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What is Systems Biology?

Systems Biology (SB) is a generic approach in Biology that is based on the fact that any biological entity (a genome, a cell, an organ, an individual, a population, etc.) represents a system formed of interacting constituents. The central idea of ​​SB is that biological processes (whether these processes are physiological, growth, regulatory, adaptive, evolving or learning processes…) come from the causal interactions between constituents and thus find their fundamental origin as than collective behavior within the systems under consideration. Thus, the organization and functioning of life is understood as emerging properties of biological systems.

Emerging behaviors within biological systems often have a significant degree of complexity due to:

  • the potentially large number of constituents (eg 2,104 human genes or 1010-1011 human neurons),
  • the heterogeneity of these constituents
  • of the heterogeneous and frequently highly non-linear interactions between these constituants (e.g. enzymatic reactions, allosteric interactions, neuronal excitability, etc.).

These behaviors can emerge within horizontal architectures, i.e. interactions in networks (of genes, signaling pathways, neurons, etc.) or vertical organizations (multi-scale interactions between the molecular, cellular, etc., up to the individual, population and the environment scales).
SB can therefore be defined, in the general sense, as being the science of interactions in Biology. It is a non-reductionist approach addressing issues related to network biology (horizontal interactions) and / or integrative biology (vertical interactions), which accounts for properties (logical, spatial, dynamical, etc.) of life as emerging collective processes.

Systems Biology is essentially based on:

  • the acquisition of large/multi-dimensional experimental data sets on the many constituents / scales of the considered systems (e.g. genomic interactions, physiological or environmental signals),
  • their specific analysis through advanced statistical and algorithmic methods (e.g. multi-dimensional statistics, Bayesian inference), as well as
  • the construction of computational mathematical models (e.g. biophysical, biochemical, neural models) to mechanistically account for the causality of the interactions involved and to produce testable predictions at different scales.