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Diffusive Spreading Across Dynamic Mitochondrial Network Architectures

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper claims that material diffusing through mitochondrial networks switches between two transport regimes—social, limited by cluster encounters, and physical, limited by diffusivity—and that one analytic expression spans the…

desk verdict A solid, genuinely useful bridge between social and physical network spreading regimes for mitochondria; the main caveat is that validation and predictions rest on the authors' own simulation model and untested cell-line numbers. read the letter →

arxiv 2506.05643 v3 pith:AXUCYNEM submitted 2025-06-06 physics.bio-ph q-bio.SC

Keaton B. Holt , Camryn Zurita , Lizzy Teryoshin , Samantha C. Lewis , Elena F. Koslover This is my paper
classification physics.bio-phq-bio.SC
keywords mitochondrialnetworkstemporaldiffusivetransportfusionandfissionfractaldimensionintracellularnetworkfillingcell-to-cellvariability
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish a single quantitative rule for how material—proteins, lipids, ions, RNA—spreads through a dynamic mitochondrial population. The rule is a connectivity-driven transition: in fragmented networks, small clusters move through three-dimensional space and exchange contents during brief fusions (a "social network" limited by encounter rate), while in well-connected networks, material tunnels through a nearly stationary, low-dimensional connected mesh (a "physical network" limited by diffusivity). The authors derive one analytic expression, their Eq. 4, that approximates the steady-state amount of material delivered from a fixed source across both regimes and the intermediate range, using only the balance of four timescales: decay, cluster filling, cluster encounter, and fission. They then extract the needed parameters from live-cell images of three human cell lines and predict that typical matrix proteins fill half the mitochondrial network in roughly six minutes in hyperfused SH-SY5Y cells and about forty-five minutes in the more disconnected IMR90 and U2OS cells. A sympathetic reader would care because this turns static morphological snapshots into testable predictions for mitochondrial mixing, with implications for how mutations, metabolites, and calcium spread within a cell.

What carries the argument

The central object is the temporal network itself: spherocylindrical mitochondrial units that diffuse with translational diffusivity $D_1$, fuse tip-to-tip ($k_{u1}$) or tip-to-side ($k_{u2}$), break by fission at rate $k_f$ (degree-dependent), and transport material along connected tubules with diffusivity $D_p$ while material decays at rate $k_d$. The argument is carried by a timescale balance: the decay time $\tau_d=1/k_d$, the cluster filling time $\tau_c = (\langle n\rangle^{1/d}\ell_0)^2/D_p$, the inter-cluster encounter time $\tau_{\mathrm{enc}}=1/(4\pi D_n b \rho)$, the fusion waiting time $\tau_u$, and the fission time $\tau_f=1/k_f$. The unifying expression, Eq. 4, combines the static-network solution—a $d$-dimensional fractal-continuum diffusion equation with modified Bessel function solutions (Eq. 1)—with the social-network mean-field solution (Eq. 3), corrected by two factors: the average concentration in a partially filled source cluster, $h_0^{(d)}$, and the fraction $f$ of a newly encountered cluster that gets filled before fission terminates the encounter. These corrections are what let a single formula interpolate between the social and physical regimes.

What would settle it

Photoconvert a fluorescent mitochondrial matrix protein in a single mitochondrion in each of the three cell lines, image the spread over time, and compare measured half-filling times to the predicted ~6 min (SH-SY5Y) and ~45 min (IMR90, U2OS) at $D_p \approx 20\ \mu\mathrm{m}^2/\mathrm{s}$. A systematic mismatch beyond experimental error, or a failure of the disconnected-cell plateau above $D_p \approx 1\ \mu\mathrm{m}^2/\mathrm{s}$, would falsify the framework.

