Computational Strategies for a System-Level Understanding of Metabolism
Abstract
:1. Introduction
- Establish from the beginning the scientific question that motivates the development of the model. Consequently, the analysis of the model is expected to increase the current knowledge on the system, thanks to novel predictions on its functioning and to their experimental validation. In this phase, initial experimental data are necessary to define a plausible mathematical model, since they can aid to discriminate among different hypotheses on the structure of the system.
- Identify the proper level of abstraction necessary to formally describe the components of thesystem and their mutual interactions. In particular, the model should take into account all available knowledge on the biochemical, physical or regulatory properties of all system components and interactions. In so doing, any detectable emergent property of the system—either in the physiological state or in response to genetic, chemical or environmental perturbations—can be discovered with the appropriate computational methods. The choice of the level of abstraction will bring to the definition of either fine-grained (e.g., mechanism-based) or coarse-grained (e.g., interaction-based or constraint-based) models. Typically, the mechanism-based approach deals with toy or core models, while the interaction-based and constraint-based approaches are more suited for the analysis of genome-wide or core models. A schematic overview of the three main modeling approaches is given in Figure 1, including a list of their principal dichotomic features [23], such as quantitative vs. qualitative, static vs. dynamic, parameterized vs. non parameterized, single volume vs. compartmental, well-stirred vs. heterogeneous (diffusion), etc.Figure 1. Schematic overview of the main modeling approaches for biological systems, together with their principal characteristics and differences. Moving from the coarse-grained (interaction-based, constraint-based) to the fine-grained (mechanism-based) approach, models vary with respect to: (i) the size of the system, defined in terms of the number of components and respective interactions included in the model, which decrease from genome-wide to core models (Section 2.1); (ii) the computational costs required for the analysis of the model, which increase from the analysis of the topological properties of the network typical of interaction-based models (Section 3.1), to the study of flux distributions typical of constraint-based models (Section 3.2), to the investigation of the system dynamics typical of mechanism-based models (Section 3.3); (iii) the nature of the computational results together with the predictive capability, which changes from qualitative to quantitative while moving from interaction-based models (characterized by a high level of abstraction) to mechanism-based models (fully parameterized and describing the system at the level of the functional chemical interactions).
- Choose the most appropriate mathematical formalism. A one-to-one correspondence between each modeling approach and a specific modeling purpose would facilitate the choice of the most suitable strategy to be employed. Unfortunately, a sharp-cutting separation is not always possible. In general, mechanism-based (dynamical) models—which are usually defined as systems of differential equations—are considered the most likely candidates to achieve a detailed comprehension of cellular processes. Nonetheless, the usual lack of quantitative parameters represents a limit to a wide applicability of this approach for large metabolic networks. Various attempts have been proposed for the automatic estimation of missing parameters or the characterization of the parameters space [24,25,26].On the other side of the spectrum of modeling approaches, interaction-based models are characterized by a simplified representation of the biological process and allow to achieve qualitative knowledge only. These models can be analyzed by using, for instance, graph theory or topological analysis to investigate the “design principles” of metabolic networks, that can be considered transversal to different organisms [27]. Moreover, they allow to easily identify the so-called hubs (highly interconnected components, essential for the existence of several metabolic processes), as well as the metabolites and reactions connecting them, which can be of particular interest within the scope of, e.g., drug target discovery [28].Considering the limitations of these modeling approaches, the common practice for the computational investigation of metabolism usually relies on constraint-based models. These models are based on the definition and manipulation of stoichiometric matrices, whose native application pertains to the field of metabolic engineering. In this case, the methodologies that were initially developed for the optimization of microbial strains or for the maximization of some product yields in biotechnological applications, are now widely used with different goals in the study of metabolic networks.
2. From Experimental Data to Models
2.1. Metabolic Network Reconstruction
2.2. Parameter Estimation
2.3. Reverse Engineering
2.4. Ensemble Modeling
3. From Models to in Silico Data
3.1. Topological Analysis
3.2. Flux Balance Analysis
3.3. Simulation of the Dynamics
4. From In Silico Data to Experimental Hypothesis
4.1. Model Validation
4.2. Sensitivity Analysis
4.3. Control Theory
5. Computational Strategies at Work: Gaining Novel Insights on Metabolism
5.1. Increase, Integrate and Validate Biological Knowledge
5.2. Generate Experimentally Testable Hypotheses: Identify Regulatory Nodes and Drug Targets
5.3. Design Microbial Strains for Metabolic Engineering and Industrial Applications
6. Conclusions and Perspectives
Pathway/Aim ofthe Model | Cell Type/Organ | Organism | Modeling Approach &Methodology | ExperimentalData | Reference |
---|---|---|---|---|---|
Glycolysis | - | T. brucei | CM, ODE | L | Achcar et al. [159] |
GW metabolic network and succinic acid production | - | S. cerevisiae | GW, FBA | M | Agren et al. [58] |
GW metabolic network | - | A. niger | GW, FBA | L | Andersen et al. [173] |
Mitochondrial energy metabolism, Na+/Ca2+ cycle, K+ cycle | Heart, liver | B. taurus, S. scrofa, R. norvegicus | CM, DAE, PE, SA | L, M | Bazil et al. [80] |
OXPHOS | Cardiomyocytes | R. norvegicus | CM, ODE | L | Beard [156] |
Electron transport chain | Heart homogenates | R. norvegicus | CM, ODE, CRL | L, M | Chang et al. [154] |
Glycolysis, OXPHOS | Not specified | Eukaryotic, H. sapiens | CM, Control theory | L | Cloutier et al. [200] |
Bow-tie architecture of metabolism | Not specified | H. sapiens | GW, Topological analysis | L | Csete et al. [118] |
Central metabolism | - | Yeast | CM, FBA | L | Damiani et al. [65] |
Energy metabolism | Skeletal muscle cell | Mammal | CM, PDE | L | Dasika et al. [165] |
Glycolysis and pentose phosphate pathway | - | E. coli | CM, ODE, SA | L | Degenring et al. [179] |
Glycolysis and pentose phosphate pathway | - | E. coli | CM, ODE, SA | L | Degenring et al. [179] |
Biosynthesis of valine and leucine | - | C. glutaminicum | CM, ODE, SDE | M | Dräger et al. [76] |
Anabolic, catabolic, chemiosmosis pathways | - | E. coli | GW, Control theory | M | Federowicw et al. [202] |
Small world behavior of metabolism | Not specified | H. sapiens | GW, Topological analysis | L | Fell et al. [116] |
GW metabolic network | Not specified | H. sapiens | GW, FBA | L | Duarte et al. [39] |
GW metabolic network | - | E. coli MG1655 | GW, FBA | M | Edwards and Palsson [175] |
GW metabolic network | - | H. influenzae | GW, FBA | L | Edwards et al. [36] |
Cancer metabolic networks | Various (NCI-60 collection) | H. sapiens | Network reconstruction, FBA, gene (pair) analysis | L | Folger et al. [208] |
GW metabolic network HepatoNet1 | Hepatocytes | H. sapiens | GW. Network reconstruction | L | Gille et al. [220] |
Cytochrome bc1 complex, ROS production | Muscle, heart, liver, kidney, brain | R. norvegicus | CM, ODE | L | Guillaud et al. [153] |
GW metabolic network EHMN | Not specified | H. sapiens | GW, Network reconstruction | L | Hao et al. [221] |
GW metabolic network | - | S. cerevisiae S288c | GW, Network reconstruction, FBA | L | Heavner et al. [3] |
GW metabolic network | - | S. cerevisiae | Network reconstruction | L | Herrgård et al. [43] |
Topological properties of metabolism | - | 43 different organisms | GW, Topological analysis | L | Jeong et al. [27] |
