Can we make biological systems

predictable?

Pamela SilverDept of Systems BiologyHarvard Medical School

Director, Harvard University Graduate Program in Systems

Biology http://silver.med.harvard.edu/

The value of models and designThe value of models and design

One example of building a system with predictable One example of building a system with predictable propertiesproperties

Training a new type of scientist - infrastructure Training a new type of scientist - infrastructure needsneeds

Overview of TalkOverview of Talk

Models and more models…….Models and more models…….

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c. 1985

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c. 2005

–We can make useful things

Redesign of a system can test our understanding of its components “What I cannot create I cannot understand.”

Richard Feynman Biology presents an array of engineering

possibilities that have thus far been unexplored

Why make a predictable

biology?

Biology and engineeringBiology and engineering

Design conceptsDesign concepts– SensationSensation– Signal processing Signal processing

& communication& communication– ModularityModularity– Easy duplicationEasy duplication

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Bugs or Features?Bugs or Features?– Self-repairSelf-repair– EvolvabilityEvolvability

Biological ModularityBiological Modularity

Examples of modularity:

Genes (promoters, ORFs, introns, enhancers)

RNA (Translation, stability, export, localization)

Proteins (Targeting, DNA binding, dimerization, degradation) Pathways (Signaling, metabolism)

Biological design can test the limits of modularity

What does Nature have to offer?What does Nature have to offer?

Standardized PartsStandardized Parts

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Bacterial DevicesBacterial Devices

The Repressilator Toggle The Repressilator Toggle SwitchSwitch (Elowitz et al) (Collins et (Elowitz et al) (Collins et al)al)

Scientific ChallengesScientific Challenges

Make functional components?Make functional components?

Measure component function quantitatively?Measure component function quantitatively?

Functional higher order networks?Functional higher order networks?

Predict the behavior of higher order networks? Predict the behavior of higher order networks?

Building cellular

memory in eukaryotes

A small success story

Activation cascades: Repression cascades:

LexALacIZif-HIV*, Zif-erbB2*ERG2, Gli1, YY1

Modular construction of Transcription Modular construction of Transcription FactorsFactors

Synthetic activator activates a minimal promoterSynthetic activator activates a minimal promoter

DIC

Reporter(YFP)

Activator(RFP)

Activator: Reporter:

+ activator- activator

~20 fold activation

Kinetics of an activation deviceKinetics of an activation device

Building Networks with Standardized Building Networks with Standardized PartsParts

Components for complex devicesComponents for complex devices

Test predictions about topology of eukaryotic networksTest predictions about topology of eukaryotic networks

age 0 age 1 age 2

vs.vs.

Auto-feedback loop as memoryAuto-feedback loop as memory

A:

B:

Memory device

Autofeedback loop

Requirements for Autofeedback LoopRequirements for Autofeedback Loop

A:

B:

A makes B

Requirements for Autofeedback Requirements for Autofeedback LoopLoop

A:

B:

A makes B B persists in the absence of A

A:

B:

A makes B B persists in the absence of A

B showsbi-stability

time = ∞

Requirements for Autofeedback Requirements for Autofeedback LoopLoop

A makes BA makes BA: B:

- A + A

DIC

Reporter(YFP)

Activator(RFP)

B persists in the absence of AB persists in the absence of A

- A + A

DIC

Reporter(YFP)

Activator(RFP)

- A

A: B:

allow to grow

Cell-based memory: B persists in the Cell-based memory: B persists in the absence of Aabsence of A

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Quantitative Properties can Predict System BehaviorQuantitative Properties can Predict System Behavior

Time derivative Time derivative based on dilution rate on dilution rate

Production rate

Systems Design with Predictable Systems Design with Predictable Properties from Synthetic PartsProperties from Synthetic Parts

Functional higher-order networksFunctional higher-order networks

Individual components predict higher-Individual components predict higher-order networkorder network

+ =

Bioenergy

Engineering microorganisms for energy production

Conclusions from the JASON report:Conclusions from the JASON report:

Boosting efficiency of fuel formation form microorganisms is THEBoosting efficiency of fuel formation form microorganisms is THE major technological application of Synthetic Biologymajor technological application of Synthetic Biology

Engineering fuel production from microbes is a SYSTEMS problemEngineering fuel production from microbes is a SYSTEMS problem (Microbes are more tractable than plants……)(Microbes are more tractable than plants……)