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Extended reading notes

Core claim

The central claim is that the dominant transport mechanism for mitochondrial contents is set by network connectivity, not by any single kinetic rate. When the mean cluster size is small, clusters behave as mobile agents that equilibrate internally on fast timescales, so spreading across the population is three-dimensional and limited by how often clusters encounter one another (fusion-limited or encounter-limited). When clusters are large, topology is nearly static and material spreads along connected tubules whose graph dimension is low ($1 \le d \lesssim 2$), so spreading is limited by the material diffusivity along the network. The paper shows that a single mean-field expression—Eq. 4, equal to the static fractal-continuum solution for the source cluster plus a social-network term with corrections for partial cluster filling before fission—reproduces the steady-state filling of explicit dynamic-network simulations over fusion rates spanning from fragmented to hyperfused networks. Applied to extracted structures from three human cell lines, the model predicts that SH-SY5Y mitochondria fall predominantly in the physical-network regime, whereas IMR90 and U2OS mitochondria fall in the social-network regime, with half-filling times around 6 and 45 minutes respectively for typical matrix proteins.

Load-bearing premise

The whole framework is validated against a prior simulation model of mitochondrial fusion, fission, and motion; if that simulation does not faithfully represent real mitochondria, the regime transition and the cell-line predictions would not transfer to living cells.

Editorial extensions

If this is right

  • In hyperfused networks, the half-filling time keeps decreasing as material diffusivity increases, whereas in disconnected networks it plateaus for $D_p \gtrsim 1\ \mu\mathrm{m}^2/\mathrm{s}$, so mixing of fast-diffusing molecules is encounter-limited rather than diffusion-limited.
  • When encounter is the slow step ($\tau_{\mathrm{enc}} \gg \tau_u$), proportionally increasing both fusion and fission rates should leave mitochondrial mixing almost unchanged; cluster mobility, not fusion waiting time, sets the spread.
  • For slowly diffusing material, partially disconnected networks can spread contents faster than hyperfused ones, because moving clusters expose each unit to more fusion partners than a static low-dimensional network does.
  • The model unifies previously separate static-network and mobile-agent approaches, so the same Eq. 4 can be applied to any temporal network of interacting, space-filling units once its cluster sizes, diffusivities, encounter rate, and fission rate are measured.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension the paper leaves implicit: the same timescale-balance framework should apply to other dynamic organelle populations, such as endosomal compartments or ER contact sites, with their own encounter and exchange rules substituted for fusion and fission.
  • The predicted regime transition near the percolation threshold could be mapped experimentally by titrating fission/fusion balance while photoconverting a matrix protein; the simulations suggest the analytic formula slightly overestimates spreading exactly in this crossover region.
  • If the half-filling predictions hold, then the roughly sevenfold difference between cell types implies that mitochondrial network architecture directly sets the timescale for genetic complementation and ROS dilution, which could be tested by measuring phenotypic rescue times in heteroplasmic cell lines with different morphologies.
  • The model's treatment of all units as identical and its neglect of directed motor-driven runs are simplifications; incorporating processive runs would likely shorten encounter times in the social regime and should be tested by comparing predictions in cell types with known directed mitochondrial transport.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This paper develops analytic approximations for the steady-state spreading of a locally produced, globally consumed material through dynamic networks of fusing and fissioning mitochondrial units. It derives a static-network solution based on a fractal continuum (Eq. 1), a mean-field 'social network' solution for fragmented clusters (Eq. 3), and a combined approximation for dynamic clusters (Eq. 4). The approximations are compared with explicit spherocylindrical simulations from the authors' prior model (Ref. [67]) over a range of connectivities and material diffusivities, and are then applied to three human cell lines to predict the time to fill half the mitochondrial network and to classify cells as hyperfused or disconnected. The central claim is that Eq. 4 captures a transition from socially limited, three-dimensional spreading to physically limited, low-dimensional transport, and that the relevant regime can be inferred from structural and dynamic imaging measurements.

