DISCOVERING REGULATORY AND SIGNALLING CIRCUITS IN MOLECULAR INTERACTION NETWORK

Ideker Bioinformatics 2002

Presented by: Omrit Zemach April 3 2013

Seminar in Algorithmic Challenges in

Analyzing Big Data* in Biology and Medicine-TAU

OUTLINE Introduction- biological terms Motivation Methods

Basic z-score calculation simulated annealing

Results Discussion

PROTEIN-PROTEIN INTERACTION

All living organisms consist of living cells All those cells, comprise the same building

blocks: RNA ,DNA and PROTEIN Protein sequences are encoded in DNA Proteins play major roles in all cellular

processes

DNA REPLICATION

TRANSCIPTION INTO mRNA

TRANSLATION OF mRNA

PROTEIN-DNA INTERACTIONS  protein binds a molecule of DNA Regulate the biological function of DNA, usually the expression of a gene.  Transcription factors that activate or repress

gene expression

GENE EXPRESSION

Gene is a sequence of the DNA . The gene decodes to a protein. the process by which information from a

gene is used in the synthesis of a functional

 protein is called gene expression It is interesting to test gene expression on

multiple conditions (experiments). Differential-express

DNA chips/ Microarrays-Simultaneous measurement of expression levels of all genes.

MOTIVATION

Databases of PROTEIN-PROTEIN & PROTEIN-DNA interactions

Widely available mRNA expression data

Generate concrete hypotheses for the underlying mechanisms governing the observed changes in gene expression

MOTIVATION Exposing the yeast galactose utilization

pathway to 20 perturbations Constructing a molecular interaction

network by screening a database of protein-protein and protein-DNA interactions

Select 362 interactions linking genes that were differentially-expressed under one or more perturbations .

Analyze changes in expression.

Conclusion: Pairs of genes linked in this network were

more likely to have correlated expression profiles than genes chosen at random

however,the general task of

Associating gene expression changes with higher order

groups of interaction was not discussed

DISCOVERING REGULATORY AND SIGNALING CIRCUITS IN MOLECULAR

INTERACTION NETWORKS Introducing method for searching the

networks to find ‘active sub-networks’ On multiple conditions , determine which

conditions significantly affect gene expression in each subnetwork.

METHODS

Z-SCORE CALCULATION Given each gene i a value pi

pi= The significance of differential expression of gene I

zi= Ф-1 (1- pi) ( z-score for gene i)

aggregate z-score for subnetwork A

Calibrating z against the background distribution

SCORING OVER MULTIPLE CONDITIONS

Extending the scoring system over multiple conditions .

Create a matrix of z-score . Rows- m conditionsColumns-genes Produce m different aggregate scores (one for each condition Sort them from highest to lowest. compute rA

max = max j (rA[j] )

Compute rA[j] for each j=1….m as follows: PZ = 1 – Ф( ZA[j] )

(the probability that any single condition has a z-score above ZA[j] )

b

(the probability that at least j of the m conditions had scores above ZA[j])

rA[j] = Ф-1 (1-pA[j) )

rAmax = max j (rA[j] )

compute rAmax

Z score of gene 1

Condition 1

Condition 2

Condition 3

Condition 4

Aggregate scores of zA1 ….. zAmc

Aggregate scores of zA1 ….. zAm sorted

Computing rA[1] … rA[m]

Taking max j (rA[j] )

Calibrating z against the background distribution

SIMULATED ANNEALING

strategy to find local maximumwe must sometimes select new points

that do not improve solutionAnnealing- Gradual cooling of liquidIncorporate a temperature parameter

into the maximization procedureAt high temperatures, explore parameter space At lower temperatures, restrict exploration

SIMULATED ANNEALING STRATEGYStart with some sample

Propose a change

Decide whether to accept change

SIMULATED ANNEALING STRATEGY Decide whether to accept change-

HOW?? Consider decreasing series of

temperatures For each temperature, iterate these

steps:Propose an update and evaluate function

Accept updates that improve solutionAccept some updates that don't

improve solution Acceptance probability depends on

“temperature” parameter

SEARCHING FOR HIGH SCORING SUBNETWORKS VIA SIMULATED ANNEALING

•Associate an active/inactive state with each node •GW = denote the working sub graph of G induced by the active nodes

THE ALGORITHM

HEURISTICS FOR IMPROVED ANNEALING Search for M subnetworks

simultaneously

Increasing the efficiency of annealing in networks with many ‘hubs’

High score node

Solution- changing step 3

Defining dmin at the beginning of the algorithm

If deg(node)> dmin

Remove all neighbors that are not in the top scoring component

Solution- changing step 3

RESULTS

RESULTSSmall network with a single perturbation

7.7 3.1

2.3

2.82.5

Z-scores

GAL4

TRANSCRIPTION FACTOR

Simulated annealing was preformed with parameters:

N=100,000 Tstart= 1 Tend= 0.01 M=5 dmin=100

Distribution of sub-network score in actual and randomized data

Large network with several perturbation

DISCUSSION

SUBNETWORKS ARE CONSISTS WITH KNOWN REGULATORY CIRCUITS

SUBNETWORKS VERSUS GENE EXPRESSION CLUSTERS

Our approach groups genes subject to the constraints of molecular interaction network

Subnetworks are scored over only a subset of conditions

Groups genes only by the significance of change, while clustering methods groups genes by both magnitude and direction of change

Our method leaves some genes unaffiliated with any subnetwork, unlike clustering which assign every gene to distinct cluster

FUTURE WORK Investigating the subnetworks we found in

the laboratory Accommodating new types of interaction

networks (protein and small molecules) Annotating each interaction with its

directionally compartments

QUESTIONS?

THANKS