Modeling and simulation of biochemical networks has become an essential activity to aid in the understanding of cellular behavior and to facilitate quantitative interpretation of modern experiments. A new approach, systems biology, is being advocated which combines modeling, simulation and quantitative experiments. Biomedical research is becoming increasingly dependent on construction and simulation of computational models. Arguably this will be even more the case with the development of personalized medicine. However, the technical aspects of modeling and simulation are overwhelming to a large number of biomedical researchers, and what is needed is a software application that is capable of providing the appropriate numerical algorithms shielded by a user interface that aides the biomedical researcher to conduct the required simulations. We propose to address this need with continuing development of our software COPASI, which is already widely used in the research community. This project will also provide support to the vibrant community of COPASI users/biomedical researchers. We will address this with the following Specific Aims:
Aim 1 : Enable COPASI with new functionality to provide advanced model capabilities by adding support for models containing noise and explicit time delays. Support for 1 and 2 dimensional compartments (filaments and membranes) will be implementes as well as support for discrete events in stochastic and delay differential simulation algorithms.
Aim 2. Improve and extend interoperability and standards compliance. COPASI will be equipped to facilitate users to create and read simulation information in SED-ML format, it will be enabled to save and read simulation and experimental result in the SBRML format, and it will support the proposed SBML Level 3.
Aim 3. Software maintenance. An existing testing plan will be continued and expanded, including appropriate collection models designed to test the various functions of the software; bug reports will be collected and fixed; suggestions for improvement will be collected from users and followed through.
Aim 4. Support the modeling community. Online support will be developed and maintained, such as video tutorials, user forum, user manual, and frequently asked questions. A COPASI User's Workshop will be held annually.

Public Health Relevance

This research will focus on developing and maintaining a computational tool, COPASI, for studying complex biological systems, including human disease models. This software application will facilitate knowledge discovery using computer simulations that are important in health and disease, including drug design. Use of this software will be available to all biomedical researchers and will contribute to understanding how diseases develop and how they can be cured.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Biodata Management and Analysis Study Section (BDMA)
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Brazhnik, Paul
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Virginia Polytechnic Institute and State University
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Mendes, Pedro (2018) Reproducible Research Using Biomodels. Bull Math Biol 80:3081-3087
Parmar, Jignesh H; Quintana, Julia; Ramírez, David et al. (2018) An important role for periplasmic storage in Pseudomonas aeruginosa copper homeostasis revealed by a combined experimental and computational modeling study. Mol Microbiol 110:357-369
Gupta, Abhishekh; Mendes, Pedro (2018) An Overview of Network-Based and -Free Approaches for Stochastic Simulation of Biochemical Systems. Computation (Basel) 6:
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Verma, Meghna; Erwin, Samantha; Abedi, Vida et al. (2017) Modeling the Mechanisms by Which HIV-Associated Immunosuppression Influences HPV Persistence at the Oral Mucosa. PLoS One 12:e0168133
Millard, Pierre; Smallbone, Kieran; Mendes, Pedro (2017) Metabolic regulation is sufficient for global and robust coordination of glucose uptake, catabolism, energy production and growth in Escherichia coli. PLoS Comput Biol 13:e1005396
Dacheux, Estelle; Malys, Naglis; Meng, Xiang et al. (2017) Translation initiation events on structured eukaryotic mRNAs generate gene expression noise. Nucleic Acids Res 45:6981-6992
Meng, Xiang; Firczuk, Helena; Pietroni, Paola et al. (2017) Minimum-noise production of translation factor eIF4G maps to a mechanistically determined optimal rate control window for protein synthesis. Nucleic Acids Res 45:1015-1025
Swainston, Neil; Smallbone, Kieran; Hefzi, Hooman et al. (2016) Recon 2.2: from reconstruction to model of human metabolism. Metabolomics 12:109
Millard, Pierre; Portais, Jean-Charles; Mendes, Pedro (2015) Impact of kinetic isotope effects in isotopic studies of metabolic systems. BMC Syst Biol 9:64

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