Biological networks and network motifs by wuxiangyu

VIEWS: 9 PAGES: 36

									Network Motifs: simple Building Blocks of
         Complex Networks

          R. Milo et. al. Science 298, 824 (2002)




                         Y. Lahini
               The cell and the environment
•   Cells need to react to their environment
•   Reaction is by synthesizing task-specific proteins, on demand.
•   The solution – regulated transcription network




•   E. Coli – 1000 protein types at any given moment >4000 genes (or possible
    protein types) – need regulatory mechanism to select the active set
•   We are interested in the design principles of this network
                Proteins are encoded by DNA

                                         Protein
                                                   translation


                                                   RNA


                                                   transcription

                                             DNA
DNA – the instruction manual, 4-letter
chemical alphabet – A,G,T,C
                                Gene Regulation
• Proteins are encoded by the DNA of the organism.
• Proteins regulate expression of other proteins by interacting
with the DNA
                                                              protein
Transcription factor
                         external signal




  DNA
               promoter region             Coding region
                       ACCGTTGCAT
Two types of Transcription Factors: 1.Activators
 X
                               No transcription                 X    Y

                            gene Y
       X binding site
                                                      Y
         Sub-second                                         Y
                                                 Y
                                                       Y
  Sx
 X        X*    Seconds

          X*                             INCREASED TRANSCRIPTION


                                             Hours
Bound activator

 Separation of time scales: TF activation level is in steady state
 Two types of Transcription Factors: Repressors

                                                   Y        X   Y
                                                        Y
Unbound repressor                              Y
                                                    Y
            X




Bound repressor
                      Sx

                  X        X*
                                     No transcription
                           X*


                       Bound repressor
                                           Equations of gene regulation
• If X* regulates Y, the net production rate of gene Y is dY  f X *  Y                                                                  
• α- Dilution/degradation rate                            dt

                                      f ( X * )   ( X *  K )                                                  f ( X * )   ( X *  K )

                                   X* Y                                                               X*     Y
    Y promoter activity




                                                                        Y promoter activity
                                                                                              




                          /2                                                                 /2




                           0                                                                   0
                               0       0.5        1         1.5     2                              0          0.5        1        1.5           2
                                     Activator concentration X*/K                                           Repressor concentration X*/K



•                         K – activation coefficient [concentration]; related to the affinity
•                         β – maximal expression level
•                         Step approximation – gene is on (rate β) or off (rate 0) with threshold K
        The gene regulatory network of E. coli
• Nodes are proteins (or the genes that encode them)
• Edges = regulatory relation between two proteins




        X      Y
                   Analyzing networks
• The idea- patterns that occur in the real network much more then in
  a randomized network, must have functional significance.
• The randomized networks share the same number of edges and
  number of nodes, but edges are assigned at random
The known E. Coli transcription network
A random graph based on the same node statistics
3-node network motif – the feedforward loop

                                   Nreal=40
                                   Nrand=7±3
         The feedforward loop : a sign sensitive filter




         The feedforward loop is a filter for transient signals while allowing fast shutdown
Mangan, Alon, PNAS, JMB, 2003
       The Feedforward loop : a sign sensitive filter




                                Vs.

                  =lacZYA             =araBAD


                                                OFF pulse




Mangan, Alon, PNAS, JMB, 2003
                    Single Input Module




                                 k3
                                 k3
                                 k2
                                 k2
                                 k1
                                 k1



         Z1Z1
         Z2Z2
         Z3Z3




    Temporal and expression level program generator
• The temporal order is encoded in a hierarchy of thresholds
• Expression levels hierarchy is encoded in hierarchy of promoter activities
Single Input Module motif is responsible for exact
          timing in the flagella assembly
Single Input Module motif is responsible for exact
          timing in the flagella assembly




Kalir et. al., science,2001
              The gene regulatory network of E. coli




                                             Single input modules

    • Shallow network, few long cascades.    Feed-forward loops
    • Modular

Shen-Orr et. al. Nature Genetics 2002
        Evolution of transcription networks


• In 1 day, 1010 copies of e-coli, 1010 replication of DNA.
• Mutation rate is 10-9
   – 10 mutations per letter in the population per day
• Even single DNA base change in the promoter can change the
  activation/repression rate

• Edges can be lost or gained (i.e. selected) easily.
     Links between WebPages – a completely
           different set of motifs is found

•   WebPages are nodes and Links are directed edges
•   3 node results:
            Structure of a nematode neuronal circuitry


                                              Head Sensory




                                                             Ventral Cord
      Ring Motor                                             Motor




[White, Brenner 1986; Durbin, Thesis, 1987]
Neurons and transcription
  share similar motifs

                            C. elegans
                            Summary

• The production of proteins in cells is regulated using a complex
  regulation network
• Network motifs: simple building blocks of complex networks
• An algorithm to identify network motifs
• Example: the transcription network of E. coli.
• The feed forward loop as a sign sensitive filter
• The single input module: exact temporal ordering of protein
  expression
Thanks
                   Equations of gene regulation
•   If X* regulates Y, the net production rate of gene Y is dY  f X *   Y
                                                            dt
•   α- Dilution/degradation rate

                     X *n                                                    
        f (X ) 
            *
                                    ( X *  K )           f (X *)                       ( X *  K )
                   K X
                    n        *n
                                                                         1    
                                                                              X
                                                                              K
                                                                                  *   n




•   K – activation coefficient [concentration]; related to the affinity
•   Β – maximal expression level
•   n – the Hill parameter (steepness of the response, usually 1-4)
•   Step approximation – gene is on (rate β) or off (rate 0) with threshold K
Actors’ web
 Mathematicians &
Computer Scientists
Sexual contacts: M. E. J. Newman, The structure and function of complex networks, SIAM Review 45, 167-256 (2003).
High school dating: Data drawn from Peter S. Bearman, James Moody, and Katherine Stovel visualized by Mark Newman
Internet as measured by Hal Burch and Bill Cheswick's Internet Mapping Project.
Metabolic networks




            KEGG database: http://www.genome.ad.jp/kegg/kegg2.html
Transcription regulatory networks




                     Single-celled eukaryote:
Bacterium: E. coli
                           S. cerevisiae
C. elegans neuronal
net
                   Dense Overlapping Regulons (DOR)




                                                 X1   X2    X3   …    Xn

                                                                           Bi-fan
                                                 Z1   Z2   Z3    …   Zm


                                                            Nreal = 203
                                                           Nrand = 47±12
                                                           Z Score = 13


                  Array of gates for hard-wired decision making


Buchler, Gerland, Hwa, PNAS 2003
Setty, Mayo, Surette, Alon, PNAS 2003

								
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