Biology:Catalog of MCA Control Patterns

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Short description: Metabolic control analysis patterns

Jannie Hofmeyr published the first catalog of control patterns in metabolic control analysis (MCA). His doctoral research.[1] concerned the use of graphical patterns to elucidate chains of interaction in metabolic regulation, later published in the European Journal of Biochemistry.[2] In his thesis, he cataloged 25 patterns for various biochemical networks. In later work, his research group, together with Carl D Christensen and Johann Rohwer, developed a Python based tool called SymCA that was part of the PySCeSToolbox toolkit [3][4] that could generate patterns automatically and symbolically from a description of the network. This software was used to generate the patterns shown below.

The control equations, especially the numerators of the equations, can give information on the relative importance and routes by which perturbations travel through a biochemical network[5]

Notation

Control patterns describe how a perturbation to a given parameter affects the steady-state level of a given variable. For example, a concentration control coefficient can describe how the overexpression of a specific enzyme can influence steady-state metabolite concentrations. Flux control coefficients are similar in that they describe how a perturbation in a given enzyme affects steady-state flux through a pathway. Such coefficients can be written in terms of elasticity coefficients.

Elasticity coefficients are local properties that describe how a single reaction is influenced by changes in the substrates and products that might influence the rate. For example, given a reaction such as:

S⟶vP

we will assume it has a rate of reaction of v. This reaction rate can be influenced by changes in the concentrations of substrate S or product P. This influence is measured by an elasticity which is defined as:

εsv=∂v∂ssv

To make the notation manageable, a specific numbering scheme is used in the following patterns. If a substrate has an index of i, then the reaction index will be vi+1. The product elasticity will also have an index of i+1. This means that a product elasticity will have identical subscripts and superscripts making them easy to identify. The source boundary species is always labeled zero as well as the label for the first reaction.

For example, the following fragment of a network illustrates this labeling:

Xo⟶v1S1⟶v2S2⟶v3

then

ε12=∂v2∂s1s1v2,ε22=∂v2∂s2s2v2,ε23=∂v3∂s2s2v3

Linear Chains

Two-Step Pathway

Xo⟶v1S1⟶v2X1

Assuming both steps are Irreversible

Ce1J=1Ce2J=0
Ce1s1=1ε12Ce2s1=−1ε12

Assuming both steps are Reversible

Cv1J=ε12ε12−ε11Cv2J=−ε11ε12−ε11
Cv1s1=1ε12−ε11Cv2s1=−1ε12−ε11

Three-Step Pathway

Xo⟶v1S1⟶v2S2⟶v3X1

Assuming the three steps are Irreversible

Denominator:

d=ε12ε23

Assume that each of the following expressions is divided by d

Ce1J=1Ce2J=0Ce3J=0

Ce1s1=ε23Ce1s2=ε12Ce2s1=−ε23Ce2s2=0Ce2s2=0Ce3s2=−ε12

Assuming the three steps are Reversible

Denominator:

d=ε12ε23−ε11ε23+ε11ε22

Assume that each of the following expressions is divided by d

Ce1J=ε12ε23Ce2J=−ε11ε23Ce3J=ε11ε22

Ce1s1=ε23−ε22Ce1s2=ε12Ce2s1=−ε23Ce2s2=−ε11Ce3s1=ε22Ce3s2=ε11−ε12

Four-Step Pathway

Xo⟶v1S1⟶v2S2⟶v3S3⟶v4X1

Denominator:

d=ε12ε23ε34−ε11ε23ε34+ε11ε22ε34−ε11ε22ε33

Assume that each of the following expressions is divided by d.

Ce1J=ε12ε23ε34Ce2J=−ε11ε23ε34Ce3J=ε11ε22ε34Ce4J=−ε11ε22ε33Ce1s1=ε22ε33−ε22ε34+ε23ε34Ce2s1=ε23ε34Ce3s1=ε22ε34Ce4s1=−ε22ε33Ce1s2=ε12ε34−ε12ε33Ce2s2=ε11ε33−ε11ε34Ce3s2=ε11ε34−ε12ε34Ce4s2=ε12ε33−ε11ε33Ce1s3=ε12ε23Ce2s3=−ε11ε23Ce3s3=ε11ε22Ce4s3=−ε11ε22+ε11ε23−ε12ε23

