BacteriumSimulatorGrid (BSGrid) - Tool for Simulating the Behavior of the Bacillus thuringiensis
2 likes843 views
The document discusses a mesoscale stochastic modeling approach for understanding the sporulation process of Bacillus thuringiensis and its δ-endotoxin production, which is significant for biopesticide development. It highlights the challenges in industrial-scale fermentation and aims to improve δ-endotoxin yields through simulations and analysis of various environmental factors, particularly oxygen levels. The proposed BSGrid application facilitates simulations using stochastic methods on both personal and high-performance computing systems, promoting cost-effective research and collaboration in the field.
BacteriumSimulatorGrid (BSGrid) - Tool for Simulating the Behavior of the Bacillus thuringiensis
- 1. 2009 Mesoscale Modeling of the Bacillus thuringiensis Sporulation Network Based on Stochastic Kinetics and Its Application for in Silico Scale-down Harold Castro, Andrés González, Sergio Orduz Mario Villamizar, Nicolás Cuervo, School of Biosciences Gabriel Lozano, Silvia Restrepo Universidad Nacional de Colombia Departments of Chemical Engineering, Medellín, Colombia Biological Sciences and Systems and Computing Engineering Universidad de los Andes Bogotá, Colombia
- 2. Introduction to Bacillus thuringiensis Bacillus thuringiensis is a gram positive bacterium widely known by its capacity of synthesizing δ-endotoxins (parasporal crystal proteins) during the sporulation process, which are used as biopesticides. This δ-endotoxins are used in some products and no toxic effects of B. thuringiensis on humans have been detected in its years of use.
- 3. Motivation These biopesticides are used in countries that require the use of organic agriculture. For instance, in Colombia they can be used for a typical problem in the insect control of maize crops. A B. thuringiensis subspecies as kurstaki can contribute to combat lepidoptera in this kind of crops.
- 4. Problem This kind of biopesticides represents 90% of the total biopesticide market and they just participate in the 5% of the total pesticide market. Industrial-scale fermentation cannot obtain a high concentration of the δ- endotoxins, so the production of biopesticides have a high cost. The δ-endotoxins are produced during the sporulation process of B. thuringiensis. It is necessary to analyze the relationship between the sporulation process and the δ-endotoxin production of the δ-endotoxins to determine the optimum conditions under which the δ-endotoxins are produced. The sporulation process is affected by intrinsic and extrinsic variables which can not be modeled using deterministic models.
- 5. Project objectives Develop a mesoscale stochastic model that predicts the sporulation process in B. thuringiensis so it allows to analyze the relationship between the sporulation process and the δ-endotoxins production, in order to increase, by fermentation processes, the δ-endotoxins production at industrial levels. Determine the effect of oxygen oscillations on the sporulation process in order to analyze the evolution of the protein synthesis on industrial scale (scale-down in silico). Validate the stochastic model results with experimental results.
- 6. Work Areas Definition of a mesoscale stochastic model for B. thuringiensis BSGrid - An application for executing simulations using stochastic algorithms UnaGrid – An Opportunistic High Performance Computing Infrastructure Comparisons with experimental data
- 7. Work Areas Definition of a mesoscale stochastic model for B. thuringiensis BSGrid - An application for executing simulations using stochastic algorithms UnaGrid – An Opportunistic High Performance Computing Infrastructure Comparisons with experimental data
- 8. A mesoscale stochastic model for B. thuringiensis Five proteins are considered: SigmaH, AbrB, KinA, Spo0A and phosporylated Spo0A. The evolution of these proteins is determined based on 27 events classified in four categories (gene transcription, protein transduction, protein degradation, degradation of messenger RNA). Messenger RNA expression is regulated with the use of the Hill equation. In the stochastic simulations the Stochastic Simulation Algorithm (SSA) of Gillespie is used. B. thuringiensis has a bimodal behavior, the planktonic population and the spore-forming population (include spore population).
