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V10 Transcriptional regulatory networks - introduction. Typical promoter region of a prokaryotic gene. The TTGACA and TATAAT sequences at positions -35 and -10 nucleotides are not essential. The preference for the correspon-ding nucleotide at each position is between 50 and 80%.

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v10 transcriptional regulatory networks introduction
V10 Transcriptional regulatory networks - introduction

Typical promoter region of a prokaryotic gene. The TTGACA and TATAAT sequences at positions -35 and -10 nucleotides are not essential. The preference for the correspon-ding nucleotide at each position is between 50 and 80%.

Example of a gene regulatory network. Solid arrows indicate direct associations between genes and proteins (via transcription and translation), between proteins and proteins (via direct physical interactions), between proteins and metabolites (via direct physical interactions or with proteins acting as enzymatic catalysts), and the effect of metabolite binding to genes (via direct interactions). Lines show direct effects, with arrows standing for activation, and bars for inhibition. The dashed lines represent indirect associations between genes that result from the projection onto 'gene space'. For example, gene 1 deactivates gene 2 via protein 1 resulting in an indirect interaction between gene 1 and gene 2 (drawn after [Brazhnik00]).

Bioinformatics III

v10 transcriptional regulatory networks introduction1
V10 Transcriptional regulatory networks - Introduction

Discovering the true connectivities from gene expression data is not trivial.

Here, three different gene connectivities may lead to similar observed co-expression patterns.

Graph representation of the gene network corresponding to the biochemical network from previous page. This figure corresponds to the lowest tier of that figure. Most genes in gene networks will have a negative effect on their own concentration because the degradation rate of their mRNA is proportional to their concentration (drawn after [Brazhnik00]).

Bioinformatics III

transcriptional regulatory network of e coli
Transcriptional regulatory network of E. coli
  • RegulonDB: database with information on transcriptional regulation and operon organization in E.coli; 105 regulators affecting 749 genes
  • 7 regulatory proteins (CRP, FNR, IHF, FIS, ArcA, NarL and Lrp) are sufficient to directly modulate the expression of more than half of all E.coli genes.
  • Out-going connectivity follows a

power-law distribution

  • In-coming connectivity follows

exponential distribution (Shen-Orr).

Martinez-Antonio, Collado-Vides, Curr Opin Microbiol 6, 482 (2003)

Bioinformatics III

structural organization of transcription regulatory networks
Structural organization of transcription/regulatory networks

Modules: observation that reg. Networks are highly interconnected, very few modules can be entirely separated from the rest of the network.

Babu et al. Curr Opin Struct Biol. 14, 283 (2004)

Bioinformatics III

frequency of co regulation
Frequency of co-regulation

Regulation by multiple TFs occurs in half of genes.

In most cases, a „gobal“ regulator (with > 10 interactions) works together with a more specific local regulator.

Martinez-Antonio, Collado-Vides, Curr Opin Microbiol 6, 482 (2003)

Bioinformatics III

regulation of tfs and club co regulation
Regulation of TFs and club co-regulation

However, in a process of decisions and

information flux, the number of controlled

or affected elements is not the only factor

to be considered.

A hierarchy of different levels of decision

is natural to our understanding of how

things get done.

In general, global regulators work

together with other global regulators.

Dynamics of decison-making is a

cooperative process of different

subsets of the network put into action

at certain moments.

Martinez-Antonio, Collado-Vides, Curr Opin Microbiol 6, 482 (2003)

Bioinformatics III

response to changes in environmental conditions
Response to changes in environmental conditions

The second function of TFs is to sense changes in environmental conditions or other internal signals encoding changes.

Global environment growth conditions in which TFs are regulating.

# in brackets indicates how many additional TFs participate in the same number of conditions.

Martinez-Antonio, Collado-Vides, Curr Opin Microbiol 6, 482 (2003)

Bioinformatics III

evolution of the gene regulatory network
Evolution of the gene regulatory network

Larger genomes tend to have more TFs per gene.

Babu et al. Curr Opin Struct Biol. 14, 283 (2004)

Bioinformatics III

do we need to rely on experiments
Do we need to rely on experiments?

