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Multi-Scale Integration Using Cellular Automata Defense Delivery

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Multi-Scale Integration Using Cellular Automata Defense Delivery Fight Fire with Fire Phage-mediated bacterial bite back Abraham Avelar, Willebaldo García, Laura Gómez, Adrián A. Granados, Luis F. Montaño, Libertad Pantoja, Enrique Paz, Jorge Quintana, Eduardo Soto, Minerva S. Trejo, Uriel Urquiza, Carlos Vargas, Arturo Velarde, Miguel A. Ramírez, Osbaldo Resendis. LCG-UNAM-Mexico Team The Project Bacteriophage infection represents an interesting process in microbiology and in- dustry. The idea of being able to contend at a population level with such infec- tions is the main motivation for the development of our project. Tet LuxI Promoter LuxR Terminator Terminator DNA LuxI LuxR Lux Box E9 + AHL RFP lysis tail capsid E3 P2 AHL Lux Box We propose a population approach based on a defense system delivered by an GFP P4 genome T3 T3 Tet Promoter Antisense Promoter LuxR T7 engineered version of the enterobacteria phage P4. The purpose of the defense Terminator Terminator Promoter LuxI T7 Prex Antisense Ribosomes Lux Box E9 Terminator construction is to provide bacteria with a system that holds back the infection RFP Sux Ori E3 Kanamycine Resistance process by triggering cellular death response when a cell encounters a specic T7genome T3genome Lux Box GFP GFP P4 genome T3 RNA polymerase T3 T3 terminator component of the infective phage. Such response must be fast enough to stop Promoter Antisense cox T7 Promoter T7 Induction the formation of viral particles, thus preventing phage proliferation and popula- Prex Antisense Terminator ogr Plasmid T7 RNA tion decline. polymerase Sux Ori Kanamycine Resistance We also propose the use of the delivery phage as a standardized method for clo- ning of synthetic biobricks based on the natural properties of phages such as P4 and P2, which transduce into a range of novel hosts. Devices Multi-Scale integration using Cellular Automata The main goal of the Cellular Automata (CA) was to Delivery integrate the information contained in the Molecu- The device was designed as a tool for delivering biobrick assemblies of up lar Distributions (particularly the BSD) with a popu- to 26 kb. We took advantage of the phages' natural ability for transducing lation simulation in order to observe the behaviour Tet DNA. The bacteriophage of choice was P4 along with basic elements of Promoter LuxR of the whole system under dierent conditions. The Terminator Terminator phage P2 that complete its cycle. In order to gain room for the insertion of LuxI experimental work with T7 and the CA show the Lux Box constructs, the non essential region for the replication of P4 was eliminated. Possible bacteria states during phage infection Growth Curve. E. coli WT E9 same overall behaviour. The targets could be several bacteria belonging to the P4 host range such RFP as Klebsiella, Salmonella, Shigella, Serratia and even Rhizobium. (referencia) E3 Lux Box GFP P4 genome Defense T3 T3 Promoter Antisense The previously described delivery device will be used for the distribution of a AHL concentration in enviroment Growth Curve. E.coli infected with T7 T7 defense system, whose main elements are two colicins employed to destroy Promoter T7 DNA and RNA. Such toxins will be transcribed by the (infectious) phage RNA- Prex Antisense polymerase, fast enough to stop phage assembly and scattering in the enviro- Terminator ment. Symultaneously, a quorum sensing signal will spread out to the non- Sux infected bacteriA warning and preparing them against future T3 or T7 infec- Ori Kanamycine Results tion. We propose the use of an antisense RNA that could be expressed in order Resistance to target phage's replication. Burst Size Distribution Burst Size Distributions were simulated for both wild type and Kamikaze E.Coli using WTM and KZM respectively. WT BSD is supported by experimentally reported data (gure 1). Cellular Automaton simulations sample BSDs in real time. Modelling Approaches Fig 2. Optical Density measurements for T3 and T7 infections on E.coli C-1a strain. Bacteriophage Infection. Blue