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Systems Biology Research

Toronto_2, Canada
(Toronto, Canada - Wei-Jiun Su)


- Overview 

Systems biology is a growing field that treats biological systems as a whole, using a range of approaches to explore networks and interactions within systems. For example, to study the function of cells, biologists working in genetics and biochemistry collaborate with mathematicians, physicists, and computer modelers to collect data on different aspects of cell signaling. This gives us a more complete picture of what's going on inside cells and other biological systems.  

Some research questions in systems biology include:

  • Gene regulatory network
  • Modeling metabolic interactions
  • Mimics antibiotic-induced protective mechanisms
  • Studying cell signaling pathways


- Computational and Systems Biology 

The field of computational and systems biology represents a synthesis of ideas and methods from the life sciences, physical sciences, computer science, and engineering. Recent advances in biology, including the Human Genome Project and massively parallel methods for probing biological samples, have created new opportunities for understanding biological problems from a systems perspective. 

Systems modeling and design are well established in engineering disciplines but newer in biology. Advances in computational and systems biology require multidisciplinary teams with skills in applying engineering and computer science principles and tools to solve biological and medical problems.

Computational and systems biology is organized around "3D" of description, distillation, and design. In many research projects, system data collection is used to create a detailed molecular or cellular level description of a system in one or more defined states.

Given the complexity of biological systems and the number of interacting components and parameters, the goal of system modeling is often to extract the essential or most important subsystems, components, and parameters and obtain simplified models that retain the ability to accurately predict the system under specific conditions . various conditions. the behavior of.

System distillation can improve model interpretability in terms of evolution and engineering principles, such as robustness, modularity, and evolvability. The resulting models can also be used to facilitate the rational design of perturbations to test understanding of systems or to alter system behavior (e.g., for therapeutic interventions), as well as efforts to design related systems or systems composed of similar biological components.



[More to come ...]


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