Dynamics of Mitochondrial Redox and Energy Networks: Insights from an Experimental–Computational Synergy - Systems Biology of Metabolic and Signaling Networks - page 114

Fig. 5.2 The
experimental—
computational synergy

The experimental - computational synergy

Simulaon - Validaon

Experiment Model

Predicon - Experimentaon

Synergy = reciprocal potentiation in successive iterative loops

pharmacological reversion, blunting oscillations, and stabilizing the action poten-
tial in different animal models, is strong proof of the involvement of the network
behavior of mitochondrial energetics.

5.1 Experimental-Computational Synergy

In the next sections we will delineate the powerful synergy arising from a combined
experimental and theoretical approach to unravel mechanisms underlying complex
dynamic behavior. Specifically, we will describe how the interaction of computa-
tional modeling and experimental work led to the vision of mitochondria behaving
as networks in architectural, topological, and dynamic senses. We also explore the
physiological and pathophysiological consequences of the network behavior of
mitochondrial function.
Figure 5.2 shows an overall flow diagram of the experimental-computational
synergy. The main driving force underlying the synergy is the continual interaction
between experiment and computational model which gives rise to iterative loops.
The intrinsic dynamic of these loops works as follows: a model is validated as can
be judged from its ability to simulate experimental results; this triggers model
prediction of new, unexpected behavior which elicits experimental verification,
followed by the discovery of novel properties revealed by the experiments which in
turn feedback on the model that can be tested again in its ability to simulate them. If
the model is unable to simulate the new behavior, then modifications (changes
either in the structure of the model, or in the rate expressions or just in parameter
values) are introduced. These changes may take the form of model upgrades to
account for new processes, or different functional relationships between the model
components already in place.
The iterative dynamic of the experiment

model loop represents the synergy
through theoretical prediction that works as a hypothesis-driven experimental test,

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