Determine homology between the domains and protein families

of TFs and regulated genes

and proteins of known 3D structure.

 Determine uncharacterized E.coli proteins with

DNA-binding domains, thus identify large majority

of E.coli TFs.

Finding: 75% of all TFs are two-domain proteins.

Analysis of domain architecture shows that 75% of

the TFs have arisen by gene duplication.

Sarah Teichmann

MRC LMB Cambridge

Madan Babu,

PhD student at LMB

Babu, Teichmann, Nucl. Acid Res. 31, 1234 (2003)

Bioinformatics III

flow chart of method to identify tfs in e coli
Flow chart of method to identify TFs in E.coli

SUPERFAMILY database (C. Chothia) contains a library of HMM models based on the sequences of proteins in SCOP for predicted proteins of completely sequenced genomes.

In addition to our set of 271 transcription factors, there are eight transcription factors without a DBD assignment that have known regulatory information.

Remove all DNA-binding proteins involved in replication/repair etc.

Babu, Teichmann, Nucl. Acid Res. 31, 1234 (2003)

Bioinformatics III

3d structures of putative and real tfs in e coli
3D structures of putative (and real) TFs in E.coli

The three-dimensional structures of the 11 DBD families seen in the 271 identified transcription factors in E.coli. The figure highlights the fact that even though the helix–turn–helix motif occurs in all families except the nucleic acid binding family, the scaffolds in which the motif occurs are very different.

Babu, Teichmann, Nucl. Acid Res. 31, 1234 (2003)

Bioinformatics III

domain architectures of tfs
Domain architectures of TFs

The 74 unique domain architectures of the 271 identified TFs. Each functional class is represented by a different shape and each family within the functional class is represented by a different colour.

The DBDs are represented as rectangles. The partner domains are represented as hexagons (small molecule-binding domain), triangles (enzyme domains), circles (protein interaction domain), diamonds (domains of unknown function) and the receiver domain has a pentagonal shape.

The letters A, R, D and U denote activators, repressors, dual regulators and TFs of unknown function, and the number of TFs of each type is given next to each domain architecture.

Architectures of known 3D structure are denoted by asterisks, and ‘+’ are cases where the regulatory function of a TF has been inferred by indirect methods, so that the DNA-binding site is not known.

Babu, Teichmann, Nucl. Acid Res. 31, 1234 (2003)

Bioinformatics III

evolution of tfs
Evolution of TFs

10% 1-domain proteins

75% 2-domain proteins

12% 3-domain proteins

3% 4-domain proteins

TFs have evolved by extensive recombination of domains.

Proteins with the same sequential arrangement of domains are likely to be direct duplicates of each other.

74 distinct domain architectures have duplicated to give rise to 271 TFs.

Babu, Teichmann, Nucl. Acid Res. 31, 1234 (2003)

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organisation of transcriptional regulatory network
Organisation of transcriptional regulatory network

For 121 TFs, there is information on their regulated genes.

They can be divided into 10 general functional categories.

Babu, Teichmann, Nucl. Acid Res. 31, 1234 (2003)

Bioinformatics III

regulatory cascades
Regulatory cascades

The TF regulatory network in E.coli.

When more than one TF regulates a gene, the order of their binding sites is as given in the figure. An arrowhead is used to indicate positive regulation when the position of the binding site is known.

Horizontal bars indicates negative regulation when the position of the binding site is known. In cases where only the nature of regulation is known, without binding site information, + and – are used to indicate positive and negative regulation.

The DBD families are indicated by circles of different colours as given in the key. The names of global regulators are in bold.

Babu, Teichmann, Nucl. Acid Res. 31, 1234 (2003)

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design principles of regulatory networks
Design principles of regulatory networks

Wiring diagrams of regulatory networks resemble somehow electrical circuits.

Try to break down networks into basic building blocks.

Search for „network motifs“ as patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks.

Uri Alon

Weizman Institute

Shen-Orr et al. Nature Gen. 31, 64 (2002)

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detection of motifs
Detection of motifs

Represent transcriptional network as a connectivity matrix M

such that Mij= 1 if operon j encodes a TF that transcriptionally regulates operon i

and Mij= 0 otherwise.

