Introduction to SystemsIntroduction to Systems Biologygy

Jessica Marjess@jimmy.harvard.edu

Jessica Marj j y

Department of Systems & Computational BiologyAlb t Ei t i C ll f M di iAlbert Einstein College of Medicine

Winter School in Mathematical & Computational Biology, 6 July 2011

Wh t i S t Bi l ?What is Systems Biology?“An attempt to explain overall systems responses (phenotypes) by detailing dynamic changes in the full spectrum of informational molecules (DNA, RNA, proteins, metabolites) and

their relationships (assemblies into complex molecular machines or 

biological networks), … [and] … integrating these dynamics 

i i i d l ”into interactive models”.

L H d & ISB TLeroy Hood & ISB Team (2008)

Bi l i C l S tBiology is a Complex System

Systems biology y gygrew out of a 

recognition that bi l i lbiology is a lot more complicated than we originally thoughtoriginally thought. 

Gene x geneGene x environment

I t ti lti l i l etcInterpreting multi‐layer signals.

The goal of systems biology is to make sense of this complexity with a view to produce predictive statements and hypotheses that can be experimentally validatedthat can be experimentally validated.

Some Fundamental Concepts in pSystems Biology

“To understand biology at the system level, we must examine the structure and dynamics of cellular and organismal functionstructure and dynamics of cellular and organismal function, rather than the characteristics of isolated parts of a cell or organism ”organism.

Kitano (2002). Science. “Systems biology: A Brief Overview”.

What elements make up a biological network?What elements make up a biological network? How does the network change over time, and 

d ff d ?over different conditions?

Some Fundamental Concepts in pSystems Biology

“Properties of systems, such as robustness, emerge as central issues, and understanding these properties may have an impactissues, and understanding these properties may have an impact on the future of medicine.”

Kitano (2002)Kitano (2002).

How does the system respond to perturbation?d h b ff hHow does the system buffer stochasticity in 

signals?gHow does the system transduce signals to drive a 

multiplicity of outcomes?multiplicity of outcomes?

Some Fundamental Concepts in pSystems Biology

“…, many breakthroughs in experimental devices, advanced software, and analytical methods are required before thesoftware, and analytical methods are required before the achievements of systems biology can live up to their much‐touted potential”. p

Kitano (2002).

How well do our models/predictions correspond to /p pwhat we observe in biology?

What can our models/predictions tell us aboutWhat can our models/predictions tell us about biology?

Breast Cancer: a disease‐related exampleBreast Cancer: a disease‐related exampleOncotype DX, MammaPrint.

Mammogram –detection: yes/no?  Histopathology & Grade:

classification based on 

Staging – TNM

histological features.

S b ll l l l

Staging  TNM classification; 0 to 4

Tissues Cells Sub‐cellular molecules (genes, proteins)

T = size of tumorN = spread to lymph nodes?M = metastasized?

Genome‐wide Microarrays & Sequencing

A good workflow for doing systems g g ybiology research

Kitano, Science (2002): vol. 295 no. 5560. 

A good place to start in bi l i ksystems biology is networks…

N t k t k l dNetworks capture knowledge

and represent a map of how individual p pcomponents relate to each other.

Wh t k i t tWhy networks are so important

• Biological processes are driven by interactions between genes and other moleculesgenes and other molecules. 

• We want to understand causal relationships in biological t h iblsystems, wherever possible.

• Correlations in gene expression can be considered to be the result of network interactions.

• Disease is considered a breakdown in key parts of theDisease is considered a breakdown in key parts of the network. To understand disease, we must first be able to understand networks in the normal state.understand networks in the normal state. 

John Quackenbush

Networks reflect known biological ginteractions.

yhw

aye Pa

thinase

AP Ki

MA

Source: GeneGO

P th t i t kPathway‐centric networksMAP Kinase PathwayMAP Kinase Pathway

http://www.genome.jp/kegg/

k d lNetworks as modelsUsing mathematical tools we want to quantify these kinds ofUsing mathematical tools, we want to quantify these kinds of relationships, and infer new ones.

gene A gene Bregulates

regulatory interactions(protein‐DNA)

bi dgene A gene B

binds functional complexB is a substrate of A(protein‐protein)

reaction product

gene A gene Bproduct

is a substrate for

metabolic pathways

John Quackenbush

Wh t’ th ll l?What’s the overall goal?Using genomic tools and methods, we are trying to better describe how each of the following pieces fit together:

G R l t N t kGene Regulatory NetworksWe can use networks as models to infer interactions andWe can use networks as models to infer interactions and relationships that improve our understanding of the biological system under studysystem under study.

This is complicated, because from DNA to protein there are man stepsprotein there are many steps.

Replication

DNA

Replication(+repair)

hnRNATranscription

mRNA

Splicing

mRNA

Translation

Polypeptide M L I V G

Folding, modification

Folded, mature

g

mature Protein

John Quackenbush

Multi‐Step Regulation p gof Gene Expression 

Degradednucleus cytosol Degraded mRNA

y

mRNA degradation control

DNA Primary RNA transcript mRNA mRNA

control

transcript

ProteinTranscription RNA RNA

mRNA translation control

control processingcontrol

transportcontrol

control

protein activity control

Active ProteinProtein

degradation control

Degraded Protein

control

John Quackenbush

Cells are dynamic systemsCells are dynamic systems

untreated treated (sensitive)

resistant(sensitive)

Network comparisons give insight into p g gbiological response.

