Lecture 5 Reaction system as ordinary differential equations Reaction system as stochastic process Metabolic network and stoichiometric matrix Graph spectral analysis/Graph spectral clustering and its application to metabolic networks Metabolomics approach for determining growth- specific metabolites in E. coli based on FT-ICR-MS

Introduction Metabolism is the process through which living cells acquire energy and building material for cell components and replenishing enzymes. Metabolism is the general term for two kinds of reactions: (1) catabolic reactions –break down of complex compounds to get energy and building blocks, (2) anabolic reactions—construction of complex compounds used in cellular functioning How can we model metabolic reactions?

What is a Model? Formal representation of a system using --Mathematics --Computer program Describes mechanisms underlying outputs Dynamical models show rate of changes with time or other variable Provides explanations and predictions

Typical network of metabolic pathways Reactions are catalyzed by enzymes. One enzyme molecule usually catalyzes thousands reactions per second (~ ) The pathway map may be considered as a static model of metabolism

Dynamic modeling of metabolic reactions is the process of understanding the reaction rates i.e. how the concentrations of metabolites change with respect to time

An Anatomy of Dynamical Models Discrete Time Discrete Variables Continuous Variables Deterministic --No Space Space -- Stochastic --No Space Space -- Finite State Machines Boolean Networks; Cellular Automata Discrete Time Markov Chains Stochastic Boolean Networks; Stochastic Cellular Automata Iterated Functions; Difference Equations Iterated Functions; Difference Equations Discrete Time Markov Chains Coupled Discrete Time Markov Chains Continuous Time Discrete Variables Continuous Variables Boolean Differential Equations Ordinary Differential Equations Coupled Boolean Differential Equations Partial Differential Equations Continuous Time Markov Chain Stochastic Ordinary Differential Equations Coupled Continuous Time Markov Chains Stochastic Partial Differential Equations

Differential equations Differential equations are based on the rate of change of one or more variables with respect to one or more other variables

An example of a differential equation Source: Systems biology in practice by E. klipp et al

An example of a differential equation Source: Systems biology in practice by E. klipp et al

An example of a differential equation

Schematic representation of the upper part of the Glycolysis Source: Systems biology in practice by E. klipp et al.

The ODEs representing this reaction system Realize that the concentration of metabolites and reaction rates v1, v2, …… are functions of time ODEs representing a reaction system

The rate equations can be solved as follows using a number of constant parameters

The temporal evaluation of the concentrations using the following parameter values and initial concentrations

Notice that because of bidirectional reactions Gluc-6-P and Fruc-6-P reaches peak earlier and then decrease slowly and because of unidirectional reaction Fruc1,6-P2 continues to grow for longer time.

The use of differential equations assumes that the concentration of metabolites can attain continuous value. But the underlying biological objects, the molecules are discrete in nature. When the number of molecules is too high the above assumption is valid. But if the number of molecules are of the order of a few dozens or hundreds then discreteness should be considered. Again random fluctuations are not part of differential equations but it may happen for a system of few molecules. The solution to both these limitations is to use a stochastic simulation approach.

Stochastic Simulation Stochastic modeling for systems biology Darren J. Wilkinson 2006

Molecular systems in cell

Molecular systems in cell [ ]: concentration of ith object [m 1(in) ] [m 2 ][m 3 ] [m 4 ] [m 5 ] [m 1(out) ] [r 1 ] [r 2 ][r 3 ] [r ４ ] [p 1 ] [p 2 ] [p 3 ] [p 4 ]

Molecular systems in cell c j : c j’ : efficiency of jth process [m 1(in) ] [m 2 ][m 3 ] [m 4 ] [m 5 ] [m 1(out) ] [r 1 ] [r 2 ][r 3 ] [r ４ ] [p 1 ] [p 2 ] [p 3 ] [p 4 ] c1c1 c2c2 c3c3 c4c4 c5c5 c6c6 c7c7 c8c8 c9c9 c 10 c 11 c 12 c 13