Scan all n × n submatrices of M generated

by choosing n nodes that lie in a connected

graph, for n = 3 and n = 4.

Submatrices were enumerated efficiently by

recursively searching for nonzero elements.

Compute a P value for submatrices representing each type of connected subgraph by comparing # of times they appear in real network vs. in random network.

For n = 3, the only significant motif is the feedforward loop.

For n = 4, only the overlapping regulation motif is significant.

SIMs and multi-input modules were identified by searching for identical rows of M.

Connectivity matrix for causal regulation of transcription factor j (row) by transcription factor i (column). Dark fields indicate regulation. (Left) Feed-forward loop motif. TF 2 regulates TFs 3 and 6, and TF 3 again regulates TF 6. (Middle) Single-input multiple-output motif. (Right) Densely-overlapping region.

Shen-Orr et al. Nature Gen. 31, 64 (2002)

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dor detection
DOR detection

Consider all operons regulated by ≥ 2 TFs.

Define (nonmetric) distance measure between operons k and j, based on the # of TFs regulating both operons:

d(k,j) = 1/ (1+n fnMk,n Mj,n)2)

Where fn = 0.5 for global TFs and fn = 1 otherwise.

Cluster operons with average-linkage algorithm.

DORs correspond to clusters with more than 10 connections

with a ratio of connections to TFs > 2.

Shen-Orr et al. Nature Gen. 31, 64 (2002)

Bioinformatics III

network motifs found in e coli transcript regul network
Network motifs found in E.coli transcript-regul network

a, Feedforward loop: a TF X regulates a second TF Y, and both jointly regulate one or more operons Z1...Zn.

b, Example of a feedforward loop (L-arabinose utilization).

c, SIM motif: a single TF, X, regulates a set of operons Z1...Zn. X is usually autoregulatory. All regulations are of the same sign. No other transcription factor regulates the operons.

d, Example of a SIM system (arginine biosynthesis).

e, DOR motif: a set of operons Z1...Zm are each regulated by a combination of a set of input transcription factors, X1...Xn. DOR-algorithm detects dense regions of connections, with a high ratio of connections to transcription factors.

f, Example of a DOR (stationary phase response).

Shen-Orr et al. Nature Gen. 31, 64 (2002)

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significance of motifs
Significance of motifs

Shen-Orr et al. Nature Gen. 31, 64 (2002)

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regulatory network
Regulatory network

Each TF appears only in a single subgraph except for

global TFs that can appear in several subgraphs.

Shen-Orr et al. Nature Gen. 31, 64 (2002)

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analysis of complexome during cell cycle
Analysis of complexome during cell cycle

Most research on biological networks has been focused on static topological properties, describing networks as collections of nodes and edges rather than as dynamic structural entities.

Here this study focusses on the temporal aspects of networks, which allows us to study the dynamics of protein complex assembly during the Saccharomycescerevisiae cell cycle.

The integrative approach combines protein-protein interactions with information on the timing of the transcription of specific genes during the cell cycle, obtained from DNA microarray time series shown before.

 a quality-controlled set of 600

periodically expressed genes,

each assigned to the point in the

cell cycle where its expression peaks.

Ulrik Lichtenberg Peer Bork

Science 307, 724 (2005)

Bioinformatics III

temporal protein interaction network in yeast cell cycle
Temporal protein interaction network in yeast cell cycle

Cell cycle proteins that are part of complexes or other physical interactions are shown within the circle.

For the dynamic proteins, the

time of peak expression is

shown by the node color;

static proteins are represented

as white nodes.

Outside the circle, the dynamic

proteins without interactions

are positioned and colored

according to their peak time.

Science 307, 724 (2005)

Bioinformatics III

just in time synthesis vs just in time assembly
Just-in-time synthesis vs. just-in-time-assembly

Transcription of cell cycle–regulated genes is generally thought to be turned on when or just before their protein products are needed: often referred to as

just-in-time synthesis.

Contrary to the cell cycle in bacteria, however, just-in-time synthesis of entire complexes is rarely observed in the network. The only large complex to be synthesized in its entirety just in time is the nucleosome, all subunits of which are expressed in S phase to produce nucleosomes during DNA replication.

