Understanding the regulation of aspartate metabolism using a model based on measured kinetic parameters

Design patterns of biological cells

Design patterns are generalized solutions to frequently recurring problems. They were initially developed by architects and computer scientists to create a higher level of abstraction for their designs. Here, we extend these concepts to cell biology to lend a new perspective on the evolved designs of cells' underlying reaction networks. We present a catalog of 21 design patterns divided into three categories: creational patterns describe processes that build the cell, structural patterns describe the layouts of reaction networks, and behavioral patterns describe reaction network function. Applying this pattern language to the E. coli central metabolic reaction network, the yeast pheromone response signaling network, and other examples lends new insights into these systems.

The Arabidopsis leaf quantitative atlas: a cellular and subcellular mapping through unified data integration

Quantitative analyses and models are required to connect a plant's cellular organisation with its metabolism. However, quantitative data are often scattered over multiple studies, and finding such data and converting them into useful information is time-consuming. Consequently, there is a need to centralise the available data and to highlight the remaining knowledge gaps. Here, we present a step-by-step approach to manually extract quantitative data from various information sources, and to unify the data format. First, data from Arabidopsis leaf were collated, checked for consistency and correctness and curated by cross-checking sources. Second, quantitative data were combined by applying calculation rules. They were then integrated into a unique comprehensive, referenced, modifiable and reusable data compendium representing an Arabidopsis reference leaf. This atlas contains the metrics of the 15 cell types found in leaves at the cellular and subcellular levels.

Red light alleviates Cd toxicity in Egeria densa by modifying carbon-nitrogen metabolism and boosting energy metabolism

Among the various pollutants detected in aquatic ecosystems, cadmium (Cd) is considered as one of the most hazardous. Freshwater macrophytes have been recognized as possible candidates for eliminating Cd from environment. Nevertheless, the impact of light quality on their ability to tolerate Cd toxicity remains unclear, and the underlying mechanisms have yet to be fully elucidated. In this study, we utilized physiological testing and metabolomics to explore the potential mechanisms by which light quality influences the ability of Egeria densa, a significant Cd hyperaccumulator, to withstand Cd toxicity. The study demonstrated that following Cd treatment, E. densa grown under red light exhibited superior photosynthetic efficiency compared to those grown under blue light, as evidenced by significantly increased photosynthetic rate, higher starch content, and greater activity of photosynthetic enzymes. Moreover, metabolomic analyses revealed that under Cd stress, E. densa grown under red light exhibited an enhanced glycolysis for increased energy production. Sucrose metabolism was also improved to generate sufficient sugar including glucose, fructose and mannose for osmotic adjustment. Moreover, under red light, the heightened production of α-ketoglutarate via tricarboxylic acid (TCA) cycle redirected nitrogen flow towards the synthesis of resilient substances such as γ-Aminobutyric Acid (GABA) and methionine. The production of these substances was ∼2.0 and 1.3 times greater than that of treatment with Cd under blue light, thereby improving E. densa's capacity to withstand Cd stress. This study represents the initial investigation into the possible mechanisms by which light quality influences the ability of E. densa to withstand Cd toxicity through regulating CN metabolism. Furthermore, these findings have the potential to improve phytoremediation strategies aimed at reducing Cd pollution.

A mutation in THREONINE SYNTHASE 1 uncouples proliferation and transition domains of the root apical meristem: experimental evidence and in silico proposed mechanism

A continuum from stem to transit-amplifying to a differentiated cell state is a common theme in multicellular organisms. In the plant root apical meristem (RAM), transit-amplifying cells are organized into two domains: cells from the proliferation domain (PD) are displaced to the transition domain (TD), suggesting that both domains are necessarily coupled. Here, we show that in the Arabidopsis thaliana mto2-2 mutant, in which threonine (Thr) synthesis is affected, the RAM lacks the PD. Through a combination of cell length profile analysis, mathematical modeling and molecular markers, we establish that the PD and TD can be uncoupled. Remarkably, although the RAM of mto2-2 is represented solely by the TD, the known factors of RAM maintenance and auxin signaling are expressed in the mutant. Mathematical modeling predicts that the stem cell niche depends on Thr metabolism and that, when disturbed, the normal continuum of cell states becomes aborted.

Carbon and nitrogen metabolic regulation in freshwater plant Ottelia alismoides in response to carbon limitation: A metabolite perspective

Carbon and nitrogen metabolism are basic, but pivotal metabolic pathways in plants and are tightly coupled. Maintaining the balance of carbon and nitrogen metabolism is critical for plant survival. Comprehensively revealing the metabolic balance of carbon-nitrogen interactions is important and helpful for understanding the adaptation of freshwater plants to CO₂ limited aqueous environment. A comprehensive metabolomics analysis combined with physiological measurement was performed in the freshwater plant Ottelia alismoides acclimated to high and low CO₂, respectively, for a better understanding of how the carbon and nitrogen metabolic adjustment in freshwater plants respond to carbon limitation. The present results showed that low CO₂ acclimated O. alismoides exhibited significant diurnal titratable acidity and malate fluctuations, as well as an opposite diel pattern of starch change and high enzymatic activities required for crassulacean acid metabolism (CAM) photosynthesis, which indicates that CAM was induced under low CO₂. Moreover, the metabolomic analysis showed that most intermediates of glycolysis, pentose phosphate pathway (PPP) and tricarboxylic acid (TCA) cycle, were increased under low CO₂, indicative of active respiration in low-CO₂-treated O. alismoides. Meanwhile, the majority of amino acids involved in pathways of glutamate and arginine metabolism, aspartate metabolism, and the branched-chain amino acids (BCAAs) metabolism were significantly increased under low CO₂. Notably, γ-aminobutyric acid (GABA) level was significantly higher in low CO₂ conditions, indicating a typical response with GABA shunt compensated for energy deprivation at low CO₂. Taken together, we conclude that in low-CO₂-stressed O. alismoides, CAM photosynthesis was induced, leading to higher carbon and nitrogen as well as energy requirements. Correspondingly, the respiration was greatly fueled via numerous starch degradation to ensure CO₂ fixation in dark, while accompanied by linked promoted N metabolism, presumably to produce energy and alternative carbon sources and nitrogenous substances for supporting the operation of CAM and enhancing tolerance for carbon limitation. This study not only helps to elucidate the regulating interaction between C and N metabolism to adapt to different CO₂ but also provides novel insights into the effects of CO₂ variation on the metabolic profiling of O. alismoides.