Molecular systems for small molecules in cell [m 1(in) ] [m 2 ][m 3 ] [m 4 ] [m 5 ] [m 1(out) ] c1c1 c2c2 c3c3 c4c4 c5c5 h 1 =c 1 [m 1(out) ]h 2 =c 2 [m 1(in) ] h 4 =c 5 [m 2 ] h 3 =c 3 [m 2 ]h 5 =c 4 [m 3 ] c 2  p 1,r 1 c 5  p 3,r 3 c 3  p 2,r 2 c 4  p 4,r 4 Stochastic selection of reaction based on(h 1, h 2, h 3, h 4, h 5 )

Molecular systems for small molecules in cell [m 1(in) ] [m 2 ][m 3 ] [m 4 ] [m 5 ] [m 1(out) ]=100 c1c1 c2c2 c3c3 c4c4 c5c5 h 1 =c 1 [m 1(out) ] = 100 c 1 h 2 =c 2 [m 1(in) ] h 4 =c 5 [m 2 ] h 3 =c 3 [m 2 ]h 5 =c 4 [m 3 ] c 2  p 1,r 1 c 5  p 3,r 3 c 5  p 2,r 2 c 4  p 4,r 4 Stochastic selection of reaction based on(100 c 1, h 2, h 3, h 4, h 5 )  Reaction 1

Molecular systems for small molecules in cell [m 1(in) ]=1 [m 2 ]=0 [m 3 ]=0 [m 4 ]=0 [m 5 ]=0 [m 1(out) ]=99 c1c1 c2c2 c3c3 c4c4 c5c5 h 1 =c 1 [m 1(out) ] = 99 c 1 h 2 =c 2 [m 1(in) ] = 1 c 2 h 4 =c 5 [m 2 ] =0 h 3 =c 3 [m 2 ] =0 h 5 =c 4 [m 3 ] =0 Stochastic selection of Reaction based on (99 c 1, 1 c 2, 0, 0, 0)  Reaction 1

Molecular systems for small molecules in cell [m 1(in) ]=2 [m 2 ]=0 [m 3 ]=0 [m 4 ]=0 [m 5 ]=0 [m 1(out) ]=98 c1c1 c2c2 c3c3 c4c4 c5c5 h 1 =c 1 [m 1(out) ] = 98 c 1 h 2 =c 2 [m 1(in) ] = 2 c 2 h 4 =c 5 [m 2 ] =0 h 3 =c 3 [m 2 ] =0 h 5 =c 4 [m 3 ] =0 Stochastic selection of Reaction based on (98 c 1, 2 c 2, 0, 0, 0)  Reaction 1

Molecular systems for small molecules in cell [m 1(in) ]=3 [m 2 ]=0 [m 3 ]=0 [m 4 ]=0 [m 5 ]=0 [m 1(out) ]=97 c1c1 c2c2 c3c3 c4c4 c5c5 h 1 =c 1 [m 1(out) ] = 97 c 1 h 2 =c 2 [m 1(in) ] = 3 c 2 h 4 =c 5 [m 2 ] =0 h 3 =c 3 [m 2 ] =0 h 5 =c 4 [m 3 ] =0 Stochastic selection of Reaction based on (97 c 1, 3 c 2, 0, 0, 0)  Reaction 2

Molecular systems for small molecules in cell [m 1(in) ]=2 [m 2 ]=1 [m 3 ]=0 [m 4 ]=0 [m 5 ]=0 [m 1(out) ]=97 c1c1 c2c2 c3c3 c4c4 c5c5 h 2 =c 2 [m 1(in) ] = 2 c 2 h 4 =c 5 [m 2 ] =1 c 5 h 3 =c 3 [m 2 ] =1 c 3 h 5 =c 4 [m 3 ] =0 h 1 =c 1 [m 1(out) ] = 97 c 1 Stochastic selection of Reaction based on (97 c 1, c 2, 1 c 3, 0, 1 c 5 )  Reaction 1

