Systems Biology: A Path to the Future Nat Goodman Institute for Systems Biology February 14, 2005.

Systems Biology:Systems Biology:

A Path to the FutureA Path to the Future

Nat GoodmanNat Goodman

Institute for Systems BiologyInstitute for Systems Biology

February 14, 2005February 14, 2005

Systems biology is hot!Systems biology is hot!

CBW Keynote Feb 14, 2005Nat Goodman

But what is it?But what is it?

System: collection of interacting parts

Everything is a system – even elementary Everything is a system – even elementary particles (if you believe string theory)!!particles (if you believe string theory)!!

Systems biology not Systems biology not study of biological study of biological systemssystems

That’s all of biology!That’s all of biology!Use of systems thinking to do biologyUse of systems thinking to do biology

Like Like molecularmolecular biology biologyMost people mean Most people mean systems molecularsystems molecular

biologybiology


Examples (1)Examples (1)

Process: Process: system whose interesting properties change system whose interesting properties change over timeover time



StructureStructure: system whose interesting properties change : system whose interesting properties change over spaceover space



Many systems have interesting properties that vary with time and space

Can often separate dimensions – study time &

space independently


Systems biology is…Systems biology is… Use of Use of systems thinkingsystems thinking to do biology to do biology

SystemSystem: collection of interacting parts: collection of interacting parts

Interesting systemInteresting system Whole greater than sum of partsWhole greater than sum of parts Properties of system nontrivial combination of properties of parts Properties of system nontrivial combination of properties of parts

plus interactionsplus interactions

Explain whole in terms of parts + interactionsExplain whole in terms of parts + interactions Rigorously describe how parts + interactions generate system Rigorously describe how parts + interactions generate system

propertiesproperties Study properties that parts + interactions can induceStudy properties that parts + interactions can induce EmergentEmergent properties: “surprising” system properties properties: “surprising” system properties

Explain how molecules and molecular interactions generate Explain how molecules and molecular interactions generate properties of cells or sub-cellular phenomenaproperties of cells or sub-cellular phenomena


Origins of systems biologyOrigins of systems biology

Systems biology

Pathway models of biological systems

Mathematical models of biological systems

High throughput laboratory technology


Origins of systems biologyOrigins of systems biology

Systems biology




Fully exploit big datasets

Don’t cherry pick!

Understand 1000s of genes

Less bias from hypotheses


The basic recipeThe basic recipe Aitchison & Galitski. Aitchison & Galitski. Inventories to insightsInventories to insights. . J Cell BiolJ Cell Biol. .

2003. PMID: 12743099.2003. PMID: 12743099.

1.1. Select experimentally tractable biological modelSelect experimentally tractable biological model

2.2. Devise Devise predictivepredictive mathematical model for phenomenon of mathematical model for phenomenon of interest (well… you’re supposed to do this interest (well… you’re supposed to do this ))

3.3. Generate / assemble Generate / assemble globalglobal datasets under baseline datasets under baseline conditionsconditions

4.4. Perturb biological modelPerturb biological model

5.5. Generate Generate globalglobal datasets under perturbed conditions datasets under perturbed conditions

6.6. Compare predictions with reality and revise model Compare predictions with reality and revise model (again… that’s the idea (again… that’s the idea ) )

7.7. Repeat until doneRepeat until done


Digression: homeostasisDigression: homeostasis

How does cell maintain target levels

of molecules?

If I knockdown expression with

RNAi, what happens to expression level?

Standard problems in control theory!

Presumably some kind of feedback

Hmm… why should this work at all??


System beingcontrolled

Control theoryControl theory

comparator

target

sensor effector


A familiar illustrationA familiar illustration


Home heating modelHome heating model

temptempt+1t+1 = temp = temptt + temp.gain – temp.loss + temp.gain – temp.loss

temp.loss(temp) =temp.loss(temp) = k klossloss ( (temp – temp.outside)temp – temp.outside)

[if heat on][if heat on]temp.gain(temp) =temp.gain(temp) = k kgaingain (temp.radiator – temp) (temp.radiator – temp)

[if heat off][if heat off]temp.gain =temp.gain = 0 0


Behavior: gain(20Behavior: gain(20ºº) = loss(20) = loss(20º)º)


