Journal:Data management and modeling in plant biology - Revision history

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	[[Category:LIMSwiki journal articles on pathology informatics]]		[[Category:LIMSwiki journal articles on pathology informatics]]
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Future perspective and conclusion

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	==Future perspective and conclusion==		==Future perspective and conclusion==
	Due to tremendous progress in experimental high-throughput analysis, well conceptualized research data management systems are becoming essential for sustainable data storage and labeling. Simultaneously, quantitative analysis of plant metabolism on large scale will support combination and comparison of complex data originating from different labs or research platforms. Bioinformatics and -mathematics play a central role both in data management and modeling due to their capability to manage, integrate, and analyze multidimensional data sets. In combination with dynamic mathematical models, network structures elucidated by (pan)genome-based network reconstruction will yield mechanistic insight into regulation of plant metabolism (Figure 3).		Due to tremendous progress in experimental high-throughput analysis, well conceptualized research data management systems are becoming essential for sustainable data storage and labeling. Simultaneously, quantitative analysis of plant metabolism on large scale will support combination and comparison of complex data originating from different labs or research platforms. Bioinformatics and -mathematics play a central role both in data management and modeling due to their capability to manage, integrate, and analyze multidimensional data sets. In combination with dynamic mathematical models, network structures elucidated by (pan)genome-based network reconstruction will yield mechanistic insight into regulation of plant metabolism (Figure 3).


	[[File:Fig3 Krantz FrontPlantSci2021 12.jpg\|900px]]		[[File:Fig3 Krantz FrontPlantSci2021 12.jpg\|900px]]