Instead, the general design principle appears to be that only some subunits of each complex are transcriptionally regulated in order to control the timing of final assembly.

Science 307, 724 (2005)

Bioinformatics III

something spectacular at the end
Something spectacular at the end 

Integrate transcriptional regulatory information and gene-expression data for multiple conditions in Saccharomyces cerevisae.

5 conditions cell cycle

sporulation

diauxic shift

DNA damage

stress response

Sarah Teichmann Mark Gerstein

Luscombe, Babu, … Teichmann, Gerstein, Nature 431, 308 (2004)

Bioinformatics III

slide26

SANDY: topological measures + network motifs

+ some post-analysis

Luscombe et al. Nature 431, 308 (2004)

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slide27

Dynamic representation of transript. regul. network

a, Schematics and summary of properties for the endogenous and exogenous sub-networks.

b, Graphs of the static and condition-specific networks. Transcription factors and target genes are shown as nodes in the upper and lower sections of each graph respectively, and regulatory interactions are drawn as edges; they are coloured by the number of conditions in which they are active. Different conditions use distinct sections of the network.

c, Standard statistics (global topological measures and local network motifs) describing network structures. These vary between endogenous and exogenous conditions; those that are high compared with other conditions are shaded. (Note, the graph for the static state displays only sections that are active in at least one condition, but the table provides statistics for the entire network including inactive regions.)

Luscombe, Babu, … Teichmann, Gerstein, Nature 431, 308 (2004)

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slide28

Interpretation

Half of the targets are uniquely expressed in only one condition; in contrast, most

TFs are used across multiple processes.

The active sub-networks maintain or rewire regulatory interactions, over half of

the active interactions are completely supplanted by new ones between conditions.

Only 66 interactions are retained across ≥ 4 conditions.

They are always „on“ and mostly regulate house-keeping functions.

The calculations divide the 5 condition-specific networks into 2 categories:

endogenous and exogenous.

Endogenous processes are multi-stage, operate with an internal transcriptional

program

Exogenous processes are binary events that react to external stimuli with a

rapid turnover of expressed genes.

Luscombe et al. Nature 431, 308 (2004)

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slide29

Figure 2 Newly derived 'follow-on' statistics for network structures.

a, TF hub usage in different cellular conditions. The cluster diagram shades cells by the normalized number of genes targeted by TF hubs in each condition. One cluster represents permanent hubs and the others condition-specific transient hubs. Genes are labelled with four-letter names when they have an obvious functional role in the condition, and seven-letter open reading frame names when there is no obvious role. Of the latter, gene names are red and italicised when functions are poorly characterized. Starred hubs show extreme interchange index values, I = 1.

b, Interaction interchange (I) of TF between conditions. A histogram of I for all active TFs shows a uni-modal distribution with two extremes. Pie charts show five example TFs with different proportions of interchanged interactions. We list the main functions of the distinct target genes regulated by each example transcription factor. Note how the TFs' regulatory functions change between conditions.

c, Overlap in TF usage between conditions. Venn diagrams show the numbers of individual TFs (large intersection) and pair-wise TF combinations (small intersection) that overlap between the two endogenous conditions.

Luscombe et al. Nature 431, 308 (2004)

Bioinformatics III

slide30

Interpretation

Most hubs (78%) are transient = they are influential in one condition, but less

so in others.

Exogenous conditions have fewer transient hubs (different ).

„Transient hub“: capacity to change interactions between connections.

Luscombe et al. Nature 431, 308 (2004)

Bioinformatics III

slide31

TF inter-regulation during the cell cycle time-course

a, The 70 TFs active in the cell cycle. The diagram shades each cell by the normalized number of genes targeted by each TF in a phase. Five clusters represent phase-specific TFs and one cluster is for ubiquitously active TFs. Both hub and non-hub TFs are included.

b, Serial inter-regulation between phase-specific TFs. Network diagrams show TFs that are active in one phase regulate TFs in subsequent phases. In the late phases, TFs apparently regulate those in the next cycle.

c, Parallel inter-regulation between phase-specific and ubiquitous TFs in a two-tiered hierarchy. Serial and parallel inter-regulation operate in tandem to drive the cell cycle while balancing it with basic house-keeping processes.

