Analog Electronics Mimic Genetic Biochemical Reactions in Living Cells
Dr. Ramez Daniel
Laboratory of Synthetic Biology & Bioelectronics (LSB2)
Biomedical Engineering , Technion
May 9, 2016
Biology Engineering
Synthetic Biology
Synthetic Biology: Control activity of the cell using principlesinspired by electrical engineering and computer science
Cytomorphic electronics : Bio- inspired, Simulation framework forscalable complex systems biology
Cytomorphic electronics
Biology is Inherently Analog-Digital Feedback-loop Hybrid Circuits
Sensory system Ras-Kinase feedback
loop amplification cascade
Turn on/off gene expression
(Hanahan et al, 2000, Cell)
Circuit Board Design
0.36nm
Distance betweenbase pairs in DNA
1nm
gate oxide thicknessin transistor
5nm
Protein
Minimum size
Size of active device 18nm Transistor
10um Human cell
Size of system 10mm
microprocessor
Frequency 10MHz 10MHz - 10GHz
Number of parts (10MHz)
20x103
(Number of genes)10,00x103
(Number of transistors)
Power (10MHz) 0.1pW 10x109pW
Specification of human cells & microelectronics
1. What are the engineering principles of life?
2. How can we use these engineering principles to buildultra low power electronics systems?
• Biology Functions: sensing, communication, actuation,feedback regulation, molecular synthesis, molecular transport,self defense and other
• Biology computes efficiently and precisely with noise andunreliable components with unreliable components on noisereal world signals (SNR = 5-10 dB)
• Biology computes in Hybrid design analog signals collectively interact via digital parts to maintain high precision.
Cytomorphic – Cells inspired electronics
Mapping between Biology to Analog Electronics
𝑺 + 𝑬 ↔ 𝑬𝑺
R. Sarpeshkar, Ultra Low Power Bioelectronics, CUP, © 2010
Biochemical Binding Reaction:
𝐸𝑆 = 𝐸𝑇𝑜𝑡𝑎𝑙𝑆/𝐾𝑑
1 + 𝑆/𝐾𝑑
Negative Feedback
𝐸𝑆 = 𝐸𝑇𝑜𝑡𝑎𝑙𝑆
𝑆 + 𝐾𝑑
KVL
• Currents in a subthreshold electronic transistor versus molecular flows in a
chemical reaction (exponential Boltzmann laws, forward /reverse currents)
• Poisson electron arrival statistics ↔ Poisson molecular flow statistics.
Noise scaling is similar.
Mapping between Biology to Analog Electronics
Mapping between Biology to Analog Electronics
𝑑𝑚𝑅𝑁𝐴
𝑑𝑡= 𝛼 ∙ 𝑅𝑁𝐴𝑝 −
𝑚𝑅𝑁𝐴
𝜏𝑚𝑅𝑁𝐴
𝑑𝑃𝑟𝑜𝑡𝑒𝑖𝑛
𝑑𝑡= 𝛼2 ∙ 𝑚𝑅𝑁𝐴 −
𝑃𝑟𝑜𝑡𝑒𝑖𝑛
𝜏𝑝
𝛼 =𝑉𝑚𝑅𝑁𝐴
𝑅𝑚𝑅𝑁𝐴+ 𝐶 ∙
𝑑𝑉𝑚𝑅𝑁𝐴
𝑑𝑡
𝛼2 ∙ 𝑉𝑚𝑅𝑁𝐴 =𝑉𝑃𝑟𝑜𝑡𝑒𝑖𝑛𝑅𝑝𝑟𝑜𝑡𝑒𝑖𝑛
+ 𝐶 ∙𝑑𝑉𝑃𝑟𝑜𝑡𝑒𝑖𝑛
𝑑𝑡
• Kirchoff’s Current Law (KCL) ↔ Flux Balance Analysis
araC
gfp
mRNA
mRNARibosome
Ribosome
Activator - Genetic circuit control and measurement
Arab
AraC
Arab
AraC
AraC
Arab
Promoter
Promoter
EGFP
Arab
VL
Iinducer
IX
VH IG
IKm
VH
IKd
IZ0
IGFP
R1
R2
VL
R1
R2
ACTIVATOR
Daniel et al, BioCAS 2011
Analog Circuits Match Experimental Data from E. coli
0,
11 1
/
d
m
G
GFP Zm
K
X inducer K
II I
I
I I I
The SPICE fit is plotted after proportional conversion of current to chemicalconcentration with 400 nA of Iinducer corresponding to 1 % concentrationof the Arabinose inducer, and 1 nA of IGFP corresponding to 100 observedfluorescence units
(R1+R2)/R2 = m = 2.8, IKm = 60 nA, IX = 50nA, IKd = 10 nA, IG = 27 nA, and IZ0 = 0.35 nA, VL = 1 V, and VH = 4 V, power supply voltage= 5 V. Iinducer = 0.04 nA to 400 nA,VT0 = 0.71 V for NMOS ,VT0 = -0.92 V for, All transistors 60μm/3μm and operated in the subthresholdregime.
lacI
gfp
mRNA
mRNARibosome
Ribosome
Repressor - Genetic circuit control and measurement
LacI
IPTG
LacI
Promoter
Promoter
EGFP
IPTG
REPRESSOR
Daniel et al, BioCAS 2011
Analog Circuits Match Experimental Data from E. coli
VL
Iinducer
IX
VH IG
IKm
VH
IKd
IZ0
IGFP
R1
R2
VL
R1
R2
The SPICE fit is plotted after proportional conversion of current to
chemical concentration with 500 nA of Iinducer corresponding to 1 mM
concentration of IPTG, and 1 nA of IGFP corresponding to 100 observed
fluorescence units
R1+R2)/R2 = m = 2.2, IKm = 1 nA, IX = 100 nA, IKd = 5 nA, IG = 25 nA, and IZ0 = 0 nA. The value of Iinducer was swept from 0.05 nA to 500 nA. VT0 = 0.71 V for NMOS ,VT0 = -0.92 V for, All transistors 60μm/3μm and operated in the subthreshold regime.
Computational Challenges of Systems Biology
Gene + Environment = Phenotype
SBML (System Biology Market Language)
Main Challenges: extremely computationally intensive1. Non linear models2. Stochastic models3. Evolution (slow learning)
{turn on/off genes Switches and Boolean Algebra}
{Analog electronics: sub-threshold transistor, RC networks}
Program and Control activity of the cell (gene regulation, protein interaction, metabolic pathways , sensing, …) using principles inspired by electrical engineering and computer science
Synthetic Biology = Re-Design the Life
Milestones in the Field of Synthetic Biology
Toggle Switch
Oscillator
Counter and Memory devices
Boolean Logic Gates
Analog Circuits
2000
2013
(Daniel, et al. Nature 2013)
(Tamsir, et al. Nature 2011)
(Gardner, et al. Nature 2000)
(Elowitz, et al. Nature 2000)
(Friedland, et al. Science 2009)
Modules Biological-devices System LevelDNA
Problems in Scaling Synthetic Biology to Large Systems
Digital abstraction is overly simplified (signals are not 1’s and 0’s, are probabilistic and analog, cross talk between parts, feedback loops..)
Too many logic gates for even a simple computation (not practical or energy efficient)
Loading between stages (downstream circuits affect upstream ones)
How to move from single part to system?