PHASE I – WORK PLAN

Computational Modelling

Modelling and Simulations

The complexity of biological systems, ranging from molecular interactions within a cell to ecosystem dynamics, poses significant challenges for researchers seeking to understand, predict, and manipulate biological processes. Computational modeling and simulation have emerged as indispensable tools in biology, enabling scientists to represent complex biological phenomena quantitatively  and explore scenarios that are difficult or impossible to study experimentally.

By abstracting biological phenomena into computational frameworks, scientists can predict behaviors, test hypotheses, and design experiments more efficiently. Despite challenges such as data limitations and computational demands, advances in technology and interdisciplinary collaboration continue to enhance the power and utility of modeling in biology.

Projects in Computational Modelling

Biological Avatars for Aging Research (BAFAR)

Developing computational models for biology provides a toolset to allow researchers to visualize and test hypotheses in biological systems, and provide predictability in translational work. Currently, modeling has been limited to discrete parts of biological systems (i.e. protein folding,
protein-to-protein interactions, etc.) meant to address specific areas of interest, and given available computational power, few models have attempted to go beyond a few of the many interrelated aspects of most systems.

The proposed project will set a computational framework for biological processes, and where possible use pre-existing models to create a baseline simulation and visualization environment, from which both Thalion and other research project results may be integrated and models developed and refined. Additionally, recent works have opened up pathways to Turing equivalent grammars and biological architectures which are compatible with both simulation and synthetic biological pipelines allowing the translation of biological intent into nucleotide, peptide, or regulatory sequences.

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