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Journal ArticleDOI

A New Framework for Metabolic Modeling under Non-Balanced Growth. Application to Carbon Metabolism of Unicellular Microalgae

TL;DR: This paper proposes a new modeling framework that manages non-balanced growth condition and hence accumulation of intracellular metabolites and provides conceptual guidelines to address this question.
About: This article is published in IFAC Proceedings Volumes.The article was published on 2013-01-01 and is currently open access. It has received 2 citations till now. The article focuses on the topics: Bioprocess.

Summary (3 min read)

1. INTRODUCTION

  • Classical modeling approaches of biological systems can be sorted into two categories: macroscopic modeling and intracellular modeling.
  • To overcome these hurdles, a commonly used hypothesis is the balanced-growth hypothesis, also called the QuasiSteady-State Approximation (QSSA).
  • Internal metabolites are assumed not to accumulate inside the microorganisms.
  • Autotrophic microalgae, as photosynthetic microorganisms, are far from the condition of balanced-growth in day/light cycles.
  • The aim of the present work is to provide the conceptual basis of a new modeling framework to model intracellular processes where accumulation of certain metabolites is present.

2. PRINCIPLE OF THE APPROACH

  • Let’s consider a batch bioprocess implying a microorganism growing in a perfectly mixed stirred-tank reactor.
  • The metabolic network of the microorganism is represented by the stoichiometric matrix containing metabolites and reactions.
  • The remaining metabolites ( ) interconnecting the subnetworks are not under the quasi-steady state constraint.
  • Groups of reactions are thus determined taking into account these intracellular mechanisms.
  • Some macromolecules composing biomass can thus be metabolites allowed to accumulate .

3.1 Metabolic Network

  • C. reinhardtii is the model organism of unicellular microalgae, which can grow heterotrophically on acetate and autotrophically on light and carbon dioxide thanks to photosynthesis (Boyle & Morgan 2009).
  • It is one of the few microalgae sequenced so far (Merchant et al. 2011), which allowed to build genome-sequencing metabolic networks (Boyle & Morgan 2009; Manichaikul et al.
  • Only the autotrophic behavior of C. reinhardtii will be considered, as lipids obtained with light and carbon dioxide are more interesting economically and sustainably for potential biofuels applications (Georgianna & Mayfield 2012).
  • The reduced network is composed of 65 internal metabolites and 60 reactions including 6 exchange reactions with the environment and 1 internal exchange reaction (between the chloroplast and the cytosol).

3.2 Formation and Reduction of each sub-network

  • Metabolic reactions were grouped by metabolic functions, taking into account compartments and metabolic pathways.
  • Five sub-networks were obtained (Fig 2) corresponding to i) photosynthesis, ii) upper part of glycolysis and carbohydrate synthesis iii) lower part of glycolysis, iv) lipids synthesis, v) citric acid cycle and oxidative phosphorylation.
  • Then, each sub-network was reduced to macroscopic reactions thanks to elementary flux mode analysis.
  • For all five sub-networks, the EFM could be computed easily, and their number was low (less than 4).
  • In the full model described in step i), 𝐾𝜖ℝ𝑛𝑚 𝑛𝑟, 𝑣 ℝ𝑛𝑟, while for the resulting model provided by their approach, 𝐾 ℝ𝑛𝑚 𝑛𝐸and 𝛼 ℝ𝑛𝐸, such that 𝑛𝑚 𝑛𝑚 and 𝑛𝐸 𝑛𝑟.

3.3.1 Photosynthesis

  • Photosynthesis allows phototrophic organisms to generate cell energy and incorporate carbon autotrophically.
  • The process takes place in the chloroplast and is decomposed into two steps commonly called the light and dark steps.
  • Then the metabolic network suggests that G3P is transformed in dihydroxyacetone phosphate (DHAP) and transported to the cytosol of the cell (R13).
  • The elementary flux analysis yields only one EFM (Table 1).
  • The stoichiometry of the macroscopic reaction obtained is in agreement with the literature: a quota of 8 photons are needed per carbon incorporated (Williams & Laurens 2010).

3.3.2 Upper glycolysis and carbohydrates synthesis

  • Carbohydrates (CARB) are complex sugars stored in the cell.
  • They are formed from simple sugars (here DHAP) by reverse glycolysis.
  • Elementary mode analysis on upper glycolysis coupled with carbohydrates synthesis results in 4 macroscopic reactions (Table 1).
  • This occurs when two metabolic pathways run simultaneously in opposite directions and have no overall effect other than to dissipate energy in the form of heat.
  • Reaction (MR3) corresponds to carbohydrates synthesis whereas reaction (MR4) corresponds to its consumption.