Interkingdom Comparison of Threonine Metabolism for Stem Cell Maintenance in Plants and Animals

In multicellular organisms, tissue generation, maintenance, and homeostasis depend on stem cells. Cellular metabolic status is an essential component of different differentiated states, from stem to fully differentiated cells. Threonine (Thr) metabolism has emerged as a critical factor required to maintain pluripotent/multipotent stem cells in both plants and animals. Thus, both kingdoms conserved or converged upon this fundamental feature of stem cell function. Here, we examine similarities and differences in Thr metabolism-dependent mechanisms supporting stem cell maintenance in these two kingdoms. We then consider common features of Thr metabolism in stem cell maintenance and predict and speculate that some knowledge about Thr metabolism and its role in stem cell function in one kingdom may apply to the other. Finally, we outline future research directions to explore these hypotheses.

A Hybrid of Particle Swarm Optimization and Harmony Search to Estimate Kinetic Parameters in Arabidopsis thaliana

Recently, modelling and simulation have been used and applied to understand biological systems better. Therefore, the development of precise computational models of a biological system is essential. This model is a mathematical expression derived from a series of parameters of the system. The measurement of parameter values through experimentation is often expensive and time-consuming. However, if a simulation is used, the manipulation of computational parameters is easy, and thus the behaviour of a biological system model can be altered for a better understanding. The complexity and nonlinearity of a biological system make parameter estimation the most challenging task in modelling. Therefore, this paper proposes a hybrid of Particle Swarm Optimization (PSO) and Harmony Search (HS), also known as PSOHS, designated to determine the kinetic parameter values of essential amino acids, mainly aspartate metabolism, in Arabidopsis thaliana. Three performance measurements are used in this paper to evaluate the proposed PSOHS: the standard deviation, nonlinear least squared error, and computational time. The proposed algorithm outperformed the other two methods, namely Simulated Annealing and the downhill simplex method, and proved that PSOHS is a more suitable algorithm for estimating kinetic parameter values.

Modeling Plant Metabolism: From Network Reconstruction to Mechanistic Models

Mathematical modeling of plant metabolism enables the plant science community to understand the organization of plant metabolism, obtain quantitative insights into metabolic functions, and derive engineering strategies for manipulation of metabolism. Among the various modeling approaches, metabolic pathway analysis can dissect the basic functional modes of subsections of core metabolism, such as photorespiration, and reveal how classical definitions of metabolic pathways have overlapping functionality. In the many studies using constraint-based modeling in plants, numerous computational tools are currently available to analyze large-scale and genome-scale metabolic networks. For ¹³C-metabolic flux analysis, principles of isotopic steady state have been used to study heterotrophic plant tissues, while nonstationary isotope labeling approaches are amenable to the study of photoautotrophic and secondary metabolism. Enzyme kinetic models explore pathways in mechanistic detail, and we discuss different approaches to determine or estimate kinetic parameters. In this review, we describe recent advances and challenges in modeling plant metabolism.

Root stem cell niche maintenance and apical meristem activity critically depend on THREONINE SYNTHASE1

Indeterminate root growth depends on the stem cell niche (SCN) and root apical meristem (RAM) maintenance whose regulation permits plasticity in root system formation. Using a forward genetics approach, we isolated the moots koom1 ('short root' in Mayan) mutant that shows complete primary RAM exhaustion and abolished SCN activity. We identified that this phenotype is caused by a point mutation in the METHIONINE OVERACCUMULATOR2 (MTO2) gene that encodes THREONINE SYNTHASE1 and renamed the mutant as mto2-2. The amino acid profile showed drastic changes, most notorious of which was accumulation of methionine. In non-allelic mto1-1 (Arabidopsis thaliana cystathionine gamma-synthetase1) and mto3-1 (S-adenosylmethionine synthetase) mutants, both with an increased methionine level, the RAM size was similar to that of the wild type, suggesting that methionine overaccumulation itself did not cause RAM exhaustion in mto2 mutants. When mto2-2 RAM is not yet completely exhausted, exogenous threonine induced de novo SCN establishment and root growth recovery. The threonine-dependent RAM re-establishment in mto2-2 suggests that threonine is a limiting factor for RAM maintenance. In the root, MTO2 was predominantly expressed in the RAM. The essential role of threonine in mouse embryonic stem cells and in RAM maintenance suggests that common regulatory mechanisms may operate in plant and animal SCN maintenance.

Workflow for Data Analysis in Experimental and Computational Systems Biology: Using Python as ‘Glue’

Bottom-up systems biology entails the construction of kinetic models of cellular pathways by collecting kinetic information on the pathway components (e.g., enzymes) and collating this into a kinetic model, based for example on ordinary differential equations. This requires integration and data transfer between a variety of tools, ranging from data acquisition in kinetics experiments, to fitting and parameter estimation, to model construction, evaluation and validation. Here, we present a workflow that uses the Python programming language, specifically the modules from the SciPy stack, to facilitate this task. Starting from raw kinetics data, acquired either from spectrophotometric assays with microtitre plates or from Nuclear Magnetic Resonance (NMR) spectroscopy time-courses, we demonstrate the fitting and construction of a kinetic model using scientific Python tools. The analysis takes place in a Jupyter notebook, which keeps all information related to a particular experiment together in one place and thus serves as an e-labbook, enhancing reproducibility and traceability. The Python programming language serves as an ideal foundation for this framework because it is powerful yet relatively easy to learn for the non-programmer, has a large library of scientific routines and active user community, is open-source and extensible, and many computational systems biology software tools are written in Python or have a Python Application Programming Interface (API). Our workflow thus enables investigators to focus on the scientific problem at hand rather than worrying about data integration between disparate platforms.

Metrics for regulated biochemical pathway systems

MOTIVATION

The assessment of graphs through crisp numerical metrics has long been a hallmark of biological network analysis. However, typical graph metrics ignore regulatory signals that are crucially important for optimal pathway operation, for instance, in biochemical or metabolic studies. Here we introduce adjusted metrics that are applicable to both static networks and dynamic systems.

RESULTS

The metrics permit quantitative characterizations of the importance of regulation in biochemical pathway systems, including systems designed for applications in synthetic biology or metabolic engineering. They may also become criteria for effective model reduction.

AVAILABILITY AND IMPLEMENTATION

The source code is available at https://gitlab.com/tienbien44/metrics-bsa.