Significance. If correct, the framework would provide a useful predictive link between mitochondrial morphology and functional mixing, with potential applicability beyond mitochondria to other spatial temporal networks. The paper's strengths are that the analytic derivations are explicit and standard, the code and imaging data are available, and the theory is validated against explicit simulations over a broad parameter range without fitting free parameters to the spreading data. The cell-line predictions are genuine in the sense that they combine independently imaged structural parameters with model-derived dynamics. The main qualification is that the simulation-based validation is against the same parent model from which several effective parameters are extracted, so the biological predictions remain untested; this should be reflected in the framing.

major comments (3)
  1. [Dynamic networks with large interacting clusters; Fig. 4] The validation of Eq. 4 in Fig. 4 uses effective parameters (mean cluster size, cluster diffusivity, steric radius, contact volume, effective fusion rate) extracted from simulations of the same spherocylindrical model [67] whose spreading output is then compared with the analytic result. This makes the agreement a test of internal consistency of the coarse-graining, not an independent validation of the biological assumptions in the parent model. Because the cell-line predictions inherit the encounter and fusion kinetics from this model, the abstract's statement that the framework provides 'a quantitative basis for predicting the homogenization of biomolecules through a mitochondrial population' overstates the current evidence. Please label Fig. 4 explicitly as a reduction test against the parent simulation model and move the untested-prediction caveat into the Results or Abstract.
  2. [Fig. 4; 'Dynamic networks with large interacting clusters'] The text acknowledges that near the percolation transition with low particle diffusivity the approximation systematically overestimates material spreading, but it does not quantify the discrepancy. Since the connectivity-driven transition between social and physical regimes is the central claim, the authors should report the magnitude of the overestimate (for example percent error in S or in the inferred t_1/2) for the affected parameter range, and state whether any of the cell-line predictions in Fig. 5 fall into this regime. This would let readers assess the practical impact of the known limitation.
  3. [Fig. 5; 'Spreading rates on mammalian mitochondrial networks'] The t_1/2 predictions for SH-SY5Y, IMR90, and U2OS combine imaging-derived structural and mobility parameters with encounter dynamics and fusion rates taken from the Ref. [67] simulation model. The Discussion appropriately notes that the predictions are untested, but the Results should state more explicitly which parameters come from imaging and which are model-inherited, and the authors should add a sensitivity analysis over the plausible range of fusion rates. Without this, the claimed 6 min versus 45 min difference between cell types is presented with more precision than the evidence supports.
minor comments (5)
  1. [Results, paragraph beginning 'We note that in the limit of arbitrarily slow particle diffusivity'] The word 'mitochodrial' should be 'mitochondrial'.
  2. [Fig. 4 caption] The word 'encompases' should be 'encompasses'.
  3. [Fig. 5 caption] The word 'algorthim' should be 'algorithm'.
  4. [Fig. 5 and Results text] The main text does not state the number of cells per line or the inter-cell variance; please add these values when describing the prototypical-cell averages and the individual-cell dashed curves.
  5. [Discussion] The statement that proportional increases in fusion and fission rates have little effect on mixing in the disconnected regime is an interesting prediction; consider adding a one-sentence derivation or an explicit reference to the SI figure where this is shown.

Circularity Check

0 steps flagged · score 2.0 of 10

No circularity by construction: Eq. 4 is a derived reduction, though its simulation testbed comes from the authors' prior model.

full rationale

The paper's central derivation is not equivalent to its inputs. Eq. 1 is obtained by solving the steady-state diffusion equation on a fractal continuum, Eq. 3 from a mean-field encounter/fusion model, and Eq. 4 by combining these limits with independently defined corrections h0^(d) and f. The effective parameters entering Eq. 4 (mean cluster size, cluster diffusivity, steric radius, contact volume, fusion rate) are extracted from the dynamic simulations, but they are not fitted to the steady-state material content S that Eq. 4 is asked to reproduce; the agreement in Fig. 4 is a reduction test, not a re-statement. The cell-line t_1/2 predictions use structural and dynamic parameters measured from imaging, so they are genuine predictions rather than fits to target data. The only notable concern is that the simulation framework used for validation and for supplying some biological parameters is Ref. [67], prior work by the same group. That makes the biological conclusions conditional on the representativeness of that simulation model, but it is a correctness/uncertainty risk, not a circular derivation. The paper even states the predictions remain to be tested ('The predictions made by this model could be tested in future work'), reinforcing that no fitted output is being relabeled as a prediction.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No numbers are fitted to match the spreading data; all inputs are either measured from images or simulations, or taken from prior literature. The structural parameters (graph dimension d, effective source coordination n_b, encounter radius b) are measured or set by geometry, not tuned to reproduce the model output. The k-means threshold for cell classification is a data-derived grouping, not a model fit.

