Course outline 1 Introduction 2 Theoretical background Biochemistry/molecular biology 3 Theoretical background computer science 4 History of the field 5 Splicing systems 6 P systems 7 Hairpins 8 Detection techniques 9 Micro technology introduction 10 Microchips and fluidics 11 Self assembly 12 Regulatory networks 13 Molecular motors 14 DNA nanowires 15 Protein computers 16 DNA computing - summery 17 Presentation of essay and discussion

CMOS Complementary Metal-Oxide Semiconductor

Introduction

Electronic pathway

Seoul subway

Pyrimidine pathway

From DNA to pathways

Biological information Two Types of Biological Information The genome, digital information Environmental, analog information

Genome information Two types of digital genome information Genes, the molecular machines of life Gene regulatory networks, specify the behavior of the genes

What is systems biology? Biological System DNA RNA Biomodules Cells Networks Proteins

A gene network …

A gene network in a physical network

Cell programming

Programming cell communities E. coli Diffusing signal proteins Interest: prog cells to perform tasks Focus: use inter. Comm. for coordinated behavior Explain substrate pic Chromosome = cell OS Design DNA, put into cell Signals=protein concentrations Describe communication – diffusion of signal chemicals

Programming cell communities Program cells to perform various tasks using Intra-cellular circuits Digital & analog components Inter-cellular communication Control outgoing signals, process incoming signals Interest: prog cells to perform tasks Focus: use inter. Comm. for coordinated behavior Explain substrate pic Chromosome = cell OS Design DNA, put into cell Signals=protein concentrations Describe communication – diffusion of signal chemicals

Programmed cell applications Biomedical combinatorial gene regulation with few inputs; tissue engineering Environmental sensing and effecting recognize and respond to complex environmental conditions Engineered crops toggle switches control expression of growth hormones, pesticides Cellular-scale fabrication cellular robots that manufacture complex scaffolds Cells are a novel substrate for engineering design -- providing an outstanding interface to the worlds of chemistry and nanotechnology. They have unique features that make them attractive for a wide variety of applications. These features include a miniature scale, energy efficiency, the ability to self reproduce, and the ability to manufacture biochemical products. But in order to effectively harness cells for our purposes, we need to address the following problem, which is how to engineer complex behaviors in cells in a fashion that is both predictable and reliable. Addressing these issues is especially important with biological substrates, where it’s often very difficult to obtain reliable and reproducible results. Effective solutions to engineering complex cell behaviors will enable applications such as sensing of complex environmental conditions, etc…

Programmed cell applications Pattern formation Cells are a novel substrate for engineering design -- providing an outstanding interface to the worlds of chemistry and nanotechnology. They have unique features that make them attractive for a wide variety of applications. These features include a miniature scale, energy efficiency, the ability to self reproduce, and the ability to manufacture biochemical products. But in order to effectively harness cells for our purposes, we need to address the following problem, which is how to engineer complex behaviors in cells in a fashion that is both predictable and reliable. Addressing these issues is especially important with biological substrates, where it’s often very difficult to obtain reliable and reproducible results. Effective solutions to engineering complex cell behaviors will enable applications such as sensing of complex environmental conditions, etc…

Programmed cell applications Analyte source detection analyte source reporter rings Cells are a novel substrate for engineering design -- providing an outstanding interface to the worlds of chemistry and nanotechnology. They have unique features that make them attractive for a wide variety of applications. These features include a miniature scale, energy efficiency, the ability to self reproduce, and the ability to manufacture biochemical products. But in order to effectively harness cells for our purposes, we need to address the following problem, which is how to engineer complex behaviors in cells in a fashion that is both predictable and reliable. Addressing these issues is especially important with biological substrates, where it’s often very difficult to obtain reliable and reproducible results. Effective solutions to engineering complex cell behaviors will enable applications such as sensing of complex environmental conditions, etc…

Biological cell programming

Biological cell programming

In vivo logic circuits

e. coli

A genetic circuit building block Title genetic circuit building block Threshold detector Amplifier delay

Logic circuit based on inverters Proteins are the wires/signals Promoter + decay implement the gates NAND gate is a universal logic element: any (finite) digital circuit can be built!

NAND and NOT gate x y NAND 1 X XY Y x NOT 1 X X

= Logic circuit based on inverters NAND NOT X R1 Z gene Y gene gene X

Why digital? We know how to program with it Signal restoration + modularity = robust complex circuits Cells do it Phage λ cI repressor: Lysis or Lysogeny? [Ptashne, A Genetic Switch, 1992] Circuit simulation of phage λ [McAdams & Shapiro, Science, 1995] Also working on combining analog & digital circuitry

Why digital?