Molecular systems for small molecules in cell [m 1(in) ]=3 [m 2 ]=1 [m 3 ]=0 [m 4 ]=0 [m 5 ]=0 [m 1(out) ]=96 c1c1 c2c2 c3c3 c4c4 c5c5 h 1 =c 1 [m 1(out) ] = 97 c 1 h 2 =c 2 [m 1(in) ] = 3 c 2 h 4 =c 5 [m 2 ] =1 c 5 h 3 =c 3 [m 2 ] =1 c 3 h 5 =c 4 [m 3 ] =0 Stochastic selection of Reaction(96 c 1, 3 c 2, 1 c 3, 0, 1 c 5 )  Reaction 3

Molecular systems for small molecules in cell [m 1(in) ]=3 [m 2 ]=0 [m 3 ]=1 [m 4 ]=0 [m 5 ]=0 [m 1(out) ]=96 c1c1 c2c2 c3c3 c4c4 c5c5 h 1 =c 1 [m 1(out) ] = 97 c 1 h 2 =c 2 [m 1(in) ] = 3 c 2 h 4 =c 5 [m 2 ] =0 h 3 =c 3 [m 2 ] =0 h 5 =c 4 [m 3 ] =1 c 4 Stochastic selection of Reaction based on (96 c 1, 3 c 2, 0, 1 c 4, 0)  …

Input data [m 1(in) ] [m 2 ][m 3 ] [m 4 ] [m 5 ] [m 1(out) ] c1c1 c2c2 c3c3 c4c4 c5c5 c1c1 m 1(out) m 1(in) c2c2 m2m2 c3c3 m2m2 m3m3 m3m3 m5m5 c4c4 m2m2 m5m5 c5c5 [m 1(out) ] [m 1(in) ][m 2 ][m 3 ][m 4 ][m 5 ] Initial concentrations Reaction parameters and Reactions

Gillespie Algorithm Step 0: System Definition objects (i = 1, 2,…, n) and their initial quantities: X i (init) reaction equations (j=1,2,…,m) R j : m (Pre) j1 X m (Pre) jn X n = m (Post) j1 X m (Post) jn X n reaction intensities: c i for R j Step 4: Quantities for individual objects are revised base on selected reaction equation [X i ] ← [X i ] – m (Pre) s + m (Post) s Step 1: [X i ]  X i (init) Step 2: h j: : probability of occurrence of reactions based on c j (j=1,2,..,m) and [X i ] (i=1,2,..,n) Step 3: Random selection of reaction Here a selected reaction is represented by index s.

Gillespie Algorithm (minor revision) Step 0: System Definition objects (i = 1, 2,…, n) and their initial quantities X i (init) reaction equations (j=1,2,…,m) R j : m (Pre) j1 X m (Pre) jn X n = m (Post) j1 X m (Post) jn X n reaction intensities: c i for R j Step 4: Quantities for individual objects are revised base on selected reaction equationX’ i = [X i ] – m (Pre) s + m (Post) s Step 1: [X i ]  X i (init) Step 2: h j: : probability of occurrence of reactions based on c j (j=1,2,..,m) and [X i ] (i=1,2,..,n) Step 3: Random selection of reaction Here a selected reaction is represented by index s. X’ i  0 No Step 5: [X j ]  X’ i Yes X’ i  X i max No Yes

Software: Simple Stochastic Simulator 1.Create stoichiometric data file and initial condition file Reaction Definition: REQ**.txt R1[X1] = [X2] R2[X2] = [X1] Reaction Parameterci[X1][X1][X2][X2] R R Stoichiometetric data and c i : REACTION**.txt c i is set by user [X1]1000 [X2]1000 Initial condition: INIT**.txt max number (for ith object, max number is set by 0 for ith, [Xi]  0 Initial quantitiy Objects used are assigned by [ ].

