Disciplines
Artificial Intelligence and Robotics | Bacterial Infections and Mycoses | Biodiversity | Bioinformatics | Databases and Information Systems | Diagnosis | Disease Modeling | Interprofessional Education | Investigative Techniques | Molecular Biology | Structural Biology | Systems Architecture | Theory and Algorithms
Abstract (300 words maximum)
Understanding how pathogens respond to physical changes in their environment is crucial for developing effective treatments and preventative measures. Current research often relies on static models or experimental data that either fail to capture the dynamic interactions within cellular environments or are not generalizable to other types of pathogens. This project aims to address this gap by creating a comprehensive cell simulation that models pathogens and their response to chemical, physical, and physiological changes. The proposed solution is a simulation that integrates biological data and computational modeling to replicate the behavior of pathogens in real time as they are affected by various changes in their surroundings. By utilizing advanced algorithms and databases, the simulation will enable users to observe the effects of different vaccines on the cellular structure and signaling pathways of pathogens. This approach not only enhances our understanding of cellular dynamics but also provides a valuable educational tool for students and researchers alike. Expected results include the successful demonstration of the simulation's ability to accurately represent cellular responses under various conditions. By validating the model against experimental data, we anticipate identifying critical thresholds at which these single-celled organisms initiate defensive mechanisms. Furthermore, this project aims to reveal novel insights into the relationship between types of invaders and host cells, potentially guiding future research into targeted therapies. Overall, this innovative cell simulation has the potential to significantly advance the field of cellular biology and enhance our ability to combat infectious diseases.
Academic department under which the project should be listed
SPCEET - Robotics and Mechatronics Engineering
Primary Investigator (PI) Name
Razvan Cristian Voicu
Included in
Artificial Intelligence and Robotics Commons, Bacterial Infections and Mycoses Commons, Biodiversity Commons, Bioinformatics Commons, Databases and Information Systems Commons, Diagnosis Commons, Disease Modeling Commons, Interprofessional Education Commons, Investigative Techniques Commons, Molecular Biology Commons, Structural Biology Commons, Systems Architecture Commons, Theory and Algorithms Commons
BLOOM: Behavioral Learning and Outcome Observation in Microbes
Understanding how pathogens respond to physical changes in their environment is crucial for developing effective treatments and preventative measures. Current research often relies on static models or experimental data that either fail to capture the dynamic interactions within cellular environments or are not generalizable to other types of pathogens. This project aims to address this gap by creating a comprehensive cell simulation that models pathogens and their response to chemical, physical, and physiological changes. The proposed solution is a simulation that integrates biological data and computational modeling to replicate the behavior of pathogens in real time as they are affected by various changes in their surroundings. By utilizing advanced algorithms and databases, the simulation will enable users to observe the effects of different vaccines on the cellular structure and signaling pathways of pathogens. This approach not only enhances our understanding of cellular dynamics but also provides a valuable educational tool for students and researchers alike. Expected results include the successful demonstration of the simulation's ability to accurately represent cellular responses under various conditions. By validating the model against experimental data, we anticipate identifying critical thresholds at which these single-celled organisms initiate defensive mechanisms. Furthermore, this project aims to reveal novel insights into the relationship between types of invaders and host cells, potentially guiding future research into targeted therapies. Overall, this innovative cell simulation has the potential to significantly advance the field of cellular biology and enhance our ability to combat infectious diseases.