Using metabolome data for mathematical modeling of plant metabolic systems

Plant metabolism is characterized by a wide diversity of metabolites, with systems far more complicated than those of microorganisms. Mathematical modeling is useful for understanding dynamic behaviors of plant metabolic systems for metabolic engineering. Time-series metabolome data has great potential for estimating kinetic model parameters to construct a genome-wide metabolic network model. However, data obtained by current metabolomics techniques does not meet the requirement for constructing accurate models. In this article, we highlight novel strategies and algorithms to handle the underlying difficulties and construct dynamic in vivo models for large-scale plant metabolic systems. The coarse but efficient modeling enables the prediction of unknown mechanisms regulating plant metabolism.

A kinetic model of sugar metabolism in peach fruit reveals a functional hypothesis of a markedly low fructose-to-glucose ratio phenotype

The concentrations of sugars in fruit vary with fruit development, environment and genotype. In general, there were weak correlations between the variations in sugar concentrations and the activities of enzymes directly related with the synthesis or degradation of sugars. This finding suggests that the relationships between enzyme activities and metabolites are often non-linear and are difficult to assess. To simulate the concentrations of sucrose, glucose, fructose and sorbitol during the development of peach fruit, a kinetic model of sugar metabolism was developed by taking advantage of recent profiling data. Cell compartmentation (cytosol and vacuole) was described explicitly, and data-driven enzyme activities were used to parameterize equations. The model correctly accounts for both annual and genotypic variations, which were observed in 10 genotypes derived from an interspecific cross. They provided important information on the mechanisms underlying the specification of phenotypic differences. In particular, the model supports the hypothesis that a difference in fructokinase affinity could be responsible for a low fructose-to-glucose ratio phenotype, which was observed in the studied population.

Construction of a network describing asparagine metabolism in plants and its application to the identification of genes affecting asparagine metabolism in wheat under drought and nutritional stress

A detailed network describing asparagine metabolism in plants was constructed using published data from Arabidopsis (Arabidopsis thaliana) maize (Zea mays), wheat (Triticum aestivum), pea (Pisum sativum), soybean (Glycine max), lupin (Lupus albus), and other species, including animals. Asparagine synthesis and degradation is a major part of amino acid and nitrogen metabolism in plants. The complexity of its metabolism, including limiting and regulatory factors, was represented in a logical sequence in a pathway diagram built using yED graph editor software. The network was used with a Unique Network Identification Pipeline in the analysis of data from 18 publicly available transcriptomic data studies. This identified links between genes involved in asparagine metabolism in wheat roots under drought stress, wheat leaves under drought stress, and wheat leaves under conditions of sulfur and nitrogen deficiency. The network represents a powerful aid for interpreting the interactions not only between the genes in the pathway but also among enzymes, metabolites and smaller molecules. It provides a concise, clear understanding of the complexity of asparagine metabolism that could aid the interpretation of data relating to wider amino acid metabolism and other metabolic processes.

An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways

Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. However, measuring these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. These local solutions can lead to a bad fit when calibrating with a model. Although the model itself can potentially match a set of experimental data, a high-performance estimation algorithm is required to improve the quality of the solutions. This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. The findings suggest that the proposed algorithm is capable of narrowing down the search space by exploiting the feasible solution areas. Hence, the proposed algorithm is able to achieve a near-optimal set of parameters at a fast convergence speed. The proposed algorithm was tested and evaluated based on two aspartate pathways that were obtained from the BioModels Database. The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. Nevertheless, the proposed algorithm is only expected to work well in small scale systems. In addition, the results of this study can be used to estimate kinetic parameter values in the stage of model selection for different experimental conditions.

Formulation, construction and analysis of kinetic models of metabolism: A review of modelling frameworks

Kinetic models are critical to predict the dynamic behaviour of metabolic networks. Mechanistic kinetic models for large networks remain uncommon due to the difficulty of fitting their parameters. Recent modelling frameworks promise new ways to overcome this obstacle while retaining predictive capabilities. In this review, we present an overview of the relevant mathematical frameworks for kinetic formulation, construction and analysis. Starting with kinetic formalisms, we next review statistical methods for parameter inference, as well as recent computational frameworks applied to the construction and analysis of kinetic models. Finally, we discuss opportunities and limitations hindering the development of larger kinetic reconstructions.

A Regulatory Hierarchy of the Arabidopsis Branched-Chain Amino Acid Metabolic Network

The branched-chain amino acids (BCAAs) Ile, Val, and Leu are essential nutrients that humans and other animals obtain from plants. However, total and relative amounts of plant BCAAs rarely match animal nutritional needs, and improvement requires a better understanding of the mechanistic basis for BCAA homeostasis. We present an in vivo regulatory model of BCAA homeostasis derived from analysis of feedback-resistant Arabidopsis thaliana mutants for the three allosteric committed enzymes in the biosynthetic network: threonine deaminase (also named l-O-methylthreonine resistant 1 [OMR1]), acetohydroxyacid synthase small subunit 2 (AHASS2), and isopropylmalate synthase 1 (IPMS1). In this model, OMR1 exerts primary control on Ile accumulation and functions independently of AHAS and IPMS AHAS and IPMS regulate Val and Leu homeostasis, where AHAS affects total Val+Leu and IPMS controls partitioning between these amino acids. In addition, analysis of feedback-resistant and loss-of-function single and double mutants revealed that each AHAS and IPMS isoenzyme contributes to homeostasis rather than being functionally redundant. The characterized feedback resistance mutations caused increased free BCAA levels in both seedlings and seeds. These results add to our understanding of the basis of in vivo BCAA homeostasis and inform approaches to improve the amount and balance of these essential nutrients in crops.

ChloroKB: A Web Application for the Integration of Knowledge Related to Chloroplast Metabolic Network

Higher plants, as autotrophic organisms, are effective sources of molecules. They hold great promise for metabolic engineering, but the behavior of plant metabolism at the network level is still incompletely described. Although structural models (stoichiometry matrices) and pathway databases are extremely useful, they cannot describe the complexity of the metabolic context, and new tools are required to visually represent integrated biocurated knowledge for use by both humans and computers. Here, we describe ChloroKB, a Web application (http://chlorokb.fr/) for visual exploration and analysis of the Arabidopsis (Arabidopsis thaliana) metabolic network in the chloroplast and related cellular pathways. The network was manually reconstructed through extensive biocuration to provide transparent traceability of experimental data. Proteins and metabolites were placed in their biological context (spatial distribution within cells, connectivity in the network, participation in supramolecular complexes, and regulatory interactions) using CellDesigner software. The network contains 1,147 reviewed proteins (559 localized exclusively in plastids, 68 in at least one additional compartment, and 520 outside the plastid), 122 proteins awaiting biochemical/genetic characterization, and 228 proteins for which genes have not yet been identified. The visual presentation is intuitive and browsing is fluid, providing instant access to the graphical representation of integrated processes and to a wealth of refined qualitative and quantitative data. ChloroKB will be a significant support for structural and quantitative kinetic modeling, for biological reasoning, when comparing novel data with established knowledge, for computer analyses, and for educational purposes. ChloroKB will be enhanced by continuous updates following contributions from plant researchers.