Software: Simple Stochastic Simulator 2. Stochastic simulation Stoichiometetric data and c i : REACTION**.txt Initial condition: INIT**.txt Reaction Parameter c: // time[X1][X2] …. Simulation results: SIM**.txt

Example of simulation results # of type of chemicals =2

[X1]  [X2] c=1, [X1]=1000, [X2]=0

[X1]  [X2] [X2]  [X1] c1=c2=1 [X1]=1000

# of type of chemicals =3

[X1]  [X2]  [X3], [X1]=1000, c=1

[X1]  [X2]  [X3], [X1]=1000, c=1

[X1]  [X2]  [X3], [X1]=1000, c=1

[X1]  [X2]  [X3],[X1]=1000, c=1

loop reaction [X1]  [X2]  [X3]  [X1], [X1]=1000, c=1

Representation of Reaction Data Set [X 1 ]2[X 1 ] c1c1 [X 1 ] + [X 2 ]2[X 2 ] c2c2 [X 2 ]Φ c3c3 Reaction DataInitial Condition [X 1 ]= X 1 (init) [X 2 ]= X 2 (init)

Example 2 EMP glcKATP + [D-glucose] -> ADP + [D-glucose-6-phosphate] glcKATP + [alpha-D-glucose] -> ADP + [D-glucose-6-phosphate] pgi[D-glucose-6-phosphate] [D-fructose-6-phosphate] pgi[D-fructose-6-phosphate] [D-glucose-6-phosphate] pgi[alpha-D-glucose-6-phosphate] [D-fructose-6-phosphate] pgi[D-fructose-6-phosphate] [alpha-D-glucose-6-phosphate] pfkATP + [D-fructose-6-phosphate] -> ADP + [D-fructose-1,6-bisphosphate] fbp[D-fructose-1,6-bisphosphate] + H(2)O -> [D-fructose-6-phosphate] + phosphate fbaA[D-fructose-1,6-bisphosphate] [glycerone-phosphate] + [D-glyceraldehyde-3-phosphate] fbaA[glycerone-phosphate] + [D-glyceraldehyde-3-phosphate] [D-fructose-1,6-bisphosphate] tpiA[glycerone-phosphate] [D-glyceraldehyde-3-phosphate] tpiA[D-glyceraldehyde-3-phosphate] [glycerone-phosphate] gapA[D-glyceraldehyde-3-phosphate] + phosphate + NAD(+) -> [1,3-biphosphoglycerate] + NADH + H(+) gapB[1,3-biphosphoglycerate] + NADPH + H(+) -> [D-glyceraldehyde-3-phosphate] + NADP(+) + phosphate pgkADP + [1,3-biphosphoglycerate] ATP + [3-phospho-D-glycerate] pgkATP + [3-phospho-D-glycerate] ADP + [1,3-biphosphoglycerate] pgm[3-phospho-D-glycerate] [2-phospho-D-glycerate] pgm[2-phospho-D-glycerate] [3-phospho-D-glycerate] eno[2-phospho-D-glycerate] [phosphoenolpyruvate] + H(2)O eno[phosphoenolpyruvate] + H(2)O [2-phospho-D-glycerate]

Example 2 EMP D-glucose alpha-D-glucose D-fructose-6-phosphatealpha-D-glucose-6-phosphate [D-fructose-1,6-bisphosphate] [D-glyceraldehyde-3-phosphate] D-glucose-6-phosphate [glycerone-phosphate] [1,3-biphosphoglycerate] [3-phospho-D-glycerate] [2-phospho-D-glycerate] [phosphoenolpyruvate]

Metabolic network and stoichiometric matrix

Typical network of metabolic pathways Reactions are catalyzed by enzymes. One enzyme molecule usually catalyzes thousands reactions per second (~ ) The pathway map may be considered as a static model of metabolism

For a metabolic network consisting of m substances and r reactions the system dynamics is described by systems equations. The stoichiometric coefficients n ij assigned to the substance S i and the reaction v j can be combined into the so called stoichiometric matrix. What is a stoichiometric matrix?

Example reaction system and corresponding stoichiometric matrix There are 6 metabolites and 8 reactions in this example system stoichiometric matrix

Binary form of N To determine the elementary topological properties, Stiochiometric matrix is also represented as a binary form using the following transformation n ij ’=0 if n ij =0 n ij ’=1 if n ij ≠0

Stiochiometric matrix is a sparse matrix Source: Systems biology by Bernhard O. Palsson

Information contained in the stiochiometric matrix Stiochiometric matrix contains many information e.g. about the structure of metabolic network, possible set of steady state fluxes, unbranched reaction pathways etc. 2 simple information: The number of non-zero entries in column i gives the number of compounds that participate in reaction i. The number of non-zero entries in row j gives the number of reactions in which metabolite j participates. So from the stoicheometric matrix, connectivities of all the metabolites can be computed

