* our design: dna killer automata
DESCRIPTION
DNA Killer Automaton (DKA) The Intelligent Nanomedicine Shaoshan Liu and Jean-Luc Gaudiot Electrical Engineering & Computer Science Dept., The Henry Samueli School of Engineering University of California, Irvine. DKA Algorithm. Object Oriented Simulation. Level 1. Experiment. Cell. DKA. - PowerPoint PPT PresentationTRANSCRIPT
* Our Design: DNA Killer Automata* Our Design: DNA Killer Automata
Existing Cancer Therapies
• Expensive
• Inefficient
• Inaccurate
• Serious Side Effects
1. DKA enter cells through injection
2. DKA detect the presence of cancer in the cell
3. DKA releases GCVTPs if all cancer indicators are detected
4. GCVTPs propagate to nearby cancer cells through homologous GJIC
5. After propagation, GCVTPs have entered over 90% of all cancer cells
DNA Killer Automaton (DKA)DNA Killer Automaton (DKA)
The Intelligent NanomedicineThe Intelligent Nanomedicine
Shaoshan Liu and Jean-Luc GaudiotShaoshan Liu and Jean-Luc GaudiotElectrical Engineering & Computer Science Dept., The Henry Samueli Electrical Engineering & Computer Science Dept., The Henry Samueli
School of EngineeringSchool of EngineeringUniversity of California, IrvineUniversity of California, Irvine
Using nanotechnology to build an automaton that cures cancer at the Using nanotechnology to build an automaton that cures cancer at the molecular level.molecular level.
* Simulation: a discrete cellular automaton model* Simulation: a discrete cellular automaton model
ExperimentExperiment
DKADKA
CytotoxinCytotoxinEnzymeEnzyme
CellCell
mRNAmRNAGJICGJIC
Object Oriented SimulationObject Oriented Simulation
Level 1Level 1
Level 2Level 2
Level 3Level 3
0
10
20
30
40
50
60
70
80
90
100
0.25 0.8 1.3 2.05 8.3 14.25 18.9 59
Percentage of Cancer Cells Entered by DKAs
Pe
rce
nta
ge
of
Ca
nc
er
Ce
lls
Kil
led
10% cancer cells
50% cancer cells
100% cancer cells
0
10
20
30
40
50
60
70
80
90
100
Number of DKAs Used
Perc
en
tag
e o
f C
an
cer
Cell
s K
ille
d
10% cancer cells
50% cancer cells
100% cancer cells
YesStartDKA
No
PositivediagnosisReleasecytotoxin
Negativediagnosis
Cytotoxinpropagation
DKAtermination
Cancer indicatorpresent
Cancer indicatorabsent
DKA AlgorithmDKA Algorithm
* Conclusion* Conclusion Our software simulation model Our software simulation model suggests that thesuggests that the bystander bystander effect effect facilitates the propagation of DKA. facilitates the propagation of DKA. Also, our simulation results suggest Also, our simulation results suggest imply that the efficacy of DKA is imply that the efficacy of DKA is linearly dependent on its dose and the linearly dependent on its dose and the degree of connectivity between degree of connectivity between cancer cells. cancer cells.
E = const. * D * NE = const. * D * N
Efficacy of cancer cell killingEfficacy of cancer cell killing
DKA efficacy Vs. DKA doseDKA efficacy Vs. DKA dose
DKA efficacy Vs. number of cells hit DKA efficacy Vs. number of cells hit
3-Stage Simulation3-Stage Simulation
•Distribution of Distribution of DKADKA
•Cancer detectionCancer detection
•Cytotoxin Cytotoxin propagationpropagation