CBET-0643548 Papin The main goal of this proposal is to increase our understanding of infectious disease processes by developing a multi-scale model of the human pathogen, Leishmaia major (L. major) based on the integration of intracellular networks. Through three specific aims, metabolic, regulatory and signaling networks will be reconstructed and integrated to develop a model linking genome-scale reconstructions of L. major and to tissue responses in host organisms. The proposed research will form the basis of research projects for training students, for computational course development and the development of a web portal designed to facilitates systems analysis associated with the role of pathogenic organisms in infectious diseases. In the long term, this research will impact the field of treatment of infectious diseases.
**We apply systems biology approaches towards understanding infectious disease mechanisms. The overarching goals of the project were to build and interrogate intracellular networks of pathogens in the context of their host environment, and use these networks to propose high-priority drug targets that serve as testable hypotheses for future experimental validation. **There is a need for the development of new drugs and treatment strategies against leishmaniasis to replace exisiting treatments that are expensive and have toxic side effects. The Leishmania major genome along with other trypanosomatids was published in 2005. This created an avenue for bottom-up network modeling approaches. With a metabolic network reconstructed, we were able to simulate gene essentiality analysis on a genome-scale level. We were able to pursue double gene deletion analysis that would have been too tedious to perform via experimentation. We built and provided for a framework for model-driven biological discovery. The hypotheses generated on the computer can be used for rational therapeutic design and development. **The principles learned with computational modeling of Leishmania were extended to other pathogenic and non-pathogenic organisms as well (e.g. Pseudomonas aeruginosa, Pseudomonas putida, and Trypanosoma cruzi). **The reconstructed metabolic networks readily serve as tools in classroom settings. The PI has incorporated material from these publications in several lectures. In addition, we are developing a textbook with a contract from Cambridge University Press with a preliminary title of Fundamentals of Systems Biology. Much of the research and educational activities supported by this grant will be integrated into this textbook. **Below, we highlight some of the major results/findings from completed works supported by this grant: --The L. major reconstruction accounted for 560 genes, 1112 reactions, 1101 metabolites and 8 unique sub-cellular localizations. The gene deletion simulations were able to identify particular genes that were both lethal for growth in L. major and unique to the parasite metabolism. An experimentally validated list of novel drugs that may be effective anti-leishmanials was also included in the analysis. --The P. aeruginosa reconstruction accounted for 1,056 genes, 1,030 proteins, and 883 reactions. An analysis of the metabolic state of the pathogen during the course of infection in the cystic fibrosis lung identified new drug targets and helped characterize principal metabolic functions that change over the course of an infection. --The P. putida metabolic network reconstruction accounted for 815 genes, 824 intracellular and 62 extracellular metabolites, and 877 reactions. The model was validated with experimental data. --The T. cruzi metabolic network reconstruction accounted for 215 genes, 158 metabolites and 162 reactions. Stage-specific proteomic data was integrated to generate a full network and an epimastigote-specific network. --A novel approach for the integration of gene expression data into a metabolic network reconstruction. This approach enables the generation of metabolic network models for particular environmental or temporal states. --An approach to map known drug targets to a metabolic network model to prioritize drugs that may be effective at inhibiting growth in a particular pathogen. --An approach to reconcile genome-scale metabolic reconstructions to enable their comparison. This method facilitates comparative systems analysis with a much higher degree of confidence. **Major Research Activities: --Genome-scale metabolic network reconstruction of Leishmania major, a pathogenic trypanosomatid and agent of leishmaniasis (Molecular Systems Biology, March 2008) --Genome-scale metabolic network reconstruction of Pseudomonas aeruginosa, an opportunistic pathogen (Journal of Bacteriology, April 2008, Cover Article) --Genome-scale metabolic network reconstruction of Pseudomonas putida, a non-pathogenic species of the Pseudomonas genus (PLoS Computational Biology, October 2008) --A comprehensive review of multi-cellular rule-based computational modeling approaches (including cellular automata and agent-based modeling) in complex immunological systems (Trends in Immunology, December 2008, Cover Article) --A book chapter introducing the basics of Flux Balance Analysis, a method used for interrogating metabolic networks (Methods in Molecular Biology, April 2009) --Central metabolic network reconstruction of Trypanosoma cruzi, a parasite in the kinetoplastid class of organisms that is an agent of Chagas disease (BMC Systems Biology, May 2009) --Functional states of the genome-scale Escherichia coli transcriptional regulatory system (PLoS Computational Biology, June 2009) --Applications of genome-scale metabolic reconstructions (Molecular Systems Biology, Nov. 2009) --A review article highlighting the application of Flux Balance Analysis to systems biology (Wiley Interdisciplinary Reviews, Nov. 2009, Invited Article) --A comparison of the P. aeruginosa and P. putida metabolic networks (PLoS Computational Biology, March 2011) --A computational analysis of Pseudomonas aerugionsa during the course of infection in the cystic fibrosis lung (Journal of Bacteriology, October 2010) --A method for the integration of gene expression data into metabolic network models (Bioinformatics, February 2011) --An in-depth review of using a metabolic network approach to identify drug targets in pathogens (Trends in Microbiology, March 2012) --A novel research framework to identify high-priority drug targets and drugs in the parasite Leishmania major. The study identified novel drugs and drug combinations that were effective against the pathogen in vitro (BMC Systems Biology, July 2012)
