Biological systems can be seen as dynamical networks, i.e. dynamical systems that are naturally endowed with an underlying network structure, because they are composed of several subsystems that interact according to an interconnection topology. Despite their large scale and complexity, they often exhibit an extraordinary robustness that preserves fundamental properties and qualitative behaviours even in the presence of huge parameter variations and environmental fluctuations.
We look for the source of the amazing robustness that often characterises biological systems, by identifying properties and emerging behaviours that exclusively depend on the system structure (the graph topology along with qualitative information), regardless of parameter values. We capture the system structure so as to enable the parameter-free assessment of important properties, including the stability of equilibria and the sign of steady-state input-output influences, thus allowing structural model falsification and structural comparison of alternative mechanisms proposed to explain the same phenomenon.
Finally, we discuss the limitations of structural methodologies, which may be overcome by integrating and complementing them with probabilistic approaches.
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