Breaking the Symmetry SEU_O Idea Outreach SEU_O System Design - - PowerPoint PPT Presentation

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Breaking the Symmetry SEU_O Idea Outreach SEU_O System Design - - PowerPoint PPT Presentation

Feb 18, 2023 14 likes •864 views

Breaking the Symmetry SEU_O Idea Outreach SEU_O System Design Experiments Scheme-Modeling Centrosymmetric Isotropy Sy Symme mmetry ry Circularsymmetry Part 1 Idea homogeneity To Break Symmetry Breaking the Symmetry

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SEU_O

Breaking the Symmetry

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SEU_O

Outreach Idea System Design Scheme-Modeling Experiments

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Part 1 Idea

Sy Symme mmetry ry

Isotropy

Circularsymmetry Centrosymmetric

homogeneity

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To Break Symmetry：

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Breaking the Symmetry

Differentiation

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Differentiation Communication Output Input

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Part 2&3 System Design && System Simulation

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Light

System Design

Input

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Light Sensor

Light Receiver Light Promoter Light Downstream Gene

Light Sensor

Input Differentiation Communication Output

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Input Differentiation Communication Output

Toggle Switch

Status 1 Status 2 Input Signal Downstream Gene Light Sensor Toggle Switch

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AHL Signal

Input Differentiation Communication Output

Light Sensor Toggle Switch

AHL Generator

Downstream Gene

AHL Signal

AHL Receptor

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Division Inhibitor

Input Differentiation Communication Output

Light Sensor Toggle Switch AHL Signal

Inhibitor

Division Inhibitor

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Light Sensor Toggle Switch AHL Signal Division Inhibitor

Light

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Light Induced System

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System Simulation

%Light Induced Micro Model

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Cellular

Applications

  • Mathematics
  • Physics
  • Complexity Science
  • Theoretical Biology
  • Microstructure Modeling
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Three Basic Functions

% Cell Division % Cell Movement % Molecule Diffusion

Light Induced Micro Model

  • -Control Group
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Light Induced Micro Model

  • -Control Group Result
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Experiment Group

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Light Induced Micro Model

  • -Experiment Group

Differentiation Communication Output Input

% Light as Input

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% Differentiation: From Green to Red

Light Induced Micro Model

  • -Experiment Group

Differentiation Communication Output Input

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% Red cell can release AHL

  • Improved AHL Diffusion

 u: AHL density;  D: diffusion constant;  q: AHL releasing rate of a single triggered cell;  γ describes the decomposition process of AHL;  m: total number of cells.

Light Induced Micro Model

  • -Experiment Group

Differentiation Communication Output Input

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Light Induced Micro Model

  • -Experiment Group

Differentiation Communication Output Input

% Red cell can release AHL % Cell Division Inhibition

  • AHL trigger the division inhibitor
  • Decrease cell division rate
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Experiment Group Control Group

Light Induced Micro Model

  • -Experiment Group

Differentiation Communication Output Input

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Light Induced Macro Model

Experiment Group Control Group

(More details @ our website)

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System Design

Input

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Auto Differentiation System

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None Random Differentiation

Input Differentiation Communication Output

Auto Differentiation System

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Input Differentiation Communication Output

Auto Differentiation System

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Chemotactic Migration

Input Differentiation Communication Output

Auto Differentiation System

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Input Differentiation Communication Output

Auto Differentiation System

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System Simulation

%Auto-Differentiation Model

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Auto-Differentiation Model

Differentiation Communication Output Input

% NO INPUT

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% New Cell Differentiate Randomly

(New Cell is Red) P P   

Pred is a constant, describing red cell rate.

Differentiation Communication Output Input

d)

red

P P    (New Cell is Green) P P    n) 1

red red

P P   

Auto-Differentiation Model

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Red Cells: Move towards AHL Release AHL Cluster

Differentiation Communication Output Input

Auto-Differentiation Model

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Move towards AHL Release AHL Cluster Return to Light Induced Model

Differentiation Communication Output Input

Auto-Differentiation Model

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Experiment Group Control Group

Differentiation Communication Output Input

Auto-Differentiation Model

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Part 4 Experiment Design

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Light Sensor Toggle Switch AHL Signal Division Inhibitor

Light Sensor

  • Red Light
  • Blue Light
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Light Sensor Toggle Switch AHL Signal Division Inhibitor

Light Sensor - Red

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Light Sensor Toggle Switch AHL Signal Division Inhibitor