line show growth without phages. To predict whether our defense system will function inside the cell and Simulations using the CA accurately reproduced the behav- iour of the T7 infection experiments. The simulations and the whether our population will survive a T7 phage infection. experiments were made under the same initial conditions. This results show the reliability of our multiscale model predic- Molecular Scale tions. We observe that the bacteria population successfully con- Based on previous works we simulated chemical-kinetic systems of the tended the whole infection. Our results suggest that the De- life cycle of phage T7 using a stochastic framework. One of the systems fense System will work as expected. corresponds to the Wild-Type Model (WTM) of the life cycle of phage T7 in E Coli. A second model, the KamikaZe Model (KZM) simulates the interplay and performance between the kamikaze system and the phage T7 infection. At a molecular level, KZM integrates contribution of ribosomes to the Fig 3. Simulation using the Cellular Automaton for the experiment described in translation rates and attack of toxins over bacterial translation machinery. Fig 2. Ensembles of runs of WTM and KZM will provide data to build Burst Size Distri- butions (BSDs)* for each model. BSDs are further used in the population models to recreate the impact of our synthetic circuit on phage infection at a population level. Fig 1. Simulated wild type Burst Size Distribution for T7. Experimentally reported values are shown as vertical red bars. +- 1 standard deviation from the mean is *Burst size: Typical number of phage released by an infected bacterium. Stochastic molecular simulations for essential reactions of T7 life shown as a horizontal red bar. cycle with kamikaze system. Population Scale Multipromoter T3/T7 multipromoter functional- We used three dierent approaches: ity was tested qualitatively with * Multi-Scale integration using Cellular Automata GFP using an IPTG-inducible T7 * Mathematical Model using Delay Dierential Equations RNA polymerase. 40X micrography of BL21 strain carry- * Agent Based Simulation Applet Fig 4. Theoretically predicted behaviour for infection process with the Defense ing multipromoter. Induced by 0.1mM Agent based simulation using Net System. Logo. IPTG. Concluding remarks References Both, delivery and defense systems represent a promising introduction of bacteriophages as rich elements in synthetic biology. Acknowledgments 1.Propagation of satellite phage P4 as a plas- David Romero Camarena, PhD mid. Goldstein R, Sedivy J, Ljungquist E. We designed a transduction system based on the natural properties of phages such as P4 and P2, which could deliver synthetic biobricks Guillermo Dávila Ramos, PhD 2.Mechanisms of Genome Propagation and into a wide range of hosts. Ian Molineux, PhD Helper Exploitation by Satellite Phage P4. José Luis Reyes Taboada, PhD Lindqvist BH, Deh˜ G, Calendar R. (1993) A possible clinical application is the sabotage of pathogenicity regulators in bacteria for manipulating disease development. Julio Collado Vides, PhD 3.Evolution of Bacteriophage T7 in a growing Karla Cedano Villavicencio, M.S. plate. Yin. (1992) Luis Kameyama, PhD 4.Intracellular Kinetics of a Growing Virus: A We have seen molecular and population models working separately, but we have never seen a multi-scale model that integrates molecular and Otto Geiger, PhD & his research group Genetically Structured Simulation for Bacte- population dynamics in such a delightful and realistic way as we did. Rafael Palacios, PhD & his research group riophage T7. Endy D., Kong D., Yin J. (1996) Richard Calendar, PhD 5. Eects of Escherichia coli Physiology on Our model successfully predicted the experimentally calculated burst sizes for T7. Sabino Pacheco, M.S. Growth of Phage T7 In Vivo and In Silico. Yin J. State Government of Morelos, Mexico (2002) Undergraduate Program in 6.Stochastic Models in Biology S. Goel. The model is a reliable tool for prediction of infected population behaviors. As well as in the analysis of the sensitivity of infection to pa- Genomic Sciences(LCG) (2003) rameter perturbations..
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