What i cannot create i do not understand the parallel synergistic pathways of design and discovery
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What i cannot create i do not understand the parallel synergistic pathways of design and discovery
“What I cannot create I do not understand”:The parallel synergistic pathways of design and discovery

Michael L. SimpsonCenter for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831Materials Science and Engineering Department, The University of Tennessee, Knoxville, TN 37996NSF Workshop on Molecular Communication:

Biological Communications Technology

February 20-21, 2008


Grand challenges
Grand Challenges

DOE – BES recently released list of ‘grand challenges”


Complexity in function usually entails complexity in in the wiring

Biological systems

4B years of harsh peer review

Scale (density), Complexity (connectivity)

Components

Complexity in function usually entails complexity in in the wiring….


Questions for s2

Understand

Design

Questions for S2

  • How do biological entities communicate?

    • How is biological information encoded onto and decoded from molecules?

    • What are the recurring architectures of biological communications?

    • What are the robustness-fragility trade-offs in biological communications systems?

  • How can different biological systems be interfaced to enhance communication?

  • How may communication mechanisms employed by biological entities be applied to create an artificial communication system using biological materials?


This talk

Understand

Design

This talk

  • What is the system environment?

    • Beyond 1st approximations

      • Information landscape (noise is everywhere)

      • Communicating in a crowd

  • Architectures that have evolved in this environment

    • Bow ties, robustness, and fragility

    • Poster child for biological communications, quorum sensing

  • With incomplete knowledge, how do we design in this environment?

    • Top-down

    • Bottom-up


From a systems view communication has three main players

Transmitter

Receiver

Channel

gene1

gene2

From a systems view communication has three main players

  • First order approximations – system-wide high fidelity

    • Transmitter encodes with high fidelity (receiver decodes) information in size or state of a molecular population/ concentration

    • There is no ‘uncertainty’ in the information

    • Free diffusion of information through a noiseless channel

    • Activity of gene2 directly proportional to activity of gene1 (e.g. Hill expression)


Information processing by cells beyond first approximations

TatA

Rev

t1/2 = 40 hrs

Tat

Vpu

Nef

5’LTR

Gag

Pol

Env

3’LTR

Vif

Vpr

Information Processing by Cells:Beyond First Approximations

Transient driven function

  • “rates” and “concentrations”.

  • reactions diffusion-limitedin a well-mixed cell.

  • Our aim: To quantify cellular information processing at the level of individual events in space and time.

Gene activity (RNA/Protein)

40 hrs

Lysis

ON

+FB

Latency

Time

Ido Golding, Dept. of Physics, UIUC

Leor Weinberger, UCSD


Noise is everywhere

Gene

P

mRNA decay

noise

kR

Transcription

noise

*

mRNA

kP

Translation

noise

Protein decay

noise

*

Protein

Noise is everywhere

  • Poisson processes

    • σ2= <M>

  • Gene expression is bursty

    • σ2~ b<M>

  • Transcriptional control is much noisier than Hill kinetics shows

  • Extrinsic noise

    • Correlated noise that couples into all gene circuits

  • Is noise always ‘bad’?

    • May convey the real uncertainty in information

  • Exists in the context of a highly networked complex system

    • what are the ‘systems’ properties of noise?


System wide look at noise
System-wide look at noise

Noise in yeast proteins

Noise structure is more complex

Noise is 2-d: magnitude (size of fluctuations); and correlation (duration of fluctuations)

High population & high noise? Why?

Simpson, et al., Proc. Nat. Acad. Sci.100, 4551-4556 (2003).

Austin, et al. Nature, 439, 608-611 (2006).

Bar-Even, et al., Nat Genet, 38, 636-643 (2006).

Newman, et al. Nature, 441, 840-846 (2006).


C onservation and distribution of stochasticity
Conservation and distribution of stochasticity

  • Fundamental question: How should stochasticity be distributed and regulated

    • Uniformly distributed?

    • Distributed by function type?

    • Is stochasticity always minimized?

BUT

Is bounded

~50M molecules, few thousand different proteins

Total stochasticity is conserved, but it can be unequally distributed across the components


The information landscape is crowded and noisy so is the physical landscape

Sender

Receiver

Channel

gene1

gene1

gene2

gene2

gene1

gene2

gene3

gene3

The information landscape is crowded and noisy…..so is the physical landscape

Marsh,et al. PNAS, 98, 2399-2406, 2001


Bow ties robustness and fragility
Bow ties, robustness, and fragility

Csete and Doyle, Trends in Biotechnology, 2004


The p53 Network – Bow-Tie Signaling ArchitectureEric Bachelor, Harvard

DNA damage repair

Apoptosis

Cell cycle arrest

Senescence

g radiation

Stalled replication forks

UV radiation

Ribosomal Stress

Oncogenes

p53


Varied p53 Dynamic Responses to DNA Damage

g radiation

UV radiation

DNA

Double Strand Breaks

Single Strand

DNA

ATM

ATR

Chk1

Chk2

Series of

Undamped

p53 Pulses

Graded

p53 Pulses

p53

Mdm2

Wip1


LuxR

O

O

O

N

H

O

Pheromone-mediated control of bioluminescence in Vibrio fischeri is a simple “quorum sensing” regulatory circuit…

“lux

box”

luxR

luxI

luxC

luxD

luxA

luxB

luxE

luxG

FMNH2 + O2 + RCHO

RCOOH + FMN + H2O + light

LuxI

3-oxo-C6-HSL

3-oxo-C6-HSL

Eric Stabb, University of Georgia


…or is it?

3OC6

3OC6

3OC6

3OC6

C8

C8

C8

C8

AI-2 AI-2

AI-2

AI-2 AI-2

OM

AI-2

LuxP

C8

acnB

phoQ

ainS

hns

lon

guaB

pstA/pstC

tfoX

topA

tRNA

LuxQ

IM

AinR

54

Hfq

C8- LuxR

P-LuxO

sRNA’s

(C8 and AI-2 lead

to less LuxO-P)

3OC6- LuxR

LuxO

LuxU

litR

LuxR

LitR

luxR

luxI

luxC

luxD

luxA

luxB

luxE

luxG

“lux

box”

LuxS

ArcA/ArcB

LuxI

bioluminescence

OH

HO

O

O

AinS

B-

O

O

O

N

HO

H

O

O

O

HO

O

N

(3OC6)

H

O

(AI-2)

(C8)

C8

3OC6

AI-2

Eric Stabb, University of Georgia


The confluence of design and discovery system design methodology

Biological systems

Scale (density), Complexity (connectivity)

  • Top-down

    • Understanding system environment &

    • rewiring complexity

Components

  • Bottom-up

    • Construction of complexity

The confluence of design and discovery:System design methodology?

“….often more is learned about existing system architecture through an attempted redesign than through analysis alone….”


Design space around the edges

Design space

Design space

Design space: Around the edges

rigidity

plasticity

plasticity

Kobayashi, H. et al. (2004) Programmable cells: Interfacing natural and engineered gene networks. Proc. Natl. Acad. Sci. U. S. A. 101, 8414–8419

Whole-Cell Synthetic Biology

(Jeff Hasty in S2)


Replacing the core
Replacing the core

rigidity

rigidity

plasticity

plasticity

Yin et al., Nature, Jan. 2008


PORE

DNA

Transcription

SENSOR

Protein

Translation

PORE

Images from www.nsf.gov

A little of both: cell mimics Material Synthesis, Information Processing, Energy ConversionM. J. Doktycz, ORNL

- Replace with synthetic micro-

and nano-fabricated structures

- Define cell size

- Predictably control material flux

- Defines volume (concentration)

- Controls presentation and release

of materials

Membrane

- Gather input

- Signal amplification

- Logic

- Generate output

- Metabolize energy

Molecular Networks

- DNA based instructions

- Modulate gene copy number,

control codon usage, cross

reactivity…

- Cell free transcription/translation

A potentially universal platform for sensing, actuating, generating energy, computing and interfacing to natural systems

A starting point for understanding integrated systems,the effect of scale on network function, and the creation of practical devices


Nano enabled synthetic biology

Sealed Container

120

ng/µl DNA

60

ng/µl DNA

Control

25 min

35 min

45 min

Nano-enabled synthetic biology

  • Macromolecular crowding and organization

    • 10–40%/V of cell volume is crowded with macromolecules

    • Reaction rates are accelerated, effective concentrations are changed, and activity coefficients are altered

    • understand transport in crowded environments

    • reveal diffusional flux at the nanoscale

    • Transport rates can depend on time/distance in crowded environments

  • Assembly and testing of genetic networks and cell-cell communication

    • DNA construct containing a gene for GFP and a T7 promoter are placed in the cell mimic

    • E. coli S30 cell free extract and small metabolites are are added

Fowlkes et al., Nanotechnology 17 (22): 5659-5668 (2006)


Speakers for s2
Speakers for S2

  • Eric Stabb – Quorum sensing

  • Eric Bachelor – P53 bow tie signaling architecture

  • John Doyle – Robustness and fragility

  • Leor Weinberger – Decision making in individual cells/transient driven function

  • Ido Golding – Information processing in cells

  • Jeff Hasty – Synthetic biology system design

  • Sasitharan Balasubramaniam – biological communication in Nano/MEMS devices


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