John Justine Villar, PhD
Associate Professor, UP Department of Computer Science
I can be reached through phone at (+632) 8981-8500 loc 3231 (DCS) or 3559 (NCTS) and through email at email@example.com.
My area of specialization is complex systems modeling, with a focus on quantum chemical systems and intelligent transport systems. Specifically, I am interested in, but not limited to, the following topics:
Mathematical Aspects of Peptide Conformational Analysis Through Quantum Chemical Calculations
Computational Network Biology (Protein Complex Detection in PPI Networks, Chemical Reaction Network Theory)
Macroscopic and Mesoscopic Modeling of Intelligent Transportation Systems
Stochastic Modelling, Numerical Optimization and Graph-Theoretic Applications in Bioinformatics and Systems Biology
Algebraic Systems Biology (Process Algebra and Natural Computing)
Reaction Kinetics Modeling (Biochemical Systems Theory)
Research, Development and Extension Projects
I am actively involved in the following ongoing research projects:
1. Construction of Potential Energy Hypersurfaces for Protein Structure Prediction through Quantum Chemical Calculations
Research project with Adrian Roy Valdez, Ph.D., Anita Ragyanszki, Ph.D (York University, Canada), David Setiadi, Ph.D. (University of Toronto, Canada) and Imre Csizmadia, Ph.D. (Hungarian Academy of Sciences 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.
2. 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)
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.
3. Maritime Transportation Information System (MARIS) (Project Leader)
This project addresses the areas of intelligent transport systems for maritime industry in the Philippines through the development and deployment of software and tools needed for a prototype forecasting system. In particular, the project targets the operation of the software components by developing and designing modular offline modules for an effective Route Capacity Measurement System.
4. Development of and Enhanced Transportation Management Suite (E-TraMS) (Project Staff Level 3)
PCIEERD-DOST funded project with Dr. Hilario Sean Palmiano, Ph.D. as the Project Leader, in collaboration with UP Department of Computer Science
This project tries to design an intelligent land vehicle control system with advanced computing and sensing capabilities to improve safety and throughput to meet increasing transportation demands. I am currently involved in the Software Development and R&D components of the project, where my team works with calibrating models of macroscopic traffic demand and traffic intersection optimization.
5. Modelling and Estimation of Transportation Energy Demand of the Philippines (Project Staff Level 3)
UP OVPAA Energy Research Fund-funded project with Dr. Karl N. Vergel, Ph.D. as the Project Leader
This project aims to estimate the baseline energy demand of road, rail, maritime and civil aviation by mode of transportation (road, rail, maritime and civil aviation), as well as to identify gaps in modeling in previous transport energy demand studies including the availability of data at the local, regional and national levels.