Mitochondrio - a Unity platform-based simulation of mitochondrial network dynamics
Bibliographic record
Abstract
Mitochondria can take on a wide range of forms, from spherical/ovoid structures to tubules to highly interconnected networks. In many cell types, mitochondria are highly malleable, capable of continually undergoing fusion and/or fission to form larger or smaller structures, respectively. In addition, mitochondria are motile, trafficked along the cytoskeleton by molecular motors to reach distant parts of the cell and then return. Computational biology can be usefully applied to understanding dynamical systems, sometimes revealing underlying relationships that are not otherwise apparent. For this purpose we developed Mitochondrio, an application that simulates mitochondrial network dynamics in a single cell. Mitochondrio is programmed in Unity, a platform specializing in creating 3D games. The application presents the user with an interface allowing them to enter parameter values for the following mitochondrial characteristics: starting density, fusion probability, fission probability, and speed. The user also defines a starting cytoskeleton, as mitochondria are tethered in silico to this cytoskeleton. When a simulation is then initiated, Mitochondrio will run continually under the specific parameters and eventually reach a steady state that can be analyzed by other platforms like MiNA. Here we will describe the development of Mitochondrio and its use to gain insight into mitochondrial dynamics that can inform experimental work. Natural Sciences and Engineering Research Council of Canada This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".