Modeling and Analysis Challenges in Biology: From Genes to Cells to Systems Francis J. Doyle III Dept. of Chemical Engineering Biomolecular Science & Engineering

Modeling and Analysis Challenges in Biology: From Genes to Cells to Systems

Francis J. Doyle IIIDept. of Chemical Engineering

Biomolecular Science & EngineeringInstitute for Collaborative Biotechnologies

http://www.ncifcrf.gov/campus/publications/how_we_can_help/clip_art/gif/NIH-bw-rev-440px.GIF

F.J. Doyle III US-EC Workshop on Infrastructure Needs for Systems Biology, Boston, May 3, 2007

Role of Models & Analysis

[Kitano, 2002]

3

BioSPICE (A Vision)

In Silico => In Vitro/Vivo Experimentation


Spectrum of Network Modeling

[Stelling, 2005]

All models are abstractions of reality [Bolouri/Davidson]

All models are wrong … some are useful [Box]

Models are most useful when they are wrong [Various]


Modeling for Analysis

Analysis Robustness – design principles, hypothesis

generation Sensitivity for design of experiment Sensitivity for ID of targets Identifiability analysis for ID of markers

Issues Context is key Multi-scale issues Stochastic issues Local vs. Global behavior Model “validation”


Validation, Verification, Consistency, etc.

Validation or verification is critical step in any model identification problem [Ljung, 1999]

Typically: ~half of data used for regression; ~half for “testing”

Matching of data (to date): “consistency” In practice, only “invalidation” is possible [Poolla et al., 1994]

Contradiction w/ data is often the most valuable role of a model Model discrimination can suggest new experiments

Competing hypotheses can be resolved

Data sets can be invalidated

Various statistical tools for model invalidation Measure of error

Confidence intervals

Likelihood ratios


Circadian Clock Circuits Across Organisms

Proteins

Genes

Networks

Cells

Organism

Organs

Length,Time

Multi-Scale Systems Analysis of Circadian Rhythm


Mammalian Circadian Clock Circuits

Traditional control engineering elements:

positive and negative feedback loopsredundant loops

time delaygain modulation

hierarchical architecture

But… what is the purpose???


Robust Yet Fragile (Gene Level)

T=transcription/translationTR=intracellular transportGR=gene regulationP=phosphorylationDP=dephosphorylationDG/DL=degradation

open=single loopfilled=double loop

3 (modified) architectures• single loop• dual loop• redundant dual loop

Insights from control-theoretic analysis:[Stelling et al., PNAS, 2004]

(i) 2-loop architecture used for clock precision(ii) robustness (local) at the expense of fragility (global)


Robust Yet Fragile (Cell Level)

[Ruoff et al., 2005]

[Herzog et al., 2004]

X

X

X

X

X

Insights from control-theoretic analysis:

(i) Timekeeping is robust to expected disturbances (Temp)(ii) Timekeeping is fragile to “attack” (VIP)


Testing Old Hypotheses

“A daily program is useless (indeed disadvantageous) unless it can be phased correctly to local time. Thus it is the phase-control, more than the period control, inherent in entrainment which is the principal dividend selection has reaped in converting a daily program into an oscillator by assuring its automatic re-initiation…”

[Pittendrigh & Daan, 1976]

Locomotor timing relative to clock

clock precision required

robust to clock error


Other Performance Metrics[Bagheri, Stelling, Doyle III, Bioinformatics, 2007]

Mouse

Drosophila

Cellular Performanceversus

Cellular Network Performance


Clock “Performance” is Context Dependent

[Herzog et al., 2004]

in vivo

explants

isolated

Period Cycle-to-cyclevariation


Model Formulation [To, Henson, Herzog, Doyle III, Biophys. J., 2007]

