Imperial College London. Synthetic Biology. Engineering Biologically-based Devices and Systems. Professor Richard I Kitney

Transcription

1 Imperial College London Synthetic Biology Engineering Biologically-based Devices and Systems Professor Richard I Kitney

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3 Developments in Biology

4 The Molecular Biology Revolution

5 April 25 th 1953

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7 The Double Helix Model Watson and Crick Nature 25 th April 1953 Scanning Tunnelling Micrograph

8 Inventing the Information Age

9 Norbert Wiener

10 Cybernetics Cybernetics (1948)

11 Claude Shannon

12 Information Theory Sampling Theory A sample A Mathematical Theory of Communication in the Bell System Technical Journal (1948)

13 The Molecular Biology Revolution

14 April 25 th 1953

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16 The Molecular Biology Revolution Sydney Brenner, et al prove the existence of mrna 1961 Brenner and Crick determine how DNA instructs cells to make specific proteins

17 The Molecular Biology Revolution Genes are transferred to bacteria, which reproduce, generating multiple copies 1977 Fred Sanger and Walter Gilbert independently develop a technique to read the DNA chemical bases

18 We stand at the dawn of a new understanding of disease Nature 409, (2001) Initial sequencing and analysis of the human genome International Human Genome Sequencing Consortium The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence. The dawn of molecular based medicine

19 The Tools and Techniques of Modern Biology

20 The Biological Continuum Systems Viscera Tissue Cells Proteins Genes

21 Advanced Web-based Information Systems User Input User Input Systems Visera Tissue Cells Proteins Genes Display Interface Visualsation 1 Interface... Display Interface Model 1 Interface Interface Visualisation 2 Interface Interface Model 2 Interface... Interface Interface... Database... Database

22 Techniques for Biological Data Gel Electrophoresis Western Blot 1D Gel 2D Gel Flow Cytometry / FACS Microarray Experiments Mass Spectrometry Microscope Images

23 Biology Levels Systems Visera Tissue Cells Proteins Genes

24 Multi-scale Modelling Population emergent properties due to interactive cell-agents Population behaviour Cell-Agent within a diffusive environment uptake death migration Petri Nets gene expression modelling secretion division differentiation Network dynamic

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27 So, what is Synthetic Biology? Taking an engineering approach to design and applying it to Biology

28 The Engineering Approach to Design Abstraction Decoupling Standardisation

29 The Engineering Approach Specifications Testing/Validation Design Implementation Standard Engineering Certified Practice Modelling

30 The Engineering Approach to Design Engineering systems are built from a hierarchy Parts Devices System At each level the characteristics of the Part, Device or System are well defined and reproducible In engineering the aim is to build a system on the basis of devices which comprise standard parts

31 Synthetic Biology: aims to build applications from Biobricks Parts encode biological functions Devices made from a collection of parts and encode human-defined functions (eg logic gates) Systems perform tasks, eg counting

32 Categories Biological Parts Biological Devices Multicellular Systems Examples The MIT Registry of Standard Biological Parts (700+) Promoters Transcriptional regulators/terminators Protein coding regions Ribosome binding sites Inverters (Yolobayashi, 2002; Karig, 2004) Biophasic switch (Michalowski, 2004) Toggle switch (Gardner, 2000) Logic gates: AND and OR gates (Rackham, 2005) Oscillators (Elowitz, 2000; Fung, 2005) Pulse generator (Basu, 2004) Programmed pattern formation (Basu, 2005) Image recording multi-cellular system (Levskaya, 2005) Terpenoids production system (Martin, 2003) Cancer-fighting drugs synthesis (Pfeifer, 2001) Biofilm formation (Kobayashi, 2004) Programmed cell population control (You, 2004) Controlled invasion of cancer cells (Anderson, 2006)

33 Genetic Circuits Switches and Logic After Christopher A Voigt 2006

34 Synthetic Biology - Building Parts, Devices and Systems The Key Components Modelling Underpinning Technology Quality Assurance Engineering Design Applications Modelling (examples) Part behaviour Engineering modelling Flux modelling Stability (of living devices) Metabolic engineering Protein networks

35 Underpinning Technology Part categorisation DNA synthesis Micro fluidics Parts into single cells Minimal organisms (synthetic host organisms) Imaging Quality Assurance Adaptation evaluation Engineering Design» Circuits» Fabrication

36 Carlson R (2003). The Pace and Proliferation of Biological Technologies. Biosecurity and Bioterrorism: Biodefense Strategy, Practice, and Science, Vol. 1, No. 3, pp

37 The Synthetic Biology Pipeline? Software Oligio Micro Arrays Assemble DNA Constructs DNA Error Correction Large Scale Assembly Data Devices Molecules

38 The MIT Registry

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40 Examples of Parts To see the catalogue of standard parts from the diagram above, hover over the part at the website below

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42 The Engineering Approach Specifications Testing/Validation Design Implementation Standard Engineering Certified Practice Modelling

43 A Case Study

44 Engineering a Molecular Predation Oscillator

45 igem Imperial Biomedical Engineers Electrical Engineer Biochemist Biologists Biomedical Engineers Biochemists Dr Mann

46 The Main Challenges Fluorescence Main challenges of past oscillators: Unstable Noisy Inflexible Repressilator Requirement for a typical engineering oscillator Our Specifications: Sustained Oscillations Stability: >10 periods High Signal to Noise Ratio SNR: High Controllable Oscillations Flexibility: Controllable Amplitude Standardized Device Frequency for Easy Modular Connectivity Design Easy Connectivity Time (min.) Figure Reference : Michael B. Elowitz & Stanislas Leibler Nature 2000

47 Initial Design Ideas Based on Large populations of molecules to reduce influence of noise Oscillations due to population dynamics A well characterized model Molecular Predator - Prey

48 The Lotka-Volterra Model d dt d X dt Y ax Prey Growth cxy Predator Growth Prey bxy Killing by Predator dy Predator Death Population Size X :Prey Y : Predator Time

49 Typical LV Simulations Graph of Prey vs. Time Low Frequency High Frequency prey prey Small Amplitude time time prey prey Large Amplitude time time

50 Required Biochemical Properties d dt A Self promoted Prey Growth expression of A A Degradation Prey Killing by of Predator A by B B A d dt B Expression of B Predator promoted Growth by AB interaction B A Degradation Predator Death of B B Population Size A B Time

51 Molecular System Prey Generator Cell Self promoted expression of A A Cell-cell communication Degradation Constraint: Chemostat of A by B A Flexibility: B Ratio of populations A A Degradation of B B B Predator Generator Cell Expression of B promoted by AB interaction

52 Designing the Prey Generator Required Dynamic Self promoted expression of A Useful BioBricks LuxI C0061 tetr LuxR F2620 plux AHL LuxR AHL AHL Final Construct LuxR LuxI tetr LuxR plux LuxI

53 Designing the Predator Generator Required Dynamic Useful BioBricks Expression of B promoted by AB interaction plux R0062 LuxR C0062 Degradation of A by B aiia C0060 Degradation of B Natural degradation Final Construct AHL LuxR AHL AHL LuxR AHL AHL Lactonase AHL Sensing plux LuxR aiia Killing

54 System Overview Prey Generator Cell LuxR AHL AHL Pool of AHL will oscillate LuxR LuxI AHL ptet LuxR plux LuxI Predator Generator Cell LuxR AHL AHL AHL LuxR AHL Lactonase LuxR aiia plux

55 Full System set-up reservoir LuxR LuxI tetr plux In-flow Cell population Input signal Change in population ratio Prey molecule generator time Wash-out [AHL] Output signal plux LuxR aiia Well mixed culture In chemostat time Predator molecule generator

56 Full System set-up reservoir LuxR LuxI [AHL] Output signal tetr plux In-flow Prey molecule generator time Wash-out Cell population plux LuxR aiia Well mixed culture In chemostat Change in population ratio time Predator molecule generator

57 Modelling the Full System d dt AHL Self promoted expression of AHL Degradation of AHL by aiia Degradation of AHL d dt LuxR Expression of LuxR Degradation of LuxR d dt aiia Expression of aiia Degradation of aiia

58 Modelling the Full System d [ AHL] AHL dt Self promoted expression of AHL Degradation of AHL by aiia Degradation of AHL d [ LuxR LuxR] dt Expression of LuxR Degradation of LuxR d [ aiia ] aiia dt Expression of aiia Degradation of aiia