Information contained in the stiochiometric matrix There are relatively few metabolites (24 or so) that are highly connected while most of the metabolites participates in only a few reactions Source: Systems biology by Bernhard O. Palsson

Information contained in the stiochiometric matrix In steady state we know that The right equality sign denotes a linear equation system for determining the rates v This equation has non trivial solution only for Rank N < r(the number of reactions) K is called kernel matrix if it satisfies NK=0 The kernel matrix K is not unique

The kernel matrix K of the stoichiometric matrix N that satisfies NK=0, contains (r- Rank N) basis vectors as columns Every possible set of steady state fluxes can be expressed as a linear combination of the columns of K Information contained in the stiochiometric matrix

- With α 1 = 1 and α 2 = 1,, i.e. at steady state v 1 =2, v 2 =-1 and v 3 =-1 Information contained in the stiochiometric matrix And for steady state flux it holds that J = α 1.k 1 + α 2.k 2 That is v2 and v3 must be in opposite direction of v1 for the steady state corresponding to this kernel matrix which can be easily realized.

Information contained in the stiochiometric matrix Reaction System Stoicheometric Matrix The stoicheomatric matrix comprises r=8 reactions and Rank =5 and thus the kernel matrix has 3 linearly independent columns. A possible solution is as follows:

Information contained in the stiochiometric matrix Reaction System The entries in the last row of the kernel matrix is always zero. Hence in steady state the rate of reaction v 8 must vanish.

Reaction System The entries for v 3, v 4 and v 5 are equal for each column of the kernel matrix, therefore reaction v 3, v 4 and v 5 constitute an unbranched pathway. In steady state they must have equal rates Information contained in the stiochiometric matrix If all basis vectors contain the same entries for a set of rows, this indicate an unbranched reaction path

Elementary flux modes and extreme pathways The definition of the term pathway in a metabolic network is not straightforward. A descriptive definition of a pathway is a set of subsequent reactions that are in each case linked by common metabolites Fluxmodes are possible direct routes from one external metabolite to another external metabolite. A flux mode is an elementary flux mode if it uses a minimal set of reactions and cannot be further decomposed.

Elementary flux modes and extreme pathways

Extreme pathway is a concept similar to elementary flux mode The extreme pathways are a subset of elementary flux modes The difference between the two definitions is the representation of exchange fluxes. If the exchange fluxes are all irreversible the extreme pathways and elementary modes are equivalent If the exchange fluxes are all reversible there are more elementary flux modes than extreme pathways One study reported that in human blood cell there are 55 extreme pathways but 6180 elementary flux modes

Elementary flux modes and extreme pathways Source: Systems biology by Bernhard O Palsson

Elementary flux modes and extreme pathways Elementary flux modes and extreme pathways can be used to understand the range of metabolic pathways in a network, to test a set of enzymes for production of a desired product and to detect non redundant pathways, to reconstruct metabolism from annotated genome sequences and analyze the effect of enzyme deficiency, to reduce drug effects and to identify drug targets etc.

Lecture7 Topic1: Graph spectral analysis/Graph spectral clustering and its application to metabolic networks Topic 2: Concept of Line Graphs Topic 3: Introduction to Cytoscape

Graph spectral analysis/ Graph spectral clustering

PROTEIN STRUCTURE: INSIGHTS FROM GRAPH THEORY by SARASWATHI VISHVESHWARA, K. V. BRINDA and N. KANNANy Molecular Biophysics Unit, Indian Institute of Science Bangalore , India

Laplacian matrix L=D-A Adjacency Matrix Degree Matrix

Eigenvalues of a matrix A are the roots of the following equation |A-λI|=0, where I is an identity matrix Let λ is an eigenvalue of A and x is a vector such that then x is an eigenvector of A corresponding to λ (1) N×NN×1 Eigenvalues and eigenvectors

Node 1 has 3 edges, nodes 2, 3 and 4 have 2 edges each and node 5 has only one edge. The magnitude of the vector components of the largest eigenvalue of the Adjacency matrix reflects this observation.

