Biosignal-Based Computing by AHL Induced Synthetic Gene Regulatory - - PowerPoint PPT Presentation

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Biosignal-Based Computing by AHL Induced Synthetic Gene Regulatory - - PowerPoint PPT Presentation

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem Biosignal-Based Computing by AHL Induced Synthetic Gene Regulatory Networks From an in vivo Flip-Flop Implementation to

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Biosignal-Based Computing by AHL Induced Synthetic Gene Regulatory Networks

From an in vivo Flip-Flop Implementation to Programmable Computing Agents Thomas Hinze1 Sikander Hayat2 Thorsten Lenser1 Naoki Matsumaru1 Peter Dittrich1

{hinze,thlenser,naoki,dittrich}@cs.uni-jena.de, s.hayat@bioinformatik.uni-saarland.de

1Bio Systems Analysis Group

Friedrich Schiller University Jena www.minet.uni-jena.de/csb

2Computational Biology Group

Saarland University www.zbi-saar.de BIOSIGNALS 2008

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Outline

Biosignal-Based Computing by AHL Induced Synthetic GRNs

  • 1. Introduction
  • 2. Gene Regulatory Networks (GRNs)
  • 3. Hill kinetics
  • 4. Case study: computational units
  • 5. RS flip-flop wetlab

implementation in Vibrio fischeri

  • 6. Synthetic GRN for

knapsack problem solution

  • 7. Conclusions, further work

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

0.2 0.4 0.6 0.8 1 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 Normalised concentration Time scale Input1: 1 Input2: 0 Input1: 1 Input2: 1 Input1: 0 Input2: 1 Input1: 0 Input2: 0 Output sensor

  • utput

regulatory circuit signal pTSM b2 pCIRb PL* Ptrc PL* PL* Plux Lux I pAHLb 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 5 10 15 20 x*x/(x*x+25) 1-x*x/(x*x+25)

z y x z x y z

NAND gate 1 1 1 1 1 1 1

&

x y a b 50% Θ Θ h+ h−− m = 2 = 5 normalised output concentration h input concentration x

EffGene RegGeneY RegGeneX complex formation AHL lux I lux R lac I lac I cl857 gfp

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

ESIGNET – Research Project

Evolving Cell Signalling Networks in silico

European interdisciplinary research project

  • University of Birmingham (Computer Science)
  • TU Eindhoven (Biomedical Engineering)
  • Dublin City University (Artificial Life Lab)
  • University of Jena (Bio Systems Analysis)

Objectives

  • Study computational properties of CSNs/GRNs
  • Develop new ways to model and predict real CSNs/GRNs
  • Gain new theoretical perspectives on real CSNs/GRNs

Collaboration partner for in vivo studies

  • BIOTEC at Dresden University of Technology

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

ESIGNET – Research Project

Evolving Cell Signalling Networks in silico

European interdisciplinary research project

  • University of Birmingham (Computer Science)
  • TU Eindhoven (Biomedical Engineering)
  • Dublin City University (Artificial Life Lab)
  • University of Jena (Bio Systems Analysis)

Objectives

  • Study computational properties of CSNs/GRNs
  • Develop new ways to model and predict real CSNs/GRNs
  • Gain new theoretical perspectives on real CSNs/GRNs

Collaboration partner for in vivo studies

  • BIOTEC at Dresden University of Technology

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

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SLIDE 5

Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

ESIGNET – Research Project

Evolving Cell Signalling Networks in silico

European interdisciplinary research project

  • University of Birmingham (Computer Science)
  • TU Eindhoven (Biomedical Engineering)
  • Dublin City University (Artificial Life Lab)
  • University of Jena (Bio Systems Analysis)

Objectives

  • Study computational properties of CSNs/GRNs
  • Develop new ways to model and predict real CSNs/GRNs
  • Gain new theoretical perspectives on real CSNs/GRNs

Collaboration partner for in vivo studies

  • BIOTEC at Dresden University of Technology

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Motivation and Intention

  • Computing in vivo
  • Synthetic/evolutionary predefined computational units
  • Implementation in micro-organisms
  • Vision: potentially miniaturised, robust, reliable,

energy-efficient and bio-compatible hardware = ⇒ Construction, programming, applicability?

www.wikipedia.org Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Biological Principles of Gene Regulation

Intercellular Information Processing of Spatial Globality within Organisms

no/few gene product gene expression gene expression genomic DNA activation pathway signalling substances gene product signalling substances (inducers) can weak repression can amplify activation repression pathway transcription factor enables gene expression transcription factor inhibits gene expression genomic DNA