Modified Neuron Model

VIP release

local VIP profile

)()( , taMt iPi

N

jjiji tt

1

)()(

D

Teq K

RC

kcAMPeq

10

T

eq

R

tC )(

*2

*

*1

**

1

1

CBK

CB

CBK

CB

CBdt

dCB

PPP

K

T

P

CkRkdt

dCrf VIP/VAPC2 complex

receptor saturation

equilibrium cAMP

fraction of phosphorylated CREB

1

1/2

1

1/2

1/√2 1/√5

1√5 1/2√2

1

1 unit

Coupling Rule

GRN Module

STN Module

ICC Module


Entrainment Behavior

0 24 48 72 96 120 144 168 192 216 2400

2

4

6

Time (hr)

per

mR

NA

(nM

)

0 24 48 72 96 120 144 168 192 216 2400

1

2

3

4

Time (hr)

Ave

rag

e pe

r m

RN

A (

nM)

VIP pulse

0 12 24 36 48 60 72 84 96 108 1200

5

10

Time (hr)

per

mR

NA

(nM

)

0 12 24 36 48 60 72 84 96 108 1200

5

10

Time (hr)

per

mR

NA

(nM

)0 12 24 36 48 60 72 84 96 108 120

0

5

10

Time (hr)

per

mR

NA

(nM

)

A.

B.

C.

VIP Entrainment Photic Entrainment

Insights from control-theoretic analysis:[To et al., Biophys J, 2007]

(i) intercellular coupling allows coherent timekeeping with relatively heterogeneous cells

(ii) synchronicity depends on cell-specific propertiesas well as network (coupling) properties

[Aton et al., 2005]

Reverse “clock genetics” showed that Cry1 and Cry2 are each dispensable in circadian behavior

WT Cry1-/- Cry2-/-Cry1-/-:Cry2-/-

van der Horst, 1999

New data [Kay lab]: Clock defects in single cells are autonomous, but not necessarily in SCN slice or animal

behavior

SingleSCN

neurons

WT Cry2-/- Cry1-/- Per1-/-


Stochastic Cellular Network Model

ContinuumM-M kinetics

Stochastic firingof elementary

reactions

i

isPsPc KC

CBTvvi

n

ijj ji

jPPii r

MM

,1 ,2

1

2

1

Coupling viaPer transcription rateCore

(molecular)oscillator

stochasticsimulation

model}

Stochastic Mutant Response[Liu et al., Cell, 2007]

cell network (Cry1 -/-) isolated cells (Cry1 -/-)


The Ultimate Level: Organism Performance

Proteins

Genes

Networks

Cells

Organism

Organs

Length,Time

Multi-scale Robust Performance Issues

Phase/period control

Protein activity/level control

Distribution control

Context-dependent control

Organism Activity Control

Insights from control analyses:

(i) Robust performance requirements vary across scales(context is key!)

(ii) Analysis of upper level in hierarchy requires appropriate detail at lower level (different from reductionism!)


Summary – Infrastructure Needs

Modeling/Analysis

Get beyond intracellular focus

Efficient/hierarchical/multi-scale/stochastic models

Seamless incorporation of analysis tools

Modular model merging? (a la CAPE-OPEN)

Formalized hypothesis testing?

Modeling and Analysis Challenges in Biology: From Genes to Cells to Systems

Francis J. Doyle IIIDept. of Chemical Engineering

Biomolecular Science & EngineeringInstitute for Collaborative Biotechnologies

Dr. Rudi Gunawan Neda Bagheri Kirsten Meeker Henry Mirsky Stephanie Taylor Tsz Leung To Melanie Zeilinger Dr. Peter Chang

Collaborators: M. Henson (UMass), E. Herzog (WashU), S. Kay (Scripps),

L. Petzold (UCSB), J. Stelling (ETH)

http://www.ncifcrf.gov/campus/publications/how_we_can_help/clip_art/gif/NIH-bw-rev-440px.GIF

Documents

Modeling and Analysis Challenges in Biology: From Genes to Cells to Systems Francis J. Doyle III Dept. of Chemical Engineering Biomolecular Science & Engineering