PP2A-B'γ modulates foliar trans-methylation capacity and the formation of 4-methoxy-indol-3-yl-methyl glucosinolate in Arabidopsis leaves

Glucosinolates (GSL) of cruciferous plants comprise a major group of structurally diverse secondary compounds which act as deterrents against aphids and microbial pathogens and have large commercial and ecological impacts. While the transcriptional regulation governing the biosynthesis and modification of GSL is now relatively well understood, post-translational regulatory components that specifically determine the structural variation of indole glucosinolates have not been reported. We show that the cytoplasmic protein phosphatase 2A regulatory subunit B'γ (PP2A-B'γ) physically interacts with indole glucosinolate methyltransferases and controls the methoxylation of indole glucosinolates and the formation of 4-methoxy-indol-3-yl-methyl glucosinolate in Arabidopsis leaves. By taking advantage of proteomic approaches and metabolic analysis we further demonstrate that PP2A-B'γ is required to control the abundance of oligomeric protein complexes functionally linked with the activated methyl cycle and the trans-methylation capacity of leaf cells. These findings highlight the key regulatory role of PP2A-B'γ in methionine metabolism and provide a previously unrecognized perspective for metabolic engineering of glucosinolate metabolism in cruciferous plants.

The Regulation of Essential Amino Acid Synthesis and Accumulation in Plants

Although amino acids are critical for all forms of life, only proteogenic amino acids that humans and animals cannot synthesize de novo and therefore must acquire in their diets are classified as essential. Nine amino acids-lysine, methionine, threonine, phenylalanine, tryptophan, valine, isoleucine, leucine, and histidine-fit this definition. Despite their nutritional importance, several of these amino acids are present in limiting quantities in many of the world's major crops. In recent years, a combination of reverse genetic and biochemical approaches has been used to define the genes encoding the enzymes responsible for synthesizing, degrading, and regulating these amino acids. In this review, we describe recent advances in our understanding of the metabolism of the essential amino acids, discuss approaches for enhancing their levels in plants, and appraise efforts toward their biofortification in crop plants.

Identification of Metabolic Pathway Systems

The estimation of parameters in even moderately large biological systems is a significant challenge. This challenge is greatly exacerbated if the mathematical formats of appropriate process descriptions are unknown. To address this challenge, the method of dynamic flux estimation (DFE) was proposed for the analysis of metabolic time series data. Under ideal conditions, the first phase of DFE yields numerical representations of all fluxes within a metabolic pathway system, either as values at each time point or as plots against their substrates and modulators. However, this numerical result does not reveal the mathematical format of each flux. Thus, the second phase of DFE selects functional formats that are consistent with the numerical trends obtained from the first phase. While greatly facilitating metabolic data analysis, DFE is only directly applicable if the pathway system contains as many dependent variables as fluxes. Because most actual systems contain more fluxes than metabolite pools, this requirement is seldom satisfied. Auxiliary methods have been proposed to alleviate this issue, but they are not general. Here we propose strategies that extend DFE toward general, slightly underdetermined pathway systems.

Tracing regulatory routes in metabolism using generalised supply-demand analysis

Background

Generalised supply-demand analysis is a conceptual framework that views metabolism as a molecular economy. Metabolic pathways are partitioned into so-called supply and demand blocks that produce and consume a particular intermediate metabolite. By studying the response of these reaction blocks to perturbations in the concentration of the linking metabolite, different regulatory routes of interaction between the metabolite and its supply and demand blocks can be identified and their contribution quantified. These responses are mediated not only through direct substrate/product interactions, but also through allosteric effects. Here we subject previously published kinetic models of pyruvate metabolism in Lactococcus lactis and aspartate-derived amino acid synthesis in Arabidopsis thaliana to generalised supply-demand analysis.

Results

Multiple routes of regulation are brought about by different mechanisms in each model, leading to behavioural and regulatory patterns that are generally difficult to predict from simple inspection of the reaction networks depicting the models. In the pyruvate model the moiety-conserved cycles of ATP/ADP and NADH/NAD ⁺ allow otherwise independent metabolic branches to communicate. This causes the flux of one ATP-producing reaction block to increase in response to an increasing ATP/ADP ratio, while an NADH-consuming block flux decreases in response to an increasing NADH/NAD ⁺ ratio for certain ratio value ranges.

In the aspartate model, aspartate semialdehyde can inhibit its supply block directly or by increasing the concentration of two amino acids (Lys and Thr) that occur as intermediates in demand blocks and act as allosteric inhibitors of isoenzymes in the supply block. These different routes of interaction from aspartate semialdehyde are each seen to contribute differently to the regulation of the aspartate semialdehyde supply block.

Conclusions

Indirect routes of regulation between a metabolic intermediate and a reaction block that either produces or consumes this intermediate can play a much larger regulatory role than routes mediated through direct interactions. These indirect routes of regulation can also result in counter-intuitive metabolic behaviour. Performing generalised supply-demand analysis on two previously published models demonstrated the utility of this method as an entry point in the analysis of metabolic behaviour and the potential for obtaining novel results from previously analysed models by using new approaches.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0236-1) contains supplementary material, which is available to authorized users.

Analysis of Loss-of-Function Mutants in Aspartate Kinase and Homoserine Dehydrogenase Genes Points to Complexity in the Regulation of Aspartate-Derived Amino Acid Contents

Biosynthesis of aspartate (Asp)-derived amino acids lysine (Lys), methionine (Met), threonine (Thr), and isoleucine involves monofunctional Asp kinases (AKs) and dual-functional Asp kinase-homoserine dehydrogenases (AK-HSDHs). Four-week-old loss-of-function Arabidopsis (Arabidopsis thaliana) mutants in the AK-HSDH2 gene had increased amounts of Asp and Asp-derived amino acids, especially Thr, in leaves. To explore mechanisms behind this phenotype, we obtained single mutants for other AK and AK-HSDH genes, generated double mutants from ak-hsdh2 and ak mutants, and performed free and protein-bound amino acid profiling, transcript abundance, and activity assays. The increases of Asp, Lys, and Met in ak-hsdh2 were also observed in ak1-1, ak2-1, ak3-1, and ak-hsdh1-1. However, the Thr increase in ak-hsdh2 was observed in ak-hsdh1-1 but not in ak1-1, ak2-1, or ak3-1. Activity assays showed that AK2 and AK-HSDH1 are the major contributors to overall AK and HSDH activities, respectively. Pairwise correlation analysis revealed positive correlations between the amount of AK transcripts and Lys-sensitive AK activity and between the amount of AK-HSDH transcripts and both Thr-sensitive AK activity and total HSDH activity. In addition, the ratio of total AK activity to total HSDH activity negatively correlates with the ratio of Lys to the total amount of Met, Thr, and isoleucine. These data led to the hypothesis that the balance between Lys-sensitive AKs and Thr-sensitive AK-HSDHs is important for maintaining the amounts and ratios of Asp-derived amino acids.

