Reference : Understanding complexity in neurodegenerative diseases: in silico reconstruction of e...
Scientific journals : Article
Life sciences : Biochemistry, biophysics & molecular biology
Understanding complexity in neurodegenerative diseases: in silico reconstruction of emergence.
[en] Understanding complexity in neurodegenerative diseases
Kolodkin, Alexey mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Simeonidis, Evangelos mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Balling, Rudi mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Westerhoff, Hans V. [> >]
Frontiers in Physiology
Multifactorial disease: network disease
[en] systems biology ; systems biology diseases ; network diseases ; weak emergence ; strong emergence ; computer modeling ; neurodegenerative disease ; Parkinson’s disease(PD)
[en] Healthy functioning is an emergent property of the network of interacting biomolecules that comprise an organism. It follows that disease (a network shift that causes malfunction) is also an emergent property, emerging from a perturbation of the network. On the one hand, the biomolecular network of every individual is unique and this is evident when similar disease-producing agents cause different individual pathologies. Consequently, a personalized model and approach for every patient may be required for therapies to become effective across mankind. On the other hand, diverse combinations of internal and external perturbation factors may cause a similar shift in network functioning. We offer this as an explanation for the multi-factorial nature of most diseases: they are "systems biology diseases," or "network diseases." Here we use neurodegenerative diseases, like Parkinson's disease (PD), as an example to show that due to the inherent complexity of these networks, it is difficult to understand multi-factorial diseases with simply our "naked brain." When describing interactions between biomolecules through mathematical equations and integrating those equations into a mathematical model, we try to reconstruct the emergent properties of the system in silico. The reconstruction of emergence from interactions between huge numbers of macromolecules is one of the aims of systems biology. Systems biology approaches enable us to break through the limitation of the human brain to perceive the extraordinarily large number of interactions, but this also means that we delegate the understanding of reality to the computer. We no longer recognize all those essences in the system's design crucial for important physiological behavior (the so-called "design principles" of the system). In this paper we review evidence that by using more abstract approaches and by experimenting in silico, one may still be able to discover and understand the design principles that govern behavioral emergence.
Luxembourg Centre for Systems Biomedicine (LCSB): Experimental Neurobiology (Balling Group)

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