- 9. Sporulation regulatory network and the Spo0A-P role The phosphorylated Spo0A protein plays an important role because when reaches high concentrations, it activates the whole sporulation process, therefore we considered that when the protein reaches a threshold value it is highly probable that the sporulation process begin
- 10. Sporulation regulatory network - Bimodal population The simulations results seem to predict a bimodal population. For finding the distribution of the populations we developed a simple Montecarlo simulation based on a probability function. f 1 , 1 , 2 , 2 , p 1 p N 1 , 1 pN 2 , 2 We used reverse engineering to find the parameters of this distribution through the development of an algorithm based on sum squares minimization. Each time t was analyzed for parameter regression using Microsoft Excel 2007® solver tool
- 11. Work Areas Definition of a mesoscale stochastic model for B. thuringiensis BSGrid - An application for executing simulations using stochastic algorithms UnaGrid – An Opportunistic High Performance Computing Infrastructure Comparisons with experimental data
- 12. BSGrid – Operation on Personal Computers An application useful for executing simulations using stochastic methods. Java J2SE. Friendly with the final user. 1. Bacterium Structure Definition through GUIs
- 13. BSGrid – Operation on Personal Computers 2. Configuration and Execution of the Simulations through GUIs
- 14. BSGrid – Operation on Personal Computers 3. Visualization and analysis of results, so he/she can decide to modify the bacterium structure and run simulations again.
- 15. BSGrid – Problems for Larger Simulations on PCs 1 Individual ≈ 63 seconds 150000 Individuals ≈ 54 Days ≈ 2 Months ¿Simulations with big populations require larger processing capabilities?
- 16. Solution: BSGrid as a Grid-Enabled application Cluster/Grid Infrastructure Independent Jobs Master XML Document Submitting BSGrid Jobs to the Cluster/Grid Infraestructure Batch Process 1. Bacterium Structure Definition through GUIs Slave 1 2. Configuration and Slave N ….. Execution of Simulations 3. Visualization and analysis of results
- 17. Solution: BSGrid as a Grid-Enabled application (2) Cluster/Grid Infrastructure Independent Jobs BSGrid job BSGrid job BSGrid job Master XML Document Submitting BSGrid Jobs to the Cluster/Grid Infraestructure Batch Process Much time to display the global statistics Slave 1 Slave N BSGrid ….. BSGrid job BSGrid job job User User ….. Analysis 1 Analysis N Relational Database Server
- 18. Solution: BSGrid as a Grid-Enabled application (3) Cluster/Grid Infrastructure Independent Jobs BSGrid job BSGrid job BSGrid job Master XML Document Submitting BSGrid Jobs to the Cluster/Grid Infraestructure Batch Process The time is reduced from minutes to seconds Slave 1 Slave N BSGrid ….. BSGrid job BSGrid job job User User Analysis 1 ….. Analysis N Relational Tables Relational Database Materialized Server Views
- 19. Friendly Graphical User Interfaces of BSGrid
- 20. Tools of the BSGrid Application BSGrid GUI Results Stochastic Algorithms PC Execution GUI Definition Bacterium Structure Model Execution of Output Data Simulations RAM Memory In PCs XML Bacterium Structure Model Execution of Output Data Input File for Simulations Database Server BSGrid In Grid/Cluster GUI Results Grid/Cluster Execution
- 21. Work Areas Definition of a mesoscale stochastic model for B. thuringiensis BSGrid - An application for executing simulations using stochastic algorithms UnaGrid – An Opportunistic High Performance Computing Infrastructure Comparisons with experimental data
- 22. A High Performance Computing Infrastructure (HPCI) This type of simulations requires large processing capabilities. Cluster and grid infrastructures regularly have dedicated computational resources so its implementation requires large financial investments.
- 23. A High Performance Computing Infrastructure (2) Dedicated infrastructures are an unviable option in organizations or countries with low financial resources. However, these organizations have many computer labs which are not fully utilized by employees or university students.
- 24. Solution: Opportunistic virtual clusters X X Cores Cores Linux Linux Processing Processing Virtual Machine Virtual Machine Physical Machine of a Physical Machine of a Computer Room Computer Room a. When there is an End User using b. When there is not an End User the physical machine using the physical machine A virtual cluster is a set of commodity and interconnected desktops executing virtual machines (VMs) in background and low-priority through virtualization technologies, these VMs take advantage of the available idle processing capabilities in computer labs on an university campus.
- 25. Solution: Opportunistic virtual clusters (2) Computer lab VM VM VM VM VM VM VM VM VM A virtual machine is executed on each computer of a lab and it supports the role of a cluster slave and all of these virtual machines on execution make up a virtual processing cluster. A dedicated node is necessary for a virtual cluster and it supports the role of the cluster master.
- 26. Solution: Opportunistic virtual clusters (2) Computer lab VM VM VM VM VM VM VM VM VM Computers in the computer lab – Virtual Cluster Slaves A virtual machine is executed on each computer of a lab and it supports the role of a cluster slave and all of these virtual machines on execution make up a virtual processing cluster. A dedicated node is necessary for a virtual cluster and it supports the role of the cluster master.
- 27. Solution: Opportunistic virtual clusters (2) Computer lab VM VM VM VM VM VM Master Dedicated computer outside the computer lab VM VM VM Computers in the computer lab – Virtual Cluster Slaves A virtual machine is executed on each computer of a lab and it supports the role of a cluster slave and all of these virtual machines on execution make up a virtual processing cluster. A dedicated node is necessary for a virtual cluster and it supports the role of the cluster master.
- 28. Opportunistic virtual clusters - Features Virtual Cluster Research Group C Cluster/Grid User Virtual Cluster Slave Slave Research Group A Cluster/Grid User Master Slave Slave Slave Slave Virtual Cluster Master Research Group B Slave Slave Slave Slave Master Slave Slave A virtual infrastructure composed by virtual clusters. The virtual clusters take advantage of the unused physical resources. An infrastructure for general purpose – Not only for biological simulations
- 29. Opportunistic virtual clusters – Features (2) GRID COMMUNITY Virtual Cluster Research Group B Cluster/Grid User Certificate Virtual Cluster Authority (CA) Research Group A Slave Slave Cluster/Grid User Master Slave Slave Middleware Slave Slave Grid Virtual Cluster Master Research Group C Slave Slave Cluster/Grid User Slave Slave Master Slave Slave Each research group can define its own virtual clusters with custom application environments (middlewares, applications, configurations, etc) A grid solution (several virtual clusters) can be deployed for supporting the processing capabilities required by some applications.
- 30. Opportunistic Grid Virtual Infrastructure Proposed Our strategy solves the problems associated with the lack or sub-utilization of preexisting computer laboratories and promotes new opportunities: The collaborative work among research groups The development of research projects that requires large processing capabilities at low cost. Limitations Best effort approach. No quality of service (QoS) is guaranteed. The capabilities of a virtual cluster depend of its configuration. Bag of tasks application.
- 31. Opportunistic Grid Virtual Infrastructure Deployed Cluster/Grid Cluster/Grid Cluster/Grid Three computer labs, each User User User Job Submission Job Submission Job Submission one with 35 computers and VMWare ESX Server windows XP as the base Globus Globus operating system. Middleware Middleware Virtual Machine Virtual Machine Virtual Machine Master Cluster Turing Master Cluster Wuaira1 Master Cluster Wuaira2 Core 2 Duo processor Computer Labs (1,86GHz) and 4 GB of RAM. Cluster Virtual Turing Cluster Virtual Wuaira Cluster Virtual Wuaira Computer Lab Computer Lab Computer Lab Three virtual clusters. Condor scheduler. How to deploy the virtual machines? VMware virtualization If the virtual machines are always in execution, software. they will be always consuming energy including when there are not cluster/grid users using the virtual infrastructure. Globus middleware. A green solution it is necessary.
- 32. Opportunistic Grid Virtual Infrastructure Deployed Three computer labs, each Cluster/Grid User Cluster/Grid User Cluster/Grid User Job Submission one with 35 computers and Job Submission Job Submission VMWare ESX Server windows XP as the base Globus Globus operating system. Middleware Middleware Virtual Machine Virtual Machine Virtual Machine Master Cluster Turing Master Cluster Wuaira1 Master Cluster Wuaira2 Core 2 Duo processor (1,86GHz) and 4 GB of RAM. Computer Labs Cluster Virtual Turing Cluster Virtual Wuaira Cluster Virtual Wuaira Computer Lab Computer Lab Computer Lab Three virtual clusters. Condor scheduler. Data Center Domain Controller Domain Controller Windows 2008 Server Windows 2003 Server VMware virtualization software. GUMA Admin. ADMONSIS Web Server Admin. Domain CAPRICA Domain Globus middleware. Cluster/Grid Cluster/Grid Cluster/Grid User User User
- 33. Deployment on Demand of the Virtual Infrastructure The deployment of virtual clusters is executed on demand through GUMA. This application allows to execute and manage virtual clusters on demand and it provides multiple services for managing the grid from light clients. It allows the monitoring of the physical and virtual machines.