The fusion of genomes leads to more options: A comparative investigation on the desulfo-glucosinolate sulfotransferases of Brassica napus and homologous proteins of Arabidopsis thaliana

Sulfotransferases (SOTs) (EC 2.8.2.-) play a crucial role in the glucosinolate (Gl) biosynthesis, by catalyzing the final step of the core glucosinolate formation. In Arabidopsis thaliana the three desulfo (ds)-Gl SOTs AtSOT16, AtSOT17 and AtSOT18 were previously characterized, showing different affinities to ds-Gls. But can the knowledge about these SOTs be generally transferred to other Gl-synthesizing plants? It was investigated how many SOTs are present in the economically relevant crop plant Brassica napus L., and if it is possible to predict their characteristics by sequence analysis. The recently sequenced B. napus is a hybrid of Brassica rapa and Brassica oleracea. By database research, 71 putative functional BnSOT family members were identified and at least eleven of those are putative ds-Gl SOTs. Besides the homologs of AtSOT16 - 18, phylogenetic analyses revealed new subfamilies of ds-Gl SOTs, which are not present in A. thaliana. Three of the B. napus ds-Gl SOT proteins were expressed and purified, and characterized by determining the substrate affinities to different ds-Gls. Two of them, BnSOT16-a and BnSOT16-b, showed a significantly higher affinity to an indolic ds-Gl, similarly to AtSOT16. Additionally, BnSOT17-a was characterized and showed a higher affinity to long chained aliphatic Gls, similarly to AtSOT17. Identification of homologs to AtSOT18 was less reliable, because putative SOT18 sequences are more heterogeneous and confirmation of similar characteristics was not possible.

Exposure of barley plants to low Pi leads to rapid changes in root respiration that correlate with specific alterations in amino acid substrates

The majority of inorganic phosphate (Pi ) stress studies in plants have focused on the response after growth has been retarded. Evidence from transcript analysis, however, shows that a Pi -stress specific response is initiated within minutes of transfer to low Pi and in crop plants precedes the expression of Pi transporters and depletion of vacuolar Pi reserves by days. In order to investigate the physiological and metabolic events during early exposure to low Pi in grain crops, we monitored the response of whole barley plants during the first hours following Pi withdrawal. Lowering the concentration of Pi led to rapid changes in root respiration and leaf gas exchange throughout the early phase of the light course. Combining amino and organic acid analysis with (15) N labelling we show a root-specific effect on nitrogen metabolism linked to specific substrates of respiration as soon as 1 h following Pi withdrawal; this explains the respiratory responses observed and was confirmed by stimulation of respiration by exogenous addition of these respiratory substrates to roots. The rapid adjustment of substrates for respiration in roots during short-term Pi -stress is highlighted and this could help guide roots towards Pi -rich soil patches without compromising biomass accumulation of the plant.

Lysine metabolism in antisense C-hordein barley grains

The grain proteins of barley are deficient in lysine and threonine due to their low concentrations in the major storage protein class, the hordeins, especially in the C-hordein subgroup. Previously produced antisense C-hordein transgenic barley lines have an improved amino acid composition, with increased lysine, methionine and threonine contents. The objective of the study was to investigate the possible changes in the regulation of key enzymes of the aspartate metabolic pathway and the contents of aspartate-derived amino acids in the nontransgenic line (Hordeum vulgare L. cv. Golden Promise) and five antisense C-hordein transgenic barley lines. Considering the amounts of soluble and protein-bound aspartate-derived amino acids together with the analysis of key enzymes of aspartate metabolic pathway, we suggest that the C-hordein suppression did not only alter the metabolism of at least one aspartate-derived amino acid (threonine), but major changes were also detected in the metabolism of lysine and methionine. Modifications in the activities and regulation of aspartate kinase, dihydrodipicolinate synthase and homoserine dehydrogenase were observed in most transgenic lines. Furthermore the activities of lysine α-ketoglutarate reductase and saccharopine dehydrogenase were also altered, although the extent varied among the transgenic lines.

From Genetics to Functional Genomics: Improvement in Drought Signaling and Tolerance in Wheat

Drought being a yield limiting factor has become a major threat to international food security. It is a complex trait and drought tolerance response is carried out by various genes, transcription factors (TFs), microRNAs (miRNAs), hormones, proteins, co-factors, ions, and metabolites. This complexity has limited the development of wheat cultivars for drought tolerance by classical breeding. However, attempts have been made to fill the lost genetic diversity by crossing wheat with wild wheat relatives. In recent years, several molecular markers including single nucleotide polymorphisms (SNPs) and quantitative trait loci (QTLs) associated with genes for drought signaling pathways have been reported. Screening of large wheat collections by marker assisted selection (MAS) and transformation of wheat with different genes/TFs has improved drought signaling pathways and tolerance. Several miRNAs also provide drought tolerance to wheat by regulating various TFs/genes. Emergence of OMICS techniques including transcriptomics, proteomics, metabolomics, and ionomics has helped to identify and characterize the genes, proteins, metabolites, and ions involved in drought signaling pathways. Together, all these efforts helped in understanding the complex drought tolerance mechanism. Here, we have reviewed the advances in wide hybridization, MAS, QTL mapping, miRNAs, transgenic technique, genome editing system, and above mentioned functional genomics tools for identification and utility of signaling molecules for improvement in wheat drought tolerance.

Moving toward a comprehensive map of central plant metabolism

Decades of intensive study have led to the discovery of the main pathways involved in central metabolism but only some of the pathways and regulatory networks in which they are embedded. In this review, we discuss techniques used to assemble these pathways into a systems biology framework that can enable accurate modeling of the response of central metabolism to changes, including ways to perturb metabolic systems and assemble the resulting data into a meaningful network. Critically, these networks are of such size and complexity that it is possible to derive them only if data from different groups can be comprehensively and meaningfully combined. We conclude that it is essential to establish common standards for the description of experimental conditions and data collection and to store this information in databases to which the whole community can contribute.

