Systems BiologySystems Biology

Two ways of looking a Two ways of looking a problemproblem

Top down or bottom upTop down or bottom up Either look at the whole organism and Either look at the whole organism and

abstract large portions of it abstract large portions of it Or try to understand each small piece Or try to understand each small piece

and then after understanding every and then after understanding every small piece assemble into the wholesmall piece assemble into the whole

Both are used, valid and complement Both are used, valid and complement each othereach other

Bottom up is traditional Bottom up is traditional approach approach

You would study a pathway in detail not You would study a pathway in detail not worrying about how that pathway might worrying about how that pathway might interact with other elements in the cell.interact with other elements in the cell.

You would strive to understand a gene You would strive to understand a gene or pathway in great detail, eventually or pathway in great detail, eventually you might extend this knowledge to you might extend this knowledge to other organisms and compare and other organisms and compare and contrast.contrast.

With top down you need other tools...With top down you need other tools...

DefinitionsDefinitions

At a recent NIH SysBio SIG retreat At a recent NIH SysBio SIG retreat almost every talk started with that almost every talk started with that speakers definition of what systems speakers definition of what systems biology is.biology is.

Leroy Hood came up with the Leroy Hood came up with the following (my summary)following (my summary) As global a view as possibleAs global a view as possible Fundamentally quantitativeFundamentally quantitative Different scales integratedDifferent scales integrated

The Systems Biology Institute take:The Systems Biology Institute take: Understand the structure of the systemUnderstand the structure of the system

Regulatory and biochemical networksRegulatory and biochemical networks Understand the dynamics of the the Understand the dynamics of the the

systemsystem Construct model with predictive capabilitiesConstruct model with predictive capabilities

Understand the control methodsUnderstand the control methods

Common “themes”Common “themes”

Cross disciplinaryCross disciplinary Lots of data/information/knowledge Lots of data/information/knowledge Concepts of networks for abstract Concepts of networks for abstract

portrayal of many interaction types.portrayal of many interaction types. Model developmentModel development

Predictive modelsPredictive models Models to drive experimentationModels to drive experimentation Models to understand processesModels to understand processes

““Inner life of a Cell”Inner life of a Cell”SIGGRAPH 2006 SIGGRAPH 2006 showcase winnershowcase winner

Need to fight infectionNeed to fight infection WBCWBC

Need to keep blood from leaking out Need to keep blood from leaking out

Requires a higher level of Requires a higher level of understandingunderstanding

Many tools “feed” into this Many tools “feed” into this understandingunderstanding MicroarraysMicroarrays Homology tools (BLAST, alignments COGS)Homology tools (BLAST, alignments COGS) Biochemical literatureBiochemical literature Genomic sequenceGenomic sequence Specialized databasesSpecialized databases

Any faults in these tools lead to Any faults in these tools lead to problems in the analysisproblems in the analysis

A complex problem A complex problem 35,000 genes either on or off (huge 35,000 genes either on or off (huge

simplification!) would have 2^35,000 simplification!) would have 2^35,000 solutionssolutions

Things can be simplified by grouping Things can be simplified by grouping and finding key genes which regulate and finding key genes which regulate many other genes and genes which may many other genes and genes which may only interact with one other geneonly interact with one other gene

In reality there are lots of subtle In reality there are lots of subtle interactions and non-binary states.interactions and non-binary states.

Some real numbers from Some real numbers from E. coliE. coli

630 transcription units controlled by 97 transcription 630 transcription units controlled by 97 transcription factors.factors.

100 enzymes that catalyse more than one biochemical 100 enzymes that catalyse more than one biochemical reaction .reaction .

68 cases where the same reaction is catalysed by more 68 cases where the same reaction is catalysed by more than one enzyme.than one enzyme.

99 cases where one reaction participates in multiple 99 cases where one reaction participates in multiple pathways.pathways.

The regulatory network is at most 3 nodes deep.The regulatory network is at most 3 nodes deep. 50 of 85 studied transcription factors do not regulate 50 of 85 studied transcription factors do not regulate

other transcription factors, lots of negative auto-other transcription factors, lots of negative auto-regulationregulation

Theoretical hurdles to Theoretical hurdles to jumpjump

Switching delay (McAdams and Arkin Switching delay (McAdams and Arkin 1997)1997) More transcripts, less protein/transcript = More transcripts, less protein/transcript =

more energy less noisemore energy less noise Fewer transcripts, More protein/transcript = Fewer transcripts, More protein/transcript =

less energy more noise.less energy more noise. Selection drives this trade-offSelection drives this trade-off Two critical times; how long after trigger does Two critical times; how long after trigger does

a protein reach a critical level how long after a protein reach a critical level how long after removal of the trigger does the protein level removal of the trigger does the protein level decline to below critical level.decline to below critical level.

