In Silico Laboratories: The Virtual Parasite Project - An Overview

1. In Silico Laboratories:The Virtual Parasite Project - An Overview SURA Grid Computing in the Life Sciences - 2006 Tarynn M. Witten, Ph.D., FGSA, FCSBC Director, Research and Development Center for the Study of Biological Complexity Visual Parasite Project Virginia Commonwealth University Richmond, VA twitten@vcu.edu

2. OVERVIEW • The Team • Computational Biology – A Larger View • In Silico Laboratories • Introduction to the VPP • Goals • Basic Parasite Information • Pathology, life cycle, and form • The VPP Program • Architecture, model, equations, benchmarks • Visualization • Patent Application • Where to From Here

3. The VPP Team

4. Computational 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”

5. In Silico Laboratories • Not a simulation of an organism but • an extensible, portable, “in silico,” multi-scale, high performance computational and mathematical laboratory for research into the dynamics of host-parasite interactions • To test this environment by examining the host-parasite dynamics of the Trypanosoma cruzi, the causative agent in Chagas Disease

6. BASIC T. CRUZI INFORMATION

7. Epidemiology – 1 • Chagas disease is the 3rd most common parasitic disease after malaria and schistosomiasis • Estimates are that over 2 billion people worldwide are affected by these 3 parasitic diseases • Mortality estimates in Africa for schistosomiasis are 200,000/year and most are children • 12 species of Trypanasoma cruzi are known to occur in the US • Trypanosoma cruzi is the causal agent in Chagas disease

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

9. T. Cruzi Lifecycle

10. Pathology in Chagas Disease • Transmitted through the feces of biting insects • Insects defecate while taking blood meal • Infected individual scratches feces into wound starting infection • Myocarditis and cardiomyopathy • Alimentary tract dysfunction manifested by megaesophagus and megacolon • Acute stage occurs 1-2 weeks after exposure

11. BASIC PROGRAM INFORMATION

12. VPP Program Architecture • Program is written in public domain languages and uses public domain software (C++, C, Fortran) • Currently VPP code exceeds 20,000 lines • Modular development of the VPP environment enables users to supply relevant lab modules for their particular research needs • Standard worlds are provided (flask, test tube, etc.) • Standard parasite forms are provided (spherical, elliptical, helicoid, amoeboid, flagellar) • Standard fluids are provided (water, plasma/blood) • Parasite we chose to model in the environment was the T. cruzi parasite • Simulation code runs in a parallel processor (MPI) environment – Sun Grizzly (32 dual processor node cluster) • Visualization interface allows user to visualize actual data in a “video/interactive” format – Sun V880 with in house developed interface

13. Initial Approach To T. cruzi Modeling • Macro-scale biophysics at a population level • Inclusion of host cells with cell cycle model • Single parasite model (sphere with tail)

15. Basic Newtonian Model • Four basic forces • Gravitational • Buoyant • Swimming • Drag

16. The Uncoupled Equations

19. VPP Visualization

25. The New Patent Application

26. Charge Gradient Patent • Results from the construction of the simulation lead to a patent application utilizing charge-gradients as a means of inhibiting and/or stopping T. cruzi invasion • Patent application on 4 June 2004 has been awarded provisional patent to VPP team • Literature research indicates that the methodology may be applicable to a class of organisms including malaria.

27. Patent Methodologies • Argument is based upon charge-charge interaction between parasite and • Charged nano-beads experiment was completed by an NSF BBSI student • In addition, other approaches such as synthesis of peptide or RNA aptamers positively charged and used to test invasion inhibition efficacy

28. WHERE TO FROM HERE?

29. Where To From Here – 1 ? • Include more biologically accurate mammalian host cell model • Develop more biologically accurate model of parasite • Expand module for environmental definitions • Include van der Waals force calculations

30. Where To From Here – 2 ? • Continue algorithm optimization and development • Generalized to n-processor distributed grid environments • Continue GUI interface development • Extension to 3D visualization (cave) • VRML interface

31. Where To From Here – 3 ? • Invasion module • Host-parasite proximity factors • Inclusion of recognition factors, reorientation factors, attachment factors • Binding factors • Transformation for invasion • Signal transduction pathways • Membrane factors • Attachment factors • Physical Invasion

32. Molecular Scale Models • Once parasites are in the proximity of the host cell, modeling must account for the various phases of invasion

33. Where To From Here – 4 ? • Extension to whole human model • Inclusion of tissue alteration factors • Pharmaco-dynamic intervention factors • Re-infection factors

34. Take Home Message • Computational Biology is not just about “omics” • Ultra-large scale environments are actively involved in addressing complex biomedical problems at super-genomic levels • These environments have similar problems with respect to programming, visualization, user interface design, data storage and access as the “omic” environments have • In silico laboratories are the next extension of HPC to biomedical research and education • Such laboratories can lead to insights into biomedical dynamics that were not here-to-fore envisioned

35. Thank You For Coming

36. Simulation Benchmark Timing -1

37. Simulation Benchmark Timing –2