A U-system approach for predicting metabolic behaviors and responses based on an alleged metabolic reaction network

Background

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.

Results

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.

Conclusions

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.

An Efficient and Very Accurate Method for Calculating Steady-State Sensitivities in Metabolic Reaction Systems

Stability and sensitivity analyses of biological systems require the ad hocwriting of computer code, which is highly dependent on the particular model and burdensome for large systems. We propose a very accurate strategy to overcome this challenge. Its core concept is the conversion of the model into the format of biochemical systems theory (BST), which greatly facilitates the computation of sensitivities. First, the steady state of interest is determined by integrating the model equations toward the steady state and then using a Newton-Raphson method to fine-tune the result. The second step of conversion into the BST format requires several instances of numerical differentiation. The accuracy of this task is ensured by the use of a complex-variable Taylor scheme for all differentiation steps. The proposed strategy is implemented in a new software program, COSMOS, which automates the stability and sensitivity analysis of essentially arbitrary ODE models in a quick, yet highly accurate manner. The methods underlying the process are theoretically analyzed and illustrated with four representative examples: a simple metabolic reaction model; a model of aspartate-derived amino acid biosynthesis; a TCA-cycle model; and a modified TCA-cycle model. COSMOS has been deposited to https://github.com/BioprocessdesignLab/COSMOS.

On the dynamics of the adenylate energy system: homeorhesis vs homeostasis

Biochemical energy is the fundamental element that maintains both the adequate turnover of the biomolecular structures and the functional metabolic viability of unicellular organisms. The levels of ATP, ADP and AMP reflect roughly the energetic status of the cell, and a precise ratio relating them was proposed by Atkinson as the adenylate energy charge (AEC). Under growth-phase conditions, cells maintain the AEC within narrow physiological values, despite extremely large fluctuations in the adenine nucleotides concentration. Intensive experimental studies have shown that these AEC values are preserved in a wide variety of organisms, both eukaryotes and prokaryotes. Here, to understand some of the functional elements involved in the cellular energy status, we present a computational model conformed by some key essential parts of the adenylate energy system. Specifically, we have considered (I) the main synthesis process of ATP from ADP, (II) the main catalyzed phosphotransfer reaction for interconversion of ATP, ADP and AMP, (III) the enzymatic hydrolysis of ATP yielding ADP, and (IV) the enzymatic hydrolysis of ATP providing AMP. This leads to a dynamic metabolic model (with the form of a delayed differential system) in which the enzymatic rate equations and all the physiological kinetic parameters have been explicitly considered and experimentally tested in vitro. Our central hypothesis is that cells are characterized by changing energy dynamics (homeorhesis). The results show that the AEC presents stable transitions between steady states and periodic oscillations and, in agreement with experimental data these oscillations range within the narrow AEC window. Furthermore, the model shows sustained oscillations in the Gibbs free energy and in the total nucleotide pool. The present study provides a step forward towards the understanding of the fundamental principles and quantitative laws governing the adenylate energy system, which is a fundamental element for unveiling the dynamics of cellular life.

Regulation of amino acid metabolic enzymes and transporters in plants

Amino acids play several critical roles in plants, from providing the building blocks of proteins to being essential metabolites interacting with many branches of metabolism. They are also important molecules that shuttle organic nitrogen through the plant. Because of this central role in nitrogen metabolism, amino acid biosynthesis, degradation, and transport are tightly regulated to meet demand in response to nitrogen and carbon availability. While much is known about the feedback regulation of the branched biosynthesis pathways by the amino acids themselves, the regulation mechanisms at the transcriptional, post-transcriptional, and protein levels remain to be identified. This review focuses mainly on the current state of our understanding of the regulation of the enzymes and transporters at the transcript level. Current results describing the effect of transcription factors and protein modifications lead to a fragmental picture that hints at multiple, complex levels of regulation that control and coordinate transport and enzyme activities. It also appears that amino acid metabolism, amino acid transport, and stress signal integration can influence each other in a so-far unpredictable fashion.

Plant metabolic modeling: achieving new insight into metabolism and metabolic engineering

Models are used to represent aspects of the real world for specific purposes, and mathematical models have opened up new approaches in studying the behavior and complexity of biological systems. However, modeling is often time-consuming and requires significant computational resources for data development, data analysis, and simulation. Computational modeling has been successfully applied as an aid for metabolic engineering in microorganisms. But such model-based approaches have only recently been extended to plant metabolic engineering, mainly due to greater pathway complexity in plants and their highly compartmentalized cellular structure. Recent progress in plant systems biology and bioinformatics has begun to disentangle this complexity and facilitate the creation of efficient plant metabolic models. This review highlights several aspects of plant metabolic modeling in the context of understanding, predicting and modifying complex plant metabolism. We discuss opportunities for engineering photosynthetic carbon metabolism, sucrose synthesis, and the tricarboxylic acid cycle in leaves and oil synthesis in seeds and the application of metabolic modeling to the study of plant acclimation to the environment. The aim of the review is to offer a current perspective for plant biologists without requiring specialized knowledge of bioinformatics or systems biology.

Crystal structure of Clostridium acetobutylicum Aspartate kinase (CaAK): An important allosteric enzyme for amino acids production

Aspartate kinase (AK) is an enzyme which is tightly regulated through feedback control and responsible for the synthesis of 4-phospho-l-aspartate from l-aspartate. This intermediate step is at an important branch point where one path leads to the synthesis of lysine and the other to threonine, methionine and isoleucine. Concerted feedback inhibition of AK is mediated by threonine and lysine and varies between the species. The crystal structure of biotechnologically important Clostridium acetobutylicum aspartate kinase (CaAK; E.C. 2.7.2.4; Mw = 48,030 Da; 437aa; SwissProt: Q97MC0) has been determined to 3 Å resolution. CaAK acquires a protein fold similar to the other known structures of AKs despite the low sequence identity (<30%). It is composed of two domains: an N-terminal catalytic domain (kinase domain) and a C-terminal regulatory domain further comprised of two small domains belonging to the ACT domain family. Pairwise comparison of 12 molecules in the asymmetric unit helped to identify the bending regions which are in the vicinity of ATP binding site involved in domain movements between the catalytic and regulatory domains. All 12 CaAK molecules adopt fully open T-state conformation leading to the formation of three tetramers unique among other similar AK structures. On the basis of comparative structural analysis, we discuss tetramer formation based on the large conformational changes in the catalytic domain associated with the lysine binding at the regulatory domains. The structure described herein is homologous to a target in wide-spread pathogenic (toxin producing) bacteria such as Clostridiumtetani (64% sequence identity) suggesting the potential of the structure solved here to be applied for modeling drug interactions. CaAK structure may serve as a guide to better understand and engineer lysine biosynthesis for the biotechnology industry.

