Systems Biology

10.11.2010

Systems biology

Systems biology is a biology-based inter-

disciplinary study field that focuses on the systematic study of complex interactions in biological

systems, thus using a new perspective (integrationinstead of reduction) to study them.

Systems biology

Systems biology is a biology-based inter-

disciplinary study field that focuses on the systematic study of complex interactions in biological

systems, thus using a new perspective (integrationinstead of reduction) to study them.

integration of complex interactions in a systematic way

Look from one to many

Slide from A. diCara

Make use of rich public data● Thousands of expression datasets available

– GEO & ArrayExpress● Protein-protein interactions

– HPRD, Intact● Curated pathway information

– KEGG, Reactome● Annotations

– Gene Ontology, literature● Regulatory knowlede

– miRNA targets

– Transcription factor targets

Merge -omics

● Each dataset can be considered as a layer

● Combinations of different layers brings us to new knowledge

Three main approaches

slide from P.Kahlem

Real world zoom-in-out

● One gene with one function and few interactions

● Find more genes having similar properties● Connect the genes based on their

functions● Model the network in order to understand

the mechanisms

Back in 2004 Chomsky predicted..

Real problems

We have more data than we can handle with traditional methods. Data has exploded

at all (regulatory) levels

Functions are distributed – cancer is usually not caused by one faulty gene

It is all about the loops

Motivation

● Network modelling helps to understand the whole picture

● Steady states represent for example differentiation stages

● Modelling as checking network reconstruction

● SQUAD dynamically models regulatory networks

Studing regulatory networks

● Modelling the behavior– ODEs describe reaction kinetics

● very demanding on the input data

– Boolean ● basic on/off data, discrete time steps

– Stochastic● time delayed reactions

Slide from I.Xenarios

Biology is rich of feedback loops

Feedforward loop

Slide from A. diCara

Are my experiment plans supported by data?

human embryonic stem cells – a story of a biological question

starting point

starting point

oct4

RNAi

starting point

oct4

RNAi

oct4Knock-down experiments

● RNAi

● detection score● p-value● ratio

Target genes

Oct4

Not all targets are directRatio does not have to be large

?

TF-DNA binding

● Array design● Replicates

Replicates may go wrong Promoters are only part of the picture

● Each program gives different results

Peak finding

● Each program gives different results● Intersection of targets are most reliable● Small number of targets is not bad

Peak finding

Assumption: Oct4 is binding to Oct4 motifs

Motifs

Binding does not mean a motif and vice versa

information layers

Oct4 targets from RNAi

Oct4 direct targets from ChIP-chip

Oct4 indirect targets from ChIP-chip

Previously published data:Oct4 ChIP-chip & RNAi

Sox2 & Nanog RNAiSox2 & Nanog ChIP-chip

DatabaseClustering genes by behaviour

Network

Research cycles

Biological experiments

Raw data analysis

Combination of datasets

Network building

Network modelling

Identification of new candidates

Embryonic Stem Cells

ESC networks

Boyer et al, 2005

van den Berg et al, 2010

Jaenish, Young 2008

Kim et al 2008

Aim of the project

● Identify genes playing a role in keeping cells in pluripotency and driving early differentiation using Boolean modelling

Our research

Network reconstruction

Literature based network

Network reconstruction

Pertubation experiments in hES and hEC cells

− Oct4, Sox2, Nanog − Gadd45g, Bmp4, Fgf2, ActA

Oct4

Oct4

RNAi Targets on microarrayTargets on microarray

Filtering the network

● Target first receptors and ligands (GO)– cell surface receptor linked signal transduction

– receptor binding

● Target genes having at least 5 incoming regulatory edges

– genes under strong regulation in ESC

AfterBefore

Network reduction

Final network

di Cara et al. BMC Bioinformatics 2007

Basics of Boolean modelling

● Each network node can be either ON or OFF

● Synchronous & asynchronous modelling -– nodes change their state one-by-one or

simultaneously

Steady states

● Network states where some genes are active, some inactive and they represent relevant biological conditions– pluripotency (OCT4, SOX2, NANOG active)– differentiation (GADD45G, BMP4 active)

Perturbations

● A node is activated on inhibited – overexpression– knockdown

● Signal is carried forward and affects the rest of the network

● A way to measure the effect of a node

Expanding and perturbing the network

Introduce a feedback loopPerturbe the nodeMeasure the effect on the network

Summary

● SysBio tries to find the behavior of the system

● Deals with interactions, not the properties of the parts

● We don't know how to build perfect models

Systems biology project

● Human data

– Given:

● Protein-protein interaction● Co-expression

– Find:

● Additional data layer (e.g. ChIP)

● Task

– Identify main hubs (5 at least, their function)

– Find largest components (after ribosome, proteasome), what is their biological function? Why are they connected?

– Find as many network motifs as possible from the data

– Write a short overview and be prepared to represent it