BIOLOGICAL REGULATION NETWORKS

1. BIOLOGICAL REGULATION NETWORKS

2. Life’s complexity pyramid: a system view of life

3. Networks • A “network” is defined by a set of distinct elements & interactions between these elements. • “Graph”: a more formal mathematical language for “network” • Networks are everywhere: - Electronic circuits - World wide web

4. Models of network that have direct impact on our understandingof biological networks: • Random networks • Scale-free networks • Hierarchical networks

5. Biological networks • The representative of multiple interactions within a cell. • In biological networks: - Nodes = genes, DNA, RNA, proteins, enzymatic substrates, metabolites, or modular structures. - Edges = molecular interactions/regulatory interactions/ bio-chemical reactions connecting the nodes, forming a functional network that governs cellular processing.

6. Interactions within biological networks • Three general classes: • Molecular/physical interactions • Regulatory/functional interactions • Genetic interactions

7. Modules • It is more practical to study smaller portions of the network that can be considered autonomous. • Such a subnetwork/subgraph unit is referred as “module”. • Module: a group of functionally or physicallylinked nodes that work together to achieve a (relatively) distinct function.

8. Network motifs • The complexity of a network can be further reduced by studying its “motifs”. • Network motifs: - Describe how single nodes connect with their neighbors - Building blocks of the networks: Transcriptional regulatory network motifs

9. Some thoughts related to the scale-free network structure • Most nodes having a small number of connections. • Only a few steps are required to join any two nodes. • Relative paucity of hubs that connect directly to one another.

10. Transcriptional regulatory networks & approachesaimed at elucidating these networks

11. Common features of transcriptional regulatory networks • Transcriptional regulatory networks control the dynamically orchestrate gene expression in the genome. • Central to transcriptional regulatory networks: - TFs activated in response to input signals. - DNA regulatory sequences, which TFs recognize and associate with. - TFs interact with to each other.

12. The main goals • 1. The identity & expression level of interacting nodes • 2. How interactions change with time/ environmentalconditions • 3. The phenotypic impacts of disrupting key nodes

13. Current approaches to elucidate transcriptional regulatory networks • Knowledge-based & computational approaches Ex: Intragenomic & comparative genomic sequence analysis. • Experimental approaches Ex: Protein-DNA interactions, DNA microarrays, gene knock-out/knock-down.

14. Computational DNA sequence-based approaches • The ones rely on the prior knowledge of TF-binding site preference • De novo motif finding algorithm

15. Some experimental tools to detect protein-DNA interactions • ChIP-chip: To identify direct target genes for a TF under a given set of conditions. • DamID: DNA adenine-methyltransferase (dam) identification;mainly used to identify DNA targets of general chromatin-binding proteins. • PBM (Protein binding microarray): A chip-based method for detection of protein-DNA binding.

16. Transcriptional regulatory networks (TRN) in bacteria

17. Structural organization of transcriptional regulation networks • Four levels of the bacterial networks: (1) A collection of TFs, their DNA binding sites of the target genes. (2) These basic units are organized into network motifs. (3) The motifs cluster into semi-independent transcriptional units. (4) The regulatory network consists of interconnecting interactions among the modules.

20. Connectivity between the external & internal sensing machinery • Exogenous TFs: activated by phosphorylation via a two-component system or by binding metabolites that are transported into the cell. • Endogenous TFs: activated by binding metabolites that are synthesized within the cell. • Hybrid TFs: binding metabolites that may be imported if external available, or intracellularly synthesized

21. Network motifs found in E. coli TRN • Feedforward loop (FFL): A TF X regulates a second TF Y, and both co-regulate one or more operons. • SIM (single input motif): A single TF X, regulates a set of operons Z1, …..Zn X is usually autoregulatory. • DOR (dense overlapping regulon) motif: A set of operons Z1, …..Zn are each regulated by a combination of a set of input TFs, X1, …..Xn.

22. Network motifs do not generally represent independent units: • They are functionally separable from the rest of the network. • Theoretical & experimental work have shown them to possess specific kinetic properties that determine the temporal program of target gene expression.

23. Transcriptional regulatory networks in yeast

28. Discovery of gene modules and regulatory networks • Integration of expression & regulator location data & results in a more accurate assignment of genes to regulators, when compared to wither type of data sets on their own. • Develop an algorithm that combines expression & location data to discover gene modules & regulatory networks. • GRAM (Genetic Regulatory Modules) algorithm

29. Evolution of the transcriptional regulatory networks

33. 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. • TFs are less conserved than target genes: - Suggesting that regulation of genes evolves faster than the genes themselves.

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35. Random networks • A fixed number (N) of nodes are connected randomly to each other with probability P • Approximately PN(N-1)/2 randomly placed links (connectivity or degree k or scale) • The node degree follow a Poisson distribution (i.e. the most nodes have approximately the same number of links, which close to the average degree <k>) • Nodes having significantly more or less links than <k> are absent or very rare

36. Scale-free networks • Scale-free networks are characterized by a power-law degree distribution • The probability that a node has exactly k links follows P(k) ~ k –γ (γ is the degree exponent & commonly 2 < γ < 3) • Scale-free networks 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”

37. Hierarchical networks • • The starting point is a small cluster (or module) • of 4 density linked nodes • • Next, three replicates of this module are • generated & the 3 external nodes (from the • replicated clusters) are connected to the central • node of the old cluster • (i.e. producing a16-node module) • • Repeat the process, producing a 64-node • module and so on • • This hierarchical model integrates a scale-free • topology with an inherent modular structure by • generating a network with a power-law degree • distribution