In SilicoIn Silico Laboratories: Laboratories:The Virtual Parasite Project - The Virtual Parasite Project -

An OverviewAn OverviewSURA Grid Computing in the Life Sciences - 2006SURA Grid Computing in the Life Sciences - 2006

Tarynn M. Witten, Ph.D., FGSA, FCSBCTarynn M. Witten, Ph.D., FGSA, FCSBCDirector, Research and DevelopmentDirector, Research and Development

Center for the Study of Biological ComplexityCenter for the Study of Biological ComplexityVisual Parasite ProjectVisual Parasite Project

Virginia Commonwealth UniversityVirginia Commonwealth UniversityRichmond, VA Richmond, VA

twitten@vcu.edu

OVERVIEWOVERVIEW The TeamThe Team Computational Biology – A Larger ViewComputational Biology – A Larger View In Silico LaboratoriesIn Silico Laboratories Introduction to the VPPIntroduction to the VPP

– GoalsGoals Basic Parasite InformationBasic Parasite Information

– Pathology, life cycle, and formPathology, life cycle, and form The VPP ProgramThe VPP Program

– Architecture, model, equations, benchmarksArchitecture, model, equations, benchmarks– VisualizationVisualization

Patent ApplicationPatent Application Where to From HereWhere to From Here

The VPP TeamThe VPP Team

Computational BiologyComputational Biology Handling the “omic” hierarchy

– It’s not just genes any more– ETA Systems Computational Medicine and

BioSciences Group Simulations of biomedical elements

– Computational Chemistry– Computational Biomechanics– Computational “Fill in your favorite noun”

Frankenstein in the machine The “in silico laboratory”

In SilicoIn Silico Laboratories Laboratories Not a simulation of an organism but Not a simulation of an organism but an extensible, portable, “in silico,” multi-scale, an extensible, portable, “in silico,” multi-scale,

high performance computational and high performance computational and mathematical laboratory for research into the mathematical laboratory for research into the dynamics of host-parasite interactionsdynamics of host-parasite interactions

To test this environment by examining the host-To test this environment by examining the host-parasite dynamics of the parasite dynamics of the Trypanosoma cruziTrypanosoma cruzi, , the causative agent in Chagas Diseasethe causative agent in Chagas Disease

BASIC BASIC T. CRUZIT. CRUZI INFORMATION INFORMATION

Epidemiology – 1 Epidemiology – 1

• Chagas disease is the 3Chagas disease is the 3rdrd most common parasitic disease after most common parasitic disease after malaria and schistosomiasis malaria and schistosomiasis•Estimates are that over 2 billion people worldwide are Estimates are that over 2 billion people worldwide are affected by these 3 parasitic diseasesaffected by these 3 parasitic diseases•Mortality estimates in Africa for schistosomiasis are Mortality estimates in Africa for schistosomiasis are 200,000/year and most are children200,000/year and most are children•12 species of Trypanasoma cruzi are known to occur in the 12 species of Trypanasoma cruzi are known to occur in the USUS•Trypanosoma cruzi is the causal agent in Chagas diseaseTrypanosoma cruzi is the causal agent in Chagas disease

Epidemiology – 2 Epidemiology – 2 • Recent estimates suggest that more than 17 million Recent estimates suggest that more than 17 million people throughout Latin America are currently infected people throughout Latin America are currently infected with T. cruzi – currently present in 18 countrieswith T. cruzi – currently present in 18 countries•4.8-5.4 million individuals currently exhibiting clinical 4.8-5.4 million individuals currently exhibiting clinical symptomssymptoms•Annual incidence 700,000 – 800,000 new casesAnnual incidence 700,000 – 800,000 new cases•45,000 deaths due to the cardiac form of the disease45,000 deaths due to the cardiac form of the disease•There is no cure and therapeutic agents are highly toxicThere is no cure and therapeutic agents are highly toxic•There is no treatment for chronic Chagas There is no treatment for chronic Chagas •Chagas disease is fatalChagas disease is fatal•The life cycle of the parasite is complexThe life cycle of the parasite is complex

T. CruziT. Cruzi Lifecycle Lifecycle

Pathology in Chagas DiseasePathology in Chagas Disease

•Transmitted through the feces of biting insectsTransmitted through the feces of biting insects•Insects defecate while taking blood mealInsects defecate while taking blood meal•Infected individual scratches feces into wound Infected individual scratches feces into wound starting infectionstarting infection•Myocarditis and cardiomyopathyMyocarditis and cardiomyopathy•Alimentary tract dysfunction manifested by Alimentary tract dysfunction manifested by megaesophagus and megacolonmegaesophagus and megacolon•Acute stage occurs 1-2 weeks after exposureAcute stage occurs 1-2 weeks after exposure

BASIC BASIC PROGRAM PROGRAM INFORMATIONINFORMATION

VPP Program ArchitectureVPP Program Architecture Program is written in public domain languages and uses public Program is written in public domain languages and uses public

domain software (C++, C, Fortran)domain software (C++, C, Fortran) Currently VPP code exceeds 20,000 linesCurrently VPP code exceeds 20,000 lines Modular development of the VPP environment enables users to Modular development of the VPP environment enables users to

supply relevant lab modules for their particular research needssupply relevant lab modules for their particular research needs– Standard worlds are provided (flask, test tube, etc.)Standard worlds are provided (flask, test tube, etc.)– Standard parasite forms are provided (spherical, elliptical, helicoid, Standard parasite forms are provided (spherical, elliptical, helicoid,

amoeboid, flagellar)amoeboid, flagellar)– Standard fluids are provided (water, plasma/blood)Standard fluids are provided (water, plasma/blood)

