1. Transcription Networks And The Cell’s Functional Organization Presenter: Roni Sharf

2. Topics to be discussed Introduction Transcription Networks Appendix C Albert-Laszlo Barabasi & Zoltan N.Oltvai Article Conclusions

3. Introduction and basic concepts What is a cell? The cell is an integrated device that is made of several thousand types of interacting proteins. A protein is a nanometer size molecular machine that carries out a specific task with exquisite precision. A cell knows how to monitor its environment and to calculate the amount of proteins and which types are needed in order to carry out a specific task.

4. Introduction and basic concepts (cont.) for example: when a sugar is sensed by the cell, the cell begins to produce proteins that will transport the sugar into the cell and benefit from it. another exmaple is when the cell is damaged, it produces the needed amount of repair proteins to fix it. The “tool” that helps the cell to determine what proteins are needed and at which rate, an information-processing function is carried out by transcription networks.

5. The cognitive problem of the cell Cells, being very complex, can sense many different signals, such as beneficial nutrients, harmful chemicals, temperature, biological signaling molecules from other cells and more. The response to these signals is presented by producing appropriate proteins that can address the given situation. In order to represent these environmental states, the cell uses special proteins called transcription factors.

6. The cognitive problem of the cell (cont.) Each active transcription factor can bind the DNA to regulate the rate at which specific target genes are read. The genes are read into mRNA, which is then translated into protein, which can act on the environment. Their activity in the cell is considered an internal representation of the environment. The most important internal representations are of cell survival and growth. Of course there are others as well that represent the cell “starvation” or the damage of the DNA. In each case the appropriate protein response is carried out.

7. The cognitive problem of the cell (cont.)

8. Elements of transcription networks The interaction between transcription factors and genes is described by transcription networks. Each gene is a stretch of DNA whose sequence encodes the information needed for production of the protein.

9. Elements of transcription networks (cont.) Transcription of a gene is the process by which RNA polymerase (in short RNAp) produces mRNA that corresponds to that gene’s coding sequence. The mRNA is then translated into a protein, that is also known as the gene product. The rate at which the gene is transcribed, the number of mRNA produced per unit time, is controlled by a regulatory region of DNA, known as the promoter, that preceds the gene.

10. Elements of transcription networks (cont.)

11. Whereas RNAp acts on virtually all of the genes, changes in the expression of specific genes are due to transcription factors. Each transcription factor modulates the transcription rate of a set of target genes. Transcription factors bind specific sites in the promoters of the regulated genes, thus affecting the transcription rate. When bound, they change the probability per unit time that RNAp binds the promoter and produces an mRNA molecule.

12. Elements of transcription networks (cont.) The transcription factors thus affect the rate at which RNAp initiates transcription of the gene. Transcription factors can act as activators, that increase the transcription rate of the gene Gene Y Increased transcription Y Y Y Y Bound activator

13. Elements of transcription networks (cont.) Transcription factors can act as repressors, that reduce the transcription rate of the gene Bound repressor No transcription

14. Elements of transcription networks (cont.) Transcription factor proteins are themselves encoded by genes, which are regulated by other transcription factors, which in turn may be regulated by other transcription factors, and so on. This set of interactions forms a transcription network. Transcription network describes all of the regulatory transcription interactions in a cell. In the network, the nodes are genes and edges represent transcriptional regulation of one gene by the protein product of another gene.

15. Elements of transcription networks (cont.)

16. A transcription network that represents about 20% of the transcription interactions in the bacterium E.coli. return

17. Properties of a transcription network graph Each edge in a transcription network corresponds to an interaction in which a transcription factor directly controls the transcription rate of a gene. These interactions can be of two types: Activation (positive control) – occurs when a transcription factor increases the rate of transcription when it binds the promoter. The sign on the edge is “+”. Repression (negative control) – occurs when a transcription factor reduces the rate of transcription when it binds the promoter. The sign on the edge is “-”.

18. Properties of a transcription network graph (cont.) Transcription factors act primarily as either activators or repressors. Nodes that send out edges with mostly “-” signs represent repressors. Activators send out mostly “+” signs. But there can be some target genes for which the Activators act as repressors and vice versa.

19. Properties of a transcription network graph (cont.)

21. Y promoter activity

22. Properties of a transcription network graph (cont.) Y promoter activity

23. APPENDIX C Transcription Networks are sparse Let us look on a network with N nodes. Each node can have an outgoing edge to each N-1 other nodes. Each node can also have a self-edge.

24. (Cont.)

26. Transcription networks are the product of evolutionary selection. Losing an edge in the network is very easy. A single mutation in the binding site of X in the promoter of Y can cause the loss of interaction! Therefore, every edge in the network is under evolutionary selection. The sparse nature of the network reflects the fact that only very few and specific interactions, with usuful functions, appear in the network.

27. Long-Tailed output degree and Compact input degree sequences Basic concepts: The number of edges that point into a node is called the node’s in-degree: The out-degree is the number of edges pointing out of a node. in-degree = 4 out-degree = 3

28. (Cont.) Incoming edges to a node correspond to transcription factors that regulate the gene. Outgoing edges correspond to the number of genes regulated by the transcription factor protein that is encoded by the gene, that corresponds to the node.

29. (Cont.)

30. That shows that not all the nodes have similar degrees! Transcription networks often have many transcription factors that regulate a few genes, fewer nodes that regulate tens of genes, and even fewer nodes that regulate hundreds of genes. The latter are called global regulators, and they usually respond to key environmental signals to control large ensembles of genes.

31. (Cont.) Thus, the out-degree distribution has a long tail and can be roughly described as a power law. The out-degree distribution is only approximately power law. It is bounded by the total number of genes N. The long-tailed distribution is sometimes called “scale-free” because there are sets of regulated genes of many different sizes with no typical scale. Nodes with many more connections than the average are called hubs.

32. (Cont.)

34. Cluster coefficients of transcription networks

35. Cont.

36. The clustering coefficient can also be measured as a function of the number of neighbors that each node has, resulting in a clustering sequence C(k). Often C(k) ~ 1/k. The more neighbors a node has, the lower its clustering coefficient.

38. Introduction Reductionism has dominated the biological research over a century, and provided a wealth of knowledge about individual cellular components and their functions. Nowadays, it is increasingly clear that a discrete biological function can only rarely be attributed to an individual molecule.

39. Introduction (cont.) Most biological characteristics arise from complex interactions between the cell’s numerous constituents, such as proteins, DNA, RNA and more. Therefore, the main focus in the 21 st century is to understand the structure and the dynamics of the complex intercellular web of interactions that contribute to the structure and function of a living cell.

40. Introduction (cont.) In order to do so, scientists developed new technology platforms, such as protein chips or semi-automated Yeast-Two Hybrid Screens, that help determine how and when these molecules interact with each other. Protein chips P-type ATPases interact with TcPKAr Yeast-Two Hybrid Screen

41. Introduction (cont.) Contemporary biology’ s major challenge is to map out, undestand and model in quantifiable terms the topological and dynamic properties of the various networks that control the behavior of the cell.

42. Basic network nomenclature The behaviour of most complex systems, such as the cell, emerges from the activity of many components that interact with each other through pairwise interactions. At a highly abstract level, we can consider the components as series of nodes that are connected to each other by links (edges). The nodes and links together form a network (or a graph).

43. An example: A graph theoretic description for a simple pathway (catalysed by Mg2+-dependant Enzymes *As we can see, the graph is directed In the most abstract approach, all interacting metabolites are considered equally *As we can see, the graph is undirected

44. the degree distribution, P(k) of the metabolic network illustrates its scale-free topology *As we can see, the greater the degree, the smaller the distribution The scaling of the clustering coefficient C(k) with the degree k illustrates the hierarchical architecture of metabolism *As we can see, the greater the degree, the smaller C(k)

45. Yeast protein interaction network. A map of protein–protein interactions, which is based on early yeast two-hybrid measurements, illustrates that a few highly connected nodes (which are also known as hubs) hold the network together. The largest cluster, which contains ~78% of all proteins, is shown. The colour of a node indicates the phenotypic effect of removing the corresponding protein (red = lethal, green = non-lethal, orange = slow growth, yellow = unknown)

46. Architectural features of cellular networks

47. Networks that are characterized by a power law degree distribution are highly non-uniform, most of the nodes have only a few links. A few nodes with a very large number of links, which are often called hubs, hold these nodes together. Networks with a power degree distribution are called scale-free. It indicates the absence of a typical node in the network (one that could be used to characterize the rest of the nodes). However, scale-free networks could easily be called scale-rich as well, as their main feature is the coexistence of nodes of widely different degrees (scales), from nodes with one or two links to major hubs.

48. Cellular networks are scale-free An important development in understanding of the cellular network architecture was the finding that most networks within the cell approximate a scale-free topology. The first evidence came from the analysis of metabolism, in which the nodes are metabolites and the links represent enzyme-catalysed biochemical reactions. As many of the reactions are irreversible, metabolic networks are directed. So, for each metabolite an ‘in’ and an ‘out’ degree can be assigned that denotes the number of reactions that produce or consume it, respectively.

49. An example of a random network where all the nodes are connected. The network is undirected The distribution of the degrees of the nodes according to Poisson distribution The clustering coefficient is independent of the node’s degree in a random network

50. An example of a scale- free network where all the nodes are connected. The network is undirected The distribution of the degrees of the nodes according to the power-law The clustering coefficient is independent of the node’s degree in this example

51. Evolutionary origin of scale-free networks most networks are the result of a growth process, during which new nodes join the system over an extended time period. A good example for it is the World Wide Web, which has grown from 1 to more than 3-billion web pages over a 10-year period. Second, nodes prefer to connect to nodes that already have many links, a process that is known as preferential attachment.

52. If a node has many links, new nodes will tend to connect to it with a higher probability. This node will therefore gain new links at a higher rate than its less connected peers and will turn into a hub. Growth and preferential attachment have a common origin in protein networks that is probably rooted in gene duplication. Duplicated genes produce identical proteins that interact with the same protein partners. Therefore, each protein that is in contact with a duplicated protein gains an extra link.

53. Highly connected proteins have a natural advantage. they are more likely to have a link to a duplicated protein than their weakly connected cousins, and therefore they are more likely to gain new links if a randomly selected protein is duplicated. This represents a preferential attachment. Although the role of gene duplication has been shown only for protein interaction networks, it probably explains, with appropriate adjustments, the emergence of the scale-free features in the regulatory and metabolic networks as well.

54. The red node is more likely to connect to node 1 than to node 2 The red circle is a duplicated gene. As we can see, the duplicated gene gains the same connections as his origin.

55. Network Motifs Connectivity patterns (sub-graphs) from which the network is composed. This idea was first presented by Uri Alon and his group, when discovered in the gene transcription network of the bacteria E.Coli. Later on were discovered in other natural networks.

56. Sub-Graphs all of the 209 bi-fan motifs (a motif with 4 nodes) that are found in the Escherichia coli transcription-regulatory network motifs that share links with other motifs are shown in blue. otherwise they are red.

57. Modularity of Cellular Networks Signatures of hierarchical modularity are present in all cellular networks that have been investigated so far.

58. An example of a hierarchical network where all the nodes are connected. The network is undirected The distribution of the degrees of the nodes according to the power-law The clustering coefficient is according to C(k)~1/k

59. Robustness of The Network Random Networks are not generally robust. Scale-free networks are robust against accidental failures: even if 80% of randomly selected nodes fail, the remaining 20% still form a compact cluster with a path connecting any two nodes. But what happens if a hub is removed?

60. Future Directions Developing new theoretical methods to characterize the network topology Enhancing our data collection abilities in order to move beyond our present level of knowledge. Integrated study of all the interactions could offer further insights

61. Conclusions The cell can be approached from the bottom up, moving from molecules to motifs and modules, or from the top to the bottom, starting from the network’s scale-free and hierarchical nature and moving to the organism-specific modules and molecules. In either case, it must be acknowledged that structure, topology, network usage, robustness and function are deeply interlinked, so we must study them and observe them as a whole.

62. What is it good for?