DRUM: a new framework for metabolic modeling under non-balanced growth. Application to the carbon metabolism of unicellular microalgae

Metabolic modeling is a powerful tool to understand, predict and optimize bioprocesses, particularly when they imply intracellular molecules of interest. Unfortunately, the use of metabolic models for time varying metabolic fluxes is hampered by the lack of experimental data required to define and calibrate the kinetic reaction rates of the metabolic pathways. For this reason, metabolic models are often used under the balanced growth hypothesis. However, for some processes such as the photoautotrophic metabolism of microalgae, the balanced-growth assumption appears to be unreasonable because of the synchronization of their circadian cycle on the daily light. Yet, understanding microalgae metabolism is necessary to optimize the production yield of bioprocesses based on this microorganism, as for example production of third-generation biofuels. In this paper, we propose DRUM, a new dynamic metabolic modeling framework that handles the non-balanced growth condition and hence accumulation of intracellular metabolites. The first stage of the approach consists in splitting the metabolic network into sub-networks describing reactions which are spatially close, and which are assumed to satisfy balanced growth condition. The left metabolites interconnecting the sub-networks behave dynamically. Then, thanks to Elementary Flux Mode analysis, each sub-network is reduced to macroscopic reactions, for which simple kinetics are assumed. Finally, an Ordinary Differential Equation system is obtained to describe substrate consumption, biomass production, products excretion and accumulation of some internal metabolites. DRUM was applied to the accumulation of lipids and carbohydrates of the microalgae Tisochrysis lutea under day/night cycles. The resulting model describes accurately experimental data obtained in day/night conditions. It efficiently predicts the accumulation and consumption of lipids and carbohydrates.

Analytical kinetic modeling: a practical procedure

This chapter describes a practical procedure to dissect metabolic systems, simplify them, and use or derive enzyme rate equations in order to build a mathematical model of a metabolic system and run simulations. We first deal with a simple example, modeling a single enzyme that follows Michaelis-Menten kinetics and operates in the middle of an unbranched metabolic pathway. Next we describe the rules that can be followed to isolate sub-systems from their environment to simulate their behavior. Finally we use examples to show how to derive suitable rate equations, simpler than those needed for mechanistic studies, though adequate to describe the behavior over the physiological range of conditions.Many of the general characteristics of kinetic models will be obvious to readers familiar with the theory of metabolic control analysis (Cornish-Bowden, Fundamentals of Enzyme Kinetics, Wiley-Blackwell, Weinheim, 327-380, 2012), but here we shall not assume such knowledge, as the chapter is directed toward practical application rather than theory.

PENDISC: a simple method for constructing a mathematical model from time-series data of metabolite concentrations

The availability of large-scale datasets has led to more effort being made to understand characteristics of metabolic reaction networks. However, because the large-scale data are semi-quantitative, and may contain biological variations and/or analytical errors, it remains a challenge to construct a mathematical model with precise parameters using only these data. The present work proposes a simple method, referred to as PENDISC (arameter stimation in a on-mensionalized -system with onstraints), to assist the complex process of parameter estimation in the construction of a mathematical model for a given metabolic reaction system. The PENDISC method was evaluated using two simple mathematical models: a linear metabolic pathway model with inhibition and a branched metabolic pathway model with inhibition and activation. The results indicate that a smaller number of data points and rate constant parameters enhances the agreement between calculated values and time-series data of metabolite concentrations, and leads to faster convergence when the same initial estimates are used for the fitting. This method is also shown to be applicable to noisy time-series data and to unmeasurable metabolite concentrations in a network, and to have a potential to handle metabolome data of a relatively large-scale metabolic reaction system. Furthermore, it was applied to aspartate-derived amino acid biosynthesis in Arabidopsis thaliana plant. The result provides confirmation that the mathematical model constructed satisfactorily agrees with the time-series datasets of seven metabolite concentrations.

Conserved changes in the dynamics of metabolic processes during fruit development and ripening across species

Computational analyses of molecular phenotypes traditionally aim at identifying biochemical components that exhibit differential expression under various scenarios (e.g. environmental and internal perturbations) in a single species. High-throughput metabolomics technologies allow the quantification of (relative) metabolite levels across developmental stages in different tissues, organs, and species. Novel methods for analyzing the resulting multiple data tables could reveal preserved dynamics of metabolic processes across species. The problem we address in this study is 2-fold. (1) We derive a single data table, referred to as a compromise, which captures information common to the investigated set of multiple tables containing data on different fruit development and ripening stages in three climacteric (i.e. peach [Prunus persica] and two tomato [Solanum lycopersicum] cultivars, Ailsa Craig and M82) and two nonclimacteric (i.e. strawberry [Fragaria × ananassa] and pepper [Capsicum chilense]) fruits; in addition, we demonstrate the power of the method to discern similarities and differences between multiple tables by analyzing publicly available metabolomics data from three tomato ripening mutants together with two tomato cultivars. (2) We identify the conserved dynamics of metabolic processes, reflected in the data profiles of the corresponding metabolites that contribute most to the determined compromise. Our analysis is based on an extension to principal component analysis, called STATIS, in combination with pathway overenrichment analysis. Based on publicly available metabolic profiles for the investigated species, we demonstrate that STATIS can be used to identify the metabolic processes whose behavior is similarly affected during fruit development and ripening. These findings ultimately provide insights into the pathways that are essential during fruit development and ripening across species.

Regulatory mechanisms after short- and long-term perturbed lysine biosynthesis in the aspartate pathway: the need for isogenes in Arabidopsis thaliana

The aspartate-derived amino acid pathway in plants is an intensively studied metabolic pathway, because of the biosynthesis of the four essential amino acids lysine, threonine, isoleucine and methionine. The pathway is mainly controlled by the key regulatory enzymes aspartate kinase (AK; EC 2.7.2.4), homoserine dehydrogenase (HSDH; EC 1.1.1.3) and 4-hydroxy-tetrahydrodipicolinate synthase (EC 4.3.3.7), formerly referred to as dihydrodipicolinate synthase (DHDPS). They are encoded by isoenzyme families and it is not known why such families are evolutionarily maintained. To gain more insight into the specific roles and regulation of the isoenzymes, we inhibited DHDPS in Arabidopsis thaliana with the chemical compound (N,N-dimethylglycinatoboranyloxycarbonylmethyl)-dimethylamine-borane (DDAB) and compared the short-term effects on the biochemical and biomolecular level to the long-term adaptations in dhdps knockout mutants. We found that DHDPS2 plays a crucial role in controlling lysine biosynthesis, thereby stabilizing flux through the whole aspartate pathway. Moreover, DHDPS2 was also shown to influence the threonine level to a large extent. In addition, the lysine-sensitive AKs, AKLYS1 and AKLYS3 control the short- and long-term responses to perturbed lysine biosynthesis in Arabidopsis thaliana.

