John Justine Villar, PhD

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Mathematical Aspects of Peptide Conformational Analysis Through Quantum Chemical Calculations
Computational Network Biology (Protein Complex Detection in PPI Networks, Chemical Reaction Network Theory in Biological Systems)
Stochastic Modelling, Numerical Optimization and Graph Theoretic Applications in Bioinformatics
Algebraic Systems Biology (Process Algebra and Natural Computing)
Reaction Kinetics Modeling (Biochemical Systems Theory)


1. Construction of Potential Energy Hypersurfaces for Protein Structure Prediction through Quantum Chemical Calculations
Ph.D. research work under the supervision of Adrian Roy Valdez, Ph.D., David Setiadi, Ph.D. (University of Toronto, Canada) and Imre Csizmadia, Ph.D. (University of Miskolc, Hungary and University of Toronto, Canada)
This study aims to construct precise potential energy surfaces (PES) of different amino acids through ab initio calculations to provide insights on the potential energy hypersurface given an amino acid sequence. We intend to compare our findings to results from well-known force fields, among others. Furthermore, the relationship between polypeptide PES and amino acid PES are also explored.

Undergraduate research project with Jerome Cary Beltran, Catalina Montes (co-supervised with Adrian Roy Valdez, Ph.D.)
This research aims to incorporate biological information into purely graph-based clustering methods to more precisely predict relevant protein complexes from protein-protein interaction networks (PPIN). The resulting algorithm will be compared to existing PPIN frameworks, in terms of the number of complexes correctly predicted from gold standard datasets, and other performance metrics.
* Second Prize, 2015 UP College of Engineering Undergraduate Project Competition

3.  Algorithms for Protein Structure Prediction using Lattice and Off-Lattice Models
Undergraduate research projects with co-supervision of Adrian Roy Valdez, Ph.D.:
This work tries to predict protein structure by determining the (approximate) relative positions of amino acids through an energy minimization algorithm, as well as to predict the final structure of the protein given an amino acid sequence and through the proposed models (lattice or off-lattice). One aspect is to characterize the properties of the proposed models and provide the bounds for the minimal energy of the resulting protein conformations.

4. Modeling Dopamine D1 Receptor Availability and Intracellular Trafficking in Renal Proximal Tubule Cells 
Research project with Carlene P. C. Pilar-Arceo, Ph.D. (Institute of Mathematics, UPD) and Eduardo Mendoza, Ph.D. (The Max Planck Institute of Biochemistry, Germany),  in collaboration with  Pedro Jose, M.D., Ph.D. and Ines Armando, M.D., Ph.D. (University of Maryland School of Medicine, USA)
This attempts to model the dynamics of dopamine D1 receptor trafficking inside human renal proximal tubule cells, as well as its availability at the cell surface. It is known that dopamine synthesis and D1 receptor function affects sodium excretion in genetically hypertensive individuals. Kinetic modeling and mathematical analyses using Chemical Reaction Network Theory help us provide insights about the existence and multiplicity of steady states, as well as their stability if it exists.


*Restricted access to SCL members