3.3.3 Lower glycolysis

  • Lower part of glycolysis is a cascading set of reactions which generates the key metabolite pyruvate (PYR) and energy cofactors (ATP, NADH) from DHAP.
  • One macroscopic reaction was obtained by Elementary Flux Analysis (Table 1).
  • Stoichiometry is in accordance with literature: after investment of one ATP in the upper part of glycolysis, 2 ATP are returned with one pyruvate (Perry et al. 2004).

3.3.4 Lipids synthesis

  • They contain at least one hydrophobic part and are constituted of long carbon chains linked to a sugar by an ether bound.
  • Unfortunately, lipid metabolism of microalgae is poorly known because it is insufficiently studied and different from bacteria and plants (Liu & Benning 2012).
  • To allow their consumption during the night, R108 was assumed reversible.
  • To group all PAs under one entity, a generic reaction (R120) was used.
  • Its stoichiometric coefficients were determined experimentally by the proportion of each class of PA present in the cell (Kliphuis et al. 2011).

3.3.5 Citric acid cycle and oxidative phosphorylation

  • Citric acid cycle takes place in the mitochondrion and transforms pyruvate into many precursor monomers for nitrogen assimilation, nucleotide and protein synthesis.
  • For each run of the cycle, energy cofactors are generated (NADH, FADH2) and can be breathed into ATP thanks to oxidative phosphorylation.
  • Reaction (MR9) corresponds only to the oxidation part, which can be compared to a futile cycle where the cell burns away energy.
  • The difference is probably due to the conversion yield of NADH and FADH to ATP (1.5 and 2.5 respectively).
  • Here the authors assumed that 42.4 % of pyruvate carbon is used for functional biomass synthesis (unpublished results, obtained taking into account the whole metabolic network of the microalgae).

3.3 Macroscopic reaction kinetics and simulations

  • The complete model has been assessed with experimental data of a continuous culture of Isochrysis affinis galbana under day/night cycle (Lacour et al. 2012).
  • For each macroscopic reaction obtained after the reduction step, simple proportional kinetics were assumed (Table 1 and 2).
  • Indeed, as observed in the experimental results, the model accurately represents lipids and carbohydrates accumulation during the day and consumption during the night (Fig 3).
  • To conclude, the authors have presented the basic principles of a new framework for modeling microbial systems under nonbalanced growth.
  • The proposed strategy results from a compromise between complexity and representativeness.

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Citations
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Journal ArticleDOI
TL;DR: Current state of the art of constraint-based modeling and computational method development is reviewed and how advanced models led to increased prediction accuracy and thus improved lipid production in microalgae is discussed.
Abstract: Production of biofuels and bioenergy precursors by phototrophic microorganisms, such as microalgae and cyanobacteria, is a promising alternative to conventional fuels obtained from non-renewable resources. Several species of microalgae have been investigated as potential candidates for the production of biofuels, for the most part due to their exceptional metabolic capability to accumulate large quantities of lipids. Constraint-based modeling, a systems biology approach that accurately predicts the metabolic phenotype of phototrophs, has been deployed to identify suitable culture conditions as well as to explore genetic enhancement strategies for bioproduction. Core metabolic models were employed to gain insight into the central carbon metabolism in photosynthetic microorganisms. More recently, comprehensive genome-scale models, including organelle-specific information at high resolution, have been developed to gain new insight into the metabolism of phototrophic cell factories. Here, we review the current state of the art of constraint-based modeling and computational method development and discuss how advanced models led to increased prediction accuracy and thus improved lipid production in microalgae.

48 citations


Cites background or methods from "A New Framework for Metabolic Model..."

  • ...Tisochrysis lutea CM EM – 157 162 2 2 [50]...

    [...]

  • ...A few other approaches have been used as an alternative or complement, such as 13C-MFA [22, 31, 34, 42] or EM [50]....

    [...]

  • ...Early metabolic models were focused on the reconstruction of core algae models, which were later expanded to include genome-scale information (Table 1) [19, 20, 35, 49, 50]....

    [...]

  • ...Several genera of microalgae have been used for biofuel production, and metabolic models now exist for organisms such as Chlamydomonas [19–30], Chlorella [31–35], Nannochloropsis [36–38], Synechocystis [39–46], Tetraselmis [47], Monoraphidium [48], Ostreococcus [49], Tisochrysis [50], and Phaeodactylum [51–54]....

    [...]