How critical is the levelHow critical is the level

Conclusions from Arkin:Conclusions from Arkin: Simulations found 3-20 minutes from Simulations found 3-20 minutes from

transcript to active protein.transcript to active protein. Many processes are stochastic (random) Many processes are stochastic (random)

not deterministic.not deterministic. The probabilities are definitely skewed but The probabilities are definitely skewed but

still have long tailsstill have long tails This means that with a large population there This means that with a large population there

are cells which may be in very different states are cells which may be in very different states than most of the rest of the population.than most of the rest of the population.

Complex interplay between regulation, lag and Complex interplay between regulation, lag and activity that has implications when trying to activity that has implications when trying to reconstruct a network.reconstruct a network.

Surviving heat shock: Surviving heat shock: Control strategies for Control strategies for

robustness and robustness and performanceperformance

Taking engineering principles and Taking engineering principles and applying them to systems biologyapplying them to systems biology

Air conditioningAir conditioning

Setpoint (temperature you set)Setpoint (temperature you set) Sensor (thermostat)Sensor (thermostat) Error signal (temp exceeded)Error signal (temp exceeded) Controller (thermostat/ac)Controller (thermostat/ac) Actuator (ac on)Actuator (ac on)

Heat shock proteinHeat shock protein

Increased heat -> mRNA -Increased heat -> mRNA -32 32 mRNA mRNA meltingmelting

Make Make 3232

Interacts with RNAP to activate specific Interacts with RNAP to activate specific sub-sets of genessub-sets of genes

Make a bunch >10,000 protein Make a bunch >10,000 protein copies to deal with heatcopies to deal with heat

Heat shock responseHeat shock response

ComponentsComponents DNAKDNAK

Chaperone representativeChaperone representative Binds to Binds to 32 32 and degraded proteinsand degraded proteins

FtsHFtsH Protease degrading Protease degrading 32 32

Titrated away by degraded proteinsTitrated away by degraded proteins 32 32

Temperature regulation at translationTemperature regulation at translation

Why make it more Why make it more difficult?difficult?

Need to turn off (cooler)Need to turn off (cooler) Don’t want to activate inappropriately Don’t want to activate inappropriately

(energy waste)(energy waste) Fast response (proteins degrading)Fast response (proteins degrading) Proportional response (it’s a little hot)Proportional response (it’s a little hot)

Theoretical types of Theoretical types of controlcontrol

Effects of Effects of control control types on types on response response

levelslevels

Adding Adding metabolic metabolic cost as a cost as a design design

parameterparameter

Using Using feedback feedback

to get to get robustnesrobustnes

ss

SummarySummary

Sometimes simple is better but:Sometimes simple is better but: Often some complexity adds Often some complexity adds

desirable featuresdesirable features Trade off between complexity, Trade off between complexity,

robustness, and economyrobustness, and economy Modules, reuseModules, reuse

““Helps” evolutionHelps” evolution Can help biologistCan help biologist

TechniquesTechniques Advanced Methods and Algorithms for Advanced Methods and Algorithms for

Biological Networks AnalysisBiological Networks Analysis““such questions are conventionally viewed as such questions are conventionally viewed as

computationally intractable. Thus, computationally intractable. Thus, biologists and engineers alike are often biologists and engineers alike are often forced to resort to inefficient simulation forced to resort to inefficient simulation methods or translate their problems into methods or translate their problems into biologically unnatural terms in order to use biologically unnatural terms in order to use available algorithms; hence the necessity available algorithms; hence the necessity for an algorithmic scalable infrastructure for an algorithmic scalable infrastructure the systematically addresses these the systematically addresses these questions”questions”

Problems of modelingProblems of modeling

Compare model to dataCompare model to data But with complex model and large But with complex model and large

parameter set any data set can be made parameter set any data set can be made to fitto fit

Could a simpler model also workCould a simpler model also work Untested parametersUntested parameters

Alternative to exhaustive Alternative to exhaustive searchessearches

Use sum of squares to generate dynamical Use sum of squares to generate dynamical behavior barriersbehavior barriers Don’t test all possible values just see where Don’t test all possible values just see where

they make a differencethey make a difference Stocastic simulation is another way but Stocastic simulation is another way but

Uses months to simulate picosecondsUses months to simulate picoseconds Robustness provides a keyRobustness provides a key

Biological systems must exhibit robustnessBiological systems must exhibit robustness This robustness also limits the search space This robustness also limits the search space

Case studiesCase studies

Consistency between literature and Consistency between literature and microarray profiles.microarray profiles.

Galactose utilization in yeast.Galactose utilization in yeast.

Case study 1: Case study 1: Microarrays -> Microarrays ->

regulatory networksregulatory networks Long been a dream, all this data Long been a dream, all this data should tell me everything.should tell me everything.

Try with E. coli:Try with E. coli: How consistent is the literature How consistent is the literature

knowledge base with the microarray knowledge base with the microarray expression profileexpression profile

Genome Research 13:2435-2443 2003Genome Research 13:2435-2443 2003 Literature compiled into the RegulonDB Literature compiled into the RegulonDB

databasedatabase Correlation was significant 70-89% but…Correlation was significant 70-89% but…

But…But… Noise filtering removed >50% of the Noise filtering removed >50% of the

genes on the microarraygenes on the microarray 83/179 known regulatory genes where 83/179 known regulatory genes where

used the rest discarded also due to used the rest discarded also due to noise filtering.noise filtering.