assumptions (4)
  • domain assumption The spherocylindrical model of mitochondrial network architecture from Ref. [67] accurately captures fusion, fission, and mechanical dynamics of real mitochondria.
    The paper uses simulations from this model as ground truth to validate the analytic spreading framework and to extract effective parameters such as cluster diffusivity and encounter rate. If the model is unrealistic, the regime transition and the extracted parameters may be wrong. Entered in 'Model Description'.
  • domain assumption The spreading process is well-described by a fixed-concentration source with first-order decay of material (rate k_d), and the steady-state total material S is the relevant observable.
    The entire model is built around this scenario. The paper acknowledges in the Discussion that real systems may have multiple sources, regulated sources, or different consumption kinetics, which would require extensions.
  • domain assumption In the social network regime, clusters can be treated as identical spherical units with uniform internal concentration, and each fusion either fully equilibrates or transfers a fixed fraction f of the cluster content.
    Used to derive Eqs. 2-4. This ignores heterogeneity in cluster sizes and internal concentration gradients during encounters.
  • domain assumption The static network can be approximated as a self-similar fractal domain characterized by a single graph dimension d, with a source sphere and reflecting outer boundary.
    Used for the continuum approximation Eq. 1. The paper shows this matches exact solutions on simulated networks, but it assumes scale invariance of the network topology at the relevant length scales.

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Cite this review

Pith. "Pith review of Diffusive Spreading Across Dynamic Mitochondrial Network Architectures." pith.science (2026). https://pith.science/paper/AXUCYNEM

@misc{pith2026250605643,
  author       = {Pith},
  title        = {Pith review of: Diffusive Spreading Across Dynamic Mitochondrial Network Architectures},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AXUCYNEM}},
  note         = {Machine review of arXiv:2506.05643}
}
read the original abstract

In eukaryotic cells, mitochondria form networks that range from highly fused interconnected structures to fragmented populations of individual organelles that undergo transient interactions. These structures can be described as temporal networks of physical units, whose dynamic topology is determined by fusion, fission, and motion of the mitochondria through intracellular space. The heterogeneity of the mitochondrial population is governed by diffusive transport and inter-unit exchange of proteins, lipids, ions, and RNA within these networks. We present a unifying framework for the dispersion of material within temporal networks of spatially embedded units that span across a broad connectivity range. Specifically, we consider filling of the networks with a locally produced but globally consumed material, demonstrating that the steady-state content is determined by the balance of timescales for spatial encounter between clusters, local fusion, fission, and diffusive transport within a cluster. As the connectivity increases, filling behavior transitions from three-dimensional spread through a `social network' limited by cluster interactions to low-dimensional transport through a largely stationary `physical network' limited by material diffusivity. We extract parameters for mitochondrial networks in three human cell lines, demonstrating that different cells can access both the social and the physical network regimes. These results provide a quantitative basis for predicting the homogenization of biomolecules through a mitochondrial population. Our framework unifies a variety of temporal network structures into an overarching theory for transport through populations of interacting and interconnected units.

Figures

Figures reproduced from arXiv: 2506.05643 by the authors.

