18. lecture ws 2005/06bioinformatics iii1 v18 – extreme pathways price et al. nature rev microbiol...

18. Lecture WS 2005/06

Bioinformatics III 1

V18 – extreme pathways

Price et al. Nature Rev Microbiol 2, 886 (2004)

Computational metabolomics: modelling constraints

Surviving (expressed) phenotypes must satisfy constraints imposed on the molecular

functions of a cell, e.g. conservation of mass and energy.

Fundamental approach to understand biological systems: identify and formulate

constraints.

Important constraints of cellular function:

(1) physico-chemical constraints

(2) Topological constraints

(3) Environmental constraints

(4) Regulatory constraints



Physico-chemical constraints


These are „hard“ constraints: Conservation of mass, energy and momentum.

Contents of a cell are densely packed viscosity can be 100 – 1000 times higher

than that of water

Therefore, diffusion rates of macromolecules in cells are slower than in water.

Many molecules are confined inside the semi-permeable membrane high

osmolarity. Need to deal with osmotic pressure (e.g. Na+K+ pumps)

Reaction rates are determined by local concentrations inside cells

Enzyme-turnover numbers are generally less than 104 s-1. Maximal rates are equal to

the turnover-number multiplied by the enzyme concentration.

Biochemical reactions are driven by negative free-energy change in forward

direction.



Topological constraints


The crowding of molecules inside cells leads to topological (3D)-constraints that affect

both the form and the function of biological systems.

E.g. the ratio between the number of tRNAs and the number of ribosomes in an E.coli

cell is about 10. Because there are 43 different types of tRNA, there is less than

one full set of tRNAs per ribosome it may be necessary to configure the

genome so that rare codons are located close together.

E.g. at a pH of 7.6 E.coli typically contains only about 16 H+ ions.

Remember that H+ is involved in many metabolic reactions.

Therefore, during each such reaction, the pH of the cell changes!



Environmental constraints


Environmental constraints on cells are time and condition dependent:

Nutrient availability, pH, temperature, osmolarity, availability of electron acceptors.

E.g. Heliobacter pylori lives in the human stomach at pH = 1

needs to produce NH3 at a rate that will maintain ist immediate surrounding at a pH

that is sufficiently high to allow survival.

Ammonia is made from elementary nitrogen H. pylori has adapted by using amino

acids instead of carbohydrates as its primary carbon source.



Regulatory constraints


Regulatory constraints are self-imposed by the organism and are subject to

evolutionary change they are no „hard“ constraints.

Regulatory constraints allow the cell to eliminate suboptimal phenotypic states and to

confine itself to behaviors of increased fitness.



Mathematical formation of constraints


There are two fundamental types of constraints: balances and bounds.

Balances are constraints that are associated

with conserved quantities as energy, mass, redox potential, momentum

or with phenomena such as solvent capacity, electroneutrality and osmotic pressure.

Bounds are constraints that limit numerical ranges of individual variables and

parameters such as concentrations, fluxes or kinetic constants.

Both bound and balance constraints limit the allowable functional states of

reconstructed cellular metabolic networks.



Genome-scale networks




Tools for analyzing network states

Price et al. Nature Rev Microbiol

2, 886 (2004)

The two steps that are used to form a solution space — reconstruction and the imposition of governing constraints — are illustrated in the centre of the figure. Several methods are being developed at various laboratories to analyse the solution space. Ci and Cj concentrations of compounds i and j; EP, extreme pathway; vi and vj fluxes through reactions i and j; v1 –v3 flux through reactions 1-3; vnet, net flux through loop.



Determining optimal states




Flux dependencies




Characterizing the whole solution space




Altered solution spaces




Extreme Pathwaysintroduced into metabolic analysis by the lab of Bernard Palsson

(Dept. of Bioengineering, UC San Diego). The publications of this lab

are available at http://gcrg.ucsd.edu/publications/index.html

The extreme pathway

technique is based

on the stoichiometric

matrix representation

of metabolic networks.

All external fluxes are

defined as pointing outwards.

Schilling, Letscher, Palsson,

J. theor. Biol. 203, 229 (2000)



Feasible solution set for a metabolic reaction network

(A) The steady-state operation of the metabolic network is restricted to the region

within a cone, defined as the feasible set. The feasible set contains all flux vectors

that satisfy the physicochemical constrains. Thus, the feasible set defines the

capabilities of the metabolic network. All feasible metabolic flux distributions lie

within the feasible set, and

(B) in the limiting case, where all constraints on the metabolic network are known,

such as the enzyme kinetics and gene regulation, the feasible set may be reduced

to a single point. This single point must lie within the feasible set.