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	==Abstract==		==Abstract==
	The study of plant-environment interactions is a multidisciplinary research field. With the emergence of quantitative large-scale and high-throughput techniques, the amount and dimensionality of experimental data have strongly increased. Appropriate strategies for data storage, [[Information management\|management]], and [[Data analysis\|evaluation]] are needed to make efficient use of experimental findings. Computational approaches to [[data mining]] are essential for deriving statistical trends and signatures contained in data matrices. Although, current biology is challenged by high data dimensionality in general, this is particularly true for plant biology. As sessile organisms, plants have to cope with environmental fluctuations. This typically results in strong dynamics of metabolite and protein concentrations, which are often challenging to quantify. Summarizing experimental output results in complex data arrays, which need computational statistics and numerical methods for building quantitative models. Experimental findings need to be combined with computational models to gain a mechanistic understanding of plant metabolism. For this, [[bioinformatics]] and mathematics need to be combined with experimental setups in physiology, biochemistry, and molecular biology. This review presents and discusses concepts at the interface of experiment and computation, which are likely to shape current and future plant biology. Finally, this interface is discussed with regard to its capabilities and limitations to develop a quantitative model of plant-environment interactions.		The study of plant-environment interactions is a multidisciplinary research field. With the emergence of quantitative large-scale and high-throughput techniques, the amount and dimensionality of experimental data have strongly increased. Appropriate strategies for data storage, [[Information management\|management]], and [[Data analysis\|evaluation]] are needed to make efficient use of experimental findings. Computational approaches to [[data mining]] are essential for deriving statistical trends and signatures contained in data matrices. Although, current biology is challenged by high data dimensionality in general, this is particularly true for plant biology. As sessile organisms, plants have to cope with environmental fluctuations. This typically results in strong dynamics of metabolite and protein concentrations, which are often challenging to quantify. Summarizing experimental output results in complex data arrays, which need computational statistics and numerical methods for building quantitative models. Experimental findings need to be combined with computational models to gain a mechanistic understanding of plant metabolism. For this, [[bioinformatics]] and mathematics need to be combined with experimental setups in physiology, biochemistry, and molecular biology. This review presents and discusses concepts at the interface of experiment and computation, which are likely to shape current and future plant biology. Finally, this interface is discussed with regard to its capabilities and limitations to develop a quantitative model of plant-environment interactions.
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	In a metabolic DE model, each differential equation describes dynamics of one metabolite. Thus, modeling a metabolic network results in a system of DEs, which needs to be solved, i.e., numerically integrated, within biochemical and physiological boundaries. Numerical integration of (O)DEs can be performed computationally using platforms like Copasi<ref>{{Cite journal \|last=Hoops \|first=S. \|last2=Sahle \|first2=S. \|last3=Gauges \|first3=R. \|last4=Lee \|first4=C. \|last5=Pahle \|first5=J. \|last6=Simus \|first6=N. \|last7=Singhal \|first7=M. \|last8=Xu \|first8=L. \|last9=Mendes \|first9=P. \|last10=Kummer \|first10=U. \|date=2006-12-15 \|title=COPASI--a COmplex PAthway SImulator \|url=https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btl485 \|journal=Bioinformatics \|language=en \|volume=22 \|issue=24 \|pages=3067–3074 \|doi=10.1093/bioinformatics/btl485 \|issn=1367-4803}}</ref><ref>{{Cite journal \|last=Kent \|first=Edward \|last2=Hoops \|first2=Stefan \|last3=Mendes \|first3=Pedro \|date=2012-12 \|title=Condor-COPASI: high-throughput computing for biochemical networks \|url=https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-6-91 \|journal=BMC Systems Biology \|language=en \|volume=6 \|issue=1 \|pages=91 \|doi=10.1186/1752-0509-6-91 \|issn=1752-0509 \|pmc=PMC3527284 \|pmid=22834945}}</ref>, Python<ref>{{Cite book \|last=van Rossum, G.; Drake Jr., F.L. \|year=1995 \|title=Python Tutorial \|publisher=Centrum voor Wiskunde en Informatica \|volume=620}}</ref>, or [[R (programming language)\|R]].<ref>{{Cite web \|last=R Core Team \|date=2021 \|title=R: A Language and Environment for Statistical Computing \|url=https://www.r-project.org/ \|publisher=R Foundation for Statistical Computing \|accessdate=16 August 2021}}</ref> Boundaries for solving ODEs arise from experiments and typically comprise information about SD/error of kinetic parameters, protein, or metabolite concentration. Within the process of parameter estimation, kinetic parameters are determined to reflect experimental data on metabolite or protein concentrations with a minimized error.<ref>{{Cite journal \|last=Moles \|first=Carmen G. \|last2=Mendes \|first2=Pedro \|last3=Banga \|first3=Julio R. \|date=2003-11 \|title=Parameter Estimation in Biochemical Pathways: A Comparison of Global Optimization Methods \|url=http://genome.cshlp.org/lookup/doi/10.1101/gr.1262503 \|journal=Genome Research \|language=en \|volume=13 \|issue=11 \|pages=2467–2474 \|doi=10.1101/gr.1262503 \|issn=1088-9051 \|pmc=PMC403766 \|pmid=14559783}}</ref> Hence, the more precise experimental quantification of such parameters and concentration is the less ambiguous are solutions of equation systems. Yet, previous findings also indicated that parameter measurements must be highly precise and complete in order to minimize “sloppiness” in parameter sensitivities and to usefully constrain model predictions.<ref>{{Cite journal \|last=Gutenkunst \|first=Ryan N \|last2=Waterfall \|first2=Joshua J \|last3=Casey \|first3=Fergal P \|last4=Brown \|first4=Kevin S \|last5=Myers \|first5=Christopher R \|last6=Sethna \|first6=James P \|date=2007-10-05 \|editor-last=Arkin \|editor-first=Adam P \|title=Universally Sloppy Parameter Sensitivities in Systems Biology Models \|url=https://dx.plos.org/10.1371/journal.pcbi.0030189 \|journal=PLoS Computational Biology \|language=en \|volume=3 \|issue=10 \|pages=e189 \|doi=10.1371/journal.pcbi.0030189 \|issn=1553-7358 \|pmc=PMC2000971 \|pmid=17922568}}</ref> Based on their findings, the authors suggest to focus rather on validation of model predictions than on model parameters. Although uncertainties about model structure, parameters, or kinetic laws can hardly be excluded from future modeling approaches due to their nested architecture<ref>{{Cite journal \|last=Liebermeister \|first=W. \|last2=Schaber \|first2=J. \|last3=Klipp \|first3=E. \|date=2009-01-01 \|title=Nested uncertainties in biochemical models \|url=https://digital-library.theiet.org/content/journals/10.1049/iet-syb_20070042 \|journal=IET Systems Biology \|language=en \|volume=3 \|issue=1 \|pages=1–9 \|doi=10.1049/iet-syb:20070042 \|issn=1751-8849}}</ref>, an iterative workflow consisting of model development, simulation, and validation by quantitative experiments will refine and advance model output and predictive power.<ref>{{Cite journal \|last=Babtie \|first=Ann C. \|last2=Stumpf \|first2=Michael P. H. \|date=2017-08 \|title=How to deal with parameters for whole-cell modelling \|url=https://royalsocietypublishing.org/doi/10.1098/rsif.2017.0237 \|journal=Journal of The Royal Society Interface \|language=en \|volume=14 \|issue=133 \|pages=20170237 \|doi=10.1098/rsif.2017.0237 \|issn=1742-5689 \|pmc=PMC5582120 \|pmid=28768879}}</ref> Such modeling approaches have revealed detailed insights into molecular processes comprising, e.g., regulatory motifs of moonlighting proteins<ref>{{Cite journal \|last=Krantz \|first=Maria \|last2=Klipp \|first2=Edda \|date=2020-01-01 \|title=Moonlighting proteins - an approach to systematize the concept \|url=https://content.iospress.com/articles/in-silico-biology/isb190473 \|journal=In Silico Biology \|language=en \|volume=13 \|issue=3-4 \|pages=71–83 \|doi=10.3233/ISB-190473 \|issn=1386-6338 \|pmc=PMC7505007 \|pmid=32285845}}</ref>, temperature compensation in reaction networks<ref>{{Cite journal \|last=Ruoff \|first=Peter \|last2=Zakhartsev \|first2=Maxim \|last3=Westerhoff \|first3=Hans V. \|date=2007-02 \|title=Temperature compensation through systems biology: Temperature compensation of fluxes \|url=https://onlinelibrary.wiley.com/doi/10.1111/j.1742-4658.2007.05641.x \|journal=The FEBS Journal \|language=en \|volume=274 \|issue=4 \|pages=940–950 \|doi=10.1111/j.1742-4658.2007.05641.x}}</ref>, or mechanisms regulating diurnal starch dynamics.<ref>{{Cite journal \|last=Pokhilko \|first=Alexandra \|last2=Flis \|first2=Anna \|last3=Sulpice \|first3=Ronan \|last4=Stitt \|first4=Mark \|last5=Ebenhöh \|first5=Oliver \|date=2014 \|title=Adjustment of carbon fluxes to light conditions regulates the daily turnover of starch in plants: a computational model \|url=http://xlink.rsc.org/?DOI=C3MB70459A \|journal=Mol. BioSyst. \|language=en \|volume=10 \|issue=3 \|pages=613–627 \|doi=10.1039/C3MB70459A \|issn=1742-206X}}</ref>		In a metabolic DE model, each differential equation describes dynamics of one metabolite. Thus, modeling a metabolic network results in a system of DEs, which needs to be solved, i.e., numerically integrated, within biochemical and physiological boundaries. Numerical integration of (O)DEs can be performed computationally using platforms like Copasi<ref>{{Cite journal \|last=Hoops \|first=S. \|last2=Sahle \|first2=S. \|last3=Gauges \|first3=R. \|last4=Lee \|first4=C. \|last5=Pahle \|first5=J. \|last6=Simus \|first6=N. \|last7=Singhal \|first7=M. \|last8=Xu \|first8=L. \|last9=Mendes \|first9=P. \|last10=Kummer \|first10=U. \|date=2006-12-15 \|title=COPASI--a COmplex PAthway SImulator \|url=https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btl485 \|journal=Bioinformatics \|language=en \|volume=22 \|issue=24 \|pages=3067–3074 \|doi=10.1093/bioinformatics/btl485 \|issn=1367-4803}}</ref><ref>{{Cite journal \|last=Kent \|first=Edward \|last2=Hoops \|first2=Stefan \|last3=Mendes \|first3=Pedro \|date=2012-12 \|title=Condor-COPASI: high-throughput computing for biochemical networks \|url=https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-6-91 \|journal=BMC Systems Biology \|language=en \|volume=6 \|issue=1 \|pages=91 \|doi=10.1186/1752-0509-6-91 \|issn=1752-0509 \|pmc=PMC3527284 \|pmid=22834945}}</ref>, Python<ref>{{Cite book \|last=van Rossum, G.; Drake Jr., F.L. \|year=1995 \|title=Python Tutorial \|publisher=Centrum voor Wiskunde en Informatica \|volume=620}}</ref>, or [[R (programming language)\|R]].<ref>{{Cite web \|last=R Core Team \|date=2021 \|title=R: A Language and Environment for Statistical Computing \|url=https://www.r-project.org/ \|publisher=R Foundation for Statistical Computing \|accessdate=16 August 2021}}</ref> Boundaries for solving ODEs arise from experiments and typically comprise information about SD/error of kinetic parameters, protein, or metabolite concentration. Within the process of parameter estimation, kinetic parameters are determined to reflect experimental data on metabolite or protein concentrations with a minimized error.<ref>{{Cite journal \|last=Moles \|first=Carmen G. \|last2=Mendes \|first2=Pedro \|last3=Banga \|first3=Julio R. \|date=2003-11 \|title=Parameter Estimation in Biochemical Pathways: A Comparison of Global Optimization Methods \|url=http://genome.cshlp.org/lookup/doi/10.1101/gr.1262503 \|journal=Genome Research \|language=en \|volume=13 \|issue=11 \|pages=2467–2474 \|doi=10.1101/gr.1262503 \|issn=1088-9051 \|pmc=PMC403766 \|pmid=14559783}}</ref> Hence, the more precise experimental quantification of such parameters and concentration is the less ambiguous are solutions of equation systems. Yet, previous findings also indicated that parameter measurements must be highly precise and complete in order to minimize “sloppiness” in parameter sensitivities and to usefully constrain model predictions.<ref>{{Cite journal \|last=Gutenkunst \|first=Ryan N \|last2=Waterfall \|first2=Joshua J \|last3=Casey \|first3=Fergal P \|last4=Brown \|first4=Kevin S \|last5=Myers \|first5=Christopher R \|last6=Sethna \|first6=James P \|date=2007-10-05 \|editor-last=Arkin \|editor-first=Adam P \|title=Universally Sloppy Parameter Sensitivities in Systems Biology Models \|url=https://dx.plos.org/10.1371/journal.pcbi.0030189 \|journal=PLoS Computational Biology \|language=en \|volume=3 \|issue=10 \|pages=e189 \|doi=10.1371/journal.pcbi.0030189 \|issn=1553-7358 \|pmc=PMC2000971 \|pmid=17922568}}</ref> Based on their findings, the authors suggest to focus rather on validation of model predictions than on model parameters. Although uncertainties about model structure, parameters, or kinetic laws can hardly be excluded from future modeling approaches due to their nested architecture<ref>{{Cite journal \|last=Liebermeister \|first=W. \|last2=Schaber \|first2=J. \|last3=Klipp \|first3=E. \|date=2009-01-01 \|title=Nested uncertainties in biochemical models \|url=https://digital-library.theiet.org/content/journals/10.1049/iet-syb_20070042 \|journal=IET Systems Biology \|language=en \|volume=3 \|issue=1 \|pages=1–9 \|doi=10.1049/iet-syb:20070042 \|issn=1751-8849}}</ref>, an iterative workflow consisting of model development, simulation, and validation by quantitative experiments will refine and advance model output and predictive power.<ref>{{Cite journal \|last=Babtie \|first=Ann C. \|last2=Stumpf \|first2=Michael P. H. \|date=2017-08 \|title=How to deal with parameters for whole-cell modelling \|url=https://royalsocietypublishing.org/doi/10.1098/rsif.2017.0237 \|journal=Journal of The Royal Society Interface \|language=en \|volume=14 \|issue=133 \|pages=20170237 \|doi=10.1098/rsif.2017.0237 \|issn=1742-5689 \|pmc=PMC5582120 \|pmid=28768879}}</ref> Such modeling approaches have revealed detailed insights into molecular processes comprising, e.g., regulatory motifs of moonlighting proteins<ref>{{Cite journal \|last=Krantz \|first=Maria \|last2=Klipp \|first2=Edda \|date=2020-01-01 \|title=Moonlighting proteins - an approach to systematize the concept \|url=https://content.iospress.com/articles/in-silico-biology/isb190473 \|journal=In Silico Biology \|language=en \|volume=13 \|issue=3-4 \|pages=71–83 \|doi=10.3233/ISB-190473 \|issn=1386-6338 \|pmc=PMC7505007 \|pmid=32285845}}</ref>, temperature compensation in reaction networks<ref>{{Cite journal \|last=Ruoff \|first=Peter \|last2=Zakhartsev \|first2=Maxim \|last3=Westerhoff \|first3=Hans V. \|date=2007-02 \|title=Temperature compensation through systems biology: Temperature compensation of fluxes \|url=https://onlinelibrary.wiley.com/doi/10.1111/j.1742-4658.2007.05641.x \|journal=The FEBS Journal \|language=en \|volume=274 \|issue=4 \|pages=940–950 \|doi=10.1111/j.1742-4658.2007.05641.x}}</ref>, or mechanisms regulating diurnal starch dynamics.<ref>{{Cite journal \|last=Pokhilko \|first=Alexandra \|last2=Flis \|first2=Anna \|last3=Sulpice \|first3=Ronan \|last4=Stitt \|first4=Mark \|last5=Ebenhöh \|first5=Oliver \|date=2014 \|title=Adjustment of carbon fluxes to light conditions regulates the daily turnover of starch in plants: a computational model \|url=http://xlink.rsc.org/?DOI=C3MB70459A \|journal=Mol. BioSyst. \|language=en \|volume=10 \|issue=3 \|pages=613–627 \|doi=10.1039/C3MB70459A \|issn=1742-206X}}</ref>