BioCircuit CAD SPICE http://bwrc.eecs.berkeley.edu/classes/icbook/SPICE/

BioCircuit CAD steady state dynamics intercellular BioSPICE a prototype biocircuit CAD tool simulates protein and chemical concentrations intracellular circuits, intercellular communication single cells, small cell aggregates

Genetic circuit elements translation RBS RBS input mRNA output mRNA ribosome ribosome transcription operator promoter RNAp

Modeling a biochemical inverter input repressor promoter output

A BioSPICE inverter simulation input repressor promoter output

Smallest memory: RS-latch flip-flop 1 1 1 1 The output a of the R-S latch can be set to 1 by momentarily setting S to 0 while keeping R at 1. When S is set back to 1 the output a stays at 1. Conversely, the output a can be set to 0 by keeping S at 1 and momentarily setting R to 0. When R is set back to 1, the output a stays at 0.

RS-latch flip-flop truth table Q ~Q (n+ 1 ) (n+ 1 ) Q ~Q Q = R + ~Q (n) (n) 1 1 ~Q = S + Q 1 1 1 1

Proof of Concept in BioSPICE RS-Latch (“flip-flop”) Ring oscillator _ [R] [A] _ R _ [S] A [B] time (x100 sec) [B] _ S B [C] [A] time (x100 sec) time (x100 sec) They work in vivo Flip-flop [Gardner & Collins, 2000] Ring oscillator [Elowitz & Leibler, 2000] However, cells are very complex environments Current modeling techniques poorly predict behavior Work in BioSPICE simulations [Weiss, Homsy, Nagpal, 1998]

The IMPLIES gate Inducers that inactivate repressors: IPTG (Isopropylthio-ß-galactoside)  Lac repressor aTc (Anhydrotetracycline)  Tet repressor Use as a logical IMPLIES gate: (NOT R) OR I Repressor Output Inducer

The IMPLIES gate active repressor inactive repressor RNAP inducer no transcription transcription RNAP promoter operator gene promoter operator gene

The toggle switch pIKE = lac/tet pTAK = lac/cIts [Gardner & Collins, 2000]

The toggle switch promoter protein coding sequence [Gardner & Collins, 2000]

The ring oscillator [Elowitz, Leibler 2000]

The ring oscillator The repressilator network. The repressilator is a cyclic negative-feedback loop composed of three repressor genes and their corresponding promoters, as shown schematically in the centre of the left-hand plasmid. It uses PLlacO1 and PLtetO1, which are strong, tightly repressible promoters containing lac and tet operators, respectively6, as well as PR, the right promoter from phage l (see Methods). The stability of the three repressors is reduced by the presence of destruction tags (denoted `lite'). The compatible reporter plasmid (right) expresses an intermediate-stability GFP variant11 (gfp-aav). In both plasmids, transcriptional units are isolated from neighbouring regions by T1 terminators from the E. coli rrnB operon (black boxes).

The ring oscillator

Evaluation of the ring oscillator Reliable long-term oscillation doesn’t work yet: Will matching gates help? Need to better understand noise Need better models for circuit design [Elowitz, Leibler 2000]

Evaluation of the ring oscillator Examples of oscillatory behaviour and of negative controls. a±c, Comparison of the repressilator dynamics exhibited by sibling cells. In each case, the fluorescence timecourse of the cell depicted in Fig. 2 is redrawn in red as a reference, and two of its siblings are shown in blue and green. a, Siblings exhibiting post-septation phase delays relative to the reference cell. b, Examples where phase is approximately maintained but amplitude varies significantly after division. c, Examples of reduced period (green) and long delay (blue). d, Two other examples of oscillatory cells from data obtained in different experiments, under conditions similar to those of a±c. There is a large variability in period and amplitude of oscillations. e, f, Examples of negative control experiments. e, Cells containing the repressilator were disrupted by growth in media containing 50mM IPTG. f, Cells containing only the reporter plasmid.