Hexokinase and not glycogen synthase controls the flux through the glycogen synthesis pathway in frog oocytes

Here we set out to evaluate the role of hexokinase and glycogen synthase in the control of glycogen synthesis in vivo. We used metabolic control analysis (MCA) to determine the flux control coefficient for each of the enzymes involved in the pathway. Acute microinjection experiments in frog oocytes were specifically designed to change the endogenous activities of the enzymes, either by directly injecting increasing amounts of a given enzyme (HK, PGM and UGPase) or by microinjection of a positive allosteric effector (glc-6P for GS). Values of 0.61 ± 0.07, 0.19 ± 0.03, 0.13 ± 0.03, and -0.06 ± 0.08 were obtained for the flux control coefficients of hexokinase EC 2.7.1.1 (HK), phosphoglucomutase EC 5.4.2.1 (PGM), UDPglucose pyrophosphorylase EC 2.7.7.9 (UGPase) and glycogen synthase EC 2.4.1.11 (GS), respectively. These values satisfy the summation theorem since the sum of the control coefficients for all the enzymes of the pathway is 0.87. The results show that, in frog oocytes, glycogen synthesis through the direct pathway is under the control of hexokinase. Phosphoglucomutase and UDPG-pyrophosphorylase have a modest influence, while the control exerted by glycogen synthase is null.

Efficient behavior of photosynthetic organelles via Pareto optimality, identifiability, and sensitivity analysis

In this work, we develop methodologies for analyzing and cross comparing metabolic models. We investigate three important metabolic networks to discuss the complexity of biological organization of organisms, modeling, and system properties. In particular, we analyze these metabolic networks because of their biotechnological and basic science importance: the photosynthetic carbon metabolism in a general leaf, the Rhodobacter spheroides bacterium, and the Chlamydomonas reinhardtii alga. We adopt single- and multi-objective optimization algorithms to maximize the CO 2 uptake rate and the production of metabolites of industrial interest or for ecological purposes. We focus both on the level of genes (e.g., finding genetic manipulations to increase the production of one or more metabolites) and on finding concentration enzymes for improving the CO 2 consumption. We find that R. spheroides is able to absorb an amount of CO 2 until 57.452 mmol h (-1) gDW (-1) , while C. reinhardtii obtains a maximum of 6.7331. We report that the Pareto front analysis proves extremely useful to compare different organisms, as well as providing the possibility to investigate them with the same framework. By using the sensitivity and robustness analysis, our framework identifies the most sensitive and fragile components of the biological systems we take into account, allowing us to compare their models. We adopt the identifiability analysis to detect functional relations among enzymes; we observe that RuBisCO, GAPDH, and FBPase belong to the same functional group, as suggested also by the sensitivity analysis.

Complementary biochemical approaches applied to the identification of plastidial calmodulin-binding proteins

Ca(2+)/Calmodulin (CaM)-dependent signaling pathways play a major role in the modulation of cell responses in eukaryotes. In the chloroplast, few proteins such as the NAD(+) kinase 2 have been previously shown to interact with CaM, but a general picture of the role of Ca(2+)/CaM signaling in this organelle is still lacking. Using CaM-affinity chromatography and mass spectrometry, we identified 210 candidate CaM-binding proteins from different Arabidopsis and spinach chloroplast sub-fractions. A subset of these proteins was validated by an optimized in vitro CaM-binding assay. In addition, we designed two fluorescence anisotropy assays to quantitatively characterize the binding parameters and applied those assays to NAD(+) kinase 2 and selected candidate proteins. On the basis of our results, there might be many more plastidial CaM-binding proteins than previously estimated. In addition, we showed that an array of complementary biochemical techniques is necessary in order to characterize the mode of interaction of candidate proteins with CaM.

Identification of cystathionine γ-synthase and threonine synthase from Cicer arietinum and Lens culinaris

In plants, cystathionine γ-synthase (CGS) and threonine synthase (TS) compete for the branch-point metabolite O-phospho-L-homoserine. These enzymes are potential targets for metabolic engineering studies, aiming to alter the flux through the competing methionine and threonine biosynthetic pathways, with the goal of increasing methionine production. Although CGS and TS have been characterized in the model organisms Escherichia coli and Arabidopsis thaliana, little information is available on these enzymes in other, particularly plant, species. The functional CGS and TS coding sequences from the grain legumes Cicer arietinum (chickpea) and Lens culinaris (lentil) identified in this study share approximately 80% amino acid sequence identity with the corresponding sequences from Glycine max. At least 7 active-site residues of grain legume CGS and TS are conserved in the model bacterial enzymes, including the catalytic base. Putative processing sites that remove the targeting sequence and result in functional TS were identified in the target species.

Control limits for accumulation of plant metabolites: brute force is no substitute for understanding

Which factors limit metabolite accumulation in plant cells? Are theories on flux control effective at explaining the results? Many biotechnologists cling to the idea that every pathway has a rate limiting enzyme and target such enzymes first in order to modulate fluxes. This often translates into large effects on metabolite concentration, but disappointing small increases in flux. Rate limiting enzymes do exist, but are rare and quite opposite to what predicted by biochemistry. In many cases however, flux control is shared among many enzymes. Flux control and concentration control can (and must) be distinguished and quantified for effective manipulation. Flux control for several 'building blocks' of metabolism is placed on the demand side, and therefore increasing demand can be very successful. Tampering with supply, particularly desensitizing supply enzymes, is usually not very effective, if not dangerous, because supply regulatory mechanisms function to control metabolite homeostasis. Some important, but usually unnoticed, metabolic constraints shape the responses of metabolic systems to manipulation: mass conservation, cellular resource allocation and, most prominently, energy supply, particularly in heterotrophic tissues. The theoretical basis for this view shall be explored with recent examples gathered from the manipulation of several metabolites (vitamins, carotenoids, amino acids, sugars, fatty acids, polyhydroxyalkanoates, fructans and sugar alcohols). Some guiding principles are suggested for an even more successful engineering of plant metabolism.