			==Future perspective and conclusion==
			Due to tremendous progress in experimental high-throughput analysis, well conceptualized research data management systems are becoming essential for sustainable data storage and labeling. Simultaneously, quantitative analysis of plant metabolism on large scale will support combination and comparison of complex data originating from different labs or research platforms. Bioinformatics and -mathematics play a central role both in data management and modeling due to their capability to manage, integrate, and analyze multidimensional data sets. In combination with dynamic mathematical models, network structures elucidated by (pan)genome-based network reconstruction will yield mechanistic insight into regulation of plant metabolism (Figure 3).

			[[File:Fig3 Krantz FrontPlantSci2021 12.jpg\|900px]]
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			{\| border="0" cellpadding="5" cellspacing="0" width="900px"
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			\| style="background-color:white; padding-left:10px; padding-right:10px;" \|<blockquote>'''Figure 3.''' Conceptual workflow for data management and modeling in plant sciences.</blockquote>
			\|-
			\|}
			\|}

			Finally, beyond its role as a tool for understanding and analyzing experimental data on plant metabolism, mathematical modeling also enables the comparison to structure and regulation of other complex systems in nature and engineering, which will support and accelerate the identification of underlying universal principles of biochemical network organization, regulation, and architecture.

			==Acknowledgements==
			We thank the members of the SFB/TR175 consortium for many fruitful discussions and DFG for funding.

			===Author contributions===
			MK and DZ are first authors and contributed equally to this manuscript. TN conceived and wrote the manuscript. All authors contributed to the article and approved the submitted version.

			===Funding===
			This work was funded by Deutsche Forschungsgemeinschaft (DFG), TR175/D02, D03, and INF.

			===Conflict of interest===
			The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

	==References==		==References==

Shawndouglas: Saving and adding more.

2021-12-20T21:17:19Z

Saving and adding more.