Behavior: balanced (gain = 2 Behavior: balanced (gain = 2 loss) loss)


Behavior: fast! (gain = 10 Behavior: fast! (gain = 10 loss) loss)


Behavior: cold! (gain Behavior: cold! (gain loss) loss)

1

0.5

0.10


Behavior: noise (50%)Behavior: noise (50%)

1

25

10

Much less stable!System out-of-control except in narrow range


Take home messageTake home message Feedback works if high enough gainFeedback works if high enough gain

Knockdown (e.g., RNAi) works if gain lowered enough to Knockdown (e.g., RNAi) works if gain lowered enough to break feedbackbreak feedback

Overexpression may have little effect or a lot (by driving Overexpression may have little effect or a lot (by driving system into feedback)system into feedback)

Noise complicates picture immenselyNoise complicates picture immensely Increasing noise can increase or decrease mean levelIncreasing noise can increase or decrease mean level

Simple systems can have complex behaviorSimple systems can have complex behavior When you see complex behavior, don’t assume complex systemWhen you see complex behavior, don’t assume complex system Complex behavior can be modeled and understoodComplex behavior can be modeled and understood


ModelsModels Abstract or theoretical representation of phenomenonAbstract or theoretical representation of phenomenon

Represents some aspects of reality and ignores others Represents some aspects of reality and ignores others

Simple biological exampleSimple biological example Elements: genes, proteins (Elements: genes, proteins (assumedassumed identical identical )) Protein-protein interactions from yeast two hybridProtein-protein interactions from yeast two hybrid Protein-DNA interactions from literatureProtein-DNA interactions from literature mRNA abundance from microarraysmRNA abundance from microarrays

protein abundance protein abundance assumedassumed identical ( identical () )

Informal vs. formal (mathematical) modelsInformal vs. formal (mathematical) models Informal communicate ideas among scientistsInformal communicate ideas among scientists Formal can be analyzed, simulated rigorouslyFormal can be analyzed, simulated rigorously

Static vs. dynamic modelsStatic vs. dynamic models

Biocarta

Informal models (apoptosis)Informal models (apoptosis)


Informal models (apoptosis)Informal models (apoptosis)

Creaghet al. Caspase-activation pathways in apoptosis and immunity. Immunol Rev. 2003 Jun;193:10-21.

Static models (graph)Static models (graph) Parts and relationshipsParts and relationships

Graphs commonly used formalism in systems biologyGraphs commonly used formalism in systems biology


Graph propertiesGraph properties Small worldSmall world

Nodes closer to each other than expectedNodes closer to each other than expected

Scale freeScale free Power law distribution of neighborsPower law distribution of neighbors More highly connected nodes (More highly connected nodes (hubshubs) than expected) than expected

Self-similarSelf-similar Graph properties preserved when clustered by distanceGraph properties preserved when clustered by distance

Or maybe notOr maybe not Recent analysis suggests properties arise from errorsRecent analysis suggests properties arise from errors


Self-similaritySelf-similarity

Song et al. Self-similarity of complex networks. Nature. 2005 Jan 27;433(7024):392-5.


Molecular interaction mapMolecular interaction map

Aladjem et al. Molecular interaction maps--a diagrammatic graphical language for bioregulatory networks. Sci STKE. 2004 Feb 24;2004(222):pe8.


Dynamic models (biological types)Dynamic models (biological types) Metabolic pathwaysMetabolic pathways

Produce substancesProduce substances

Signal transduction pathwaysSignal transduction pathways Transmit and transform information Transmit and transform information

Gene regulatory networksGene regulatory networks Control gene expressionControl gene expression Maintain steady state or guide cell to new steady stateMaintain steady state or guide cell to new steady state Major focus of systems biologyMajor focus of systems biology

General General regulatory networksregulatory networks Combine all of above Combine all of above


Dynamic models (mathematical types -1)Dynamic models (mathematical types -1)

MechanisticMechanisticprotein A binds B activating C which travels to protein A binds B activating C which travels to nucleus and promotes transcription of Dnucleus and promotes transcription of D

FunctionalFunctionalproteins A & B required for expression of Dproteins A & B required for expression of D