Journal ArticleDOI
TL;DR: This research studied the identification of a class of switched linear biological models with single input and the system matrix dependent on the intensity of excitation, which allowed determination of model parameters that would otherwise be difficult to determine uniquely.
Abstract: Pulse is often used to excite biological systems. The inputs such as irrigation, therapy, and treatments to biological systems are also equivalent to pulses. This makes the biological system behave as switched models under the function of the input. To reduce difficulty in model parameter estimation, the system could be represented as a switched linear model under the pulse excitation. In this research, we studied the identification of a class of switched linear biological models with single input and the system matrix dependent on the intensity of excitation. System identifiability and identification were discussed. A recurrent-pulse excitation method was devised to provide necessary constraints for parameter estimation. The recurrent-pulse technique allowed determination of model parameters that would otherwise be difficult to determine uniquely. The usefulness of the method was demonstrated by examples including delayed fluorescence from photosystem II, which was well known as a versatile tool for sensing plant physiological status and environmental changes in the literature.

3 citations


Cites background from "A New Framework for Metabolic Model..."

  • ...Introduction Mathematical models are often used for analysis of biological systems (Banik et al., 2007; Baroukh et al., 2013; Fallon and Lauffenburger, 2000; Ropers et al., 2006)....

    [...]

  • ...Mathematical models are often used for analysis of biological systems (Banik et al., 2007; Baroukh et al., 2013; Fallon and Lauffenburger, 2000; Ropers et al., 2006)....

    [...]

References
More filters
Journal ArticleDOI
Yusuf Chisti1
TL;DR: As demonstrated here, microalgae appear to be the only source of renewable biodiesel that is capable of meeting the global demand for transport fuels.

9,030 citations


"A New Framework for Metabolic Model..." refers background in this paper

  • ...In microalgae, only Triacylglycerols (TAGs) can be transformed into biofuels (Chisti 2007)....

    [...]

  • ...For instance, microalgae can store lipids, which can be transformed into third generation biofuels, providing a promising solution for renewable energies (Chisti 2007)....

    [...]

Journal ArticleDOI
TL;DR: This primer covers the theoretical basis of the approach, several practical examples and a software toolbox for performing the calculations.
Abstract: Flux balance analysis is a mathematical approach for analyzing the flow of metabolites through a metabolic network. This primer covers the theoretical basis of the approach, several practical examples and a software toolbox for performing the calculations.

3,229 citations


"A New Framework for Metabolic Model..." refers methods in this paper

  • ...Most of the metabolic modeling and analysis frameworks rely on the balanced-growth hypothesis: Flux Balance Analysis (FBA) (Orth et al. 2010), Dynamical Flux Balance Analysis (DFBA) (Mahadevan et al....

    [...]

  • ...Most of the metabolic modeling and analysis frameworks rely on the balanced-growth hypothesis: Flux Balance Analysis (FBA) (Orth et al. 2010), Dynamical Flux Balance Analysis (DFBA) (Mahadevan et al. 2002), Elementary Flux Modes (EFM) (Schuster et al. 1999), Flux Coupling Analysis (FCA) (Burgard et…...

    [...]

  • ...Most of the metabolic modeling and analysis frameworks rely on the balanced-growth hypothesis: Flux Balance Analysis (FBA) (Orth et al. 2010), Dynamical Flux Balance Analysis (DFBA) (Mahadevan et al. 2002), Elementary Flux Modes (EFM) (Schuster et al. 1999), Flux Coupling Analysis (FCA) (Burgard et al. 2004), Macroscopic Bioreaction Models (MBM) (Provost et al. 2006), Hybrid Cybernetic Models (HCM) (Song et al. 2009) and Lumped Hybrid Cybernetic Models (L-HCM) (Song et al. 2010)....

    [...]