Edwards & Palsson PNAS 97, 5528 (2000)



Extreme Pathways – theorem

Theorem. A convex flux cone has a set of systemically independent generating

vectors. Furthermore, these generating vectors (extremal rays) are unique up to

a multiplication by a positive scalar. These generating vectors will be called

„extreme pathways“.

(1) The existence of a systemically independent generating set for a cone is

provided by an algorithm to construct extreme pathways (see below).

(2) uniqueness?

Let {p1, ..., pk} be a systemically independent generating set for a cone.

Then follows that if pj = c´+ c´´ both c´and c´´ are positive multiples of pj.


J. theor. Biol. 203, 229 (2000)



Extreme Pathways – uniqueness

To show that this is true, write the two pathways c´and c´´ as non-negative linear

combinations of the extreme pathways:

Since the pi are systemically independent,

Therefore both c´and c´´ are multiples of pj.

If {c1, ..., ck} was another set of extreme pathways, this argument would show that

each of the ci must be a positive multiple of one of the pi.


J. theor. Biol. 203, 229 (2000)

k

iiiij

k

iiiii

k

iii

1

1

1

0,

pccp

pc

pc

0,0 iiji



Extreme Pathways – algorithm - setup

The algorithm to determine the set of extreme pathways for a reaction network

follows the pinciples of algorithms for finding the extremal rays/ generating

vectors of convex polyhedral cones.

Combine n n identity matrix (I) with the transpose of the stoichiometric

matrix ST. I serves for bookkeeping.


J. theor. Biol. 203, 229 (2000)

S

I ST



separate internal and external fluxes

Examine constraints on each of the exchange fluxes as given by

j bj j

If the exchange flux is constrained to be positive do nothing.

If the exchange flux is constrained to be negative multiply the

corresponding row of the initial matrix by -1.

If the exchange flux is unconstrained move the entire row to a temporary

matrix T(E). This completes the first tableau T(0).

T(0) and T(E) for the example reaction system are shown on the previous slide.

Each element of this matrices will be designated Tij.

Starting with x = 1 and T(0) = T(x-1) the next tableau is generated in the following

way:


J. theor. Biol. 203, 229 (2000)



idea of algorithm

(1) Identify all metabolites that do not have an unconstrained exchange flux

associated with them.

The total number of such metabolites is denoted by .

For the example, this is only the case for metabolite C ( = 1).

What is the main idea?

- We want to find balanced extreme pathways

that don‘t change the concentrations of

metabolites when flux flows through

(input fluxes are channelled to products not to

accumulation of intermediates).

- The stochiometrix matrix describes the coupling of each reaction to the

concentration of metabolites X.

- Now we need to balance combinations of reactions that leave concentrations

unchanged. Pathways applied to metabolites should not change their

concentrations the matrix entries

need to be brought to 0.Schilling, Letscher, Palsson,

J. theor. Biol. 203, 229 (2000)



keep pathways that do not change concentrations of internal metabolites

(2) Begin forming the new matrix T(x) by copying

all rows from T(x – 1) which contain a zero in the

column of ST that corresponds to the first

metabolite identified in step 1, denoted by index c.

(Here 3rd column of ST.)

Schilling, Letscher, Palsson, J. theor. Biol. 203, 229 (2000)

1 -1 1 0 0 0

1 0 -1 1 0 0

1 0 1 -1 0 0

1 0 0 -1 1 0

1 0 0 1 -1 0

1 0 0 -1 0 1

1 -1 1 0 0 0

T(0) =

T(1) =

+



balance combinations of other pathways

(3) Of the remaining rows in T(x-1) add together

all possible combinations of rows which contain

values of the opposite sign in column c, such that

the addition produces a zero in this column.

Schilling, et al.

JTB 203, 229

1 -1 1 0 0 0

1 0 -1 1 0 0

1 0 1 -1 0 0

1 0 0 -1 1 0

1 0 0 1 -1 0

1 0 0 -1 0 1

T(0) =

T(1) =

1 0 0 0 0 0 -1 1 0 0 0

0 1 1 0 0 0 0 0 0 0 0

0 1 0 1 0 0 0 -1 0 1 0

0 1 0 0 0 1 0 -1 0 0 1

0 0 1 0 1 0 0 1 0 -1 0

0 0 0 1 1 0 0 0 0 0 0

0 0 0 0 1 1 0 0 0 -1 1



remove “non-orthogonal” pathways

(4) For all of the rows added to T(x) in steps 2 and 3 check to make sure that no

row exists that is a non-negative combination of any other sets of rows in T(x) .

