The Karyote physico-chemical genomic, proteomic, metabolic cell modeling system

Area of Science:

• Systems Biology
• Computational Biology
• Biophysics

Background:

• Cellular dynamics involve complex physical and chemical processes across multiple scales.
• Integrating diverse biological data (genomic, proteomic, metabolic) for cell modeling is challenging.

Purpose of the Study:

• To advance cell modeling by developing a system that seamlessly integrates data archiving, quantitative physico-chemical modeling, and information theory.
• To introduce the Karyote, CellX, and VirusX modeling systems for analyzing cellular dynamics, self-organization, and viral processes.

Main Methods:

• Developed the Karyote software system with modules for model building, data archiving, simulation, and an information theory module (ITM) for automated calibration.
• Utilized rigorous multiple scale analysis to handle complex dynamics, including fast/slow reactions and minority species effects.
• Introduced CellX for modeling dynamic intracellular structures and VirusX for studying viral self-assembly and infection using molecular mechanics and continuum theory.

Main Results:

• The Karyote system automates the development and calibration of predictive cell models from large biological datasets.
• CellX captures the self-organizing behavior of cells by modeling spatial distributions of variables.
• VirusX integrates different theoretical approaches to investigate viral interactions with host cells.

Conclusions:

• The presented modeling system offers a powerful, integrated approach to understanding complex cellular dynamics and data.
• CellX and VirusX extend modeling capabilities to address dynamic structural changes and viral phenomena, respectively.
• This work facilitates a deeper, predictive understanding of cellular life from molecular to system levels.