- 34. Work Areas Definition of a mesoscale stochastic model for B. thuringiensis BSGrid - An application for executing simulations using stochastic algorithms UnaGrid – An Opportunistic High Performance Computing Infrastructure Comparisons with experimental data
- 35. Experimental tests Three fermentations were carried out and the B. thuringiensis subsp. kurstaki HD1-1999 were used. One single colony was inoculated in 50 mL culture at 30 oC for 72 h. Oxygen was controlled by adding a mix of air-pure oxygen. pH and temperature were maintained at 6.5 and 30 oC respectively. The population of planktonic, spore-forming and spores populations were evaluated using phase contrast microscope.
- 36. Experimental results Our results seem to indicate that the sporulation process is triggered around the 20th hour possibly influenced by intrinsic and extrinsic noise, and due to poor oxygen transfer in Bogotá (2600 AMSL) we believe that the spore content did not pass over 60%, contrary to several reports.
- 37. In silico results - Bimodal population The model was run for 150000 cells. The analysis was carried out for 2900 cells up to 80000 seconds. In order to save computational resources, results were saved every 500 s. In order to assure the presence of two subpopulations in the proposed mesoscale model, we adjust our histograms to continue Gaussian distribution curves and the bimodal population describes the presence of planktonic cells (low Spo0AP) and spores (high Spo0A-P) along the time.
- 38. In silico results Interestingly, high Spo0A-P population increases when augmenting time clearly indicating the augmenting of spores until reaching steady state (right figure). These results describe a similar dynamics compared to the spore concentration in the fermentor (left figure). Our analysis in silico predicts that the sporulation process takes around 8 h to be completed while the experimental results display that the process takes within 20 h. A deeper study is required.
- 39. System response to oxygen oscillations Keep into account that Oxygen tension partially controls KinA activity therefore affecting Spo0A phosphorylation rate described by: Spo 0 A Spo 0 A P c A sin 2 t + d n KinA c KMsp * KinA n K n T kasp The stochastic kinetic constant A : Wave amplitude c was modified according to: T : Oscilation period d : M ean value of the sinusoidal function Parameters Simulation A T d Five hundred simulations were 1 0,5 0,5 0,5 performed for each of these 2 0,5 1,0 0,5 conditions. 3 0,625 1,0 0,625 4 0,25 1,0 1,0 5 0,5 1,0 1,0
- 40. Spo0A-P response to oscillations in the oxygen tension The results of these simulations with oscillations in the oxygen tension predict a reduction in the size of the high Spo0A-P population demonstrating the effects of the industrial-scale oscillations on the sporulation process.
- 41. Results of processing time and data generated Processing time required on a personal computer: Amount of Time required for each CPU Total time Model name bacteria bacterium (sec) numbers (days) B. thruring. 150000 63 2 54,69 Processing time required on the opportunistic virtual cluster infrastructure: Amount of Time required for each CPU Total time Model name bacteria bacterium (sec) numbers (days) B. thruring. 150000 111 70 2,75 These results confirm the benefits of our strategy and performance tests confirm the transparency of our model. We found that 10GB were generated by the model simulated.
- 42. Conclusions Stochastic model In the model developed we demonstrate the presence of multistability for B. thuringiensis and we also can demonstrate that cycling the oxygen decreases the population of spore-forming cells. BSGrid application BSGrid application is a tool for simulating biological systems using stochastic methods and algorithms in PCs and HPCIs. Virtual infrastructure and parallel computing Parallel computing provides advantages for this type of simulations through the generation of a large number of independent jobs. The infrastructure proposed allows the execution of this and other applications using an opportunistic strategy (cost close to zero).
- 43. Future work Stochastic model The proposed model predicts an elapsed time of 8 h for the sporulation process. Nevertheless our experimental results indicate a longer process therefore more studies are required in order to understand the triggering process. Analysis with new parameters in the model are required for analyzing the relationship between the sporulation process and the δ-endotoxins production. Experimental results In the fermentation process were not possible to differentiate between spores populations and spore-forming populations so an analysis more detailed should be used for validating the mesoscale model using reporter genes related with the sporulation.
- 44. Future work BSGrid application Adapt and publish BSGrid as an open source application. Given its modular design, BSGrid is ready to be extended to handle new stochastic methods and algorithms. Infrastructure Researchers want to work now with larger populations, more complex structures and get more accurate answers.
- 45. Thanks for your attention! Questions?