← Older revision		Revision as of 21:17, 20 December 2021
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	===Differential equation models for quantitative analysis of biochemical network dynamics===		===Differential equation models for quantitative analysis of biochemical network dynamics===
	Mathematical models of plant metabolism are frequently based on systems of DEs. For example, dynamics of metabolite concentrations are mathematically described in such models by the sum of synthesizing and interconverting/degrading enzyme reactions. Typically, time is considered to be the only independent variable, and, thus, ordinary differential equations (ODEs) are applied for simulating biochemical networks. (Andrews ~~and~~ Arkin, 2006) If two or more independent variables are considered, e.g., time and space, partial differential equations (PDEs) are applied.		Mathematical models of plant metabolism are frequently based on systems of DEs. For example, dynamics of metabolite concentrations are mathematically described in such models by the sum of synthesizing and interconverting/degrading enzyme reactions. Typically, time is considered to be the only independent variable, and, thus, ordinary differential equations (ODEs) are applied for simulating biochemical networks.<ref>{{Cite journal \|last=Andrews \|first=Steven S. \|last2=Arkin \|first2=Adam P. \|date=2006-07 \|title=Simulating cell biology \|url=https://linkinghub.elsevier.com/retrieve/pii/S0960982206017751 \|journal=Current Biology \|language=en \|volume=16 \|issue=14 \|pages=R523–R527 \|doi=10.1016/j.cub.2006.06.048}}</ref> If two or more independent variables are considered, e.g., time and space, partial differential equations (PDEs) are applied.

	To briefly illustrate the suitability of (ordinary) differential equations for dynamic modeling of metabolism, consider an arbitrary enzyme catalyzed two-substrate reaction:		To briefly illustrate the suitability of (ordinary) differential equations for dynamic modeling of metabolism, consider an arbitrary enzyme catalyzed two-substrate reaction:
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	<math>{- \frac{d\left\lbrack A \right\rbrack}{dt} = - \frac{d\left\lbrack B \right\rbrack}{dt} = \frac{d\left\lbrack C \right\rbrack}{dt} = k\left\lbrack A \right\rbrack\left\lbrack B \right\rbrack = f\left( {A,B,C} \right)}</math>		<math>{- \frac{d\left\lbrack A \right\rbrack}{dt} = - \frac{d\left\lbrack B \right\rbrack}{dt} = \frac{d\left\lbrack C \right\rbrack}{dt} = k\left\lbrack A \right\rbrack\left\lbrack B \right\rbrack = f\left( {A,B,C} \right)}</math>

	The right side of the ODEs can be summarized by metabolic functions ''f''(A, B, C) comprising all (kinetic) terms, which contribute to changes in concentration of substrate and product molecules. While in this arbitrary example metabolic functions only comprise one kinetic term, the composition of such functions in metabolic systems are much more complex due to various enzyme reactions, which contribute to synthesis, degradation, or transport of metabolites. Also, while kinetics in this equation are described as constantly proportional to substrate concentrations without regulatory impact, enzyme catalyzed reactions typically follow kinetics with saturation, inhibition, and activation. Systems of DEs mathematically amalgamate different kinetic laws with dynamic substrate, product, and effector concentrations, which enable quantitative simulation of metabolism. Further, DEs enable different types of kinetic modeling focusing on dynamic (time-series) data or steady-state approaches. (Rohwer, 2012) However, for simulation of kinetic DE models within physiologically relevant boundaries, sets of kinetic parameters and metabolite concentrations need to be quantified. As a consequence, due to experimental limitations, the applicability of (O)DE-based models is frequently limited to relatively small networks and narrow time frames, in which the model can explain or reliably predict experimental data. Nevertheless, DEs constitute a very important approach for modeling of metabolic networks because of the inbuilt consideration of substrate and product concentrations on metabolic functions, i.e., a changing substrate concentration has a direct effect on its own metabolic function. (Nägele, 2014)		The right side of the ODEs can be summarized by metabolic functions ''f''(A, B, C) comprising all (kinetic) terms, which contribute to changes in concentration of substrate and product molecules. While in this arbitrary example metabolic functions only comprise one kinetic term, the composition of such functions in metabolic systems are much more complex due to various enzyme reactions, which contribute to synthesis, degradation, or transport of metabolites. Also, while kinetics in this equation are described as constantly proportional to substrate concentrations without regulatory impact, enzyme catalyzed reactions typically follow kinetics with saturation, inhibition, and activation. Systems of DEs mathematically amalgamate different kinetic laws with dynamic substrate, product, and effector concentrations, which enable quantitative simulation of metabolism. Further, DEs enable different types of kinetic modeling focusing on dynamic (time-series) data or steady-state approaches.<ref>{{Cite journal \|last=Rohwer \|first=Johann M. \|date=2012-03 \|title=Kinetic modelling of plant metabolic pathways \|url=https://academic.oup.com/jxb/article-lookup/doi/10.1093/jxb/ers080 \|journal=Journal of Experimental Botany \|language=en \|volume=63 \|issue=6 \|pages=2275–2292 \|doi=10.1093/jxb/ers080 \|issn=1460-2431}}</ref> However, for simulation of kinetic DE models within physiologically relevant boundaries, sets of kinetic parameters and metabolite concentrations need to be quantified. As a consequence, due to experimental limitations, the applicability of (O)DE-based models is frequently limited to relatively small networks and narrow time frames, in which the model can explain or reliably predict experimental data. Nevertheless, DEs constitute a very important approach for modeling of metabolic networks because of the inbuilt consideration of substrate and product concentrations on metabolic functions, i.e., a changing substrate concentration has a direct effect on its own metabolic function.<ref>{{Cite journal \|last=Nägele \|first=Thomas \|date=2014-11-06 \|title=Linking metabolomics data to underlying metabolic regulation \|url=http://journal.frontiersin.org/article/10.3389/fmolb.2014.00022/abstract \|journal=Frontiers in Molecular Biosciences \|volume=1 \|doi=10.3389/fmolb.2014.00022 \|issn=2296-889X \|pmc=PMC4428386 \|pmid=25988163}}</ref>