Glycolysis, OXPHOS | - | Not specified | CM, ODE, Game theory | - | Kareva [189] |
Whole-cell life cycle model | - | M. genitalium | GW, FBA, ODE | L, M | Karr et al. [204] |
Glycolysis, pentose phosphate pathway | - | T. brucei | CM, ODE | L | Kerkhoven et al. [64] |
Energy metabolism | Colorectal cells | H. sapiens | CM, FBA, EM | M | Khazaei et al. [214] |
GW metabolic network | - | Synechocystis sp. PCC 6803 | GW, FBA | L | Knoop et al. [37] |
Glycolysis, gluconeogenesys, glycogen metabolism | Hepatocytes | H. sapiens | CM, ODE | L | König et al. [157] |
Adenine nucleotide translocase | Heart mitochondria | B. taurus | CM, ODE, PE, SA | L | Metelkin et al. [152] |
GW metabolic network | - | Z. mays L. subsp. mays | GW, Network reconstruction | L | Monaco et al. [40] |
Xylose metabolism | - | L. lactis IO-1 | CM, ODE, SA | M | Oshiro et al. [183] |
GW metabolic network | - | S. cerevisiae | GW, Network reconstruction, FBA | L | Österlund et al. [222] |
GW metabolic network and succinic acid production | - | S. cerevisiae | GW, FBA | M | Otero et al. [57] |
Topological properties of metabolism | - | 43 different organisms, E. coli | GW, Topological analysis | L | Ravasz et al. [122] |
One-carbon metabolism, trans-sulfuration pathway, synthesis of glutathione | Hepatocyte | H. sapiens | CM, ODE | L | Reed et al. [158] |
Glycolysis, TCA cycle, pentose phosphate pathway, glutaminolysis, OXPHOS | HeLa cell | H. sapiens | CM, FBA | M | Resendis-Antonio et al. [67] |
Modularity of metabolism | Not specified | H. sapiens | GW, Topological analysis | L | Resendis-Antonio et al. [120] |
GW metabolic network | Not specified | H. sapiens | GW, Network reconstruction | L | Sahoo et al. [223] |
Acetone, butanol and ethanol production | - | C. acetobutylicum | CM, ODE, SA | M | Shinto et al. [184] |
Cancer metabolic networks | Various (NCI-60 collection) | H. sapiens | FBA | L | Shlomi et al. [206] |
GW metabolic network | - | S. cerevisiae | GW, FBA | L | Simeonidis et al. [130] |
Glycolysis | - | S. cerevisiae | CM, ODE | M | Teusink et al. [62] |
GW metabolic network | Not specified | H. sapiens | GW, FBA | L | Thiele et al. [4] |
Primary metabolism | - | E. coli | CM, ODE, EM | - | Tran et al. [101] |
Fueling reaction network | - | E. coli W3110 | CM, FBA | M | Varma et al. [174] |
Reduced model of cell metabolism | - | - | CM, FBA | L | Vazquez et al. [61] |
Small-world property of metabolism | - | E. coli | GW. Topological analysis | L | Wagner et al. [117] |
GW metabolic network | - | C. glabrata | GW, FBA | L | Xu et al. [38] |
Erythrocyte metabolism | Red blood cell | H. sapiens | Hybrid: ODE + MFA | - | Yugi et al. [166] |
Mitochondrial energy metabolism | Various tissues | Mammal | CM, ODE | - | Yugi [224] |
Modularity of metabolism | Not specified | H. sapiens | GW, Topological analysis | L | Zhao et al. [119] |
ROS-induced ROS release in mitochondria network | Cardiomyocytes | C. porcellus | CM, ODE, PDE, RD, Finite Difference Method | M | Zhou et al. [164] |
Tool name | Purpose | Interaction-based | Constraint-Based | Mechanism-Based | Reference |
---|---|---|---|---|---|
BioMet Toolbox | Genome-wide metabolic model validation, FBA, probabilistic FBA, gene set analysis | √ | [225] | ||
Cobra Toolbox | FBA, FVA, dFBA, gap filling, network visualization | √ | [226] | ||
COPASI | Determinstic, stochastic and hybrid simulation, PE, SA, MCA | √ | [227,228] | ||
cupSODA | Deterministic simulations on GPUs | √ | [171] | ||