QuantitativeQuantitativeaa units of A and units of A and bb units of B leads to units of B leads to dd units of D units of D

Many are graphicalMany are graphical


Dynamic models (mathematical types -2)Dynamic models (mathematical types -2) Basic definitionsBasic definitions

StateState: ensemble of properties at one point: ensemble of properties at one point State spaceState space: set of all possible states: set of all possible states Transition rulesTransition rules: function that maps state into next state: function that maps state into next state

Allowable states (for abundance or concentration)Allowable states (for abundance or concentration) BooleanBoolean: on / off: on / off QualitativeQualitative: e.g., high, medium, low: e.g., high, medium, low StochasticStochastic: integers, e.g., number of molecules: integers, e.g., number of molecules ContinuousContinuous: real numbers: real numbers

Transition rulesTransition rules Deterministic or probabilisticDeterministic or probabilistic Mathematical frameworkMathematical framework

differential equations, difference equationsdifferential equations, difference equationsboolean logic, general mathematical or computational logicboolean logic, general mathematical or computational logicBayesian or other probabilistic networksBayesian or other probabilistic networks


Dynamic models (usage)Dynamic models (usage) Model constructionModel construction

Automatic inference from data – central topic in Automatic inference from data – central topic in systems biologysystems biology

Manual, hand crafted by expertsManual, hand crafted by experts

PredictionPrediction SimulationSimulation Mathematical analysis possible sometimesMathematical analysis possible sometimes


endo16 cisendo16 cis-regulatory system-regulatory system

Davidson et al. A genomic regulatory network for development. Science. 2002 Mar 1;295(5560):1669-78.


Data types (1)Data types (1) Systems biology has voracious appetite for dataSystems biology has voracious appetite for data

Omnivorous!Omnivorous!

Large scale laboratory datasetsLarge scale laboratory datasets Genome and gene sequencesGenome and gene sequences mRNA abundance (aka gene expression profiles)mRNA abundance (aka gene expression profiles) Protein abundance and identificationProtein abundance and identification Protein-protein interactionsProtein-protein interactions Protein-DNA interactions (aka transcription factor binding Protein-DNA interactions (aka transcription factor binding

sites)sites) Gene-phenotype and gene-gene relationshipsGene-phenotype and gene-gene relationships Sub-cellular protein localizationSub-cellular protein localization


Data types (2)Data types (2) Lab data augmented by computational predictionsLab data augmented by computational predictions

Protein-protein interactions inferred from other speciesProtein-protein interactions inferred from other species Protein-DNA interactions (aka transcription factor binding site Protein-DNA interactions (aka transcription factor binding site

prediction)prediction) Identification of protein binding domains from sequence or Identification of protein binding domains from sequence or

structurestructure Functional clustering through data and text miningFunctional clustering through data and text mining

Biological interpretation requires connecting novel Biological interpretation requires connecting novel data to biological “truth”data to biological “truth”

Manually curated datasets produced by expertsManually curated datasets produced by experts Ontologies, e.g., GOOntologies, e.g., GO Some curated datasets quite large and greatly expand the data Some curated datasets quite large and greatly expand the data

available from large scale experimentsavailable from large scale experiments


Data fusionData fusion Large scale data often has high error ratesLarge scale data often has high error rates

Protein-protein interactions studied extensivelyProtein-protein interactions studied extensively 50% false positives50% false positives Unknown but probably higher false negative rateUnknown but probably higher false negative rate

Garbage in, garbage outGarbage in, garbage out


Garbage in, dinner outGarbage in, dinner out

Data fusionData fusion Large scale data often has high error ratesLarge scale data often has high error rates

Protein-protein interactions studied extensivelyProtein-protein interactions studied extensively 50% false positives50% false positives Unknown but probably higher false negative rateUnknown but probably higher false negative rate

Data fusionData fusion Combine data from multiple sources to reduce errorCombine data from multiple sources to reduce error Central topic in systems biologyCentral topic in systems biology

Protein-protein interactionsProtein-protein interactions

Lack of Lack of concordance concordance among four large among four large Y2H projectsY2H projects

Numbers in Numbers in parentheses from parentheses from small studiessmall studies

Deane et al. Protein interactions: two methods for assessment of the reliability of high throughput observations. Mol Cell Proteomics 2002.

