The Virtual Parasite Project: a high performance computational laboratory to understand host-parasite Dynamics

Investigators:

  Computing Team
  Tarynn M. Witten, Project Director
  Ford Sleeman
  Gurav Rana
  Daniela Puiu
  Katie Youell

  Biology Team
  Gregory A. Buck, Director
  Luiz S. Ozaki
  Patricio A. Manque
  Joao M. P. Alves
  Myrna Serrano

Trypanosoma cruzi is a flagellate protozoan parasite, a member of the order Kinetoplastida and of the family Trypanosomatidae. Despite nearly 100 years of research on the T. cruzi parasite, we still understand very little about its dynamics. While there is a treatment for the acute stage (a highly toxic drug), if recognized in time, there is still no treatment for chronic Chagas’ Disease, which infects millions of individuals worldwide. Because of its complex lifecycle, T. cruzi provides one of the most fascinating and complex, yet sophisticated model systems for investigating host-parasite dynamics. These organisms also are of biological interest since they are able to change their morphology according to the environment where they live, through a process of reversible cell transformation.

Therefore, new treatment strategies must be devised, and these will very likely be derived from application of contemporary molecular modeling strategies to develop inhibitors of novel processes in the basic biology and pathogenesis of the parasites. To do so will require looking at host-parasite dynamics using new and more sophisticated tools than those that have been employed over the past century. The experimental research strength of the VCU microbiology group in this area provides the necessary biological input to theoretical analysis of the process.

Our methodology is based upon an integrated mathematical, in silico modeling approach that is directly coupled to biological experimentation. The initial goal of this research is to create an extensible, portable, in silico, multi-scale, high performance computational model of the T. cruzi life cycle. The long-term goal is to apply novel mathematical and computational modeling technologies, well informed by biological experimentation, to specific host-parasite systems in order to develop new paradigms for understanding the infectious disease process, for the purpose of developing new therapeutic and public health interventions and strategies. Successful development of this project will allow potential application of this methodology to viruses, Cryptosporidium parvum (for which there is no treatment and which is a potential bioterrorism agent), worms, bacteria and potentially to larger systems.

Model development incorporates the basic Newtonian equations, but alters the form of the T. cruzi approximation to be cylindrical in nature and adjusts all of the fluid dynamics equations to address the dynamics of the swimming tail, which is used to pull the T. cruzi parasite forward in the water rather that to push it forward as in other organisms.

See explanation of the model in paragraph before picture.


The data generated by the simulation is passed to a visualizing program that allows the user to view the motion of the T. cruzi parasite in three dimensions. Computations take place in a simulated laboratory environment defined by the experimentalist. In this figure, blue spheres represent the hypothetical mammalian cells plated on the bottom of the dish. The T. cruzi parasites are represented by the white spheres. The green vectors indicate the traveling direction and the velocity of the T-cruzi. The length of the vector is proportional to how fast it is swimming. The user interface keeps track of all basic simulation data. An interface allows the user to navigate through the actual simulation and examine the simulation from any given perspective.

We intend the model will ultimately include host-parasite proximity factors (recognition, reorientation, binding and attachment), host-parasite transformation for invasion (signal transduction pathway factors, membrane factors), modeling of the physical invasion (entry) of the parasite, along with optimization of the computational methods to adjust for the increased complexity of the simulation. The dynamics of recognition, orientation and attachment is little understood but highly complex.