One method used is as follows:

let A(i) = set of column indices j for with the elements of row i = 0.

For the example above Then check to determine if there exists

A(1) = {2,3,4,5,6,9,10,11} another row (h) for which A(i) is a

A(2) = {1,4,5,6,7,8,9,10,11} subset of A(h).

A(3) = {1,3,5,6,7,9,11}

A(4) = {1,3,4,5,7,9,10} If A(i) A(h), i h

A(5) = {1,2,3,6,7,8,9,10,11} where

A(6) = {1,2,3,4,7,8,9} A(i) = { j : Ti,j = 0, 1 j (n+m) }

then row i must be eliminated from T(x)

Schilling et al.

JTB 203, 229



repeat steps for all internal metabolites

(5) With the formation of T(x) complete steps 2 – 4 for all of the metabolites that do

not have an unconstrained exchange flux operating on the metabolite,

incrementing x by one up to . The final tableau will be T().

Note that the number of rows in T () will be equal to k, the number of extreme

pathways.

Schilling et al.

JTB 203, 229



balance external fluxes

(6) Next we append T(E) to the bottom of T(). (In the example here = 1.)

This results in the following tableau:

Schilling et al.

JTB 203, 229

T(1/E) =

1 -1 1 0 0 0

1 1 0 0 0 0 0

1 1 0 -1 0 1 0

1 1 0 -1 0 1 0

1 1 0 1 0 -1 0

1 1 0 0 0 0 0

1 1 0 0 0 -1 1

1 -1 0 0 0 0

1 0 -1 0 0 0

1 0 0 0 -1 0

1 0 0 0 0 -1




(7) Starting in the n+1 column (or the first non-zero column on the right side),

if Ti,(n+1) 0 then add the corresponding non-zero row from T(E) to row i so as to

produce 0 in the n+1-th column.

This is done by simply multiplying the corresponding row in T(E) by Ti,(n+1) and

adding this row to row i .

Repeat this procedure for each of the rows in the upper portion of the tableau so

as to create zeros in the entire upper portion of the (n+1) column.

When finished, remove the row in T(E) corresponding to the exchange flux for the

metabolite just balanced.

Schilling et al.

JTB 203, 229




(8) Follow the same procedure as in step (7) for each of the columns on the right

side of the tableau containing non-zero entries.

(In this example we need to perform step (7) for every column except the middle

column of the right side which correponds to metabolite C.)

The final tableau T(final) will contain the transpose of the matrix P containing the

extreme pathways in place of the original identity matrix.

Schilling et al.

JTB 203, 229



pathway matrix

T(final) =

PT =

Schilling et al.

JTB 203, 229

1 -1 1 0 0 0 0 0 0

1 1 0 0 0 0 0 0

1 1 -1 1 0 0 0 0 0 0

1 1 -1 1 0 0 0 0 0 0

1 1 1 -1 0 0 0 0 0 0

1 1 0 0 0 0 0 0

1 1 -1 1 0 0 0 0 0 0

1 0 0 0 0 0 -1 1 0 0

0 1 1 0 0 0 0 0 0 0

0 1 0 1 0 0 0 -1 1 0

0 1 0 0 0 1 0 -1 0 1

0 0 1 0 1 0 0 1 -1 0

0 0 0 1 1 0 0 0 0 0

0 0 0 0 1 1 0 0 -1 1

v1 v2 v3 v4 v5 v6 b1 b2 b3 b4

p1 p7 p3 p2 p4 p6 p5



Extreme Pathways for model system

Schilling et al.

JTB 203, 229

1 0 0 0 0 0 -1 1 0 0

0 1 1 0 0 0 0 0 0 0

0 1 0 1 0 0 0 -1 1 0

0 1 0 0 0 1 0 -1 0 1

0 0 1 0 1 0 0 1 -1 0

0 0 0 1 1 0 0 0 0 0

0 0 0 0 1 1 0 0 -1 1

v1 v2 v3 v4 v5 v6 b1 b2 b3 b4

p1 p7 p3 p2 p4 p6 p5

2 pathways p6 and p7 are not shown (right below) because all exchange fluxes with the exterior are 0.Such pathways have no net overall effect on the functional capabilities of the network.They belong to the cycling of reactions v4/v5 and v2/v3.



How reactions appear in pathway matrix

In the matrix P of extreme pathways, each column is an EP and each row

corresponds to a reaction in the network.

The numerical value of the i,j-th element corresponds to the relative flux level

through the i-th reaction in the j-th EP.