Cytoscape | Complex networks visualization and topological analysis | √ | [229,230] | ||
FAME | Web based FBA and FVA | √ | [231] | ||
FASIMU | FBA, FVA, gene deletion analysis, gap filling | √ | [232] | ||
OptFlux | FBA, FVA, EFM, gene deletion analysis | √ | [233] | ||
Pathway Tools | GW reconstruction, FBA, gap filling | √ | [234] | ||
Raven Toolbox | GW reconstructions, FBA, network analysis and visualization | √ | √ | [235] | |
SurreyFBA | FBA, FVA, EFM | √ | [236] |
Database | Contents | Reference |
---|---|---|
BiGG | Genome-scale metabolic networks | [237] |
BioCyc | Collection of more than 3000 pathways / genome databases | [238] |
BioModels | SBML models of biological processes | [239] |
Brenda | Molecular and biochemical information on enzymes | [240] |
CellML | XML-based models of biological processes | [241] |
Ensembl | Genome browser for genomic information | [242] |
ExPASy | Portal to existing databases and tools categorized by life science areas | [243] |
GeneCards | Omics data on human genes | [244] |
HumanCyc | Human metabolism pathways | [245] |
Human Metabolic Atlas | Human metabolism models | [52] |
Human Protein Atlas | Human protein expression profiles with spatial localization in tissues and cells | [53] |
JWS | Curated models of biochemical pathways and simulation tools | [246] |
KEGG | Manually curated pathway maps integrating molecular-level information | [32] |
Acknowledgments
Author Contributions
Conflicts of Interest
List of Acronyms
BDO | 1,4-butanediol |
CM | Core Model |
DE | Differential Evolution |
dFBA | Dynamic Flux Balance Analysis |
dNTP | Deoxyribonucleotide Triphosphate |
EFM | Elementary Flux Modes |
EM | Ensemble Modeling |
FBA | Flux Balance Analysis |
FDG-PET | 18F-fluorodeoxyglucose–positron emission tomography |
FVA | Flux Variability Analysis |
GA | Genetic Algorithm |
GC | Gas Chromatography |
GC-MS | Gas Chromatography Mass Spectrometry |
GP | Genetic Programming |
GPU | Graphical Processing Unit |
GW | Genome-Wide |
MCA | Metabolic Control Analysis |
MFA | Metabolic Flux Analysis |
MID | Mass Isoptomers Distribution |
MS | Mass Spectrometry |
OAT | One-factor-at-A-Time |
ODE | Ordinary Differential Equation |
OF | Objective Function |
PE | Parameter Estimation |
PSO | Particle Swarm Optimization |
RD | Reaction-Diffusion |
RE | Reverse Engineering |
ROS | Reactive Oxygen Species |
SA | Sensitivity Analysis |
SiAn | Simulated Annealing |
SDE | Stochastic Differential Equation |
TCA | Tricarboxylic Acid |
Appendix I: Experimental Methodologies for Metabolic Data Generation
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Cazzaniga, P.; Damiani, C.; Besozzi, D.; Colombo, R.; Nobile, M.S.; Gaglio, D.; Pescini, D.; Molinari, S.; Mauri, G.; Alberghina, L.; et al. Computational Strategies for a System-Level Understanding of Metabolism. Metabolites 2014, 4, 1034-1087. https://doi.org/10.3390/metabo4041034
Cazzaniga P, Damiani C, Besozzi D, Colombo R, Nobile MS, Gaglio D, Pescini D, Molinari S, Mauri G, Alberghina L, et al. Computational Strategies for a System-Level Understanding of Metabolism. Metabolites. 2014; 4(4):1034-1087. https://doi.org/10.3390/metabo4041034
Chicago/Turabian StyleCazzaniga, Paolo, Chiara Damiani, Daniela Besozzi, Riccardo Colombo, Marco S. Nobile, Daniela Gaglio, Dario Pescini, Sara Molinari, Giancarlo Mauri, Lilia Alberghina, and et al. 2014. "Computational Strategies for a System-Level Understanding of Metabolism" Metabolites 4, no. 4: 1034-1087. https://doi.org/10.3390/metabo4041034