Ring oscillator with mismatched inverters A = original cI/λP(R) B = repressor binding 3X weaker C = transcription 2X stronger

Device physics in steady state “Ideal” inverter Transfer curve gain (flat,steep,flat) adequate noise margins “gain” [output] 1 [input] Curve can be achieved with certain dna-binding proteins Inverters with these properties can be used to build complex circuits

Measuring a transfer curve Construct a circuit that allows: Control and observation of input protein levels Simultaneous observation of resulting output levels inverter CFP R YFP “drive” gene output gene Also, need to normalize CFP vs YFP

Flow cytometry (FACS)

Drive input levels by varying inducer IPTG (uM) lacI [high] YFP (Off) P(lacIq) P(lac) IPTG P(lacIq) lacI 100 IPTG P(lac) YFP 1000 promoter protein coding sequence

Measuring a transfer curve for lacI/p(lac) aTc YFP lacI CFP tetR [high] (Off) P(LtetO-1) P(R) P(lac) measure TC P(R) tetR P(lac) YFP aTc P(Ltet-O1) lacI CFP

Transfer curve data points 01 undefined 10 1 ng/ml aTc 10 ng/ml aTc 100 ng/ml aTc

lacl/p(lac) transfer curve aTc YFP lacI CFP tetR [high] (Off) P(LtetO-1) P(R) P(lac) gain = 4.72

Evaluating the transfer curve Gain / Signal restoration Noise margins high gain note: graphing vs. aTc (i.e. transfer curve of 2 gates)

Signal processing circuits

Cell-cell communication circuits VAI Receiver cells Sender cells tetR P(tet) luxI P(Ltet-O1) aTc GFP(LVA) Lux P(R) luxR Lux P(L) + Sender cells Receiver cells LuxR GFP tetR luxI VAI aTc VAI aTc pLuxI-Tet-8 pRCV-3

2:4 multiplexer C(4)HSL qsc box C(6)HSL lux box Cell Color none 1 none 1 Green (GFP) Red (HcRED) Cyan (CFP)

Significance of multiplexer With a 2:4 mux, the combination of 2 inputs produces 4 different output states / expressed proteins In Eukaryotic cells, these proteins could potentially differentiate the cell into one of four cell types Applications include tissue engineering and more understanding for stem cell fate and determination

Mux the sum of three circuits qsc lux A 1 green qsc lux B 1 red + + qsc lux C 1 cyan qsc lux D 1 green red cyan =

Case A C6HSL C4HSL luxR GFP lux box qsc box RhlR qsc lux A 1 green

Case B C6HSL C4HSL luxR HcRED qsc box lux box RhlR qsc lux B 1 red

Case C, AND gate cI lux box CFP λP(R) qsc lux C 1 cyan cI qsc box

luxR RhlR GFP HcRED Case A + B C6HSL C4HSL lux box qsc box qsc lux AxorB 1 green red

Design considerations qsc binding site plasmid copy number production of C(x)HSL

Phenotype tests triple plasmid, regulatory double plasmid, antisensing double plasmid, antisensing + regulatory chromosome, antisensing + regulatory

Case A pASK-102: Single “Parent” Offspring QSC box

Case A Plasmid 1

Case A Plasmid 2 Parents: pASK-102-qsc117 (vector) pECP61.5 (insert)

Detecting chemical gradients signal O O O N H O N H analyte source H N O O O N H O N H O N H O N H O N H O N H O N H O N H O O H N O O reporter rings Biomat fab: scale, new materials possibilities Env. Sensing : toxin source pinpointing Forward engineering: understanding development Toxin source detection uses the concentration detect circuit Split this slide into two Toxin source detection slide Analyte source detection

Circuit components Components Acyl-HSL detect Low threshold x R O N H P(lux) X Y Z P(W) GFP P(Z) P(X) W P(Y) luxR P(R) Components Acyl-HSL detect Low threshold High threshold Negating combiner

Detecting chemical gradients acyl-hSL detection L u x R O N H P(lux) X Y Z P(W) GFP P(Z) P(X) W P(Y) luxR P(R) X used for low threshold and Y used as high threshold X low threshold Y high threshold

Detecting chemical gradients low threshold detection L u x R O N H P(lux) X Y Z2 P(W) GFP P(Z) Z1 P(X) W P(Y) luxR P(R)

Detecting chemical gradients high threshold detection L u x R O N H P(lux) X Y Z2 P(W) GFP P(Z) Z1 P(X) W P(Y) luxR P(R)

Detecting chemical gradients protein Z determines range L u x R O N H P(lux) X Y Z2 P(W) GFP P(Z) Z1 P(X) W P(Y) luxR P(R)

Detecting chemical gradients negating combiner L u x R O N H P(lux) X Y Z2 P(W) GFP P(Z) Z1 P(X) W P(Y) luxR P(R)

Engineering circuit characteristics HSL-mid: the midpoint where GFP has the highest concentration HSL-width: the range where GFP is above 0.3uM Draw a figure explaining about hsl-width and hsl-mid HSL-mid 0.3 HSL-width