Node 1 has 3 edges, nodes 2, 3 and 4 have 2 edges each and node 5 has only one edge. Also the magnitude of the vector components of the largest eigenvalue of the Laplacian matrix reflects this observation.

 The largest eigenvalue (lev) depends upon the highest degree in the graph.  For any k regular graph G (a graph with k degree on all the vertices), the eigenvalue with the largest absolute value is k.  A corollary to this theorem is that the lev of a clique of n vertices is n − 1.  In a general connected graph, the lev is always less than or equal to (≤ ) to the largest degree in the graph.  In a graph with n vertices, the absolute value of lev decreases as the degree of vertices decreases.  The lev of a clique with 11 vertices is 10 and that of a linear chain with 11 vertices is a linear chain with 11 vertices

In graphs 5(a)-5(e), the highest degree is 6. In graphs 5(f)-5(i), the highest degree is 5, 4, 3 and 2 respectively.

It can be noticed that the lev is generally higher if the graph contains vertices of high degree. The lev decreases gradually from the graph with highest degree 6 to the one with highest degree 2. In case of graphs 5(a)-5(e), where there is one common vertex with degree 6 (highest degree) and the degrees of the other vertices are different (less than 6 in all cases), the lev differs i.e. the lev also depends on the degree of the vertices adjoining the highest degree vertex.

This paper combines graph 4(a) and graph 4(b) and constructs a Laplacian matrix with edge weights (1/dij ), where dij is the distance between vertices i and j. The distances between the vertices of graph 4(a) and graph 4(b) are considered to be very large (say 100) and thus the matrix elements corresponding to a vertex from graph 4(a) and the other from graph 4(b) is considered to have a very small value of The Laplacian matrix of 8 vertices thus considered is diagonalized and their eigenvalues and corresponding vector components are given in Table 3.

The vector components corresponding to the second smallest eigenvalue contains the desired information about clustering, where the cluster forming residues have identical values. In Fig. 4, nodes 1-5 form a cluster (cluster 1) and 6-8 form another cluster (cluster 2).

Metabolome Based Reaction Graphs of M. tuberculosis and M. leprae: A Comparative Network Analysis by Ketki D. Verkhedkar1, Karthik Raman2, Nagasuma R. Chandra2, Saraswathi Vishveshwara1* 1 Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India, 2 Bioinformatics Centre, Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore, India PLoS ONE | September 2007 | Issue 9 | e881

Construction of network R1 R2 R3 R4 Stoichrometric matrix Following this method the networks of metabolic reactions corresponding to 3 organisms were constructed

Analysis of network parameters

Giant component of the reaction network of e.coli

Giant components of the reaction networks of M. tuberculosis and M. leprae

Analyses of sub-clusters in the giant component Graph spectral analysis was performed to detect sub-clusters of reactions in the giant component. To obtain the eigenvalue spectra of the graph, the adjacency matrix of the graph is converted to a Laplacian matrix (L), by the equation: L=D-A where D, the degree matrix of the graph, is a diagonal matrix in which the ith element on the diagonal is equal to the number of connections that the ith node makes in the graph. It is observed that reactions belonging to fatty acid biosynthesis and the FAS-II cycle of the mycolic acid pathway in M. tuberculosis form distinct, tightly connected sub-clusters.

Identification of hubs in the reaction networks In biological networks, the hubs are thought to be functionally important and phylogenetically oldest. The largest vector component of the highest eigenvalue of the Laplacian matrix of the graph corresponds to the node with high degree as well as low eccentricity. Two parameters, degree and eccentricity, are involved in the identification of graph spectral (GS) hubs.

