Analog Electronics Mimic Genetic Biochemical Reactions in Living Cells

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1 Analog Electronics Mimic Genetic Biochemical Reactions in Living Cells Dr. Ramez Daniel Laboratory of Synthetic Biology & Bioelectronics (LSB 2 ) Biomedical Engineering, Technion May 9, 2016

2 Cytomorphic electronics Biology Engineering Synthetic Biology Cytomorphic electronics : Bio- inspired, Simulation framework for scalable complex systems biology Synthetic Biology: Control activity of the cell using principles inspired by electrical engineering and computer science

3 Biology is Inherently Analog-Digital Feedback-loop Hybrid Circuits Sensory system Ras-Kinase feedback loop amplification cascade Turn on/off gene expression Circuit Board Design (Hanahan et al, 2000, Cell)

4 Specification of human cells & microelectronics Minimum size Distance between base pairs in DNA 0.36nm gate oxide thickness in transistor 1nm Size of active device Protein 5nm Transistor 18nm Size of system Human cell 10um microprocessor 10mm Frequency 10MHz 10MHz - 10GHz Number of parts (10MHz) 20x10 3 (Number of genes) 10,00x10 3 (Number of transistors) Power (10MHz) 0.1pW 10x10 9 pw

5 Cytomorphic Cells inspired electronics Biology Functions: sensing, communication, actuation, feedback regulation, molecular synthesis, molecular transport, self defense and other Biology computes efficiently and precisely with noise and unreliable components with unreliable components on noise real world signals (SNR = 5-10 db) Biology computes in Hybrid design analog signals collectively interact via digital parts to maintain high precision. 1. What are the engineering principles of life? 2. How can we use these engineering principles to build ultra low power electronics systems?

6 Mapping between Biology to Analog Electronics Biochemical Binding Reaction: S + E ES ES = E Total Negative Feedback S/K d 1 + S/K d ES = E Total S S + K d KVL R. Sarpeshkar, Ultra Low Power Bioelectronics, CUP, 2010

7 Mapping between Biology to Analog Electronics 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.

8 Mapping between Biology to Analog Electronics dmrna dt = α RNAp mrna τ mrna dprotein dt = α 2 mrna Protein τ p α = V mrna + C dv mrna R mrna dt α 2 V mrna = V Protein + C dv Protein R protein dt Kirchoff s Current Law (KCL) Flux Balance Analysis

9 Activator - Genetic circuit control and measurement Arab AraC Arab AraC Ribosome mrna Promoter arac Arab Ribosome mrna AraC EGFP Promoter gfp Arab

10 Analog Circuits Match Experimental Data from E. coli ACTIVATOR V H I G V H I Kd I inducer I Km R 1 R 1 I Z0 R 2 R 2 I GFP I X Daniel et al, BioCAS 2011 V L V L I GFP IG I K 1 d 1 1 I X Iinducer / I Km m I Z 0, The SPICE fit is plotted after proportional conversion of current to chemical concentration with 400 na of Iinducer corresponding to 1 % concentration of the Arabinose inducer, and 1 na of I GFP corresponding to 100 observed fluorescence units (R 1 +R 2 )/R 2 = m = 2.8, I Km = 60 na, I X = 50nA, I Kd = 10 na, I G = 27 na, and I Z0 = 0.35 na, V L = 1 V, and V H = 4 V, power supply voltage = 5 V. I inducer = 0.04 na to 400 na,v T0 = 0.71 V for NMOS,V T0 = V for, All transistors 60μm/3μm and operated in the subthreshold regime.

11 Repressor - Genetic circuit control and measurement LacI Ribosome mrna Promoter laci IPTG LacI Ribosome mrna EGFP Promoter gfp IPTG

12 Analog Circuits Match Experimental Data from E. coli REPRESSOR V H I G V H I Kd I inducer I Km R 1 R 1 I Z0 R 2 R 2 I GFP Daniel et al, BioCAS 2011 V L I X V L The SPICE fit is plotted after proportional conversion of current to chemical concentration with 500 na of I inducer corresponding to 1 mm concentration of IPTG, and 1 na of I GFP corresponding to 100 observed fluorescence units R 1 +R 2 )/R 2 = m = 2.2, I Km = 1 na, I X = 100 na, I Kd = 5 na, I G = 25 na, and I Z0 = 0 na. The value of Iinducer was swept from 0.05 na to 500 na. V T0 = 0.71 V for NMOS,V T0 = V for, All transistors 60μm/3μm and operated in the subthreshold regime.

13 Computational Challenges of Systems Biology Gene + Environment = Phenotype SBML (System Biology Market Language) Main Challenges: extremely computationally intensive 1. Non linear models 2. Stochastic models 3. Evolution (slow learning)

14 Synthetic Biology = Re-Design the Life Program and Control activity of the cell (gene regulation, protein interaction, metabolic pathways, sensing, ) using principles inspired by electrical engineering and computer science {turn on/off genes Switches and Boolean Algebra} {Analog electronics: sub-threshold transistor, RC networks}

15 Milestones in the Field of Synthetic Biology 2000 Toggle Switch Boolean Logic Gates (Gardner, et al. Nature 2000) Oscillator (Tamsir, et al. Nature 2011) (Elowitz, et al. Nature 2000) Analog Circuits Counter and Memory devices (Daniel, et al. Nature 2013) 2013 (Friedland, et al. Science 2009)

16 Problems in Scaling Synthetic Biology to Large Systems How to move from single part to system? DNA Biological-devices Modules System Level 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)

17 Positive Feedback (PF) and Shunt Motif - Results Daniel et al., Nature, 2013

18 It s Just the Beginning Thank You