Systems Biology

Systems Biology has emerged in the last years as a new, holistic approach based on the global understanding of cells, instead of only focusing on their individual molecules, in order to better understand their complexity. In other words, it is the study of organisms (or other biological systems), viewed as integrated and interrelated network of genes, proteins, and biochemical reactions which create complex biological processes.

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Why is Systems Biology essential for researchers?

To understand complex biological systems such as cells, tissues, or even the human body, it is not sufficient to identify and characterize the individual molecules of the system. It is also necessary to obtain a thorough understanding of the interactions between molecules and biological pathways. This is even truer for comprehending complex diseases such as cancer, multiple sclerosis, etc.

Thus, with the recent advances enabling researchers to monitor complex cellular processes on the molecular level, the focus is shifting from the study of individual cell components toward the interpretation of the data generated by these so-called omics technologies. Mathematical models based on Systems Biology offer numerous benefits to researchers, such as:

  •  Allowing them to integrate data from different experiments, revealing complex properties that may not be apparent from single experiments, and to integrate and interpret enormous amounts of information about the functioning of biological systems.
  •  Allowing them to investigate the connections between complex processes and how their disruptions may contribute to the development of diseases, helping to systematically analyse system perturbations and ultimately assessing the suitability of specific molecules as novel therapeutic targets.
  •  Being more versatile than wet experiments. Computer simulations are less constrained than wet experiments, and can assess more experimental settings in less time. This allows researchers to focus the wet lab resources for the more relevant experiments, and to explore novel scenarios and develop hypotheses to guide the design of new and promising experiments. This approach is particularly helpful for difficult or expensive wet experiments.
  •  Helping to systematically determine the relevance of a specific molecule or pathway for the overall behaviour of the system. Not all components of a reaction or pathway are equally important, and many biological processes are controlled by relatively small subsystems. Comparison of computer simulations and actual experimental data may help the researcher to readily identify simpler subsystems that are enough to understand the features of the much more difficult-to-treat full biological system.
  •  Allowing researchers to combine approaches in exploring biological phenomena. Mathematical models integrate data, helping to generate new ideas (or hypotheses). They may be tested using both "wet experiments" and "dry experiments" (computer simulations), which generate new data that can help researchers form new hypotheses and refine their mathematical models
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