Successful engineering requires a basic understanding of the systemSuccessful engineering requires a basic understanding of the system to be engineered (multiple feedback loops, etc)to be engineered (multiple feedback loops, etc)

Need to minimize the oxygen sensitivity of fuel-forming catalysts inNeed to minimize the oxygen sensitivity of fuel-forming catalysts in biological systems (logical engineering of systems and proteins)biological systems (logical engineering of systems and proteins)

Study Leader Mike Brenner; 6/23/06Study Leader Mike Brenner; 6/23/06

Training Training Scientists for Scientists for

the Futurethe Future

General systems approachGeneral systems approach

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An emerging fieldAn emerging field

* * We welcome students from biology, computer science, mathematics, chemistry, physics, engineering…

* * We use interdisciplinary approaches to address important biological and medical questions

* While most other Ph.D. Programs will teach you * While most other Ph.D. Programs will teach you the state the state of the art in the field, of the art in the field, this program expects this program expects students students to help create it!to help create it!

Our challenges and goals

• Can we enable collaboration and synergy amongst our students?

• Can we teach the biologists mathematical modeling?

• Can we teach the modelers to answer biological problems?

Applied Mathematics (1)

Mathematics (1)

ElectricalEngineering (2)

ComputationalBiology (2)

Immunology (1)

Medicine (1)Biology (3)

Biochemistry (3)

Mathematical Biology (1)

Genetics (1)

Computer Science (1)

Microbiology (1)

Distribution of Systems Biology graduate students

Defining a systems biology curriculum

• Systems biology is an emerging field, without a defined curriculum

• biochemistry glycolysis, oxidative phosphorylation, etc. • molecular bio transcription, translation, etc. • systems bio ???

• No unified principles yet, no coherent textbook

• What role does ‘omics’ play?

• Dynamical systems & foundations (Gunawardena)

SB200: mathematical models in systems biology

• ODE models• eigenvectors, eigenvalues• phase plane

• (multi)stability• hysteresis• oscillators

• Linked equilibria & biological networks (Fontana)

• equilibrium, thermodynamics• binding, multiple substrates• kinase/phosphatase cascades

• adaptation• motifs & logic• graph theory

• Stochastics (Paulsson)

• probability and statistics • stochastic chemical reactions• numerical simulation

• kinetics, sensitivity• fluctuations• noise

Essential computational tools

• numerical solutions to ODE models• stochastic simulations• matrix manipulations• phase portraits (pplane)• etc.

• symbolic and numerical calculations • algebra• analytical solutions to a range of DEs• notebook files• etc.

Neither program is free for academic use. Possible free alternatives: Octave, R

The learning curve for biologists

• Quantitative thinking & simulation when intuition is lacking, e.g.:

mRNA -- synthesis constant, degradation constantprotein -- synthesis 1st order in mRNA, degradation 1st order in protein

k1

k2

k3 k4

Which rate constants determine the time at which the protein reaches steady state?

Which determine the steady state concentration of protein?

The learning curve for biologists

k1

k2

k3k4

The learning curve for biologists

• Mathematical tools

k1

k2

k3k4

• Differential equations, parameter space, phase space, stability• Linear algebra: matrix manipulations, basis, Jacobian, eigen analysis• Probability & statistics

x = Ax + bdt

d

The learning curve for modelers

• Biological reality vs. models -- all models are wrong to some degree

• Understanding physical principles of biology:

• Is it okay to assume that phosphorylation and dephosphorylation are irreversible processes? If so, when are they irreversible? Why?

• Is there any meaningful way to compare a 1st order rate constant to a 2nd order rate constant? Is it really okay to eliminate one of these constants because it’s ‘slow’?

The learning curve for modelers

• Understanding physical principles of biology:

• Is the model based on sound principles?

noff

on

DPk

knPD ⏐⏐ ⏐←

⏐⏐ →⏐+

• Is the model robust? Biology doesn’t operate in narrow parameter regimes!

The learning curve for everybody

• Understanding when and how a complex system can be simplified into a useful model

Does an abstract diagram communicate the essence of what it depicts?

Spider doing a handstand(Droodles, Roger Price [Pencil on napkin, ca 1953])

Thank You!Thank You!

Research funding from:Research funding from:NIH, DOD, Merck, HHMI, Keck FoundationNIH, DOD, Merck, HHMI, Keck FoundationOffice of the Provost, Harvard UniversityOffice of the Provost, Harvard University