GENIE – GEne Network Inference with Ensemble of trees Van Anh Huynh-Thu Department of Electrical Engineering and Computer Science, Systems and Modeling, University of Liege, Belgium

Inference of GRNs  Gene regulatory networks (GRNs) are behind the scene players in gene expression  How do we determine the regulators of each gene?  Input: Gene expression data in different conditions/time points A subset of the genes that contains all the regulators (without GENIE accuracy plummets)

Underlying Model  Every reverse engineering tool assumes an underlying model  GENIE assume that the GRN is a Boolean network  Therefore, the regulation of each gene is a Boolean function

GENIE Strategy Outline  Not to make strong assumptions about the possible regulatory interactions (e.g. a strong assumption is linearity)  Treat time-series as static experiments  Solve the problem for each gene separately, and combine the results  The final output is a ranking of potential interactions in descending confidence

GENIE workflow

Tree-based Ensemble Methods  A regulation function is a binary tree – at each node a binary test according to a different regulator is performed  The prediction is at the leaf  For each gene, randomly select a set of samples and produce a tree from each one (the root is the single gene that splits K random conditions of the target best, and so on)  Rank the regulators according to their importance in the trees

Ranking of regulators #S is the number of samples that reach the node N #S t (S f ) is the number of samples with output true (false) Var() is the variance of the output In order to avoid bias towards highly variable genes, the expression values are first normalized to unit variance

Best performer in DREAM5 network inference

The Genetic Landscape of the Cell Charles Boone University of Toronto, Donnelly Center

Synthetic Genetic Arrays No growth Single mutant strand (query gene) is crossed with all other single mutants Double mutants are selected Currently done for budding yeast, e.coli and s.pombe

Genetic Interactions  Positive interaction: The double knockout is more viable than would be expected by the separate contributions of the single knockouts  Negative interaction: The double knockout is less viable than would be expected by the separate contributions of the single knockouts  They crossed ~1700 yeast single mutants with ~3,800 single mutants, and after filtering failures they got ~5.4 million double mutants

Yeast Interaction Map Edges are interactions that pass cutoff threshold (170,000) Proximity in the layout is according to similarity in interaction profiles Colored sets = GO enrichment

Proximity between clusters and related functions Proximate clusters Both require cytoskeleton genes

Zoom in on pathway Red – Negative Green - Positive Budding Required for polarization and growth Cell division Interactions between pathways and complexes were often monochromatic Translation

Positive vs. negative interactions Negative interactions are ~two times more prominent than positive No interaction

Degree distribution Severe fitness defects in single mutants correlate with degree Hubs are less numerous

Gene duplicates interact less

Correlation between degree and gene properties Black - PPI # morphological phenotypes # chemical perturbations unstable structure

Genetic interactions between cellular processes Cell cycle is more buffered?

Hubs in the chemical interaction networks match hubs in GI network DNA repair Hydroxyurea blocks DNA synthesis Erodoxin (new) similar to protein Folding-related gene Single mutant + chemical = chemical interaction

Discovering Master Regulators of Alcohol Addiction William Shin Center for Computational Biology and Bioinformatics Columbia University

Rat Model of Alcohol Addiction Control Alcohol Self Administration Alcohol Vapor Treatment (Chronic alcohol addiction) Control Non Dependent No Alcohol Vapor

Rat model of alcohol addiction Alcohol self- administration (lever pressing) Alcohol Intake during early withdrawal Dependent (exposed to alcohol vapor) Non-dependent (exposed to air) Baseline Alcohol responding (0.5 hr) * Induction of alcohol- dependence

Identification of TF-target interactions  Rat Brain regions were sliced and used as microarray samples 92 samples from Dependent, Non-Dependent, Control Rats across 8 regions that are known as sites-of-action for of addictive drugs.  Applied ARACNE to this data Information-theory based (MI) Tests triplets of genes for indirect interactions  130,000 TF-target interactions in total

Screening of false positives Targets of TF 1 TF 1 TF 2 TF 1 shadows TF 2 : TF 2 appears enriched only because it shares common targets with TF 1 Targets of TF 2

Masters regulators in the Accumbens shell

Activity profile at different brain regions

siRNA validation has 50-75% success rate NOT ALL TARGETS WERE TESTED YET