Identification of hubs in the reaction networks Alternatively, hubs can be ranked based on their connectivity alone (degree hubs). It was observed that the top 50 degree hubs in the reaction networks of the three organisms comprised reactions involving the metabolite L-glutamate as well as reactions involving pyruvate. However, the top 50 GS hubs of M. tuberculosis and M. leprae exclusively comprised reactions involving L-glutamate while the top GS hubs in E. coli only consisted of reactions involving pyruvate. The difference in the degree and GS hubs suggests that the most highly connected reactions are not necessarily the most central reactions in the metabolome of the organism

91 Metabolomics approach for determining growth-specific metabolites based on FT-ICR-MS

92 [1] Metabolomics Metabolite 1Metabolite 2Metabolite 3 Metabolite 4 Metabolite 5 Metabolite 6 BC DE F IL HK Interpretation of Metabolome Species Molecular weight and formula Fragmentation Pattern Metabolite information Species Metabolites Tissue Samples Species-Metabolite relation DB Experimental Information MS

Data Processing from FT-MS data acquisition of a time series experiment to assessment of cellular conditions Time (min) OD 600 T1 T2 T3 T4 T5 T6 T7 T8 (a)Metabolite quantities for time series experiments Metabolites M M+1 M/2 (e) Assessment of cellular condition by metabolite composition m/z Time point (b) Data preprocessing and constructing data matrix (d) Annotation of ions as metabolites (c) Classification of ions into metabolite-derivative group Detected m/z Theoretical m/z Molecular formula Exact massErrorCandidateSpecies C2H2O3C2H2O Glyoxylic acidEscherichia coli C 8 H 16 O Octanoic acidEscherichia coli C 21 H 27 N 7 O 14 P NADEscherichia coli C 21 H 29 N 7 O 14 P NADHEscherichia coli E. coli

94 time time 1 time 8 time 2 metab.1metab.200 (b) Data matrix Software are provided by T. Nishioka (Kyoto Univ./Keio Univ.)

, PG5 PG7 PG9 PG3 PG1 PG6 PG2 PG4 PG10 PG8 M-1 M-2 M-3 M-4 M-5 M-6 M-7 M-8 M-9 M-10 M-11 M-12 M-13 M-14 M-15 M-16 M-17 (c) Classification of ions into metabolite- derivative group (DPClus) Correlation network for individual ions. Intensity ratio between Monoisotope (M) and Isotope (M+1)  # of Carbons in molecular formula:

96 (d) Annotation of ions as metabolites using KNApSAcK DB Detected m/z a Theoretical m/z Molecular formula Exact massErrorCandidateSpecies C2H2O3C2H2O Glyoxylic acidEscherichia coli C 8 H 16 O Octanoic acidEscherichia coli C 16 H 30 O omega-Cycloheptanenonanoic acidAlicyclobacillus acidocaldarius C 16 H 30 O omega-Cycloheptanenonanoic acidAlicyclobacillus acidocaldarius C 18 H 34 O Oleic acidEscherichia coli C 18 H 34 O cis-11-Octadecanoic acidLactobacillus plantarum C 18 H 34 O omega-Cycloheptylundecanoic acidAlicyclobacillus acidocaldarius C 18 H 34 O alpha-Cycloheptaneundecanoic acidAlicyclobacillus acidocaldarius C 18 H 34 O alpha-Cycloheptaneundecanoic acidAlicyclobacillus acidocaldarius C 18 H 34 O alpha-Cycloheptaneundecanoic acidAlicyclobacillus acidocaldarius C 10 H 15 N 2 O 8 P dTMPEscherichia coli K C 10 H 14 N 5 O 7 P AMPEscherichia coli C 10 H 14 N 5 O 7 P '-AMPEscherichia coli C 10 H 14 N 5 O 7 P dGMPEscherichia coli C 10 H 16 N 2 O 11 P dTDPEscherichia coli C 9 H 14 N 2 O 12 P UDPEscherichia coli C 10 H 15 N 5 O 10 P Adenosine 3',5'-bisphosphateEscherichia coli C 10 H 15 N 5 O 10 P ADPEscherichia coli C 10 H 15 N 5 O 10 P dGDPEscherichia coli C 20 H 19 Cl 2 NO Antibiotic MI F18A2Actinomadura spiralis MI178-34F18 C 20 H 19 Cl 2 NO Antibiotic MI F18C2Actinomadura spiralis MI178-34F C 15 H 22 N 7 O 8 P Phosmidosine BStreptomyces sp. strain RK C 24 H 20 N 2 O Kinamycin AStreptomyces murayamaensis sp. nov. C 24 H 20 N 2 O Kinamycin CStreptomyces murayamaensis sp. nov C 10 H 16 N 5 O 13 P ATP,dGTPEscherichia coli C 16 H 26 N 2 O 15 P dTDP-L-rhamnoseEscherichia coli C 15 H 24 N 2 O 17 P UDP-D-glucoseEscherichia coli C 15 H 24 N 2 O 17 P UDP-D-galactoseEscherichia coli C 17 H 27 N 3 O 17 P UDP-N-acetyl-D-mannosamineEscherichia coli C 17 H 27 N 3 O 17 P UDP-N-acetyl-D-glucosamineEscherichia coli C 17 H 27 N 5 O 16 P ADP-L-glycero-beta-D-manno- heptopyranose Escherichia coli C 21 H 27 N 7 O 14 P NADEscherichia coli C 21 H 29 N 7 O 14 P NADHEscherichia coli C 32 H 62 N 12 O Argimicin ASphingomonas sp C 41 H 65 N 5 O BE 32030BNocardia sp. A C 41 H 46 N 10 O 9 S Argyrin GArchangium gephyra Ar 8082 C 45 H 56 C l2 N 2 O Decatromicin BActinomadura sp. MK73-NF4 C 39 H 50 N 8 O 12 S Napsamycin CStreptomyces sp. HIL Y-82,11372