Five-Step Pathway

Xo⟶v1S1⟶v2S2⟶v3S3⟶v4S4⟶v5X1

Denominator:

d=ε12ε23ε34ε45−ε11ε23ε34ε45+ε11ε22ε34ε45−ε11ε22ε33ε45+ε11ε22ε33ε44

Assume that each of the following expressions is divided by d

Ce1J=ε12ε23ε34ε45Ce2J=−ε11ε23ε34ε45Ce3J=ε11ε22ε34ε45Ce4J=−ε11ε22ε33ε45Ce5J=ε11ε22ε33ε44Ce1s1=ε23ε34ε45−ε22ε34ε45+ε22ε33ε45−ε22ε33ε44Ce2s1=−ε23ε34ε45Ce3s1=ε22ε34ε45Ce4s1=−ε22ε33ε45Ce5s1=ε22ε33ε44Ce1s2=ε12ε34ε45−ε12ε33ε45+ε12ε33ε44Ce2s2=−ε11ε34ε45+ε11ε33ε45−ε11ε33ε44Ce3s2=−ε12ε34ε45+ε11ε34ε45Ce4s2=ε12ε33ε45−ε11ε33ε45Ce5s2=−ε12ε33ε44+ε11ε33ε44Ce1s3=ε12ε23ε45−ε12ε23ε44Ce2s3=−ε11ε23ε45+ε11ε23ε44Ce3s3=ε11ε22ε45−ε11ε22ε44Ce4s3=−ε12ε23ε45+ε11ε23ε45−ε11ε22ε45Ce5s3=ε12ε23ε44−ε11ε23ε44+ε11ε22ε44Ce1s4=ε12ε23ε34Ce2s4=−ε11ε23ε34Ce3s4=ε11ε22ε34Ce4s4=−ε11ε22ε33Ce5s4=−ε12ε23ε34+ε11ε23ε34−ε11ε22ε34+ε11ε22ε33

Linear Chains with Negative Feedback

Three-Step Pathway

File:Negative Feedback Loop with three reaction steps.pdf

Denominator:

d=ε11ε22−ε11ε23+ε12ε23−ε21ε12

Assume that each of the following expressions is divided by d.

Ce1J=ε12ε23Ce2J=−ε11ε23Ce3J=ε11ε22−ε21ε12Ce1s1=ε23−ε22Ce2s1=−ε23−ε21Ce3s1=ε22−ε21Ce1s2=ε12Ce2s2=−ε11Ce3s2=ε11−ε12

Four-Step Pathway

Denominator:

d=ε11ε22ε34−ε11ε23ε34−ε31ε12ε23+ε12ε23ε34−ε11ε22ε33

Assume that each of the following expressions is divided by d.

Cv1J=ε12ε23ε34Cv2J=−ε11ε23ε34Cv3J=ε11ε22ε34Cv4J=−ε11ε22ε33−ε31ε12ε23Cv1S1=ε22ε33−ε22ε34+ε23ε34Cv2S1=ε31ε23−ε23ε34Cv3S1=−ε31ε22+ε22ε34Cv4S1=ε31ε22−ε31ε23−ε22ε33Cv1S2=−ε12ε33+ε12ε34Cv2S2=ε11ε33−ε11ε34Cv3S2=εB1ε34+ε31ε12−ε12ε34Cv4S2=−ε11ε33−ε31ε12+ε12ε33Cv1S3=ε12ε23Cv2S3=−ε11ε23Cv3S3=ε11ε22Cv4S3=−ε11ε22+ε11ε23−ε12ε23

Branched Pathways

File:Simple Branched Metabolic Pathway.png

At steady-state v1=v2+v3, therefore define the following two terms:

α=v2v11−α=v3v1

Denominator:

d=εs2α+εs3(1−α)−εs1

Assume that each of the following expressions is divided by d.

Ce1J1=εs3(1−α)+εs2αCe1J1=−εs1αCe1J1=−εs1(1−α)+εs2α

See also

References

  1. ↑ Hofmeyr, Jan-Hendrik (1986). Studies in steady-state modelling and control analysis of metabolic systems. University of Stellenbosch. 
  2. ↑ Hofmeyr, J.-H. S. (1989). "Control-pattern analysis of metabolic pathways: Flux and concentration control in linear pathways". Eur. J. Biochem. 186 (1–2): 343–354. doi:10.1111/j.1432-1033.1989.tb15215.x. PMID 2598934. 
  3. ↑ Christensen, Carl D; Hofmeyr, Jan-Hendrik S; Rohwer, Johann M (1 January 2018). "PySCeSToolbox: a collection of metabolic pathway analysis tools". Bioinformatics 34 (1): 124–125. doi:10.1093/bioinformatics/btx567. PMID 28968872. 
  4. ↑ Akhurst, Timothy (2008). Symbolic Control Analysis of Cellular Systems (PhD). Stellenbosch University.
  5. ↑ Christensen, Carl D.; Hofmeyr, Jan-Hendrik S.; Rohwer, Johann M. (28 November 2018). "Delving deeper: Relating the behaviour of a metabolic system to the properties of its components using symbolic metabolic control analysis". PLOS ONE 13 (11). doi:10.1371/journal.pone.0207983. PMID 30485345. Bibcode: 2018PLoSO..1307983C.