Light Sensor - Red

  • Parts
  • BBa_M30109
  • BBa_S05053

(cph8)

  • BBa_S05054

(Ompc+RFP)

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Light Sensor Toggle Switch AHL Signal Division Inhibitor

Light Sensor - Blue

Light

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Toggle Switch

Light Sensor Toggle Switch AHL Signal Division Inhibitor Status 2 Status 1 cI Promoter lacI lac promoter cI UV Light

Input Output

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Toggle Switch

Light Sensor Toggle Switch AHL Signal Division Inhibitor

  • Parts
  • BBa_S05055(Lac promoter+cI)
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AHL Signal

Light Sensor Toggle Switch AHL Signal Division Inhibitor LuxI

SAM

LuxI

AHL AHL AHL AHL

LuxR

Lux pR

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Division Inhibitor

Light Sensor Toggle Switch AHL Signal Division Inhibitor FtsZ DNA ( From Genome) FtsZ mRNA FtsZ protein Cell Division Expression Vector Antisense FtsZ mRNA Fragment A Fragment

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Division Inhibitor

Light Sensor Toggle Switch AHL Signal Division Inhibitor

  • Parts
  • BBa_K897720(asFtsZ+ Paired termini)
  • BBa_K897624(asFtsZ+terminator)
  • BBa_K897318(Paired termini)
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Division Inhibitor

Light Sensor Toggle Switch AHL Signal Division Inhibitor

  • Results

Experiment group Control group IPTG 0mM/L 0.5mM/L 1mM /L 2mM /L

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Part 5 Outreach

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Overall Prospects

  • -----more than creating a star

Break the symmetry More accurate microarray Differentiation

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Complex Microstructure Construction More Direct Bio-sensor Evolution Research

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A Specific Example

  • Bio-Computer:

Rational interconnection of synthetic switches networks that execute input- triggered genetic instructions

Bio-logical Gates ------ Basic Units of Bio-computer

Feasibility: Turing Machine; Advantages: Simple&&direct; Realization: Boolean algebra&&Threshold;

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Bio-logical Gates(1)

  • 1. A 2-Input NAND Gate

Light Input Location: 35, 45. Cellular Density Output Location: 40.

A 1 1 B 1 L 1 1

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  • 2. A 2-Input NOR Gate

Light Input Location: 38, 42. Cellular Density Output Location: 40.

A 1 B 1 1 L 1

Bio-logical Gates(2)

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Model Extension

  • Model for a Combinational Logical Gate
  • L=not((A*B)+C)
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Model Extension

  • Model for a Combinational Logical Gate
  • L=not((A*B)+C)
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Model Extension

  • Model for a Combinational Logical Gate
  • L=not((A*B)+C)
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Model Extension

  • Model for a Combinational Logical Gate
  • L=not((A*B)+C)
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Model Extension

  • Model for a Combinational Logical Gate
  • L=not((A*B)+C)
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Model Extension

  • Model for a Combinational Logical Gate
  • L=not((A*B)+C)

A B C L 1 1 1 1 1 1 1 1 1 1

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Part Application

  • A New Standard for Biosafety:

Light-Controlled Colony Growth

  • A newly-built protection

for antisense RNA sequences (paired termini structure)

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Human Practice

  • Main Theme: Biosafety
  • 1. Domestic Survey
  • 2. Biosafety Training
  • 3. Innovation: a safer general transgenic

vector

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Scheme

  • Attach another division repressing part to

the former light sensor

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Others

  • Video
  • A short flash video named Synthetic Biology---

yesterday, today and tomorrow .

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Others

  • Domestic Competition Organization
  • Lectures
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Acknowledgements

  • We would like to convey our

sincere thanks to many people ,

  • rganizations and all our team

members.

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reference

  • [1] Oleg A. Igoshin et al. Breaking symmetry in myxobacteria, Curr Biol. 2004 Jun

22;14(12):R459-62.

  • [2] Alvin Tamsir et al. Robust multicellular computing using genetically encoded

NOR gates and chemical ‘wires’, Nature 469, 212–215

  • [3] Simon Ausländer et al. Programmable single-cell mammalian biocomputers,

Nature (2012) doi:10.1038

  • [4]Anselm Levskaya et al. Synthetic biology: Engineering Escherichia coli to see
  • light. Nature,November 24,2005.438:441-442
  • [5]Jeffrey J.Tabor et al. A Synthetic Genetic EdgeDetection Program. Cell,June

26,137: 1272-1281

  • [6] Hideki Kobayashi et al. Programmable cells: Interfacing natural and engineered

gene networks. PNAS, April 26, 2004

  • [7] http://partsregistry.org/Featured_Parts:Cell-Cell-Signaling
  • [8] http://partsregistry.org/wiki/index.php/Part:BBa_F2620
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reference

  • [9] Effect of different antisense RNA sequence on ftsZ gene silencing in

Escherichia coli, CHEN Yin，QIAN Xiu-ping，HE Jian-yong，GE Mei，YIN Yu, Journal of Shenyang Pharmaceutical University , Vol. 28 No. 7 Jul． 2011

  • p. 564
  • [10] Antisense technology in molecular and cellular bioengineering, Current

Opinion in Biotechnology, Volume 14, Issue 5, October 2003, Pages 505-511 , Li Kim Lee, Charles M Roth

  • [11] Paired termini stabilize antisense RNAs and enhance conditional gene

silencing in Escherichia coli, Nobutaka Nakashima, Tomohiro Tamura and Liam Good, Nucleic Acids Research, 2006, Vol. 34, No. 20

  • [12] Molecular dynamics simulation of GTPase activity in polymers of the cell

division protein FtsZ, Fernando Martín-García, Estefanía Salvarelli , Jesús Ignacio Mendieta-Moreno , Miguel Vicente, Jesús Mingorance , Jesús Mendieta, Paulino Gómez-Puertas , F. Martín-García et al. / FEBS Letters,586(2012).

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reference

  • [13] Discovery of novel inhibitors of the ZipA/FtsZ complex by NMR

fragment screening coupled with structure-based design, Bioorganic & Medicinal Chemistry 14 (2006) 7953–7961, Desiree H. H. Tsao, Alan G. Sutherland, Lee. D. Jennings, Yuanhong Li, Thomas S. Rush, Juan C. Alvarez, Weidong Ding, Elizabeth G. Dushin, Russell G. Dushin, Steve A. Haney, Cynthia H. Kenny, A. Karl Malakian,Ramaswamy Nilakantana and Lidia Mosyaka.

  • [14]Pritchard,J.,Seielstad,M.T.,Perez-Lezaun,A.&Feldman,M.W.1999

Population growth of human Y chromosomes: a study of Y chromosome

  • microsatellites. Mol. Biol.Evo.16, 1791-1798.
  • [15]Marjoram, P.,Molitor,J., Plagnol, V.&Tavare,S.2003 Markov chain Monte

Carlo without likelihoods. Proc.Natl Acad. Sci. USA 100,15 324-15 328.

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SEU_O

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Thank you!

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Q&A

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Light Induced Micro Model

  • -Control Group

1 , ,

1 0

n n i j i j r r r

No Cell       

         

% Cell Division

  • Division Probability: Pdiv
  • Cellular States: Φ(n;i,j)

 ‘n’ represents the n th period  ‘(i, j)’ represents the location of the cell.

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% Cell Division

  • Division Probability: Pdiv
  • Cellular States: Φ(n;i,j)

 ‘n’ represents the n th period  ‘(i, j)’ represents the location of the cell.

1 , ,

1 0

n n i j i j r r r

No Cell       

         

Light Induced Micro Model

  • -Control Group
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Light Induced Micro Model

  • -Control Group

% Cell Division % Cell Movement

  • Move Probability: Pmov
  • constant velocity: Vg
  • Uniform Distributed: θ
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Light Induced Micro Model

  • -Control Group

% Cell Division % Cell Movement

  • Move Probability: Pmov
  • constant velocity: Vg
  • Uniform Distributed: θ
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u: Liquid density; D: diffusion constant; γ describes the decomposition process;

% Cell Division % Cell Movement % Liquid Diffusion

Degradation Diffusion Source

Light Induced Micro Model

  • -Control Group
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% Red Cells Move towards AHL

  • Go straight

Maintain original direction

  • Turn around

Choose a direction randomly

  • AHL concentration gradient↑ Pturn ↑

Auto-Differentiation Model

Differentiation Communication Output Input

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% Red cells move towards AHL

  • Relationship between

Δu and Pturn

Differentiation Communication Output Input

Auto-Differentiation Model

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Cell division

From:Effect of different antisense RNA sequence on ftsZ gene silencing in Escherichia coli, CHEN Yin，QIAN Xiu-ping，HE Jian-yong，GE Mei，YIN Yu, Journal of Shenyang Pharmaceutical University , Vol. 28 No. 7 Jul． 2011 p. 564

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Cell division

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Cell division

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