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Reverse Engineering of Regulatory Networks

Reverse Engineering of Regulatory Networks. Ka-Lok Ng Department of Bioinformatics Asia University. Contents. Introduction – Gene regulatory network (GRN) The steady-state approach - time-series model. Introduction – Gene regulatory network (GRN).

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Reverse Engineering of Regulatory Networks

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  1. Reverse Engineering of Regulatory Networks Ka-Lok Ng Department of Bioinformatics Asia University

  2. Contents • Introduction – Gene regulatory network (GRN) • The steady-state approach - time-series model

  3. Introduction – Gene regulatory network (GRN) • One gene can affect the expression of another gene by binding of the gene product of one gene to the promoter region of another gene • > 2 genes, we refer to the regulatory network as the regulatory interactions between the genes • Given a larger number measurements of the expression levels of a number of genes, we should be able to model or reverse engineer the regulatory network that controls their expression level. • 2 approaches  the time-series and steady-state approaches

  4. The time-series approach • Expression level of a gene at a certain time point xj(t) can be modeled as some function of the expression levels of all other genes at all previoustime points xi(t-1), where i may or may not equal j, if i = j that means self regulate where ri,jis a weight factor representing how gene i affects gene j, that is,ij (activate) or i -| j (inhibit), positively or negatively Problem ! Many more genes > number of time points Suppose there are g genes  g2 possible connections among them (for instance, g = 4  16 possibilities, including self-regulation) There are g(g+1)/2 possible interactions It is called one has a dimensionality problem A possible solution is cluster the genes that have similar behavior into gene clusters

  5. The time-series approach Example At t =0, gene c is induced, at t = 1,2,3,4 we follow the expression level of gene c and three other genes, a, b and d Deduce a GRN from the time-series data (5 time points). c d b a gene c ↑, but genes (a, b, d) ↓ gene c represses gene a ? -1, 0, 1  inhibit, no interaction, activate

  6. The time-series approach 4 genes  16 possible regulation relations Consider the system of equation governing the regulation of gene a 4 equations, 4 unknowns ri,j = -1, 0, 1  inhibit, no interaction, activate

  7. The time-series approach

  8. The time-series approach

  9. The time-series approach Interaction matrix between four genes

  10. Reference • Knudsen S. (2002). A biologist’s guide to analysis of data microarray data. J. Wiley

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