BiomodulesBiomodules

Cluster protein-protein interactions (both axes)Cluster protein-protein interactions (both axes)

Recapitulates known pathwaysRecapitulates known pathways

Rives, Galitski. Modular organization of cellular networks. PNAS 2003.


Gene co-expression networksGene co-expression networks

Meta-analysis of ~3,000 microarray gene expression Meta-analysis of ~3,000 microarray gene expression experiments across human, fly, worm, yeastexperiments across human, fly, worm, yeast

Yielded ~3,000 meta-genesYielded ~3,000 meta-genes

Recapitulates known conserved processesRecapitulates known conserved processes

Stuart et al. A gene-coexpression network for global discovery of conserved genetic modules. Science 2003.


Gene co-expression networksGene co-expression networks

Stuart et al. A gene-coexpression network for global discovery of conserved genetic modules. Science 2003.


Date vs. party hubsDate vs. party hubs

Combines protein-protein Combines protein-protein interactions & microarrayinteractions & microarray

Party hubsParty hubs: interaction partners : interaction partners have correlated expressionhave correlated expression

Date hubsDate hubs: others: others

Date hubs more important for Date hubs more important for graph connectivitygraph connectivity

Party hubs have more spatial Party hubs have more spatial localizationlocalization

Hanet al. Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature. 2004 Jul 1;430(6995):88-9.


Date vs. party hubsDate vs. party hubs

Hanet al. Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature. 2004 Jul 1;430(6995):88-9.

ActiveActivesubnetworkssubnetworks

Ideker et al. Discovering regulatory and signaling circuits in molecular interaction networks. Bioinformatics 2002.

Combines Combines protein-protein & protein-protein & protein-DNA protein-DNA interactions & interactions & microarraymicroarray

Found subgraphs Found subgraphs with correlated with correlated expressionexpression


Condition-specific modelsCondition-specific models

Luscombe et al. Genomic analysis of regulatory network dynamics reveals large topological changes. Nature. 2004 Sep 16;431(7006):308-12.

Combines regulatory Combines regulatory interactions from genetic, interactions from genetic, biochemical, ChIP-chipbiochemical, ChIP-chip

Found active subgraphs under Found active subgraphs under various conditions: various conditions: endogenous vs. exogenousendogenous vs. exogenous


Condition-specific modelsCondition-specific models

Luscombe et al. Genomic analysis of regulatory network dynamics reveals large topological changes. Nature. 2004 Sep 16;431(7006):308-12.

Combines regulatory Combines regulatory interactions from genetic, interactions from genetic, biochemical, ChIP-chipbiochemical, ChIP-chip

Found active subgraphs under Found active subgraphs under various conditions: various conditions: endogenous vs. exogenousendogenous vs. exogenous

Permanent vs. transient hubsPermanent vs. transient hubs

Exogenous subgraphs simpler.Exogenous subgraphs simpler.

Smaller hubsSmaller hubs

Fewer transient hubsFewer transient hubs

Interferon response in liver cellsInterferon response in liver cells

Yan et al. System-based proteomic analysis of the interferon response in human liver cells. Genome Biol. 2004;5(8):R54.

Combines protein-Combines protein-protein interactions & protein interactions & protein abundanceprotein abundance

Found many known Found many known IFN-regulated IFN-regulated proteins, pathways proteins, pathways and some new onesand some new ones

Note: sparse graph Note: sparse graph compared to yeast compared to yeast examplesexamples

Interferon response in liver Interferon response in liver cellscells

Yan et al. System-based proteomic analysis of the interferon response in human liver cells. Genome Biol. 2004;5(8):R54.


Systems biology: A…Systems biology: A…

Systems biology





Path to the futurePath to the future

Biotech has given us an embarrassment of riches

More data than we can eat!

Don’t pig-out on old-style research!

Aim for deep, rigorous understanding of biological systems

Path to the futurePath to the future

Convert all this progress into real richesfor science, society, our patients

Systems Biology: A Path to the Future Nat Goodman Institute for Systems Biology February 14, 2005.

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Transcript of Systems Biology: A Path to the Future Nat Goodman Institute for Systems Biology February 14, 2005.