	In a metabolic DE model, each differential equation describes dynamics of one metabolite. Thus, modeling a metabolic network results in a system of DEs, which needs to be solved, i.e., numerically integrated, within biochemical and physiological boundaries. Numerical integration of (O)DEs can be performed computationally using platforms like Copasi (Hoops ~~et al~~., 2006; Kent ~~et al~~.~~, 2012)~~, Python (van Rossum, 1995), or [[R (programming language)\|R]]. (R Core Team, 2021) Boundaries for solving ODEs arise from experiments and typically comprise information about SD/error of kinetic parameters, protein, or metabolite concentration. Within the process of parameter estimation, kinetic parameters are determined to reflect experimental data on metabolite or protein concentrations with a minimized error. (Moles ~~et al~~., 2003) Hence, the more precise experimental quantification of such parameters and concentration is the less ambiguous are solutions of equation systems. Yet, previous findings also indicated that parameter measurements must be highly precise and complete in order to minimize “sloppiness” in parameter sensitivities and to usefully constrain model predictions. (Gutenkunst ~~et al~~.~~, 2007)~~ Based on their findings, the authors suggest to focus rather on validation of model predictions than on model parameters. Although uncertainties about model structure, parameters, or kinetic laws can hardly be excluded from future modeling approaches due to their nested architecture (Schaber ~~et al~~., 2009), an iterative workflow consisting of model development, simulation, and validation by quantitative experiments will refine and advance model output and predictive power. (Babtie ~~and~~ Stumpf, 2017) Such modeling approaches have revealed detailed insights into molecular processes comprising, e.g., regulatory motifs of moonlighting proteins (Krantz ~~and~~ Klipp, 2020), temperature compensation in reaction networks (Ruoff ~~et al~~., 2007), or mechanisms regulating diurnal starch dynamics. (Pokhilko ~~et al~~.~~, 2014)~~		In a metabolic DE model, each differential equation describes dynamics of one metabolite. Thus, modeling a metabolic network results in a system of DEs, which needs to be solved, i.e., numerically integrated, within biochemical and physiological boundaries. Numerical integration of (O)DEs can be performed computationally using platforms like Copasi<ref>{{Cite journal \|last=Hoops \|first=S. \|last2=Sahle \|first2=S. \|last3=Gauges \|first3=R. \|last4=Lee \|first4=C. \|last5=Pahle \|first5=J. \|last6=Simus \|first6=N. \|last7=Singhal \|first7=M. \|last8=Xu \|first8=L. \|last9=Mendes \|first9=P. \|last10=Kummer \|first10=U. \|date=2006-12-15 \|title=COPASI--a COmplex PAthway SImulator \|url=https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btl485 \|journal=Bioinformatics \|language=en \|volume=22 \|issue=24 \|pages=3067–3074 \|doi=10.1093/bioinformatics/btl485 \|issn=1367-4803}}</ref><ref>{{Cite journal \|last=Kent \|first=Edward \|last2=Hoops \|first2=Stefan \|last3=Mendes \|first3=Pedro \|date=2012-12 \|title=Condor-COPASI: high-throughput computing for biochemical networks \|url=https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-6-91 \|journal=BMC Systems Biology \|language=en \|volume=6 \|issue=1 \|pages=91 \|doi=10.1186/1752-0509-6-91 \|issn=1752-0509 \|pmc=PMC3527284 \|pmid=22834945}}</ref>, Python<ref>{{Cite book \|last=van Rossum, G.; Drake Jr., F.L. \|year=1995 \|title=Python Tutorial \|publisher=Centrum voor Wiskunde en Informatica \|volume=620}}</ref>, or [[R (programming language)\|R]].<ref>{{Cite web \|last=R Core Team \|date=2021 \|title=R: A Language and Environment for Statistical Computing \|url=https://www.r-project.org/ \|publisher=R Foundation for Statistical Computing \|accessdate=16 August 2021}}</ref> Boundaries for solving ODEs arise from experiments and typically comprise information about SD/error of kinetic parameters, protein, or metabolite concentration. Within the process of parameter estimation, kinetic parameters are determined to reflect experimental data on metabolite or protein concentrations with a minimized error.<ref>{{Cite journal \|last=Moles \|first=Carmen G. \|last2=Mendes \|first2=Pedro \|last3=Banga \|first3=Julio R. \|date=2003-11 \|title=Parameter Estimation in Biochemical Pathways: A Comparison of Global Optimization Methods \|url=http://genome.cshlp.org/lookup/doi/10.1101/gr.1262503 \|journal=Genome Research \|language=en \|volume=13 \|issue=11 \|pages=2467–2474 \|doi=10.1101/gr.1262503 \|issn=1088-9051 \|pmc=PMC403766 \|pmid=14559783}}</ref> Hence, the more precise experimental quantification of such parameters and concentration is the less ambiguous are solutions of equation systems. Yet, previous findings also indicated that parameter measurements must be highly precise and complete in order to minimize “sloppiness” in parameter sensitivities and to usefully constrain model predictions.<ref>{{Cite journal \|last=Gutenkunst \|first=Ryan N \|last2=Waterfall \|first2=Joshua J \|last3=Casey \|first3=Fergal P \|last4=Brown \|first4=Kevin S \|last5=Myers \|first5=Christopher R \|last6=Sethna \|first6=James P \|date=2007-10-05 \|editor-last=Arkin \|editor-first=Adam P \|title=Universally Sloppy Parameter Sensitivities in Systems Biology Models \|url=https://dx.plos.org/10.1371/journal.pcbi.0030189 \|journal=PLoS Computational Biology \|language=en \|volume=3 \|issue=10 \|pages=e189 \|doi=10.1371/journal.pcbi.0030189 \|issn=1553-7358 \|pmc=PMC2000971 \|pmid=17922568}}</ref> Based on their findings, the authors suggest to focus rather on validation of model predictions than on model parameters. Although uncertainties about model structure, parameters, or kinetic laws can hardly be excluded from future modeling approaches due to their nested architecture<ref>{{Cite journal \|last=Liebermeister \|first=W. \|last2=Schaber \|first2=J. \|last3=Klipp \|first3=E. \|date=2009-01-01 \|title=Nested uncertainties in biochemical models \|url=https://digital-library.theiet.org/content/journals/10.1049/iet-syb_20070042 \|journal=IET Systems Biology \|language=en \|volume=3 \|issue=1 \|pages=1–9 \|doi=10.1049/iet-syb:20070042 \|issn=1751-8849}}</ref>, an iterative workflow consisting of model development, simulation, and validation by quantitative experiments will refine and advance model output and predictive power.<ref>{{Cite journal \|last=Babtie \|first=Ann C. \|last2=Stumpf \|first2=Michael P. H. \|date=2017-08 \|title=How to deal with parameters for whole-cell modelling \|url=https://royalsocietypublishing.org/doi/10.1098/rsif.2017.0237 \|journal=Journal of The Royal Society Interface \|language=en \|volume=14 \|issue=133 \|pages=20170237 \|doi=10.1098/rsif.2017.0237 \|issn=1742-5689 \|pmc=PMC5582120 \|pmid=28768879}}</ref> Such modeling approaches have revealed detailed insights into molecular processes comprising, e.g., regulatory motifs of moonlighting proteins<ref>{{Cite journal \|last=Krantz \|first=Maria \|last2=Klipp \|first2=Edda \|date=2020-01-01 \|title=Moonlighting proteins - an approach to systematize the concept \|url=https://content.iospress.com/articles/in-silico-biology/isb190473 \|journal=In Silico Biology \|language=en \|volume=13 \|issue=3-4 \|pages=71–83 \|doi=10.3233/ISB-190473 \|issn=1386-6338 \|pmc=PMC7505007 \|pmid=32285845}}</ref>, temperature compensation in reaction networks<ref>{{Cite journal \|last=Ruoff \|first=Peter \|last2=Zakhartsev \|first2=Maxim \|last3=Westerhoff \|first3=Hans V. \|date=2007-02 \|title=Temperature compensation through systems biology: Temperature compensation of fluxes \|url=https://onlinelibrary.wiley.com/doi/10.1111/j.1742-4658.2007.05641.x \|journal=The FEBS Journal \|language=en \|volume=274 \|issue=4 \|pages=940–950 \|doi=10.1111/j.1742-4658.2007.05641.x}}</ref>, or mechanisms regulating diurnal starch dynamics.<ref>{{Cite journal \|last=Pokhilko \|first=Alexandra \|last2=Flis \|first2=Anna \|last3=Sulpice \|first3=Ronan \|last4=Stitt \|first4=Mark \|last5=Ebenhöh \|first5=Oliver \|date=2014 \|title=Adjustment of carbon fluxes to light conditions regulates the daily turnover of starch in plants: a computational model \|url=http://xlink.rsc.org/?DOI=C3MB70459A \|journal=Mol. BioSyst. \|language=en \|volume=10 \|issue=3 \|pages=613–627 \|doi=10.1039/C3MB70459A \|issn=1742-206X}}</ref>

