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

Sriyudthsak K, Sawada Y, Chiba Y, Yamashita Y, Kanaya S, Onouchi H, Fujiwara T, Naito S, Voit EO, Shiraishi F, Hirai MY - BMC Syst Biol (2014)

Bottom Line: 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.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 can effectively predict metabolic behaviors and responses based on structures of an alleged metabolic reaction network.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

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.

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Related in: MedlinePlus

Qualitative predictions of consequences of gene knockdown. The simplified aspartate-family biosynthesis pathway enlarged from Figure 4. Metabolites (AdoMet, S-adenosyl-methionine; Ala, alanine; Arg, arginine; Asn, asparagine; Asp, aspartate; AspSA, aspartate-semialdehyde; Cit, citrate; CisAc, cis-aconitate; Cys, cysteine; CysTA, cystathionine; DHDP, dihydrodipicolinate; Fum, fumalate; Gln, glutamine; Glu, glutamate; Gly, glycine; G6P, glucose-6-phosphate; Hcys, homocysteine; HSer, homoserine; His, histidine; Ile, iso-leucine; IsoCit, iso-citrate; α-KG, α-ketoglutarate; Leu, leucine; Lys, lysine; Mal, malate; Met, methionine; OAc, oxaloacetate; OPH, O-phosphohomoserine; Orn, ornitine; Phe, phenylalanine; Pro, proline; Pyr, pyruvate; Ser, serine; Suc, succinate; SucCoA, succinyl CoA; Thr, threonine; Trp, tryptophan; Tyr, tyrosine; Val, valine) are represented in black letters whereas the knocked-down gene (mto2, MTO2 encoding threonine synthase) is represented in green letters. Two squares boxes indicate the comparisons between experimental results (the former) and in silico prediction (the latter).
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Figure 8: Qualitative predictions of consequences of gene knockdown. The simplified aspartate-family biosynthesis pathway enlarged from Figure 4. Metabolites (AdoMet, S-adenosyl-methionine; Ala, alanine; Arg, arginine; Asn, asparagine; Asp, aspartate; AspSA, aspartate-semialdehyde; Cit, citrate; CisAc, cis-aconitate; Cys, cysteine; CysTA, cystathionine; DHDP, dihydrodipicolinate; Fum, fumalate; Gln, glutamine; Glu, glutamate; Gly, glycine; G6P, glucose-6-phosphate; Hcys, homocysteine; HSer, homoserine; His, histidine; Ile, iso-leucine; IsoCit, iso-citrate; α-KG, α-ketoglutarate; Leu, leucine; Lys, lysine; Mal, malate; Met, methionine; OAc, oxaloacetate; OPH, O-phosphohomoserine; Orn, ornitine; Phe, phenylalanine; Pro, proline; Pyr, pyruvate; Ser, serine; Suc, succinate; SucCoA, succinyl CoA; Thr, threonine; Trp, tryptophan; Tyr, tyrosine; Val, valine) are represented in black letters whereas the knocked-down gene (mto2, MTO2 encoding threonine synthase) is represented in green letters. Two squares boxes indicate the comparisons between experimental results (the former) and in silico prediction (the latter).

Mentions: Moreover, we were able to predict the behaviors of concentrations of aspartate-4-semialdehyde (X80) (Figure 7d) and O-phospho-homoserine (X82) (Figure 7e), which can hardly be detected due to technical limitations. They are not only located at important branched points in the metabolic map but also related to many regulations including both inhibitions and activations (Figure 8). Thus, the information of these metabolites could allow us to comprehend metabolic system. Again, the aspartate-family amino acid biosynthesis includes various inhibitions and activations, so that these dynamic behaviors will not be reasonably observed by normal mass action equations.


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

Sriyudthsak K, Sawada Y, Chiba Y, Yamashita Y, Kanaya S, Onouchi H, Fujiwara T, Naito S, Voit EO, Shiraishi F, Hirai MY - BMC Syst Biol (2014)

Qualitative predictions of consequences of gene knockdown. The simplified aspartate-family biosynthesis pathway enlarged from Figure 4. Metabolites (AdoMet, S-adenosyl-methionine; Ala, alanine; Arg, arginine; Asn, asparagine; Asp, aspartate; AspSA, aspartate-semialdehyde; Cit, citrate; CisAc, cis-aconitate; Cys, cysteine; CysTA, cystathionine; DHDP, dihydrodipicolinate; Fum, fumalate; Gln, glutamine; Glu, glutamate; Gly, glycine; G6P, glucose-6-phosphate; Hcys, homocysteine; HSer, homoserine; His, histidine; Ile, iso-leucine; IsoCit, iso-citrate; α-KG, α-ketoglutarate; Leu, leucine; Lys, lysine; Mal, malate; Met, methionine; OAc, oxaloacetate; OPH, O-phosphohomoserine; Orn, ornitine; Phe, phenylalanine; Pro, proline; Pyr, pyruvate; Ser, serine; Suc, succinate; SucCoA, succinyl CoA; Thr, threonine; Trp, tryptophan; Tyr, tyrosine; Val, valine) are represented in black letters whereas the knocked-down gene (mto2, MTO2 encoding threonine synthase) is represented in green letters. Two squares boxes indicate the comparisons between experimental results (the former) and in silico prediction (the latter).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4305983&req=5

Figure 8: Qualitative predictions of consequences of gene knockdown. The simplified aspartate-family biosynthesis pathway enlarged from Figure 4. Metabolites (AdoMet, S-adenosyl-methionine; Ala, alanine; Arg, arginine; Asn, asparagine; Asp, aspartate; AspSA, aspartate-semialdehyde; Cit, citrate; CisAc, cis-aconitate; Cys, cysteine; CysTA, cystathionine; DHDP, dihydrodipicolinate; Fum, fumalate; Gln, glutamine; Glu, glutamate; Gly, glycine; G6P, glucose-6-phosphate; Hcys, homocysteine; HSer, homoserine; His, histidine; Ile, iso-leucine; IsoCit, iso-citrate; α-KG, α-ketoglutarate; Leu, leucine; Lys, lysine; Mal, malate; Met, methionine; OAc, oxaloacetate; OPH, O-phosphohomoserine; Orn, ornitine; Phe, phenylalanine; Pro, proline; Pyr, pyruvate; Ser, serine; Suc, succinate; SucCoA, succinyl CoA; Thr, threonine; Trp, tryptophan; Tyr, tyrosine; Val, valine) are represented in black letters whereas the knocked-down gene (mto2, MTO2 encoding threonine synthase) is represented in green letters. Two squares boxes indicate the comparisons between experimental results (the former) and in silico prediction (the latter).
Mentions: Moreover, we were able to predict the behaviors of concentrations of aspartate-4-semialdehyde (X80) (Figure 7d) and O-phospho-homoserine (X82) (Figure 7e), which can hardly be detected due to technical limitations. They are not only located at important branched points in the metabolic map but also related to many regulations including both inhibitions and activations (Figure 8). Thus, the information of these metabolites could allow us to comprehend metabolic system. Again, the aspartate-family amino acid biosynthesis includes various inhibitions and activations, so that these dynamic behaviors will not be reasonably observed by normal mass action equations.

Bottom Line: 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.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 can effectively predict metabolic behaviors and responses based on structures of an alleged metabolic reaction network.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus