SlideShare una empresa de Scribd logo
1 de 45
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
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.
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.
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.
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.
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
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
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).
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
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
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
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
BSGrid – Operation on Personal Computers




2. Configuration and
  Execution of the
Simulations through
        GUIs
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.
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?
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
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
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
Friendly Graphical User Interfaces of BSGrid
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
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
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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
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.
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
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.
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.
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.
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.
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
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.
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.
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).
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.
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.
Thanks for your attention!




                 Questions?

Más contenido relacionado

Similar a BacteriumSimulatorGrid (BSGrid) - Tool for Simulating the Behavior of the Bacillus thuringiensis

Thesis: Slicing of Java Programs using the Soot Framework (2006)
Thesis:  Slicing of Java Programs using the Soot Framework (2006) Thesis:  Slicing of Java Programs using the Soot Framework (2006)
Thesis: Slicing of Java Programs using the Soot Framework (2006) Arvind Devaraj
 
IRJET- Generating 3D Models Using 3D Generative Adversarial Network
IRJET- Generating 3D Models Using 3D Generative Adversarial NetworkIRJET- Generating 3D Models Using 3D Generative Adversarial Network
IRJET- Generating 3D Models Using 3D Generative Adversarial NetworkIRJET Journal
 
2D Gaussian Filter for Image Processing A Study.pdf
2D Gaussian Filter for Image Processing  A Study.pdf2D Gaussian Filter for Image Processing  A Study.pdf
2D Gaussian Filter for Image Processing A Study.pdfLisa Riley
 
Hardware software co simulation of edge detection for image processing system...
Hardware software co simulation of edge detection for image processing system...Hardware software co simulation of edge detection for image processing system...
Hardware software co simulation of edge detection for image processing system...eSAT Publishing House
 
Creating Objects for Metaverse using GANs and Autoencoders
Creating Objects for Metaverse using GANs and AutoencodersCreating Objects for Metaverse using GANs and Autoencoders
Creating Objects for Metaverse using GANs and AutoencodersIRJET Journal
 
Partial Object Detection in Inclined Weather Conditions
Partial Object Detection in Inclined Weather ConditionsPartial Object Detection in Inclined Weather Conditions
Partial Object Detection in Inclined Weather ConditionsIRJET Journal
 
AIA Hsinchu Orange3
AIA Hsinchu Orange3AIA Hsinchu Orange3
AIA Hsinchu Orange3Chun Yi Wu
 
IRJET- A Review Paper on Object Detection using Zynq-7000 FPGA for an Embedde...
IRJET- A Review Paper on Object Detection using Zynq-7000 FPGA for an Embedde...IRJET- A Review Paper on Object Detection using Zynq-7000 FPGA for an Embedde...
IRJET- A Review Paper on Object Detection using Zynq-7000 FPGA for an Embedde...IRJET Journal
 
AN EFFICIENT FPGA IMPLEMENTATION OF MRI IMAGE FILTERING AND TUMOUR CHARACTERI...
AN EFFICIENT FPGA IMPLEMENTATION OF MRI IMAGE FILTERING AND TUMOUR CHARACTERI...AN EFFICIENT FPGA IMPLEMENTATION OF MRI IMAGE FILTERING AND TUMOUR CHARACTERI...
AN EFFICIENT FPGA IMPLEMENTATION OF MRI IMAGE FILTERING AND TUMOUR CHARACTERI...VLSICS Design
 
An Efficient FPGA Implemenation of MRI Image Filtering and Tumour Characteriz...
An Efficient FPGA Implemenation of MRI Image Filtering and Tumour Characteriz...An Efficient FPGA Implemenation of MRI Image Filtering and Tumour Characteriz...
An Efficient FPGA Implemenation of MRI Image Filtering and Tumour Characteriz...VLSICS Design
 
Quality assessment of stereoscopic 3 d image compression by binocular integra...
Quality assessment of stereoscopic 3 d image compression by binocular integra...Quality assessment of stereoscopic 3 d image compression by binocular integra...
Quality assessment of stereoscopic 3 d image compression by binocular integra...Shakas Technologies
 
Branch and-bound nearest neighbor searching over unbalanced trie-structured o...
Branch and-bound nearest neighbor searching over unbalanced trie-structured o...Branch and-bound nearest neighbor searching over unbalanced trie-structured o...
Branch and-bound nearest neighbor searching over unbalanced trie-structured o...Michail Argyriou
 
DEEP LEARNING BASED BRAIN STROKE DETECTION
DEEP LEARNING BASED BRAIN STROKE DETECTIONDEEP LEARNING BASED BRAIN STROKE DETECTION
DEEP LEARNING BASED BRAIN STROKE DETECTIONIRJET Journal
 
IRJET-Hardware Co-Simulation of Classical Edge Detection Algorithms using Xil...
IRJET-Hardware Co-Simulation of Classical Edge Detection Algorithms using Xil...IRJET-Hardware Co-Simulation of Classical Edge Detection Algorithms using Xil...
IRJET-Hardware Co-Simulation of Classical Edge Detection Algorithms using Xil...IRJET Journal
 
Photo Editing And Sharing Web Application With AI- Assisted Features
Photo Editing And Sharing Web Application With AI- Assisted FeaturesPhoto Editing And Sharing Web Application With AI- Assisted Features
Photo Editing And Sharing Web Application With AI- Assisted FeaturesIRJET Journal
 
An Open Source solution for Three-Dimensional documentation: archaeological a...
An Open Source solution for Three-Dimensional documentation: archaeological a...An Open Source solution for Three-Dimensional documentation: archaeological a...
An Open Source solution for Three-Dimensional documentation: archaeological a...Giulio Bigliardi
 
A Literature Survey: Neural Networks for object detection
A Literature Survey: Neural Networks for object detectionA Literature Survey: Neural Networks for object detection
A Literature Survey: Neural Networks for object detectionvivatechijri
 

Similar a BacteriumSimulatorGrid (BSGrid) - Tool for Simulating the Behavior of the Bacillus thuringiensis (20)

Thesis: Slicing of Java Programs using the Soot Framework (2006)
Thesis:  Slicing of Java Programs using the Soot Framework (2006) Thesis:  Slicing of Java Programs using the Soot Framework (2006)
Thesis: Slicing of Java Programs using the Soot Framework (2006)
 
IRJET- Generating 3D Models Using 3D Generative Adversarial Network
IRJET- Generating 3D Models Using 3D Generative Adversarial NetworkIRJET- Generating 3D Models Using 3D Generative Adversarial Network
IRJET- Generating 3D Models Using 3D Generative Adversarial Network
 
2D Gaussian Filter for Image Processing A Study.pdf
2D Gaussian Filter for Image Processing  A Study.pdf2D Gaussian Filter for Image Processing  A Study.pdf
2D Gaussian Filter for Image Processing A Study.pdf
 
Hardware software co simulation of edge detection for image processing system...
Hardware software co simulation of edge detection for image processing system...Hardware software co simulation of edge detection for image processing system...
Hardware software co simulation of edge detection for image processing system...
 
dc09ttp-2011-thesis
dc09ttp-2011-thesisdc09ttp-2011-thesis
dc09ttp-2011-thesis
 
Creating Objects for Metaverse using GANs and Autoencoders
Creating Objects for Metaverse using GANs and AutoencodersCreating Objects for Metaverse using GANs and Autoencoders
Creating Objects for Metaverse using GANs and Autoencoders
 
Partial Object Detection in Inclined Weather Conditions
Partial Object Detection in Inclined Weather ConditionsPartial Object Detection in Inclined Weather Conditions
Partial Object Detection in Inclined Weather Conditions
 
AIA Hsinchu Orange3
AIA Hsinchu Orange3AIA Hsinchu Orange3
AIA Hsinchu Orange3
 
IRJET- A Review Paper on Object Detection using Zynq-7000 FPGA for an Embedde...
IRJET- A Review Paper on Object Detection using Zynq-7000 FPGA for an Embedde...IRJET- A Review Paper on Object Detection using Zynq-7000 FPGA for an Embedde...
IRJET- A Review Paper on Object Detection using Zynq-7000 FPGA for an Embedde...
 
AN EFFICIENT FPGA IMPLEMENTATION OF MRI IMAGE FILTERING AND TUMOUR CHARACTERI...
AN EFFICIENT FPGA IMPLEMENTATION OF MRI IMAGE FILTERING AND TUMOUR CHARACTERI...AN EFFICIENT FPGA IMPLEMENTATION OF MRI IMAGE FILTERING AND TUMOUR CHARACTERI...
AN EFFICIENT FPGA IMPLEMENTATION OF MRI IMAGE FILTERING AND TUMOUR CHARACTERI...
 
An Efficient FPGA Implemenation of MRI Image Filtering and Tumour Characteriz...
An Efficient FPGA Implemenation of MRI Image Filtering and Tumour Characteriz...An Efficient FPGA Implemenation of MRI Image Filtering and Tumour Characteriz...
An Efficient FPGA Implemenation of MRI Image Filtering and Tumour Characteriz...
 
Quality assessment of stereoscopic 3 d image compression by binocular integra...
Quality assessment of stereoscopic 3 d image compression by binocular integra...Quality assessment of stereoscopic 3 d image compression by binocular integra...
Quality assessment of stereoscopic 3 d image compression by binocular integra...
 
Branch and-bound nearest neighbor searching over unbalanced trie-structured o...
Branch and-bound nearest neighbor searching over unbalanced trie-structured o...Branch and-bound nearest neighbor searching over unbalanced trie-structured o...
Branch and-bound nearest neighbor searching over unbalanced trie-structured o...
 
DEEP LEARNING BASED BRAIN STROKE DETECTION
DEEP LEARNING BASED BRAIN STROKE DETECTIONDEEP LEARNING BASED BRAIN STROKE DETECTION
DEEP LEARNING BASED BRAIN STROKE DETECTION
 
IRJET-Hardware Co-Simulation of Classical Edge Detection Algorithms using Xil...
IRJET-Hardware Co-Simulation of Classical Edge Detection Algorithms using Xil...IRJET-Hardware Co-Simulation of Classical Edge Detection Algorithms using Xil...
IRJET-Hardware Co-Simulation of Classical Edge Detection Algorithms using Xil...
 
Deep MIML Network
Deep MIML NetworkDeep MIML Network
Deep MIML Network
 
Photo Editing And Sharing Web Application With AI- Assisted Features
Photo Editing And Sharing Web Application With AI- Assisted FeaturesPhoto Editing And Sharing Web Application With AI- Assisted Features
Photo Editing And Sharing Web Application With AI- Assisted Features
 
thesis
thesisthesis
thesis
 
An Open Source solution for Three-Dimensional documentation: archaeological a...
An Open Source solution for Three-Dimensional documentation: archaeological a...An Open Source solution for Three-Dimensional documentation: archaeological a...
An Open Source solution for Three-Dimensional documentation: archaeological a...
 
A Literature Survey: Neural Networks for object detection
A Literature Survey: Neural Networks for object detectionA Literature Survey: Neural Networks for object detection
A Literature Survey: Neural Networks for object detection
 

Más de Mario Jose Villamizar Cano

e-Clouds A Platform and Marketplace to Access and Publish Scientific Applicat...
e-Clouds A Platform and Marketplace to Access and Publish Scientific Applicat...e-Clouds A Platform and Marketplace to Access and Publish Scientific Applicat...
e-Clouds A Platform and Marketplace to Access and Publish Scientific Applicat...Mario Jose Villamizar Cano
 
Frameworks y herramientas de desarrollo ágil para emprendedores y startups
Frameworks y herramientas de desarrollo ágil para emprendedores y startupsFrameworks y herramientas de desarrollo ágil para emprendedores y startups
Frameworks y herramientas de desarrollo ágil para emprendedores y startupsMario Jose Villamizar Cano
 
Desarrollo de Soluciones Escalables de Software como Servicio (SaaS)
Desarrollo de Soluciones Escalables de Software como Servicio (SaaS)Desarrollo de Soluciones Escalables de Software como Servicio (SaaS)
Desarrollo de Soluciones Escalables de Software como Servicio (SaaS)Mario Jose Villamizar Cano
 
An Overview of Internet Startups and Entrepreneurship
An Overview of Internet Startups and EntrepreneurshipAn Overview of Internet Startups and Entrepreneurship
An Overview of Internet Startups and EntrepreneurshipMario Jose Villamizar Cano
 
Energy-aware VM Allocation on An Opportunistic Cloud Infrastructure
Energy-aware VM Allocation on An Opportunistic Cloud InfrastructureEnergy-aware VM Allocation on An Opportunistic Cloud Infrastructure
Energy-aware VM Allocation on An Opportunistic Cloud InfrastructureMario Jose Villamizar Cano
 
Emprendimiento en Internet / Internet Startups
Emprendimiento en Internet / Internet StartupsEmprendimiento en Internet / Internet Startups
Emprendimiento en Internet / Internet StartupsMario Jose Villamizar Cano
 
e-Clouds: a SaaS Marketplace for Scientific Computing
e-Clouds: a SaaS Marketplace for Scientific Computinge-Clouds: a SaaS Marketplace for Scientific Computing
e-Clouds: a SaaS Marketplace for Scientific ComputingMario Jose Villamizar Cano
 
Cloud computing oportunidades para empresarios y emprendedores
Cloud computing oportunidades para empresarios y emprendedoresCloud computing oportunidades para empresarios y emprendedores
Cloud computing oportunidades para empresarios y emprendedoresMario Jose Villamizar Cano
 
UnaCloud: Opportunistic Cloud Computing Infrastructure as a Service
UnaCloud: Opportunistic Cloud Computing Infrastructure as a ServiceUnaCloud: Opportunistic Cloud Computing Infrastructure as a Service
UnaCloud: Opportunistic Cloud Computing Infrastructure as a ServiceMario Jose Villamizar Cano
 
Bio-UnaGrid: Easing bioinformatics workflow execution
Bio-UnaGrid: Easing bioinformatics workflow executionBio-UnaGrid: Easing bioinformatics workflow execution
Bio-UnaGrid: Easing bioinformatics workflow executionMario Jose Villamizar Cano
 
Taxonomía de los modelos de entrega de servicios, despliegue y facturación en...
Taxonomía de los modelos de entrega de servicios, despliegue y facturación en...Taxonomía de los modelos de entrega de servicios, despliegue y facturación en...
Taxonomía de los modelos de entrega de servicios, despliegue y facturación en...Mario Jose Villamizar Cano
 
Una grid una solución oportunista para la HPC en colombia
Una grid una solución oportunista para la HPC en colombiaUna grid una solución oportunista para la HPC en colombia
Una grid una solución oportunista para la HPC en colombiaMario Jose Villamizar Cano
 
Infraestructura computacional: Computación en grid
Infraestructura computacional: Computación en gridInfraestructura computacional: Computación en grid
Infraestructura computacional: Computación en gridMario Jose Villamizar Cano
 

Más de Mario Jose Villamizar Cano (20)

Emprendimiento en internet y startups 2017
Emprendimiento en internet y startups 2017Emprendimiento en internet y startups 2017
Emprendimiento en internet y startups 2017
 
e-Clouds A Platform and Marketplace to Access and Publish Scientific Applicat...
e-Clouds A Platform and Marketplace to Access and Publish Scientific Applicat...e-Clouds A Platform and Marketplace to Access and Publish Scientific Applicat...
e-Clouds A Platform and Marketplace to Access and Publish Scientific Applicat...
 
Frameworks y herramientas de desarrollo ágil para emprendedores y startups
Frameworks y herramientas de desarrollo ágil para emprendedores y startupsFrameworks y herramientas de desarrollo ágil para emprendedores y startups
Frameworks y herramientas de desarrollo ágil para emprendedores y startups
 
Desarrollo de Soluciones Escalables de Software como Servicio (SaaS)
Desarrollo de Soluciones Escalables de Software como Servicio (SaaS)Desarrollo de Soluciones Escalables de Software como Servicio (SaaS)
Desarrollo de Soluciones Escalables de Software como Servicio (SaaS)
 
An Overview of Internet Startups and Entrepreneurship
An Overview of Internet Startups and EntrepreneurshipAn Overview of Internet Startups and Entrepreneurship
An Overview of Internet Startups and Entrepreneurship
 
Energy-aware VM Allocation on An Opportunistic Cloud Infrastructure
Energy-aware VM Allocation on An Opportunistic Cloud InfrastructureEnergy-aware VM Allocation on An Opportunistic Cloud Infrastructure
Energy-aware VM Allocation on An Opportunistic Cloud Infrastructure
 
Emprendimiento en Internet / Internet Startups
Emprendimiento en Internet / Internet StartupsEmprendimiento en Internet / Internet Startups
Emprendimiento en Internet / Internet Startups
 
e-Clouds: a SaaS Marketplace for Scientific Computing
e-Clouds: a SaaS Marketplace for Scientific Computinge-Clouds: a SaaS Marketplace for Scientific Computing
e-Clouds: a SaaS Marketplace for Scientific Computing
 
Cloud computing oportunidades para empresarios y emprendedores
Cloud computing oportunidades para empresarios y emprendedoresCloud computing oportunidades para empresarios y emprendedores
Cloud computing oportunidades para empresarios y emprendedores
 
CLOUD COMPUTING HOY: Todo como Servicio.
CLOUD COMPUTING HOY: Todo como Servicio.CLOUD COMPUTING HOY: Todo como Servicio.
CLOUD COMPUTING HOY: Todo como Servicio.
 
UnaCloud: Opportunistic Cloud Computing Infrastructure as a Service
UnaCloud: Opportunistic Cloud Computing Infrastructure as a ServiceUnaCloud: Opportunistic Cloud Computing Infrastructure as a Service
UnaCloud: Opportunistic Cloud Computing Infrastructure as a Service
 
Bio-UnaGrid: Easing bioinformatics workflow execution
Bio-UnaGrid: Easing bioinformatics workflow executionBio-UnaGrid: Easing bioinformatics workflow execution
Bio-UnaGrid: Easing bioinformatics workflow execution
 
Taxonomía de los modelos de entrega de servicios, despliegue y facturación en...
Taxonomía de los modelos de entrega de servicios, despliegue y facturación en...Taxonomía de los modelos de entrega de servicios, despliegue y facturación en...
Taxonomía de los modelos de entrega de servicios, despliegue y facturación en...
 
An Opportunistic Storage System for UnaGrid
An Opportunistic Storage System for UnaGridAn Opportunistic Storage System for UnaGrid
An Opportunistic Storage System for UnaGrid
 
Una grid una solución oportunista para la HPC en colombia
Una grid una solución oportunista para la HPC en colombiaUna grid una solución oportunista para la HPC en colombia
Una grid una solución oportunista para la HPC en colombia
 
Infraestructura computacional: Computación en grid
Infraestructura computacional: Computación en gridInfraestructura computacional: Computación en grid
Infraestructura computacional: Computación en grid
 
APO1 - Presentacion nivel 6
APO1 - Presentacion nivel 6APO1 - Presentacion nivel 6
APO1 - Presentacion nivel 6
 
APO1 - Presentacion nivel 4
APO1 - Presentacion nivel 4APO1 - Presentacion nivel 4
APO1 - Presentacion nivel 4
 
APO2 - Presentacion nivel 10
APO2 - Presentacion nivel 10APO2 - Presentacion nivel 10
APO2 - Presentacion nivel 10
 
APO2 - Presentacion nivel 9
APO2 - Presentacion nivel 9APO2 - Presentacion nivel 9
APO2 - Presentacion nivel 9
 

Último

30-de-thi-vao-lop-10-mon-tieng-anh-co-dap-an.doc
30-de-thi-vao-lop-10-mon-tieng-anh-co-dap-an.doc30-de-thi-vao-lop-10-mon-tieng-anh-co-dap-an.doc
30-de-thi-vao-lop-10-mon-tieng-anh-co-dap-an.docdieu18
 
Material Remains as Source of Ancient Indian History & Culture.ppt
Material Remains as Source of Ancient Indian History & Culture.pptMaterial Remains as Source of Ancient Indian History & Culture.ppt
Material Remains as Source of Ancient Indian History & Culture.pptBanaras Hindu University
 
3.14.24 The Selma March and the Voting Rights Act.pptx
3.14.24 The Selma March and the Voting Rights Act.pptx3.14.24 The Selma March and the Voting Rights Act.pptx
3.14.24 The Selma March and the Voting Rights Act.pptxmary850239
 
PHARMACOGNOSY CHAPTER NO 5 CARMINATIVES AND G.pdf
PHARMACOGNOSY CHAPTER NO 5 CARMINATIVES AND G.pdfPHARMACOGNOSY CHAPTER NO 5 CARMINATIVES AND G.pdf
PHARMACOGNOSY CHAPTER NO 5 CARMINATIVES AND G.pdfSumit Tiwari
 
Pharmacology chapter No 7 full notes.pdf
Pharmacology chapter No 7 full notes.pdfPharmacology chapter No 7 full notes.pdf
Pharmacology chapter No 7 full notes.pdfSumit Tiwari
 
Metabolism , Metabolic Fate& disorders of cholesterol.pptx
Metabolism , Metabolic Fate& disorders of cholesterol.pptxMetabolism , Metabolic Fate& disorders of cholesterol.pptx
Metabolism , Metabolic Fate& disorders of cholesterol.pptxDr. Santhosh Kumar. N
 
Riti theory by Vamana Indian poetics.pptx
Riti theory by Vamana Indian poetics.pptxRiti theory by Vamana Indian poetics.pptx
Riti theory by Vamana Indian poetics.pptxDhatriParmar
 
POST ENCEPHALITIS case study Jitendra bhargav
POST ENCEPHALITIS case study  Jitendra bhargavPOST ENCEPHALITIS case study  Jitendra bhargav
POST ENCEPHALITIS case study Jitendra bhargavJitendra Bhargav
 
UNIT I Design Thinking and Explore.pptx
UNIT I  Design Thinking and Explore.pptxUNIT I  Design Thinking and Explore.pptx
UNIT I Design Thinking and Explore.pptxGOWSIKRAJA PALANISAMY
 
25 CHUYÊN ĐỀ ÔN THI TỐT NGHIỆP THPT 2023 – BÀI TẬP PHÁT TRIỂN TỪ ĐỀ MINH HỌA...
25 CHUYÊN ĐỀ ÔN THI TỐT NGHIỆP THPT 2023 – BÀI TẬP PHÁT TRIỂN TỪ ĐỀ MINH HỌA...25 CHUYÊN ĐỀ ÔN THI TỐT NGHIỆP THPT 2023 – BÀI TẬP PHÁT TRIỂN TỪ ĐỀ MINH HỌA...
25 CHUYÊN ĐỀ ÔN THI TỐT NGHIỆP THPT 2023 – BÀI TẬP PHÁT TRIỂN TỪ ĐỀ MINH HỌA...Nguyen Thanh Tu Collection
 
DLL Catch Up Friday March 22.docx CATCH UP FRIDAYS
DLL Catch Up Friday March 22.docx CATCH UP FRIDAYSDLL Catch Up Friday March 22.docx CATCH UP FRIDAYS
DLL Catch Up Friday March 22.docx CATCH UP FRIDAYSTeacherNicaPrintable
 
ASTRINGENTS.pdf Pharmacognosy chapter 5 diploma in Pharmacy
ASTRINGENTS.pdf Pharmacognosy chapter 5 diploma in PharmacyASTRINGENTS.pdf Pharmacognosy chapter 5 diploma in Pharmacy
ASTRINGENTS.pdf Pharmacognosy chapter 5 diploma in PharmacySumit Tiwari
 
DNA and RNA , Structure, Functions, Types, difference, Similarities, Protein ...
DNA and RNA , Structure, Functions, Types, difference, Similarities, Protein ...DNA and RNA , Structure, Functions, Types, difference, Similarities, Protein ...
DNA and RNA , Structure, Functions, Types, difference, Similarities, Protein ...AKSHAYMAGAR17
 
ICS2208 Lecture4 Intelligent Interface Agents.pdf
ICS2208 Lecture4 Intelligent Interface Agents.pdfICS2208 Lecture4 Intelligent Interface Agents.pdf
ICS2208 Lecture4 Intelligent Interface Agents.pdfVanessa Camilleri
 
LEAD6001 - Introduction to Advanced Stud
LEAD6001 - Introduction to Advanced StudLEAD6001 - Introduction to Advanced Stud
LEAD6001 - Introduction to Advanced StudDr. Bruce A. Johnson
 
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - HK2 (...
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - HK2 (...HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - HK2 (...
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - HK2 (...Nguyen Thanh Tu Collection
 
AI Uses and Misuses: Academic and Workplace Applications
AI Uses and Misuses: Academic and Workplace ApplicationsAI Uses and Misuses: Academic and Workplace Applications
AI Uses and Misuses: Academic and Workplace ApplicationsStella Lee
 

Último (20)

30-de-thi-vao-lop-10-mon-tieng-anh-co-dap-an.doc
30-de-thi-vao-lop-10-mon-tieng-anh-co-dap-an.doc30-de-thi-vao-lop-10-mon-tieng-anh-co-dap-an.doc
30-de-thi-vao-lop-10-mon-tieng-anh-co-dap-an.doc
 
Material Remains as Source of Ancient Indian History & Culture.ppt
Material Remains as Source of Ancient Indian History & Culture.pptMaterial Remains as Source of Ancient Indian History & Culture.ppt
Material Remains as Source of Ancient Indian History & Culture.ppt
 
ANOVA Parametric test: Biostatics and Research Methodology
ANOVA Parametric test: Biostatics and Research MethodologyANOVA Parametric test: Biostatics and Research Methodology
ANOVA Parametric test: Biostatics and Research Methodology
 
3.14.24 The Selma March and the Voting Rights Act.pptx
3.14.24 The Selma March and the Voting Rights Act.pptx3.14.24 The Selma March and the Voting Rights Act.pptx
3.14.24 The Selma March and the Voting Rights Act.pptx
 
PHARMACOGNOSY CHAPTER NO 5 CARMINATIVES AND G.pdf
PHARMACOGNOSY CHAPTER NO 5 CARMINATIVES AND G.pdfPHARMACOGNOSY CHAPTER NO 5 CARMINATIVES AND G.pdf
PHARMACOGNOSY CHAPTER NO 5 CARMINATIVES AND G.pdf
 
t-test Parametric test Biostatics and Research Methodology
t-test Parametric test Biostatics and Research Methodologyt-test Parametric test Biostatics and Research Methodology
t-test Parametric test Biostatics and Research Methodology
 
Pharmacology chapter No 7 full notes.pdf
Pharmacology chapter No 7 full notes.pdfPharmacology chapter No 7 full notes.pdf
Pharmacology chapter No 7 full notes.pdf
 