Luscombe et al. (2004) compared gene regulatory networks for yeast exposed to different conditions.yeast exposed to different conditions.

What have we learnt from comparing p gdifferent networks?

• Yeast cells reroute the signals through their gene l k diff l d di hregulatory networks differently, depending on the 

condition they find themselves in (e.g. d )endogenous versus exogenous).

• In this rewiring we see changes in topology andIn this rewiring, we see changes in topology and also in activation – these reflect different 

lregulatory responses.

f h l• Certain motif structures characterize a particular condition’s response.

Contrasting different cell types reveals g ypdifferent expression programs

A li i i f 200 hi hl i li d ll

Neural CellsNeural Cells

A mammalian organism consists of over 200 highly‐specialized cell types. 

Neural Cells Cardiac MuscleNeural Cells Cardiac Muscle

Fertilized Egg Pluripotent Stem Cells

Fertilized Egg Pluripotent Stem Cells

ggStem Cells

ggStem Cells

Most cell types share the same genome.

Epigenetic modification and transcription factor networks generate the h i f ll t ifi di it

yp g

mechanism for cell type‐specific diversity. 

A cell type's unique program is manifested by its transcriptional profile.

Deciphering the rewiring rules of transcriptional regulatory networks for cell fate transitions.

Deconstructing a Cell's Gene Expression g pProgram

Isolating the active biological pathways that are specific to a cell type allows us to begin to model the transcriptional landscape of cellular states.

Linking gene signatures to cell lines is a start. 

pluripotent stem cells

progenitor cells

fibroblasts

Adapted from Sui Huang, Bioessays 31:546, 2009

Our goal is to go further, and (eventually) model cell fate transitions.

Embryonic stem cells transition between different statesEmbryonic stem cells transition between different states to become more differentiated and less pluripotent.

embryonic stem cell trajectorymost

pluripotent

least pluripotent

induced pluripotent stem

?induced pluripotent stem

cell trajectory

Cells alter their transcriptional programs to transition to different states.

• Stuart Kauffman presented the idea of a gene expression landscape with attractors.

• ~250 stable cell types each represent attractors.

• Cells can be "pushed" or induced to converge to an attractor. 

• Once in the attractor, a cell is robust to small perturbations.

Huang and Ingber (2008) Breast Disease.

Constructing a Trajectory in Gene g j yExpression Space

T time pointsGene expression matrix of time series data⎥

⎥⎤

⎢⎢⎡ Txx

LLLL

LL 111

nes

T time points

series data.

⎥⎥⎥

⎦⎢⎢⎢

⎣− TN

xxx

LL

LLL

LLLL

)1(

Ngen

E.g. trajectory for cell cycle⎦⎣ NTN xx LL1

T

.g. trajectory for cell cycle

Principal 

T time

ctor 2

Components Analysis

e poin envec

Analysis ts

Eige

First 2 Eigenvectors Eigenvector 1

Differentiation of Human Promyelocytesto Neutrophils

AffymetrixTime 0

Promyeloctyes

AffymetrixGeneChipRA used in 

differentiation 

(HL‐60 Cell Line) therapy for acute promyelocyticleukemia.

Dimethyl All-Trans ~6 days

leukemia.

Sulfoxide(DMSO)

Retinoic Acid (ATRA)

N t hil

Combined with chemotherapy, 

l t i iNeutrophil‐like Cells

complete remission rates as high as 90‐95% can be 

Day 7Collins et al. (1978) PNAS 

achieved.

Trajectories Converge to an AttractorTrajectories Converge to an AttractorTwo different perturbations ‐ DMSO and ATRA ‐ to induceTwo different perturbations  DMSO and ATRA  to induce differentiation of HL‐60 promyelocytes into neutrophil‐like cells.

Affymetrix Hgu95av2 GeneChips to measure expression changes at 12 time points over 7 day period. DMSO, ATRAConstructed expression trajectories:

Initial divergence followed by

SO,

Initial divergence followed by convergence demonstrates the existence of an attractor.e ste ce o a att acto

Because of this divergence, Huang discounts the presence of adiscounts the presence of a "specific, unique differentiation pathway".

Huang et al. (2005) Physics Reviews Letters

p y

What genes are driving the cell fate transition?Transient Pathway (Perturbation 2)

State A(Perturbation 2)

Core Differentiation Pathway

State BPathway

Transient Pathway (Perturbation 1)( )

Observed Trajectory (Perturbation 1)

State A(Perturbation 1)

State B

Ob d T j tObserved Trajectory (Perturbation 2)

Mar & Quackenbush (2009) PLoS Comp Bio.

C d T i t GCore and Transient GenesCore Genes

• Integral to the differentiation 

ssion

Perturbation 1process.

• Profiles are well preserved  xpres

pacross perturbations. 