	==References==		==References==

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Differential equation models for quantitative analysis of biochemical network dynamics

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	===Differential equation models for quantitative analysis of biochemical network dynamics===		===Differential equation models for quantitative analysis of biochemical network dynamics===
			Mathematical models of plant metabolism are frequently based on systems of DEs. For example, dynamics of metabolite concentrations are mathematically described in such models by the sum of synthesizing and interconverting/degrading enzyme reactions. Typically, time is considered to be the only independent variable, and, thus, ordinary differential equations (ODEs) are applied for simulating biochemical networks. (Andrews and Arkin, 2006) If two or more independent variables are considered, e.g., time and space, partial differential equations (PDEs) are applied.

			To briefly illustrate the suitability of (ordinary) differential equations for dynamic modeling of metabolism, consider an arbitrary enzyme catalyzed two-substrate reaction:

			<math>{A + B\overset{k}{\rightarrow}\ C}</math>

			Here, two substrate molecules A and B react to form a product C with the rate constant ''k''. Changes of substrate and product concentrations within a time period ''Δt'' (infinitesimally written as ''dt'') are captured by the corresponding ODEs:

			<math>{- \frac{d\left\lbrack A \right\rbrack}{dt} = - \frac{d\left\lbrack B \right\rbrack}{dt} = \frac{d\left\lbrack C \right\rbrack}{dt} = k\left\lbrack A \right\rbrack\left\lbrack B \right\rbrack = f\left( {A,B,C} \right)}</math>

			The right side of the ODEs can be summarized by metabolic functions ''f''(A, B, C) comprising all (kinetic) terms, which contribute to changes in concentration of substrate and product molecules. While in this arbitrary example metabolic functions only comprise one kinetic term, the composition of such functions in metabolic systems are much more complex due to various enzyme reactions, which contribute to synthesis, degradation, or transport of metabolites. Also, while kinetics in this equation are described as constantly proportional to substrate concentrations without regulatory impact, enzyme catalyzed reactions typically follow kinetics with saturation, inhibition, and activation. Systems of DEs mathematically amalgamate different kinetic laws with dynamic substrate, product, and effector concentrations, which enable quantitative simulation of metabolism. Further, DEs enable different types of kinetic modeling focusing on dynamic (time-series) data or steady-state approaches. (Rohwer, 2012) However, for simulation of kinetic DE models within physiologically relevant boundaries, sets of kinetic parameters and metabolite concentrations need to be quantified. As a consequence, due to experimental limitations, the applicability of (O)DE-based models is frequently limited to relatively small networks and narrow time frames, in which the model can explain or reliably predict experimental data. Nevertheless, DEs constitute a very important approach for modeling of metabolic networks because of the inbuilt consideration of substrate and product concentrations on metabolic functions, i.e., a changing substrate concentration has a direct effect on its own metabolic function. (Nägele, 2014)

			In a metabolic DE model, each differential equation describes dynamics of one metabolite. Thus, modeling a metabolic network results in a system of DEs, which needs to be solved, i.e., numerically integrated, within biochemical and physiological boundaries. Numerical integration of (O)DEs can be performed computationally using platforms like Copasi (Hoops et al., 2006; Kent et al., 2012), Python (van Rossum, 1995), or [[R (programming language)\|R]]. (R Core Team, 2021) Boundaries for solving ODEs arise from experiments and typically comprise information about SD/error of kinetic parameters, protein, or metabolite concentration. Within the process of parameter estimation, kinetic parameters are determined to reflect experimental data on metabolite or protein concentrations with a minimized error. (Moles et al., 2003) Hence, the more precise experimental quantification of such parameters and concentration is the less ambiguous are solutions of equation systems. Yet, previous findings also indicated that parameter measurements must be highly precise and complete in order to minimize “sloppiness” in parameter sensitivities and to usefully constrain model predictions. (Gutenkunst et al., 2007) Based on their findings, the authors suggest to focus rather on validation of model predictions than on model parameters. Although uncertainties about model structure, parameters, or kinetic laws can hardly be excluded from future modeling approaches due to their nested architecture (Schaber et al., 2009), an iterative workflow consisting of model development, simulation, and validation by quantitative experiments will refine and advance model output and predictive power. (Babtie and Stumpf, 2017) Such modeling approaches have revealed detailed insights into molecular processes comprising, e.g., regulatory motifs of moonlighting proteins (Krantz and Klipp, 2020), temperature compensation in reaction networks (Ruoff et al., 2007), or mechanisms regulating diurnal starch dynamics. (Pokhilko et al., 2014)

	==References==		==References==

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	This article aims to summarize and discuss current advances and limitations of integrative molecular analysis, computational modeling, and data science. It focuses on both experimental and theoretical methodology to support design and analysis of interdisciplinary research in plant biology. A particular focus is laid on methodologies for capturing system dynamics of plant metabolism induced by a changing environment.		This article aims to summarize and discuss current advances and limitations of integrative molecular analysis, computational modeling, and data science. It focuses on both experimental and theoretical methodology to support design and analysis of interdisciplinary research in plant biology. A particular focus is laid on methodologies for capturing system dynamics of plant metabolism induced by a changing environment.