Luscombe et al. Nature 431, 308 (2004)

Bioinformatics III

slide32

Summary

  • Integrated analysis of transcriptional regulatory information and condition-specific
  • gene-expression data; post-analysis, e.g.
  • Identification of permanent and transient hubs
  • interchange index
  • overlap in TF usage across multiple conditions.
  • Large changes in underlying network architecture
  • in response to diverse stimuli, TFs alter their interactions to varying degrees,

thereby rewiring the network

  • some TFs serve as permanent hubs, most act transiently
  • environmental responses facilitate fast signal propagation
  • cell cycle and sporulation proceed via multiple stages
  • Many of these concepts may also apply to other biological networks.

Luscombe et al. Nature 431, 308 (2004)

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slide33

additional slides (not used)

Luscombe et al. Nature 431, 308 (2004)

Bioinformatics III

cross organism comparison
Cross-organism comparison

Many TF families are specific to individual phylogenetic groups or greatly expanded in some genomes.

In contrast to the high level of conservation of other regulatory and signalling systems across the crown group eukaryotes,

some of the TF families are dramatically different in the various lineages.

Babu et al. Curr Opin Struct Biol. 14, 283 (2004)

Bioinformatics III

regulatory interactions across organisms
Regulatory interactions across organisms

Are regulatory interactions conserved among organisms? Apparently yes.

Orthologous TFs regulate orthologous target genes.

As expected, the conservation of genes and interaction is related to the phylogenetic difference between organisms.

Above: Many interactions of (a) can be mapped to pathogenetic Pseudomonas aeruginosa that is related to E.coli (b).

Very few interactions can be mapped from (a) to (c).

Babu et al. Curr Opin Struct Biol. 14, 283 (2004)

Bioinformatics III

regulatory interactions across organisms1
Regulatory interactions across organisms
  • Observation: there is no bias towards conservation of network motifs.
  • Regulatory interactions in motifs are lost or retained at the same rate as the other interactions in the network.
  • The transcriptional network appears to evolve in a step-wise manner, with loss and gain of individual interactions probably playing a greater role than loss and gain of whole motifs or modules.

Observation: TFs are less conserved than target genes, which suggests that regulation of genes evolves faster than the genes themselves.

Babu et al. Curr Opin Struct Biol. 14, 283 (2004)

Bioinformatics III

mathematical aspects of the inverse problem
Mathematical Aspects of the Inverse Problem

A network with two or more connected components, i.e. two or more sub-networks, has as fixed configurations the combination (Cartesian product) of all fixed configurations of each sub-network.

We say that the fixed configurations are factorizable.

Thus, the inverse problem consists of determining whether a fixed configurations set is factorizable.

In this way, we can obtain some information on the connectivity of the network.

Aracena & Demongeot, Acta Biotheoretica 52, 391 (2004)

Bioinformatics III

factorization
Factorization

Given S  {0,1}n and a permutation function : {1,...,n}  {1,...,n},

we denote by (S), or simply S the set {s(1)s(2) ... s(n) : s1s2...sn S }.

A set S  {0,1}n is said to be factorizable if there exist sets of vectors

S1  {0,1}j(1) and S2  {0,1}j(2) and , ..., Sk  {0,1}j(3) and a permutation function

: {1, ..., n}  {1,...,n} such that S can be written as S = (S1 S2 ...  Sk) ,

where the symbol „“ is the cartesian product between sets.

If S is a factorizable set, then j(1) + j(2) + ... + j(k) = n.

The set defined by F = {S1,S2, ...,Sk} is called a factorization of S and each

Sj F a factor of S.

F is called a maximal factorization if every factor Sj F is not factorizable.

Aracena & Demongeot, Acta Biotheoretica 52, 391 (2004)

Bioinformatics III

examples
Examples

i) S = {0100, 0111, 1000, 1011} = {01, 10}  {00, 11}.

Here, the permutation function is the identity.

ii) S = {0010, 0111, 1000, 1101} =

({0100, 0111, 1000, 1011})(2,3) = ({01, 10}  {00, 11})(2,3) ,

where (2,3) is the function which permutes the second and third coordinates.