Papin, Price, Palsson,

Genome Res. 12, 1889 (2002)

PPP TLM



Papin, Price, Palsson, Genome Res. 12, 1889 (2002)

A symmetric Pathway Length Matrix PLM can be calculated:

where the values along the diagonal correspond to the length of the EPs.

PPP TLM

Properties of pathway matrix

The off-diagonal terms of PLM are the number of reactions that a pair of extreme

pathways have in common.




One can also compute a reaction participation matrix PPM from P:

where the diagonal correspond to the number of pathways in which the given

reaction participates.

TPM PPP

Properties of pathway matrix



EP Analysis of H. pylori and H. influenza

Amino acid synthesis in Heliobacter pylori vs.

Heliobacter influenza studied by EP analysis.




Extreme Pathway Analysis

Calculation of EPs for increasingly large networks is computationally intensive and

results in the generation of large data sets.

Even for integrated genome-scale models for microbes under simple conditions,

EP analysis can generate thousands of vectors!

Interpretation:

- the metabolic network of H. influenza has an order of magnitude larger degree of

pathway redundancy than the metabolic network of H. pylori

Found elsewhere: the number of reactions that participate in EPs that produce a

particular product is poorly correlated to the product yield and the molecular

complexity of the product.

Possible way out?




Linear Matrices

ijAjiA T ,, Transpose of a linear matrix A:

U is a unitary n n matrix with the n n identity matrix In.

In linear algebra singular value decomposition (SVD) is an important

factorization of a rectangular real or complex matrix, with several applications in

signal processing and statistics.

This matrix decomposition is analogous to the diagonalization of symmetric or

Hermitian square matrices using a basis of eigenvectors given by the spectral

theorem.

nIUUUU **



Diagonalisation of pathway matrix?

Suppose M is an m n matrix with real or complex entries.

Then there exists a factorization of the form

M = U V* where

U : m m unitary matrix,

Σ : is an m n matrix with nonnegative numbers on the diagonal and

zeros off the diagonal,

V* : the transpose of V, an n n unitary matrix of real or complex

numbers.

Such a factorization is called a singular-value decomposition of M.

U describes the rows of M with respect to the base vectors associated with

the singular values.

V describes the columns of M with respect to the base vectors associated

with the singular values. Σ contains the singular values

One commonly insists that the values Σi,i be ordered in non-increasing

fashion. In this case, the diagonal matrix Σ is uniquely determined by M

(though the matrices U and V are not).



Single Value Decomposition of EP matrices

For a given EP matrix P np, SVD decomposes P into 3 matrices

Price et al. Biophys J 84, 794 (2003)

T

pn

V00

0ΣUP

where U nn : orthonormal matrix of the left singular vectors,

V pp : an analogous orthonormal matrix of the right singular vectors,

rr :a diagonal matrix containing the singular values i=1..r arranged in

descending order where r is the rank of P.

The first r columns of U and V, referred to as the left and right singular vectors, or

modes, are unique and form the orthonormal basis for the column space and row

space of P.

The singular values are the square roots of the eigenvalues of PTP. The magnitude

of the singular values in indicate the relative contribution of the singular vectors in

U and V in reconstructing P.

E.g. the second singular value contributes less to the construction of P than the first

singular value etc.



Single Value Decomposition of EP: Interpretation


The first mode (as the other modes) corresponds to a valid biochemical pathway

through the network.

The first mode will point

into the portions of the

cone with highest density

of EPs.



SVD applied for Heliobacter systems


Cumulative fractional contributions for the singular value decomposition of the EP

matrices of H. influenza and H. pylori.

This plot represents the

contribution of the first

n modes to the overall

description of the system.



Application of elementary modesMetabolic network structure of E.coli determines

key aspects of functionality and regulation

Compute EFMs for central

metabolism of E.coli.

Catabolic part: substrate uptake

reactions, glycolysis, pentose

phosphate pathway, TCA cycle,

excretion of by-products (acetate,

formate, lactate, ethanol)

Anabolic part: conversions of

precursors into building blocks like

amino acids, to macromolecules,

and to biomass.

Stelling et al. Nature 420, 190 (2002)

Elementary modes will be covered in V19.

The concept is closely related to extreme

pathways. In this example , we will simply

ignore the small difference.



Metabolic network topology and phenotypeThe total number of EFMs for given

conditions is used as quantitative

measure of metabolic flexibility.

a, Relative number of EFMs N enabling

deletion mutants in gene i ( i) of E. coli

to grow (abbreviated by µ) for 90 different

combinations of mutation and carbon

source. The solid line separates

experimentally determined mutant

phenotypes, namely inviability (1–40)

from viability (41–90).