Inhibition (negative gene regulation) Activation (positive gene regulation)

regulator gene regulator gene effector gene effector gene

Feedback loops: gene products can act as transcription factors and signalling substances forming gene regulatory networks

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Hill Kinetics – Sigmoid-Shaped Threshold Functions

  • Model cooperative and competitive aspects of interacting

gene regulatory units dynamically and quantitatively

  • Homogeneous and analytic
  • Formulate relative intensity of

gene regulations by sigmoid- shaped threshold functions h+, h− : R × R × N → R

  • x ≥ 0: input concentration of

transcription factor activating/ inhibiting gene expression

  • Θ > 0: threshold (50% level)
  • m ∈ N+: degree of regulation

activation (upregulation) h+(x, Θ, m) =

xm xm+Θm

inhibition (downregulation) h−(x, Θ, m) = 1 − h−(x, Θ, m)

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 5 10 15 20 x*x/(x*x+25) 1-x*x/(x*x+25) 50% Θ Θ h+ h−− m = 2 = 5 normalised output concentration h input concentration x

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Hill Kinetics – Sigmoid-Shaped Threshold Functions

  • Model cooperative and competitive aspects of interacting

gene regulatory units dynamically and quantitatively

  • Homogeneous and analytic
  • Formulate relative intensity of

gene regulations by sigmoid- shaped threshold functions h+, h− : R × R × N → R

  • x ≥ 0: input concentration of

transcription factor activating/ inhibiting gene expression

  • Θ > 0: threshold (50% level)
  • m ∈ N+: degree of regulation

activation (upregulation) h+(x, Θ, m) =

xm xm+Θm

inhibition (downregulation) h−(x, Θ, m) = 1 − h−(x, Θ, m)

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 5 10 15 20 x*x/(x*x+25) 1-x*x/(x*x+25) 50% Θ Θ h+ h−− m = 2 = 5 normalised output concentration h input concentration x

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Hill Kinetics – Modelling Dynamical Network Behaviour

  • Several interacting (competing) transcription factors

influence gene expression

  • Activators Ai, inhibitors Ij and proportional factor c1 > 0:

determine production rate of a gene product

  • Additional assumption of

linear spontaneous decay rate c2 · [GeneProduct] with c2 > 0

  • Differential equation for

corresponding gene product: d [GeneProduct ] dt = ProductionRate − c2[GeneProduct ] = c1 · h+(A1, ΘA1, m) · . . . · h+(An, ΘAn, m) · (1 − h+(I1, ΘI1, m) · . . . · h+(Ip, ΘIp, m)) −c2 · [GeneProduct]

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

A n A 1 I 1 I p

Gene GeneProduct

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Hill Kinetics – Modelling Dynamical Network Behaviour

  • Several interacting (competing) transcription factors

influence gene expression

  • Activators Ai, inhibitors Ij and proportional factor c1 > 0:

determine production rate of a gene product

  • Additional assumption of

linear spontaneous decay rate c2 · [GeneProduct] with c2 > 0

  • Differential equation for

corresponding gene product: d [GeneProduct ] dt = ProductionRate − c2[GeneProduct ] = c1 · h+(A1, ΘA1, m) · . . . · h+(An, ΘAn, m) · (1 − h+(I1, ΘI1, m) · . . . · h+(Ip, ΘIp, m)) −c2 · [GeneProduct]

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

A n A 1 I 1 I p

Gene GeneProduct

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Case Study: Inverter

Input: concentration levels of transcription factor x Output: concentration level of gene product y

y y x x y

NOT gate 1 1

&

x a

RegulatorGene EffectorGene

Dynamical behaviour depicted for m = 2, Θj = 0.1, j ∈ {x, a},

a(0) = 0, y(0) = 0, x(t) =  0

for

0 ≤ t < 10; 20 ≤ t < 30 1

for

10 ≤ t < 20; 30 ≤ t < 40

0.2 0.4 0.6 0.8 1 5 10 15 20 25 30 35 40 Normalised concentration Time scale Input: 0 Input: 1 Input: 0 Input: 1 Output

˙ a = h+(x, Θx, m) − a ˙ y = h−(a, Θa, m) − y

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Case Study: NAND Gate

Input: concentration levels of transcription factors x (inp.1), y (inp.2) Output: concentration level of gene product z

z y x z x y z

NAND gate 1 1 1 1 1 1 1

&

x y a b

EffGene RegGeneY RegGeneX complex formation

Dynamical behaviour depicted for m = 2, Θj = 0.1, j ∈ {x, y, a, b}

0.2 0.4 0.6 0.8 1 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 Normalised concentration Time scale Input1: 1 Input2: 0 Input1: 1 Input2: 1 Input1: 0 Input2: 1 Input1: 0 Input2: 0 Output

˙ a = h+(x, Θx, m) − a ˙ b = h+(y, Θy, m) − b ˙ z = 1 − h+(a, Θa, m) · h+(b, Θb, m) − z

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Case Study: RS Flip-Flop

Input: concentration levels of transcription factors S, R Output: concentration level of gene product Q

Q S R Q R S Q

low active RS flip−flop

& &

1 1 1 1 1 hold −

S a b R

EffGene RegGeneSetState RegGeneResetState

Dynamical behaviour depicted for m = 2, Θj = 0.1, j ∈ {a, b, R, S}

0.2 0.4 0.6 0.8 1 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 Normalised concentration Time scale Set

  • S: 0, -R: 1

Store

  • S: 0, -R: 0

Reset

  • S: 1, -R: 0

Store

  • S: 0, -R: 0

Output

˙ a = 1 − h+(b, Θb, m) · h−(S, ΘS, m) − a ˙ b = 1 − h+(a, Θa, m) · h−(R, ΘR, m) − b ˙ Q = h+(b, Θb, m) · h−(S, ΘS, m) − Q

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Quorum Sensing via AHL

Quorum sensing (autoinduction)

  • Intercellular communication between bacteria
  • Regulation of gene expression based on

bacteria-population density, e.g. in Vibrio fischeri

cell i cell j signal sensor

  • utput

regulatory circuit pTSM b2 pCIRb PL* Ptrc PL* PL* Plux Lux I pAHLb AHL AHL AHL lux I lux R lac I lac I cl857 gfp AHL cellular extra

AHL (N-acyl homoserine lactone)

  • Signal molecule
  • Autoinducer
  • Produced and released by bacterial cells
  • Critical concentration −

→ activation of gene expression

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Quorum Sensing via AHL

Quorum sensing (autoinduction)

  • Intercellular communication between bacteria
  • Regulation of gene expression based on

bacteria-population density, e.g. in Vibrio fischeri

cell i cell j signal sensor

  • utput

regulatory circuit pTSM b2 pCIRb PL* Ptrc PL* PL* Plux Lux I pAHLb AHL AHL AHL lux I lux R lac I lac I cl857 gfp AHL cellular extra

AHL (N-acyl homoserine lactone)

  • Signal molecule
  • Autoinducer
  • Produced and released by bacterial cells
  • Critical concentration −

→ activation of gene expression

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Bioluminescence in Vibrio fischeri

Enzyme catalysed reaction emitting photons

www.carleton.edu

FMNH2: flavin mononucleotide (luciferin)

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Wetlab Implementation of GRN-Based RS Flip-Flop

Experimental Setup

  • in vivo system (bistable toggle switch in Vibrio fischeri)

mimics RS flip-flop

  • Encoding of all genes using two constructed plasmids
  • Quantification of its performance using flow cytometry
  • Presence or absence of inducers AHL and IPTG acts as

input signals, green fluorescent protein (gfp) as output

Collaboration with S. Hayat, at this time Dresden University of Technology, BIOTEC laboratories. Thanks to J.J. Collins, W. Pompe, G. Rödel, K. Ostermann, L. Brusch for their support. Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Flow Cytometry

  • Technique for counting, examining,

and sorting microscopic particles

  • Particles focused in fluid stream
  • Measuring point surrounded by array
  • f laser detectors emitting light beam
  • Each passing particle scatters light
  • Fluorescent chemicals within particles

emit light at lower frequency

  • Fluctuation of brightness analysed

at each detector − → particle count

  • Quantification of gfp amount
  • Cytometer used for experimental studies:

Becton Dickinson LSR II (488nm laser)

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

www.ncifcrf.gov

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Wetlab Experimental Results

B A

5.000 10.000 30.000 5.000 10.000 15.000 12 24 36 48 60 72 1 1 30°C 42°C GFP mean (average units) Flip−flop output Setting IPTG, Resetting AHL, Reset Set Reset Store Store Store low (0) high (1) time (hrs) GFP mean: 188 units after 12 hrs GFP mean: 312 units after 24 hrs GFP mean: 32.178 units after 36 hrs GFP mean: 4.106 units after 48 hrs 644 units GFP mean: after 60 hrs GFP mean: 373 units after 72 hrs GFP mean: 14.803 units GFP mean: 4.856 units GFP mean: 1.108 units GFP mean: 601 units GFP mean: 15.621 units GFP mean: 7.073 units after 12 hrs after 24 hrs after 36 hrs after 48 hrs after 60 hrs after 72 hrs 12 24 36 48 60 72 1 1 30°C 42°C GFP mean (average units) Flip−flop output Setting IPTG, Resetting AHL, Set Reset Set Store Store Store low (0) high (1) time (hrs)

Repeated activation and deactivation of the toggle switch based on inducers and temperature. Temperature was switched every 24 hours. Cells were incubated with inducers for 12 hours, followed by growth for 12 hours without inducers, initially kept at 30◦C (A) and 42◦C (B). The cells successfully switched states thrice. Collaboration with S. Hayat, at this time Dresden University of Technology, BIOTEC laboratories. Thanks to J.J. Collins, W. Pompe, G. Rödel, K. Ostermann, L. Brusch for their support. Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Knapsack Problem

NP-complete, exponential need of resources for exact solution Problem definition

There are n nat. numbers a1, . . . , an and reference number b ∈ N Is there a subset I ⊆ {1, . . . , n} with

i∈I

ai = b ?

Explanation

a1, . . . , an: weights of objects 1, . . . , n. Is there a possibility to pack a selection of these objects into the knapsack and to meet the overall weight b exactly?

Example

.

9 9 5 7 7

2 2

a = 7 a = 2 b = 9

1 2 3

a = 5

?

  • bject 2
  • bject 1
  • bject 3

"yes" solution

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Solution to the Knapsack Problem: Strategy

Dynamic programming approach − → finite automaton Example instance: n = 3, a1 = 3, a2 = 1, a3 = 2, b = 3

a 1 = 3 a 2 = 1 a 3 = 2

111 101 100 010 011 110 yes 1 1 1 1 1 0,1 no 0,1 00*

v n+1 b+1 nodes nodes v(0,0)

(b,n)

  • finite automaton −

→ circuit (based on NAND gates, RS-FFs, clock generator)

  • circuit −

→ artificial GRN

  • artificial GRN −

→ dynamical simulation (Hill kinetics)

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Solution to the Knapsack Problem: Strategy

Dynamic programming approach − → finite automaton Example instance: n = 3, a1 = 3, a2 = 1, a3 = 2, b = 3

a 1 = 3 a 2 = 1 a 3 = 2

111 101 100 010 011 110 yes 1 1 1 1 1 0,1 no 0,1 00*

v n+1 b+1 nodes nodes v(0,0)

(b,n)

  • finite automaton −

→ circuit (based on NAND gates, RS-FFs, clock generator)

  • circuit −

→ artificial GRN

  • artificial GRN −

→ dynamical simulation (Hill kinetics)

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Circuit Construction from Finite Automaton

& & & & & & & & & & & & & & & S R Q Q S R Q Q S R Q Q & & & & & >1 & S R Q Q

b1 b2 b3 x clock

Q Q & & & & S R C & &

=

&

z

NAND gate

x y a b

OR gate low active RS flip−flop

unification to NAND gates

=

&

=

& & AND gate NAND gate denoted as artificial GRN

EffGene RegGeneY RegGeneX complex formation

Gray code and Karnaugh optimisation for minimal boolean functions

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Artificial GRN from Circuit Description

  • Clock generator:

repressilator GRN (Elowitz et al.)

  • ODE derived from Hill kinetics for GRN

representing whole circuit (115 regulatory processes)

  • Simulation of dynamical behaviour (Copasi)
  • Diagram depicts variable bits b1 and b3

from path 110 → 010

1

→ 011

1

→ 111

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

0.2 0.4 0.6 0.8 1 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 Normalised concentration Time scale x=0 x=1 x=1 b1 b3

b2 b3 b1 b’2 b’3 b’1 x

1 1 1 1 1 1 1 1 1 1 1 1 1 final state reached

repressilator gfp

LacI lambda cI GFP TetR

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Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem

Conclusions and Further Work

Conclusions

  • GRNs suitable for performing computations
  • Definition and composition of computational units
  • Presented study as a proof of concept
  • Promising simulation results obtained by Hill kinetics
  • Adjust parameters to achieve stable/reliable switching

behaviour

  • Computing agent: complex (artificial) GRN for specific task

Further work

  • Coupling of computational units in vivo
  • Acceleration of GRN-based computations by parallelisation
  • Comparison of synthesised artificial GRNs with

evolutionary arisen counterparts addressing functional units

Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich