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S ynthetic M ulticellular B acterium

S ynthetic M ulticellular B acterium. SMB: Synthetic Multicellular Bacterium. Introduction Design & models Experimental validation of the design Applications & Perspectives: E. colight Conclusions. Why a Synthetic Multicellular Bacterium.

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S ynthetic M ulticellular B acterium

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  1. Synthetic Multicellular Bacterium

  2. SMB: Synthetic Multicellular Bacterium • Introduction • Design & models • Experimental validation of the design • Applications & Perspectives: E. colight • Conclusions

  3. Why a Synthetic Multicellular Bacterium • Multicellularity as a backbone for complex synthetic biology • Tool for metabolic engineering: • decoupling growth and transgene expression • Studying fundamental aspects of multicellularity

  4. Decoupling functions in complex systems • The germline & soma solution Differentiation Tradeoff between growth & transgene expression Partial dissociation between growth & transgene expression

  5. Towards a Synthetic Multicellular Bacterium Feeding Differentiation Differentiation E coli Soma Germline Turns ON Feeding Reproduction Reproduction

  6. Proof of Feasibility • growth • differentiation • death G = Germline S = Soma Basin of attraction Exponential growth Stability and fixed point analysis • Population collapses • Population size remains constant • Population growth is exponential There are sets of parameters for which exponential growth exist

  7. Design of Devices • Combining both devices T T lox71 lox66 dapA ftsK • Differentiation device chromosome T lox66 X lox71 Y Differentiation control Irreversible recombination cre loxscar Y • Feeding device (+) In a dapA strain Differentiation dapA

  8. Choices • No simple bypass or reversion • Overproduction and excretion • Survival in DAP starvation • No growth in LB • DAP sensitive expression mechanism Essential gene: Auxotrophy Metabolite: • Different Soma / Germline phenotypes • Longevity • Little impact on metabolism • Genetic isolation ftsK  Cellular division • dapA Subtilis • Peptidoglycan and lysine pathways •  Feedback insensitive

  9. Concept & Implementation Germline cell Differentiation control cre dapA ftsK gfp T T lox71 lox66 Differentiation DAP starvation >> RECOMBINATION >> Differentiation Somatic cell ftsK gfp T No replication origin cre DAP feeding T loxScar dapA loxSc

  10. Models overviewFour approaches to answer four questions Qualitative models How does differentiation induces feeding? How do spatial organization and distribution evolve? Multi-agents based system Cellular automaton Quantitative models How sensitive is the system to noise? How robust and tunable is the system? Gillespie based simulation Kinetic model

  11. Spatial Simulation • An Agent Based Model • Mechanical model • Masses/springs system • Delaunay triangulation neighborhood • Biological model • Differentiation • DAP production/consumption/diffusion • Cells volume growth • Coupling both models Cells volume growth modifies the mechanical constraints and neighborhood • Simulations reproduce the 3 formerly predicted behaviors Exponential growth Differentiation rate + Stability Differentiation rate ++ Red = Germline Green = Soma

  12. G+S G S Biochemical Kinetic model • Quantitative analysis on an ODE model • Molecular level • Mean concentration values on the population Outcome of the simulation: • Range of valid parameters  Optimization and Robustness • Critical parameters: • DAP excretion • Differentiation rate Pop. size Time

  13. Differentiation by Recombination : Influence of Frequency Experimental analysis of recombination frequency KnR pBAD lox lox Growth on kanamycin cre NO growth On kan pBAD lox cre C.F.U / ML 36% recombination rate per generation Time Exploring the impact of recombination Frequency through simulation Growth rate Differentiation rate There is an optimum differentiation rate for growth

  14. Differentiation through Recombination:Influence of frequency Maximize growth G Increase germline proportion Decrease germline generation time Differentiation division G G S Increase DAP concentration G G S Decrease differentiation rate Increase differentiation rate G G S Trade off Tradeoff between: 50% recombination per generation  stability • germline generation time / germline proportion

  15. Inhibits Feeding DAP ara + dapAp pBAD cre rbs cre rbs Differentiation through Recombination: Introducing Feedback Differentiation E coli Soma Germline Turns ON Reproduction Reproduction Tunable constant differentiation Conditional differentiation

  16. Differentiation through Recombination: Introducing Feedback Soma with Retrocontrol Germline with retrocontrol Population size Soma Germline time Soma with retrocontrol Germline with retrocontrol Soma Germline Retrocontrol can increase robustness

  17. Differentiation through Recombination: Introducing feedback ? DAP dapA promoter rfp rbs Mean Fluorescence (AU) DAP Concentration 0µM 100µM dapA promoter can be used in the SMB to provide retro-control on differentiation

  18. dapA strain on limited DAP concentration Range of limiting growth [DAP] = range of dapAp activation = 0-100µM

  19. Experimental validation of DAP choice Coculture and survival dapA- cell Coculture dapA- cell Prototroph cell DAP? Survival DAP feeding supports survival

  20. Construction Process Germline cell dapAp Cre dapA subtilis ftsK gfp T T lox71 lox66 DAP starvation >> RECOMBINATION >> Differentiation Somatic cell ftsK gfp T No replication origin Cre T loxScar dapA subtilis loxSc • Chromosomal insertion T gfp lox71 DapA lox66 ftsK In a dapA strain Differentiation control cre

  21. Perspectives & Applications Differentiation DAP feeding • SMB as a tool for biological engineering. Tradeoff between growth & transgene expression Partial dissociation between growth & transgene expression

  22. E. colight: potential application of the SMB as a “metabolic plant” Differentiation Diacylglycerol DAP feeding Free fatty acid Triglyceride DGAT Acyl-coA Phospholipid Triglyceride inclusion • Triglycerides synthesis only in Soma •  Soma isolation through differentiation induction • Ingestion to absorb the fatty acids as you eat Eat fat don’t get fat

  23. E. colight: experimental proof - sodium oleate + sodium oleate (2mM) IPTG - IPTG + IPTG + IPTG - IPTG + DGAT + DGAT - • Cloning of DGAT of acinetobacter ADP1 under pLac control • Specific triglycerides coloration: Nile Red • E.colight makes triglycerides!

  24. Achievements • A new synthetic organism !! • Computational proof of principle • Experimental & computational analysis orienting the design process • Construction of the SMB genetic cassettes • 19 New Biobricks added & characterized in the registry • Inserting a transcription factor in both Somatic in germline cassettes enables full modularity of our device • Full traceability of molecular biology work and full wiki documentation

  25. Acknowledgements

  26. Who did what ? Wet Lab: David Bikard Thomas Landrain David Puyraimond Eimad Shotar Modeling team: Gilles Vieira Aurélien Rizk Modeling tools Biocham MGS Interface Wet/Dry: David Guegan Nicolas Chiaruttini Logistics: Thomas Clozel Thomas Landrain Instructors and advisors: Samuel Botanni Franck delaplace Francois Kepes Ariel Lindner Vincent Schächter Antoine Spicher Alfonso Jaramillo

  27. dapA DapA Allosteric control Genetic feedback DAP peptidoglycan Lysine

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