The # of EFMs for mutant strain

allows correct prediction of

growth phenotype in more than 90%

of the cases.



Robustness analysis

The # of EFMs qualitatively indicates whether a mutant is viable or not, but does

not describe quantitatively how well a mutant grows.

Define maximal biomass yield Ymass as the optimum of:

ei is the single reaction rate (growth and substrate uptake) in EFM i selected for

utilization of substrate Sk.


ki Si

iSXi e

eY

/,



Software: FluxAnalyzer

Dependency of the mutants' maximal

growth yield Ymax( i) (open circles) and the

network diameter D( i) (open squares) on

the share of elementary modes

operational in the mutants. Data were

binned to reduce noise. Stelling et al. Nature 420, 190 (2002)

Central metabolism of E.coli behaves in a highly robust manner because

mutants with significantly reduced metabolic flexibility show a growth yield

similar to wild type.



Distribution of growth-supporting

elementary modes in wild type (rather

than in the mutants), that is, share of

modes having a specific biomass

yield (the dotted line indicates equal

distribution).


Multiple, alternative pathways exist

with identical biomass yield.

Growth-supporting elementary modes



Assume that optimization during biological evolution can be characterized by the

two objectives of flexibility (associated with robustness) and of efficiency.

Flexibility means the ability to adapt to a wide range of environmental conditions,

that is, to realize a maximal bandwidth of thermodynamically feasible flux

distributions (maximizing # of EFMs).

Efficiency could be defined as fulfilment of cellular demands with an optimal

outcome such as maximal cell growth using a minimum of constitutive elements

(genes and proteins, thus minimizing # EFMs).

These 2 criteria pose contradictory challenges.

Optimal cellular regulation needs to find a trade-off.

Can regulation be predicted by EFM analysis?




Compute control-effective fluxes for each reaction l by determining the efficiency of any EFM

ei by relating the system‘s output to the substrate uptake and to the sum of all absolute

fluxes.

With flux modes normalized to the total substrate uptake, efficiencies i(Sk, ) for

the targets for optimization -growth and ATP generation, are defined as:

Can regulation be predicted by EFM analysis?


l

li

ATPi

ki

l

li

iki

e

eATPS

e

eS ,and,

Control-effective fluxes vl(Sk) are obtained by averaged weighting of the product of reaction-

specific fluxes and mode-specific efficiencies over all EFMs using the substrate under

consideration:

lki

i

liki

SAl

ki

i

liki

SXkl ATPS

eATPS

YS

eS

YSv

kk,

,1

,

,1

max/

max/

YmaxX/Si and Ymax

A/Si are optimal yields of biomass production and of ATP synthesis.

Control-effective fluxes represent the importance of each reaction for efficient and flexible

operation of the entire network.



Prediction of gene expression patterns

As cellular control on longer timescales

is predominantly achieved by genetic

regulation, the control-effective fluxes

should correlate with messenger RNA

levels.

Compute theoretical transcript ratios

(S1,S2) for growth on two alternative

substrates S1 and S2 as ratios of

control-effective fluxes.

Compare to exp. DNA-microarray data

for E.coli growin on glucose, glycerol,

and acetate.

Excellent correlation!Stelling et al. Nature 420, 190 (2002)

Calculated ratios between gene expression levels

during exponential growth on acetate and

exponential growth on glucose (filled circles

indicate outliers) based on all elementary modes

versus experimentally determined transcript

ratios19. Lines indicate 95% confidence intervals

for experimental data (horizontal lines), linear

regression (solid line), perfect match (dashed

line) and two-fold deviation (dotted line).



Predicted transcript ratios for acetate

versus glucose for which, in contrast to

a, only the two elementary modes with

highest biomass and ATP yield

(optimal modes) were considered.

This plot shows only weak correlation.

This corresponds to the approach

followed by Flux Balance Analysis.


Prediction of transcript ratios



Summary (extreme pathways)


Extreme pathway analysis provides a mathematically rigorous way to dissect

complex biochemical networks.

The matrix products PT P and PT P are useful ways to interpret pathway lengths

and reaction participation.

However, the number of computed vectors may range in the 1000sands.

Therefore, meta-methods (e.g. singular value decomposition) are required that

reduce the dimensionality to a useful number that can be inspected by humans.

Single value decomposition may be one useful method ... and there are more to

come.

18. lecture ws 2005/06bioinformatics iii1 v18 – extreme pathways price et al. nature rev microbiol...

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