Quantifying the impact of different forms of stress on fungal growth: an inference method based on high-resolution pictures of the mycelial network
Résumé
In previous work [5], a complete methodology for monitoring the growth of a filamentous fungus was introduced, covering all aspects of this complex task going from the multi-scale imaging of the network of filaments to the automated extraction of the graph structure and its key statistics at regular time points. This methodology was applied to the fungus Podospora anserina grown in the lab under various conditions. In parallel, a stochastic growth-fragmentation model for the dynamics of such mycelial networks was introduced and studied in [23]. This simple model depends on three parameters only: the elongation speed v of a single filament, the branching rate b_1 of a filament at its open end, and the per unit length rate b_2 at which a budding event happens, resulting in a new filament branching off from an existing one. In this work, we develop a statistical inference method based on the large-time behaviour of the growth-fragmentation model shown in [23], and on the high-resolution pictures of the mycelial network obtained using the methodology described in [5], to reconstruct the parameters v, b_1 and b_2 from experimental data. We use this method to analyse the growth of P. anserina observed under standard conditions and when several forms of stress are applied, in order to quantify the effect of these stresses on the different mechanisms of fungal growth. By comparing with the parameter estimates obtained from the dynamical tracking of individual filaments, we show that reliable estimates of the individual elongation speed and branching rates can be computed from the easily accessible data consisting in a single panorama of the filament network pictured after several hours of growth and an empirical measure of the exponential growth rate of the number of branch points and free extremities.
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