Book
22 Oct 2013
TL;DR: The general dynamical model of bioreactors was extended to include extended Luenberger and Kalman observers and asymptotic observers for state estimation when the reaction rates are unknown, and a general solution to the linearizing control problem for a class of CST bioreacts was found.
Abstract: Chapter 1. Dynamical Models of Bioreactors. Introduction. The basic dynamics of microbial growth in stirred tank reactors. Extensions to the basic dynamics. Models of the specific growth rate. The reaction scheme of a biotechnological process. General dynamical model of bioreactors. Examples of state space models. A basic structural property of the general dynamical model. Reduction of the general dynamical model. Stability analysis. Extending the general dynamical model. References and bibliography. Chapter 2. Kinetic Modelling, Estimation and Control in Bioreactors: An Overview. Introduction. Difficulties in modelling the reactor kinetics. Minimal modelling of reaction kinetics. Software sensors for bioreactors. Adaptive control of bioreactors. Conclusions and perspectives. References and bibliography. Chapter 3. State and Parameter Estimation with Known Yield coefficients. Introduction. On state observation in bioreactors. Extended Luenberger and Kalman observers. Asymptotic observers for state estimation when the reaction rates are unknown. On-line estimation of reaction rates. References and bibliography. Chapter 4. State and Parameter Estimation with unknown yield coefficients. Introduction. On-line estimation of the specific reaction rates. Joint estimation of yield coefficients and specific reaction rates. Adaptive observers. Estimation of yield coefficients. Other parameter estimation issues in bioreactors. References and bibliography. Chapter 5. Adaptive Control of Bioreactors. Introduction. Principle of linearizing control and remarks on closed loop stability. Singular perturbation design of linearizing controllers. Adaptive linearizing control (known yield coefficients). A general solution to the linearizing control problem for a class of CST bioreactors. Adaptive linearizing control (unknown yield coefficients). Practical aspects of implementation. Case study: Adaptive linearizing control of fed-batch reactors. Case study: Adaptive control of the gaseous production rate of a synthesis product. References and bibliography. Appendix 1. Models of the Specific Growth Rate. Appendix 2. Elements of Stability Theory. Appendix 3. Persistence of excitation. Convergence of Adaptive Estimators. Nomenclature. Index.

1,371 citations


"A New Framework for Metabolic Model..." refers background in this paper

  • ...Macroscopic models rely on macroscopic reactions where microorganisms act as catalyzers (Bastin & Dochain 1990)....

    [...]

Journal ArticleDOI
TL;DR: The main conclusions are that the biochemical composition of the biomass influences the economics, in particular, increased lipid content reduces other valuable compounds in the biomass; the “biofuel only” option is unlikely to be economically viable; and among the hardest problems in assessing the economics are the cost of the CO2 supply and uncertain nature of downstream processing.
Abstract: Following scrutiny of present biofuels, algae are seriously considered as feedstocks for next-generation biofuels production. Their high productivity and the associated high lipid yields make them attractive options. In this review, we analyse a number aspects of large-scale lipid and overall algal biomass production from a biochemical and energetic standpoint. We illustrate that the maximum conversion efficiency of total solar energy into primary photosynthetic organic products falls in the region of 10%. Biomass biochemical composition further conditions this yield: 30 and 50% of the primary product mass is lost on producing cell protein and lipid. Obtained yields are one third to one tenth of the theoretical ones. Wasted energy from captured photons is a major loss term and a major challenge in maximising mass algal production. Using irradiance data and kinetic parameters derived from reported field studies, we produce a simple model of algal biomass production and its variation with latitude and lipid content. An economic analysis of algal biomass production considers a number of scenarios and the effect of changing individual parameters. Our main conclusions are that: (i) the biochemical composition of the biomass influences the economics, in particular, increased lipid content reduces other valuable compounds in the biomass; (ii) the “biofuel only” option is unlikely to be economically viable; and (iii) among the hardest problems in assessing the economics are the cost of the CO2 supply and uncertain nature of downstream processing. We conclude by considering the pressing research and development needs.

1,128 citations

Journal ArticleDOI
TL;DR: This study demonstrates how the combination of in silico and experimental biology can be used to obtain a quantitative genotype–phenotype relationship for metabolism in bacterial cells.
Abstract: A significant goal in the post-genome era is to relate the annotated genome sequence to the physiological functions of a cell. Working from the annotated genome sequence, as well as biochemical and physiological information, it is possible to reconstruct complete metabolic networks. Furthermore, computational methods have been developed to interpret and predict the optimal performance of a metabolic network under a range of growth conditions. We have tested the hypothesis that Escherichia coli uses its metabolism to grow at a maximal rate using the E. coli MG1655 metabolic reconstruction. Based on this hypothesis, we formulated experiments that describe the quantitative relationship between a primary carbon source (acetate or succinate) uptake rate, oxygen uptake rate, and maximal cellular growth rate. We found that the experimental data were consistent with the stated hypothesis, namely that the E. coli metabolic network is optimized to maximize growth under the experimental conditions considered. This study thus demonstrates how the combination of in silico and experimental biology can be used to obtain a quantitative genotype-phenotype relationship for metabolism in bacterial cells.

1,039 citations

Frequently Asked Questions (1)
Q1. What contributions have the authors mentioned in the paper "A new framework for metabolic modeling under non-balanced growth. application to carbon metabolism of unicellular microalgae" ?

In this paper, the authors provide conceptual guidelines to address this question. The authors propose a new modeling framework that manages non-balanced growth condition and hence accumulation of intracellular metabolites. The basis of their approach is illustrated for the carbon metabolic network of unicellular microalgae.