59 Modelling the Full System d [ AHL] AHL dt Gene Expression a a 0 [ AHL] + [ AHL] Degradation of AHL by aiia Degradation of AHL d [ LuxR LuxR] dt [ ][ ] Production c AHL LuxRof c + [ LuxR AHL][ LuxR] 0 Degradation of LuxR d [ aiia ] aiia dt c [ AHL][ LuxR] Production of c + [ AHL aiia][ LuxR] 0 Degradation of aiia

60 Modelling the Full System Gene Expression Enzymatic Reaction d [ AHL] AHL dt a a 0 [ AHL] + [ AHL] b [ aiia][ AHL] b + [ AHL] 0 Degradation of AHL d [ LuxR LuxR] dt [ ][ ] Production c AHL LuxRof c + [ LuxR AHL][ LuxR] 0 Degradation of LuxR d [ aiia ] aiia Production c[ AHL][ LuxRof ] dt c0 + [ AHL aiia][ LuxR] Degradation of aiia

61 Modelling the Full System Gene Expression Enzymatic Reaction Degradation d [ AHL] AHL dt a a 0 [ AHL] + [ AHL] b [ aiia][ AHL] b + [ AHL] 0 Degradation e[ AHL] of AHL d [ LuxR] LuxR dt [ ][ ] Production c AHL LuxRof c + [ LuxR AHL][ LuxR] 0 d 1 [ LuxR] d [ aiia ] c[ AHL][ LuxR] aiia dt Production of c0 + [ AHL aiia][ LuxR] d [ aiia] 2

62 Full System Simulations Graph of Prey vs. Time Low Frequency High Frequency prey prey Small Amplitude time time prey prey Large Amplitude time time

63 Typical System Behaviours Oscillations with limit cycles Predator No oscillations Predator Prey Prey Prey Prey Time Time

64 Modelling the Full System Population dependent d [ AHL] AHL dt Gene Expression a a 0 [ AHL] + [ AHL] Enzymatic Reaction b [ aiia][ AHL] b + [ AHL] 0 Degradation Degradation e[ AHL] of AHL d [ LuxR] LuxR dt [ ][ ] Production c AHL LuxRof c + [ AHL LuxR ][ LuxR] d [ aiia ] aiia Production c[ AHL][ LuxRof ] dt c0 + [ AHL aiia][ LuxR] 0 Wash-out related d 1 [ LuxR] d [ aiia] 2 Constant

65 Characterisation Predator Sensing Test part Predictive model transfer function [GFP] Experimental Data Average with variance and curve fit Experimental data [AHL]

66 Characterisation Predator Sensing Test part Predictive model transfer function [GFP] Average with variance and curve fitting Experimental data Fitting model to data Parameter extractions [AHL]

67 Implementation Registry Catalogue Parts Prey J37034 RS+J37034 Prey with Riboswitch + Assembly process J37023 J37024 AiiA Test Construct J37025 Final predator J37033 J37019 Sensing predator J37031 Sensing predator J

68 The Project Cycle Specifications Testing/Validation Design Implementation Modelling

69 The Wiki Documentation Communication Organization

70 A 3 rd Industrial Revolution in the Making (?) Synthetic Biology promises a shift comparable in importance to the ICT revolution with the power to revolutionise many sectors of the economy including: Parts, Devices and Systems Biofuels Biomaterials Medicines/Drugs/Vaccines Biosensors

71 The Companies

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73 John Mulligan Genetics Engineer It is possible to design a protein with a specific configuration on a computer and hen to access Blue heron s software system to construct the DNA sequence that would produce it inside the cell. The protein and DNA may not exist in nature

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75 The Hierarchy of Synthetic Biology and Quantitative Systems Biology Next 10yrs and beyond Synthetic Biology Systems Biology Healthcare Applications Level 3 Synthesis of Engineering Devices and Systems and industrial applications Now Systems Biology Synthetic Biology Systems, Devices, Parts - Engineering Specification Level 2 Engineering Systems and Signal Theory Biology and Basic Medical Science Level 1

76 Educational Aspects Some thoughts from the UK

77 Systems biology training based on the Bologna model 1. BSc/BEng - three years, e.g. a first degree in engineering or physics 2. MSc - two years, a masters in biology or basic medical science 3. PhD - three years (minimum), a doctorate in Systems Biology

78 Concluding Remarks We are now at a similar point to the late 19 th Century where the great industries of the 20 th Century (automotive, aircraft, ICT etc) did not exist Synthetic Biology, coupled to Systems Biology, will produce a third industrial revolution

79 The End