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 E coli Differentiation Feeding Germlin e Soma Reproduction Turns ON

6. Basin of attraction Exponential growth Proof of Feasibility 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 differentiation death G = Germline S = Soma

7. Design of Devices Differentiation device T Irreversible recombination loxscar Y Feeding device Differentiation In a dapA strain (+) lox71lox66 ftsK dapA T T Combining both devices cre Differentiation control chromosome lox66 Y lox71 X dapA

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

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

10. Models overview Four approaches to answer four questions Qualitative models Quantitative models How does differentiation induces feeding? How do spatial organization and distribution evolve? How sensitive is the system to noise? How robust and tunable is the system? Cellular automaton Multi-agents based 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 Stability Red = Germline Green = Soma Differentiation rate ++ Differentiation rate +

12. 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 S G G+SG+S Time Pop. size

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

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

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

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

17. 100µM 0µM Differentiation through Recombination: Introducing feedback DAP Concentration Mean Fluorescence (AU) rfp rbs dapA promoter DAP ? 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. Coculture Experimental validation of DAP choice DAP feeding supports survival dapA- cell Prototroph cell DAP? Survival Coculture and survival

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

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

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

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

25. 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 Achievements

27. Acknowledgements

28. 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

29. DAP Allosteric control dapA Lysine DapA Genetic feedback peptidoglycan