			==On a large scale: How does genome-scale metabolic network reconstruction support data integration in plant biology?==
			The availability of comprehensive genome information has enabled the reconstruction of genome-scale metabolic networks, which predict, based on gene annotation, a functional cellular network structure. This crucially supports the interpretation of gene functions and makes pathways accessible to computational biology and mathematics.<ref>{{Cite journal \|last=Oberhardt \|first=Matthew A \|last2=Palsson \|first2=Bernhard Ø \|last3=Papin \|first3=Jason A \|date=2009-01 \|title=Applications of genome‐scale metabolic reconstructions \|url=https://onlinelibrary.wiley.com/doi/10.1038/msb.2009.77 \|journal=Molecular Systems Biology \|language=en \|volume=5 \|issue=1 \|pages=320 \|doi=10.1038/msb.2009.77 \|issn=1744-4292 \|pmc=PMC2795471 \|pmid=19888215}}</ref> Further, reconstructed networks significantly facilitate a mechanistic description of genotype-phenotype relationships and enable the application of constraint-based analysis methods.<ref>{{Cite journal \|last=Lewis \|first=Nathan E. \|last2=Nagarajan \|first2=Harish \|last3=Palsson \|first3=Bernhard O. \|date=2012-04 \|title=Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods \|url=http://www.nature.com/articles/nrmicro2737 \|journal=Nature Reviews Microbiology \|language=en \|volume=10 \|issue=4 \|pages=291–305 \|doi=10.1038/nrmicro2737 \|issn=1740-1526 \|pmc=PMC3536058 \|pmid=22367118}}</ref><ref>{{Cite journal \|last=Ramon \|first=Charlotte \|last2=Gollub \|first2=Mattia G. \|last3=Stelling \|first3=Jörg \|date=2018-10-26 \|title=Integrating –omics data into genome-scale metabolic network models: principles and challenges \|url=https://portlandpress.com/essaysbiochem/article/62/4/563/78519/Integrating-omics-data-into-genome-scale-metabolic \|journal=Essays in Biochemistry \|language=en \|volume=62 \|issue=4 \|pages=563–574 \|doi=10.1042/EBC20180011 \|issn=0071-1365}}</ref> Major constraints are thermodynamics, mass and charge conservation, and the substrate/enzyme availability. Constraints dramatically reduce the parameter space, which explains a genotype-phenotype relationship, and, hence, strongly increases the probability to find physiologically relevant solutions for underlying equation systems. Thus, it is not surprising that, in current plant biology, genome-scale reconstruction has become an integral part from single-cell to multi-tissue modeling.<ref>{{Cite journal \|last=Gomes de Oliveira Dal’Molin \|first=Cristiana \|last2=Nielsen \|first2=Lars Keld \|date=2018-02 \|title=Plant genome-scale reconstruction: from single cell to multi-tissue modelling and omics analyses \|url=https://linkinghub.elsevier.com/retrieve/pii/S0958166917301052 \|journal=Current Opinion in Biotechnology \|language=en \|volume=49 \|pages=42–48 \|doi=10.1016/j.copbio.2017.07.009}}</ref> For example, model reconstructions have been applied to analyze metabolic regulation in autotrophic and heterotrophic tissues, to study C<sub>4</sub> plant metabolism, to evaluate diurnal metabolic interactions in plant leaf tissue and to analyze photorespiration.<ref>{{Cite journal \|last=de Oliveira Dal’Molin \|first=Cristiana Gomes \|last2=Quek \|first2=Lake-Ee \|last3=Palfreyman \|first3=Robin William \|last4=Brumbley \|first4=Stevens Michael \|last5=Nielsen \|first5=Lars Keld \|date=2010-12-01 \|title=C4GEM, a Genome-Scale Metabolic Model to Study C4 Plant Metabolism \|url=https://academic.oup.com/plphys/article/154/4/1871/6108787 \|journal=Plant Physiology \|language=en \|volume=154 \|issue=4 \|pages=1871–1885 \|doi=10.1104/pp.110.166488 \|issn=1532-2548 \|pmc=PMC2996019 \|pmid=20974891}}</ref><ref>{{Cite journal \|last=de Oliveira Dal'Molin \|first=Cristiana Gomes \|last2=Quek \|first2=Lake-Ee \|last3=Palfreyman \|first3=Robin William \|last4=Brumbley \|first4=Stevens Michael \|last5=Nielsen \|first5=Lars Keld \|date=2010-02-03 \|title=AraGEM, a Genome-Scale Reconstruction of the Primary Metabolic Network in Arabidopsis \|url=https://academic.oup.com/plphys/article/152/2/579/6108441 \|journal=Plant Physiology \|language=en \|volume=152 \|issue=2 \|pages=579–589 \|doi=10.1104/pp.109.148817 \|issn=1532-2548 \|pmc=PMC2815881 \|pmid=20044452}}</ref><ref>{{Cite journal \|last=Cheung \|first=C.Y. Maurice \|last2=Poolman \|first2=Mark G. \|last3=Fell \|first3=David. A. \|last4=Ratcliffe \|first4=R. George \|last5=Sweetlove \|first5=Lee J. \|date=2014-06-02 \|title=A Diel Flux Balance Model Captures Interactions between Light and Dark Metabolism during Day-Night Cycles in C3 and Crassulacean Acid Metabolism Leaves \|url=https://academic.oup.com/plphys/article/165/2/917/6113238 \|journal=Plant Physiology \|language=en \|volume=165 \|issue=2 \|pages=917–929 \|doi=10.1104/pp.113.234468 \|issn=1532-2548 \|pmc=PMC4044858 \|pmid=24596328}}</ref><ref>{{Cite journal \|last=Yuan \|first=Huili \|last2=Cheung \|first2=C.Y. Maurice \|last3=Poolman \|first3=Mark G. \|last4=Hilbers \|first4=Peter A. J. \|last5=Riel \|first5=Natal A. W. \|date=2016-01 \|title=A genome‐scale metabolic network reconstruction of tomato ( Solanum lycopersicum L.) and its application to photorespiratory metabolism \|url=https://onlinelibrary.wiley.com/doi/10.1111/tpj.13075 \|journal=The Plant Journal \|language=en \|volume=85 \|issue=2 \|pages=289–304 \|doi=10.1111/tpj.13075 \|issn=0960-7412}}</ref>