Metabolism , Metabolic Fate& disorders of cholesterol.pptx
Metabolism , Metabolic Fate& disorders of cholesterol.pptxMetabolism , Metabolic Fate& disorders of cholesterol.pptx
Metabolism , Metabolic Fate& disorders of cholesterol.pptx
 
Riti theory by Vamana Indian poetics.pptx
Riti theory by Vamana Indian poetics.pptxRiti theory by Vamana Indian poetics.pptx
Riti theory by Vamana Indian poetics.pptx
 
POST ENCEPHALITIS case study Jitendra bhargav
POST ENCEPHALITIS case study  Jitendra bhargavPOST ENCEPHALITIS case study  Jitendra bhargav
POST ENCEPHALITIS case study Jitendra bhargav
 
UNIT I Design Thinking and Explore.pptx
UNIT I  Design Thinking and Explore.pptxUNIT I  Design Thinking and Explore.pptx
UNIT I Design Thinking and Explore.pptx
 
25 CHUYÊN ĐỀ ÔN THI TỐT NGHIỆP THPT 2023 – BÀI TẬP PHÁT TRIỂN TỪ ĐỀ MINH HỌA...
25 CHUYÊN ĐỀ ÔN THI TỐT NGHIỆP THPT 2023 – BÀI TẬP PHÁT TRIỂN TỪ ĐỀ MINH HỌA...25 CHUYÊN ĐỀ ÔN THI TỐT NGHIỆP THPT 2023 – BÀI TẬP PHÁT TRIỂN TỪ ĐỀ MINH HỌA...
25 CHUYÊN ĐỀ ÔN THI TỐT NGHIỆP THPT 2023 – BÀI TẬP PHÁT TRIỂN TỪ ĐỀ MINH HỌA...
 
DLL Catch Up Friday March 22.docx CATCH UP FRIDAYS
DLL Catch Up Friday March 22.docx CATCH UP FRIDAYSDLL Catch Up Friday March 22.docx CATCH UP FRIDAYS
DLL Catch Up Friday March 22.docx CATCH UP FRIDAYS
 
ASTRINGENTS.pdf Pharmacognosy chapter 5 diploma in Pharmacy
ASTRINGENTS.pdf Pharmacognosy chapter 5 diploma in PharmacyASTRINGENTS.pdf Pharmacognosy chapter 5 diploma in Pharmacy
ASTRINGENTS.pdf Pharmacognosy chapter 5 diploma in Pharmacy
 
DNA and RNA , Structure, Functions, Types, difference, Similarities, Protein ...
DNA and RNA , Structure, Functions, Types, difference, Similarities, Protein ...DNA and RNA , Structure, Functions, Types, difference, Similarities, Protein ...
DNA and RNA , Structure, Functions, Types, difference, Similarities, Protein ...
 
ICS2208 Lecture4 Intelligent Interface Agents.pdf
ICS2208 Lecture4 Intelligent Interface Agents.pdfICS2208 Lecture4 Intelligent Interface Agents.pdf
ICS2208 Lecture4 Intelligent Interface Agents.pdf
 
LEAD6001 - Introduction to Advanced Stud
LEAD6001 - Introduction to Advanced StudLEAD6001 - Introduction to Advanced Stud
LEAD6001 - Introduction to Advanced Stud
 
Least Significance Difference:Biostatics and Research Methodology
Least Significance Difference:Biostatics and Research MethodologyLeast Significance Difference:Biostatics and Research Methodology
Least Significance Difference:Biostatics and Research Methodology
 
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - HK2 (...
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - HK2 (...HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - HK2 (...
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - HK2 (...
 
AI Uses and Misuses: Academic and Workplace Applications
AI Uses and Misuses: Academic and Workplace ApplicationsAI Uses and Misuses: Academic and Workplace Applications
AI Uses and Misuses: Academic and Workplace Applications
 

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?