97 PLS Y Responses X N=8 M=220K=1 N=8 PLS (Partial Least Square regression model) -- extract important combinations of metabolites. N (biol.condition) << M (metabolites) (e) Estimation of cell condition based on a function of the composition of metabolites. Y(Cell density)= a 1 x 1 +…+ a j x j +….+ a M x M x j, the quantity for jth metabolites cell condition measurement points Metabolites Time (min) OD 600 T1 T2 T3 T4 T5 T6 T7 T8

ajaj UDP-glucose, UDP-galactose NAD Parasperone A UDP-N-acetyl-D-glucosamine UDP-N-acetyl-D-mannosamine ADP, Adenosine 3',5'-bisphosphate, dGDP UDP omega-Cycloheptyl-alpha-hydroxyundecanoate Octanoic acid dTMP, dGMP, 3'-AMP NADH Argyrin G dTDP ATP, dGTP Lenthionine omega-Cycloheptylnonanoate dTDP-6-deoxy-L-mannose omega-Cycloheptylundecanoate, cis-11-Octadecanoic acid ADP-(D,L)-glycero-D-manno-heptose Glyoxylate omega-Cycloheptyl-alpha-hydroxyundecanoate Stationary-phase dominantExponential-phase dominant y(OD 600 Cell Density)= a 1 x 1 +…+ a j x j +….+ a M x M a j > 0, stationary phase-dominant metabolites x j, the quantity for jth a j < 0, exponential phase-dominant metabolites (e) Assessment of cellular condition by metabolite composition Detection of stage-specific metabolites (PLS model of OD600 to metabolite intensities) Red: E.coli metabolites;Black: Other bacterial metabolites PG1,3,5,7,9 MS/MS analyses 120 metabolites 80 metabolites MS/MS analyses PG2,4,6,8,10

10 Phosphatidylglycerols detected by MS/MS spectra (b) Relation of mass differences among PG1 to 10  marker molecules PG5 30:1(14:0,16:1) PG1 32:1(16:0,16:1) PG3 34:1(16:0,18:1) PG6 31:0(14:0,c17:0) PG2 33:0(16:0,c17:0) PG4 34:5(16:0,c19:0) PG7 34:2(16:1,18:1) PG9 36:2(18:1,18:1) PG8 35:1(16:1,c19:1) PG10 37:1(18:1,c19:0) (Cluster 1) (Cluster 2) CFA ∆(CH 2 ) 2 US ∆(CH 2 ) 2  Cyclopropane Formation of PGs occurs in the transition from exponential to stationary phase. Exponential phase Stationary phase Cyclopropane Formaiton of PGs unsaturated PGs cyclopropanated PGs