Simple conditions: Minimal media, Simple conditions: Minimal media, anaerobic and stationary phase growth.anaerobic and stationary phase growth.

32% of the 83 where always off.32% of the 83 where always off. Fell to ~40% if effector metabolites not Fell to ~40% if effector metabolites not

considered.considered.

Case study 2: Figure Case study 2: Figure out Galactose out Galactose

utilization in yeastutilization in yeast Classic last line: “As technologies for Classic last line: “As technologies for

cellular perturbation and global cellular perturbation and global measurement mature, these measurement mature, these approaches will soon become feasible approaches will soon become feasible in higher eukaryotes”in higher eukaryotes”

Combines: literature knowledge, Combines: literature knowledge, microarray, proteomics, visualization, microarray, proteomics, visualization, and network techniques to refine and network techniques to refine what is known about galactose what is known about galactose utilization in yeast.utilization in yeast.

Science 292:929-934Science 292:929-934

Utilization of galactose is well studied Utilization of galactose is well studied 1625 papers in PubMed dating back to 1625 papers in PubMed dating back to the 1950s. the 1950s.

Simple process get Galactose into the Simple process get Galactose into the cell then modify this sugar into the cell then modify this sugar into the more usable form of glucose-6-P; don’t more usable form of glucose-6-P; don’t waste a lot of energy doing it if: (1.) waste a lot of energy doing it if: (1.) there is no gal or (2.) you have plenty of there is no gal or (2.) you have plenty of glucose.glucose.

Galactose metabolismGalactose metabolism

The ProcessThe Process Define all genes in the genome, Define all genes in the genome,

particularly the subset of genes and other particularly the subset of genes and other small molecules that are involved in the small molecules that are involved in the gal pathway (DONE)gal pathway (DONE)

For each gene or condition change (ie For each gene or condition change (ie delete the gene) and measure the global delete the gene) and measure the global effect on both mRNA and protein levels.effect on both mRNA and protein levels.

Integrate the changes in respect to the Integrate the changes in respect to the first point with all known protein-protein first point with all known protein-protein and protein-DNA networksand protein-DNA networks

Form new hypothesises and testForm new hypothesises and test

Expression Expression measurementsmeasurements

Visualizing the dataVisualizing the dataBlue line (pp)Yellow line (pd)

Networks the “system” of Networks the “system” of systems biologysystems biology

Humans produce some pretty complex Humans produce some pretty complex structures.structures. Computer chipsComputer chips Oil refineriesOil refineries AirplanesAirplanes

The goals for these structures are similar The goals for these structures are similar to life forms to life forms SurviveSurvive Do it at a cheap costDo it at a cheap cost Reproduce/evolve??Reproduce/evolve??

Basic network Basic network terminologyterminology

NodesNodes EdgesEdges Scale-freeScale-free

Power lawsPower laws Exponential/Random networksExponential/Random networks

RobustnessRobustness Ability to respond to different conditionsAbility to respond to different conditions Robust yet fragileRobust yet fragile

ComplexityComplexity Not the number of parts… consider a lump of coalNot the number of parts… consider a lump of coal The number of different parts AND the organization The number of different parts AND the organization

of those partsof those parts

Graph theory, networksGraph theory, networks Two types of Two types of

networksnetworks Exponential and Exponential and

scale freescale free Most cellular Most cellular

networks are scale networks are scale freefree

It makes the most It makes the most sense to study the sense to study the interactions of the interactions of the central nodes not central nodes not the outer nodesthe outer nodes

High Throughput data High Throughput data sourcessources

Microarray dataMicroarray data Already well covered in the last couple of weeks.Already well covered in the last couple of weeks. Probably the most mature Probably the most mature

ProteomicsProteomics Several processesSeveral processes

Separation of the productsSeparation of the products Digest the productsDigest the products Find the mass of the productsFind the mass of the products

ProblemsProblems Contamination Contamination Phosphorylation, glycosylation, Acylation, methylation, Phosphorylation, glycosylation, Acylation, methylation,

cleavage.cleavage.

CytoscapeCytoscape Software tool to manage data and develop predictive Software tool to manage data and develop predictive

models (Genome Research Shannon et al. 2003) models (Genome Research Shannon et al. 2003) Not directed specifically to a cellular process or disease Not directed specifically to a cellular process or disease

pathwaypathway Combine Combine

Protein-protein interactionsProtein-protein interactions RNA expressionRNA expression Genetic interactionsGenetic interactions Protein-dna interactionsProtein-dna interactions Protein abundanceProtein abundance Protein phosphorylationProtein phosphorylation Metabolite concentrationsMetabolite concentrations

Integrate (global) molecular interactions and state Integrate (global) molecular interactions and state measurements.measurements.

Organized around a network graphOrganized around a network graph