation experiment, RM28 medium supplemented with 10 mM L-lysine and 1 mM L-threonine  was used at the third medium change. For a control experiment, RM28 without supplementation was used. Sucrose in RM28 medium was a sole carbon source for callus culture. The experiments were carried out in triplicate.
For both metabolome and amino acid analyses, calli were collected prior to lysine and threonine treatment (0 h), and 2, 6, 12, 24, 36, 48, 60, 72, 84 and 96 h after the treatment. The calli were immediately frozen in liquid nitrogen and stored at -80°C. Prior to analyses, the frozen samples were lyophilized using a freeze dryer (FDU-2100, EYELA) in a vacuum.
Metabolites were extracted by homogenizing lyophilized callus in 500 uL 80% methanol in 0.1% formic acid per 2 mg dry weight callus with 5 mm zirconia beads (no.5-4060-13, AS ONE Co. Ltd.) in 2.0 mL sampling tubes (no.132-620C, WATSON Co., LTD) for 5 min using shake master NEO (Bio Medical Science, Tokyo, Japan). After centrifugation using a high speed refrigerated micro centrifuge (TOMY MX-300) at 14,000 r.p.m. at 4°C, 250 uL supernatant was dried up in 96 well plate and the residue was dissolved in 120 uL ultrapure water (no. 210-01303, Wako Pure Chemical Industries, Ltd.). One uL of the solution was subjected to widely targeted metabolome analysis  by LC-MS using UPLC-TQD system (Waters, Milford, MA, USA). The peak intensities were normalized by those of internal standards (10-campher sulfonate and lidocaine). For mathematical modeling, the normalized peak intensities of treated samples were subtracted from those of control samples to eliminate effects of cell growth and obtain relative metabolite concentrations.
Amino acid analysis
Metabolites were extracted by homogenizing lyophilized callus in 500 uL of the extraction solution (methanol: milliQ water = 4:1) per 2 mg dry weight in 2.0 mL sampling tubes using Mixer Mill MM300. After centrifugation at 15,000 r.p.m. at 25°C for 10 min, the 400 uL supernatant was used for the analysis performed according to the protocol based on the EZ:faast amino acid derivatization technique for GC-MS (Phenomenex, Torrance, CA) with slight modification. The reagent 1 (internal standard solution) was diluted to 10-fold while the reagent 6 (re-dissolution solvent) was added only 50uL to concentrate amino acid concentrations in samples. Then, 1 uL of solution was subjected to amino acid analysis by GC-MS using GCMS-QP2010 Plus (Shimadzu, Kyoto, Japan).
The correlation coefficients, testing for the significance of the correlation coefficients, and student's t continuous random variable based on a survival function were calculated for comparing model simulations with experimental data.
Arabiodopsis callus model
Information for metabolic pathways including their inhibition and activation was assembled from KEGG (http://www.genome.jp/kegg/) [37, 38] and AraCyc (http://arabidopsis.org/tools/aracyc/)  databases. A mathematical model was constructed based on the metabolic reaction network including 351 metabolites and 441 fluxes, which cover a wide variety of metabolic pathways related to amino acid biosynthesis, glycolysis pathway, TCA cycle, pentose phosphate pathway, GSL biosynthesis, and purine and pyrimidine biosynthesis (Figure 4; Supplementary Model - additional file 2). The relative concentrations of 86 metabolites among 268 analyzed in metabolome analysis were included in the model. Twenty (square purple boxes in Figure 4) out of the 86 metabolite concentrations included in the model (23.2%) showed considerable changes.
The present study employs a GMA-system model to which the U-system approach was applied for characterizing metabolic behaviors in a large-scale metabolic reaction network related to aspartate-derived amino acids, namely, aspartate, lysine, threonine, and methionine. The effects of feedback inhibition and activation were also included based on information available from the literature and pertinent databases [12, 40]. Kinetic orders of metabolites were set to 1, while kinetic orders for inhibition and activation signals were set to -0.5 and 0.5, respectively. In the experiments to obtain relative metabolite concentrations, lysine and threonine was supplemented to the culture medium and transported into the callus cells. Then Fick's law of diffusion  was included in the equations for the supplemented metabolites lysine and threonine. Also, their parameters were appropriately fitted with available data of Lys and Thr obtained by metabolome analysis. Metabolite concentrations at the steady state were calculated by simultaneously solving the ordinary differential equations for all 351 metabolites (Figure 4). Since these concentrations were optimized according to the mass balance of the system, the magnitude of the concentration values become smaller as the location of metabolites on the metabolic map were farther away from glucose, which was the main initial metabolite in the present study. It is noted that time-series metabolome data from the experiment were given in relative rather than absolute concentrations. Thus, it is reasonable to take into account the magnitudes of metabolite concentrations while considering predicted behaviors of the metabolite concentrations.
We thank Mss. Saeko Yasokawa, Eriko Tanaka and Hitomi Sekihara for technical assistance in callus cultures, Ms. Akane Sakata and Mrs. Muneo Sato and Yutaka Yamada for technical assistance in metabolome analysis, and Mr. Hiroshi Kiyota for technical assistance in amino acid analysis. This work was supported in part by the Japan Science and Technology Agency, CREST to MYH, MEXT KAKENHI grant numbers 25119719 to FS and 22119006 to SN, JSPS KAKENHI grant number 25660084 to MYH, Grant-in-Aid from the NC-CARP project of MEXT to MYH, and a grant from the U.S. National Science Foundation (Project MCB-0946595) to EOV.
Publication charges for this article were funded by KAKENHI grant number 25660084.
This article has been published as part of BMC systems Biology Volume 8 Supplement 5, 2014: Proceedings of the 25th International Conference on Genome Informatics (GIW/ISCB-Asia): Systems Biology. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcsystbiol/supplements/8/S5.
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