Given the sets I  {1, ..., n} and S  (0,1)n, let PI(S) be the projection set defined by

PI(S) = {(sj(1),sj(2), ...,sj(I)): s  S, j(k)  I, k = 1, ..., | I |, and j(k) < j(l) for all k < l }.

Aracena & Demongeot, Acta Biotheoretica 52, 391 (2004)

Bioinformatics III

proposition 2
Proposition 2

Proposition 2

If a set S  {0,1}n is factorizable, then the maximal factorization of S is unique.

Proof

Let F = {S1,S2, ...,Sk} and G = {T1,T2, ...,Tk} be two distinct maximal factorizations of S.

S = (S1  S2  ... Sk)1 = (T1  T2  ... Tk)2

Hence, the permutation  = (1)-1○ 2 is such that

S1  S2  ... Sk = (T1  T2  ... Tk)

Since F and G are maximal factorizations, there is a factor of F not included into G,

which is supposed to be S1  {0,1}q, q  {1, ..., n}.

Let T = T1  T2  ...  Tm , so S1 = P{1,...,q} (T)

Hence, if we denote by I(k)  {1, ..., n} the set of indices such that

PI(k)(T) = T, for every k = 1, ...,m and by J = { j  {1,...,m}: I(j)  {(1),...,(p)} }

then there exists a permutation function ‘ such that

Therefore, S1 is factorizable, a contradiction.

Aracena & Demongeot, Acta Biotheoretica 52, 391 (2004)

Bioinformatics III

algorithm
Algorithm

Let : {0,1}n  {0,1}n  P({1,...,n}) be the function called the difference function where P({1,...,n}) is the set of subsets of {1,...,n} and defined by

(x,y) = {i: xi  yi}, where x,y  {0,1}n.

Given S  {0,1}n, the idea of the Factorization algorithm is first to construct a matrix with all the values of (x,y) for every x,y  S.

Next, for each row i of the matrix we construct a finite and undirected graph

Gi = (Vi,Ei), where the set of nodes Vi is equal to the set {1,...,n} and the set of arcs Ei is determined by the values of each row of the matrix, according to the algorithm.

Finally, the connected components of the union of all graphs Gi determine the factors of the maximal factorization of S.

In the case that S is not factorizable, the output of the algorithm will be a graph with a unique connected component.

Aracena & Demongeot, Acta Biotheoretica 52, 391 (2004)

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algorithm1
Algorithm

Aracena & Demongeot, Acta Biotheoretica 52, 391 (2004)

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theorem 3
Theorem 3

Given a set S  {0,1}n, if I = { I(1), I(2), ..., I(k) } is the output of the Factorization algorithm with input S,

then F = { P(I)(S): I = 1, ..., k) is the maximal factorization of S

and the complexity of the algorithm is O(|S|3 + n2)

Aracena & Demongeot, Acta Biotheoretica 52, 391 (2004)

Bioinformatics III

example 2
Example 2

Let S = { x1 = 000, x2 = 001, x3= 100, x4= 010, x5= 011, x6= 110}.

The difference matrix is

and the partial graphs and the

output graph of the algorithm are:

The output is I(1) = {1,3} and I(2) = {2}.

 the maximal factorization of S is given

by

S = (PI(1)(S)  PI(2) (S))(2,3)

= ({00,01,10}  {0,1})(2,3)

where (2,3) is the permutation of the

second and third coordinates.

Aracena & Demongeot, Acta Biotheoretica 52, 391 (2004)

Bioinformatics III

example 3
Example 3

The following set of vectors corresponds to the observed fixed points of the A.thaliana regulatory network, considering only genes whose activity is not constant.

Let S = { x1 = 0010000, x2 = 0011011, x3= 0000100, x4= 0001111, x5= 1100000, x6= 1101011}.

The difference matrix is

The graph G of the algorithm and the

connected components

I(1) = {1,2,3,5} and I(2) = {4,6,7} are:

The maximal factorization of S is given

by

S = (PI(1)(S)  PI(2) (S))(4,5)

= ({0010,0001,1100)  (000,111)}(4,5)

Aracena & Demongeot, Acta Biotheoretica 52, 391 (2004)

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