			The experimental basis for constraining, validating, and optimizing large-scale models are high-throughput experiments, i.e., omics analyses. For example, to investigate effects of nitrogen assimilation on metabolism in maize (Zea mays), a genome-scale metabolic model for maize leaf was created comprising more than 5,800 genes, 8,500 reactions, and 9,000 metabolites.<ref name=":0">{{Cite journal \|last=Simons \|first=Margaret \|last2=Saha \|first2=Rajib \|last3=Amiour \|first3=Nardjis \|last4=Kumar \|first4=Akhil \|last5=Guillard \|first5=Lenaïg \|last6=Clément \|first6=Gilles \|last7=Miquel \|first7=Martine \|last8=Li \|first8=Zhenni \|last9=Mouille \|first9=Gregory \|last10=Lea \|first10=Peter J. \|last11=Hirel \|first11=Bertrand \|date=2014-11-05 \|title=Assessing the Metabolic Impact of Nitrogen Availability Using a Compartmentalized Maize Leaf Genome-Scale Model \|url=https://academic.oup.com/plphys/article/166/3/1659/6111218 \|journal=Plant Physiology \|language=en \|volume=166 \|issue=3 \|pages=1659–1674 \|doi=10.1104/pp.114.245787 \|issn=1532-2548 \|pmc=PMC4226342 \|pmid=25248718}}</ref> Using a combination of transcriptomic and proteomic data to constrain metabolic flux predictions, the authors were able to reproduce experimentally determined metabolomic data to significantly higher accuracy than without these constraints. Applying a combination of publicly available data on maize metabolism, reaction networks, and results from omics experiments, information about reaction stoichiometry, directionality, and compartmentalization was derived. Algorithmic model curation was combined with manual modification to, for example, resolve gaps in the network model with reactions from similar organisms. Information about transcripts and proteins, which were experimentally observed to significantly differ in mutants and under variable nitrogen supply, were then incorporated into the model by switching on/off corresponding reactions. Flux predictions through the metabolic network were compared to metabolomics measurements. With this integrated setup, model application unraveled genes coding for enzymes, which are involved in regulation of biomass formation under variable nitrogen supply.<ref name=":0" /> In another study, publicly available transcriptomics and metabolomics data were used within a constraint-based modeling approach to investigate network structure and flux distribution in root cell types and tissue layers of ''Arabidopsis thaliana''. Based on transcriptomics and metabolomics data, it was possible to extract tissue and cell type specific models from a general genome-scale model of root metabolism. By this, the authors were able to simulate and analyze cell types as autonomous subsystems, which communicate with each other via metabolites or proteins. But it was also shown and discussed that further experimental evidence and constraints are essential to support hypotheses derived from their simulations.<ref>{{Cite journal \|last=Scheunemann \|first=Michael \|last2=Brady \|first2=Siobhan M. \|last3=Nikoloski \|first3=Zoran \|date=2018-12 \|title=Integration of large-scale data for extraction of integrated Arabidopsis root cell-type specific models \|url=http://www.nature.com/articles/s41598-018-26232-8 \|journal=Scientific Reports \|language=en \|volume=8 \|issue=1 \|pages=7919 \|doi=10.1038/s41598-018-26232-8 \|issn=2045-2322 \|pmc=PMC5962614 \|pmid=29784955}}</ref> This example nicely illustrates how large-scale data integration can (i) unravel novel and detailed mechanistic insights into plant metabolism, and also (ii) indicate design and research focus of follow-up studies to prove model predictions. By placing metabolites, proteins, or transcripts into a pathway and network context, genome-scale models significantly support the biochemical and physiological interpretation of molecular data.

			Also, in a biotechnological context, such data integration strategies have become an important and promising tool to advance and improve bioengineering strategies. As an example, a genome-scale metabolic network reconstruction for green microalgal model species ''Chlamydomonas reinhardtii'' has been developed which reliably and quantitatively predicts growth depending on the light source.<ref>{{Cite journal \|last=Chang \|first=Roger L \|last2=Ghamsari \|first2=Lila \|last3=Manichaikul \|first3=Ani \|last4=Hom \|first4=Erik F Y \|last5=Balaji \|first5=Santhanam \|last6=Fu \|first6=Weiqi \|last7=Shen \|first7=Yun \|last8=Hao \|first8=Tong \|last9=Palsson \|first9=Bernhard Ø \|last10=Salehi‐Ashtiani \|first10=Kourosh \|last11=Papin \|first11=Jason A \|date=2011-01 \|title=Metabolic network reconstruction of Chlamydomonas offers insight into light‐driven algal metabolism \|url=https://onlinelibrary.wiley.com/doi/10.1038/msb.2011.52 \|journal=Molecular Systems Biology \|language=en \|volume=7 \|issue=1 \|pages=518 \|doi=10.1038/msb.2011.52 \|issn=1744-4292 \|pmc=PMC3202792 \|pmid=21811229}}</ref> This metabolic network comprises 10 compartments, accounting for more than 1,000 genes associated with more than 2,000 reactions and over 1,000 metabolites. Regulatory effects arising from different light conditions are covered by the model, which enables estimation of growth under different laboratory conditions. The model has been refined using metabolite profiling to include further branches of metabolism, e.g., amino acids and peptides as nitrogen sources.<ref>{{Cite journal \|last=Chaiboonchoe \|first=Amphun \|last2=Dohai \|first2=Bushra Saeed \|last3=Cai \|first3=Hong \|last4=Nelson \|first4=David R. \|last5=Jijakli \|first5=Kenan \|last6=Salehi-Ashtiani \|first6=Kourosh \|date=2014-12-10 \|title=Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling \|url=http://journal.frontiersin.org/article/10.3389/fbioe.2014.00068/abstract \|journal=Frontiers in Bioengineering and Biotechnology \|volume=2 \|doi=10.3389/fbioe.2014.00068 \|issn=2296-4185 \|pmc=PMC4261833 \|pmid=25540776}}</ref> Although, it developed a decade ago, the original model (named iRC1080) still represents a valid and supportive platform for data interpretation, and it still fruitfully initiates further model development and validation.<ref>{{Cite journal \|last=Shene \|first=Carolina \|last2=Asenjo \|first2=Juan A. \|last3=Chisti \|first3=Yusuf \|date=2018-12 \|title=Metabolic modelling and simulation of the light and dark metabolism of Chlamydomonas reinhardtii \|url=https://onlinelibrary.wiley.com/doi/10.1111/tpj.14078 \|journal=The Plant Journal \|language=en \|volume=96 \|issue=5 \|pages=1076–1088 \|doi=10.1111/tpj.14078}}</ref> These examples, together with many other studies that have been summarized recently<ref>{{Cite journal \|last=Tong \|first=Hao \|last2=Nikoloski \|first2=Zoran \|date=2021-02 \|title=Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data \|url=https://linkinghub.elsevier.com/retrieve/pii/S0176161720302443 \|journal=Journal of Plant Physiology \|language=en \|volume=257 \|pages=153354 \|doi=10.1016/j.jplph.2020.153354}}</ref>, provide strong evidence for the capability of genome-scale metabolic models to couple statistics with metabolic models.

	==References==		==References==