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Dalle Pezze, P.

Publications and source records attributed to Dalle Pezze, P..

2 recordsLinked to original sources

Dynamic modelling suggests differential mechanisms for initiation of non-selective autophagy and mitophagy

During autophagy, the ULK complex nucleates autophagic precursors which give rise to autophagosomes. We analysed by live imaging and mathematical modelling translocation of ATG13 (part of ULK complex) to autophagic puncta in starvation-induced autophagy and ivermectin-induced mitophagy. In non-selective autophagy, the intensity and duration of ATG13 translocation approximated a normal distribution whereas wortmannin reduced this and shifted to a log-normal distribution. During mitophagy, multiple translocations of ATG13, with increasing time between peaks were observed. We hypothesised that these multiple translocations arise because engulfment of mitochondrial fragments requires successive nucleations of multiple phagophores on the same target, and a mathematical model based on this idea reproduced the oscillatory behaviour. Significantly, model and experimental data were also in agreement that the number of ATG13 translocations is directly proportional to the diameter of the targeted mitochondrial fragments. Our data provide novel insights into the early dynamics of selective and non-selective autophagy.

systems biology

SBpipe: a collection of pipelines for automating repetitive simulation and analysis tasks

Background: The rapid growth of the number of mathematical models in Systems Biology fostered the development of many tools to simulate and analyse them. The reliability and precision of these tasks often depend on multiple repetitions and they can be optimised if executed as pipelines. In addition, new formal analyses can be performed on these repeat sequences, revealing important insights about the accuracy of model predictions.\n\nResults: Here we introduce SBpipe, an open source software tool for automating repetitive tasks in model building and simulation. Using basic configuration files, SBpipe builds a sequence of repeated model simulations or parameter estimations, performs analyses from this generated sequence, and finally generates a LaTeX/PDF report. The parameter estimation pipeline offers analyses of parameter profile likelihood and parameter correlation using samples from the computed estimates. Specific pipelines for scanning of one or two model parameters at the same time are also provided. Pipelines can run on multicore computers, Sun Grid Engine (SGE), or Load Sharing Facility (LSF) clusters, speeding up the processes of model building and simulation. SBpipe can execute models implemented in Copasi, Python or coded in any other programming language using Python as a wrapper module. Future support for other software simulators can be dynamically added without affecting the current implementation.\n\nConclusions: SBpipe allows users to automatically repeat the tasks of model simulation and parameter estimation, and extract robustness information from these repeat sequences in a solid and consistent manner, facilitating model development and analysis. The source code and documentation of this project are freely available at the web site: https://pdp10.github.io/sbpipe/.

systems biology