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?

Daniel et al., Nature, 2013

Positive Feedback (PF) and Shunt Motif - Results

ThankYou

It’s Just the

Beginning