Parasite we chose to model in the environment was the Parasite we chose to model in the environment was the T. cruzi T. cruzi parasiteparasite

Simulation code runs in a parallel processor (MPI) environment – Simulation code runs in a parallel processor (MPI) environment – Sun Grizzly (32 dual processor node cluster)Sun Grizzly (32 dual processor node cluster)

Visualization interface allows user to visualize actual data in a Visualization interface allows user to visualize actual data in a “video/interactive” format – Sun V880 with in house developed “video/interactive” format – Sun V880 with in house developed interfaceinterface

Initial Approach To Initial Approach To T. cruziT. cruzi Modeling Modeling Macro-scale biophysics at a population Macro-scale biophysics at a population

levellevel Inclusion of host cells with cell cycle modelInclusion of host cells with cell cycle model Single parasite model (sphere with tail)Single parasite model (sphere with tail)

Basic Newtonian ModelBasic Newtonian Model Four basic forcesFour basic forces

– GravitationalGravitational– BuoyantBuoyant– SwimmingSwimming– DragDrag

The Uncoupled EquationsThe Uncoupled Equations

VPP VisualizationVPP Visualization

The New Patent ApplicationThe New Patent Application

Charge Gradient PatentCharge Gradient Patent Results from the construction of the simulation Results from the construction of the simulation

lead to a patent application utilizing charge-lead to a patent application utilizing charge-gradients as a means of inhibiting and/or gradients as a means of inhibiting and/or stopping T. cruzi invasionstopping T. cruzi invasion

Patent application on 4 June 2004 has been Patent application on 4 June 2004 has been awarded provisional patent to VPP teamawarded provisional patent to VPP team

Literature research indicates that the Literature research indicates that the methodology may be applicable to a class of methodology may be applicable to a class of organisms including malaria.organisms including malaria.

Patent MethodologiesPatent MethodologiesArgument is based upon charge-charge Argument is based upon charge-charge

interaction between parasite andinteraction between parasite and– Charged nano-beads experiment was Charged nano-beads experiment was

completed by an NSF BBSI studentcompleted by an NSF BBSI student– In addition, other approaches such as In addition, other approaches such as

synthesis of peptide or RNA aptamers synthesis of peptide or RNA aptamers positively charged and used to test invasion positively charged and used to test invasion inhibition efficacyinhibition efficacy

WHERE TO FROM HERE?WHERE TO FROM HERE?

Where To From Here – 1 ?Where To From Here – 1 ?Include more biologically accurate Include more biologically accurate

mammalian host cell modelmammalian host cell modelDevelop more biologically accurate model of Develop more biologically accurate model of

parasiteparasiteExpand module for environmental Expand module for environmental

definitionsdefinitionsInclude van der Waals force calculationsInclude van der Waals force calculations

Where To From Here – 2 ?Where To From Here – 2 ?Continue algorithm optimization and Continue algorithm optimization and

developmentdevelopment– Generalized to n-processor distributed grid Generalized to n-processor distributed grid

environmentsenvironments

Continue GUI interface developmentContinue GUI interface development– Extension to 3D visualization (cave)Extension to 3D visualization (cave)– VRML interfaceVRML interface

Where To From Here – 3 ?Where To From Here – 3 ? Invasion moduleInvasion module

– Host-parasite proximity factorsHost-parasite proximity factors Inclusion of recognition factors, reorientation factors, attachment Inclusion of recognition factors, reorientation factors, attachment

factorsfactors

– Binding factorsBinding factors– Transformation for invasionTransformation for invasion

Signal transduction pathwaysSignal transduction pathways Membrane factorsMembrane factors Attachment factorsAttachment factors

– Physical InvasionPhysical Invasion

Molecular Scale ModelsMolecular Scale Models Once parasites are in Once parasites are in

the proximity of the the proximity of the host cell, modeling host cell, modeling must account for the must account for the various phases of various phases of invasioninvasion

Where To From Here – 4 ?Where To From Here – 4 ?

Extension to whole Extension to whole human modelhuman model– Inclusion of tissue Inclusion of tissue

alteration factorsalteration factors– Pharmaco-dynamic Pharmaco-dynamic

intervention factorsintervention factors– Re-infection factorsRe-infection factors

Take Home MessageTake Home Message Computational Biology is not just about “omics”Computational Biology is not just about “omics” Ultra-large scale environments are actively involved in Ultra-large scale environments are actively involved in

addressing complex biomedical problems at super-genomic addressing complex biomedical problems at super-genomic levelslevels

These environments have similar problems with respect to These environments have similar problems with respect to programming, visualization, user interface design, data programming, visualization, user interface design, data storage and access as the “omic” environments havestorage and access as the “omic” environments have

In silicoIn silico laboratories are the next extension of HPC to laboratories are the next extension of HPC to biomedical research and educationbiomedical research and education

Such laboratories can lead to insights into biomedical Such laboratories can lead to insights into biomedical dynamics that were not here-to-fore envisioneddynamics that were not here-to-fore envisioned

Thank You For ComingThank You For Coming

Simulation Benchmark Timing -1Simulation Benchmark Timing -1

-1

-0.5

0

0.5

1

1.5

2

2.5

3

100 1,000 10,000 100,000

Number of Parasites in Simulation

log(

Tot

al S

imul

atio

n T

ime)

124

Simulation Benchmark Timing –2Simulation Benchmark Timing –2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3

100 1,000 10,000 100,000

Number of Parasites in Simulation

log(

Tot

al S

imu

lati

on T

ime) 1

1 Threaded