Time

Ex Perturbation 2

Transient Genes

• Directly induced by the

Time

n• Directly induced by the perturbation.

• DMSO specific ATRA specific ession Perturbation 2

• DMSO‐specific, ATRA‐specific. 

• Poorly correlated profiles b i

Expr

Perturbation 1across perturbations.

Time

Cl if i C d T i t GF h f h 3841

Classifying Core and Transient GenesFor each of the 3841 genes:

Fit both modelsUse likelihood ratio test to determine which is more appropriate

}:)|(sup{)( 0Ω∈=Λ

θθ xLx ),,,,,,,( 32103210 ββββαααα=Ωwhere

Use likelihood ratio test to determine which is more appropriate. 

}:)|(sup{)(

Ω∈Λ

θθ xLx where

Benjamini‐Hochberg correction for multiple testing on 3841 P‐values. 

Classification Rule: Genes with adjusted P‐values < 0.1 are in the transient group.

Core Gene Model1428 genes

DMSO, ATRA

sion

 Log

 atio

Expres Ra

Time

T i t GTransient Gene Model

1462 genes

DMSO ATRA

on Log

 o

DMSO, ATRA

Expressio

Ratio

Time

E

Wh t H W L t?What Have We Learnt?Transition from one state to another is driven by twoTransition from one state to another is driven by two classes of genes: Core genes whose sustained 

h d

l

expression carry the system down developmental pathways.

Promyelocytes

Neutrophilsp

Transient genes that fire in response to a stimulus and whoseTransient genes that fire in response to a stimulus, and whose expression decays over time. Th i t t l i ki ki th t i t thThese are instrumental in kicking the system into the transition. 

More generally we can think about other transitionsMore generally, we can think about other transitions between states. 

PromyelocytesCell Type 1

NeutrophilsCell Type 2

Or other types of transitions between cell fates:Or other types of transitions between cell fates: 

+ TFA

MCF‐7

A

TFDifferentiationMutant

+ TFA

+ TFB

TF Proliferation+ TFB

RIKEN Omics Science Centre, JapanDr Mariko Okada

In the presence of diseaseIn the presence of disease:

Disease PopulationCell Type 1

p

Cell Type 2Cell Type 2

Control Population

Within any one population of individuals we can thinkWithin any one population of individuals, we can think of individuals each having their unique trajectory.

Variability within a 

State 1

ypopulation.

St t 2State 2

IndividualIndividual 

A D i P hAverage Dominant Path

In biology variation is an inherent propertyPhysical variants 

In biology, variation is an inherent property

Confers robustness

protein

y(variation in receptors, post‐translational 

Confers robustness and underlies 

phenotypic diversity pmodifications, etc). 

G i

phenotypic diversity.

RNAGene expressionmicroRNA

Structural variants:Deletions,

Isoforms

DNA

Deletions, Duplications & Inversions.

SNPs & Haplotypes

DNAPromoter complexity – Transcriptional Start Sites, 

Enhancers InsulatorsEnhancers, Insulators.

Epigenetics

Cell populations are inherently p p yheterogeneous

Variance in expression can be a surrogate measure which fl h hreflects this heterogeneity.

St h ti it i G E iStochasticity in Gene ExpressionThe fate and production of a single mRNA transcript involvesThe fate and production of a single mRNA transcript involves a multi‐step process.

Transcription factors and other small molecules bind upstream of the gene to activate transcription.

Transcription factors are usually present at low concentrations inside the cellinside the cell.

Binding is probabilistic, giving transcription a stochastic 

Kaern et al. Nature Reviews Genetics 2005. component.

Single cell assays demonstrated no two g ycells are ever identical

Before single cell In situ hybridization in Single‐cell gene Before single cell assays were invented:

In situ hybridization in 1989 gave snapshots of individual nuclei. 

g gexpression profiling in 

2001. 

Genes are eitherCells express genes heterogeneouslyCells were thought Genes are either 

“on” or “off”.heterogeneously 

around a distribution of levels. 

Cells were thought to be identical.

Implications for model‐building in p gsystems biology

• Advances in technology have shown that biological h d hsystems are heterogeneous and contain stochastic 

components.  

• To be truly informative and predictive, our models must therefore evolve to be able to capture this stochasticitytherefore evolve to be able to capture this stochasticity. 

• Growing evidence suggests that disease may g gg ycorresponds to a breakdown in the plasticity of regulatory networks.regulatory networks. 

Cross‐species comparisons: different p pimplementations of the same system.

The same gene circuitry may manifest with variations in its components in different species. 

What evolutionary advantages might this confer?might this confer? What are the consequences for design principles orfor design principles or regulatory events?

Where Is Systems Biology Headed?Where Is Systems Biology Headed?

A k l d tAcknowledgements

John Quackenbush Christine Wells

TrajectoriesSui HuangChristine Wells

Sean GrimmondM k R

Sui HuangDonald IngberMariko OkadaMark Ragan

Tim Littlejohn

Mariko OkadaAlistair Forrest

Alvis Brazma

ARC Centre of Excellence in Bioinformatics

Institute for Molecular Bioscience UQInstitute for Molecular Bioscience, UQ