Figure 1
Figure 1. FIG. 1. Dynamic network simulation framework. (a) [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. Filling of static networks depends on network dimensionality. Two different sets of networks are considered: near-linear [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. Steady-state material content supplied by a source [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: FIG. 4. Spreading on simulated networks exhibits a transition between the static network and social network regime. The [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: FIG. 5. Extracted structures of mammalian mitochondrial networks exemplify both the static network and social network [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

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Reference graph

Works this paper leans on

113 extracted references · 79 canonical work pages

  1. [4]

    Epidemic processes on self-propelled particles: Continuum and agent-based modeling.Phys Rev Res, 4(4):043160, 2022

    Jorge P Rodr ´ ıguez, Matteo Paoluzzi, Demian Levis, and Michele Starnini. Epidemic processes on self-propelled particles: Continuum and agent-based modeling.Phys Rev Res, 4(4):043160, 2022

    2022

  2. [24]

    Mitochondrial network branching enables rapid protein spread with slower mitochondrial dynamics.PRX Life, 2(4):043005, 2024

    Prabha Chuphal, Jordan D Lanctˆ ot, Sean P Cornelius, and Aidan I Brown. Mitochondrial network branching enables rapid protein spread with slower mitochondrial dynamics.PRX Life, 2(4):043005, 2024

    2024

  3. [67]

    Spatiotemporal modeling of mito- chondrial network architecture.PRX Life, 2(4):043002, 2024

    Keaton B Holt, Julius Winter, Suliana Manley, and Elena F Koslover. Spatiotemporal modeling of mito- chondrial network architecture.PRX Life, 2(4):043002, 2024

    work page 2024

  4. [1]

    Global transport networks and infectious disease spread

    Andrew J Tatem, David J Rogers, and Simon I Hay. Global transport networks and infectious disease spread. Adv Parasit, 62:293–343, 2006

    2006

  5. [2]

    Spread of epidemic disease on net- works.Phys Rev E, 66(1):016128, 2002

    Mark EJ Newman. Spread of epidemic disease on net- works.Phys Rev E, 66(1):016128, 2002

    2002

  6. [3]

    Dynamics and steady states in excitable mobile agent systems.Phys Rev Lett, 100(16):168103, 2008

    Fernando Peruani and Gustavo J Sibona. Dynamics and steady states in excitable mobile agent systems.Phys Rev Lett, 100(16):168103, 2008

    2008

  7. [5]

    The spread of innovations in social networks.P Natl Acad Sci, 107 (47):20196–20201, 2010

    Andrea Montanari and Amin Saberi. The spread of innovations in social networks.P Natl Acad Sci, 107 (47):20196–20201, 2010

    2010

  8. [6]

    Automated monitoring of behavior reveals bursty inter- action patterns and rapid spreading dynamics in honey- bee social networks.P Natl Acad Sci, 115(7):1433–1438, 2018

    Tim Gernat, Vikyath D Rao, Martin Middendorf, Harry Dankowicz, Nigel Goldenfeld, and Gene E Robinson. Automated monitoring of behavior reveals bursty inter- action patterns and rapid spreading dynamics in honey- bee social networks.P Natl Acad Sci, 115(7):1433–1438, 2018

    2018

Show all 113 references
  1. [7]

    Time-ordered networks reveal limitations to information flow in ant colonies.PloS one, 6(5):e20298, 2011

    Benjamin Blonder and Anna Dornhaus. Time-ordered networks reveal limitations to information flow in ant colonies.PloS one, 6(5):e20298, 2011

    2011

  2. [8]

    Extracellular sheets and tunnels modulate glutamate diffusion in hippocampal neuropil.J Comp Neurol, 521(2):448–464, 2013

    Justin P Kinney, Josef Spacek, Thomas M Bartol, Chandrajit L Bajaj, Kristen M Harris, and Terrence J Sejnowski. Extracellular sheets and tunnels modulate glutamate diffusion in hippocampal neuropil.J Comp Neurol, 521(2):448–464, 2013

    2013

  3. [9]

    Diffusion in brain extracellular space.Physiol Rev, 88(4):1277–1340, 2008

    Eva Sykov´ a and Charles Nicholson. Diffusion in brain extracellular space.Physiol Rev, 88(4):1277–1340, 2008

    2008

  4. [10]

    Predicting oil recovery using perco- lation.Physica A: Statistical mechanics and its Appli- cations, 266(1-4):107–114, 1999

    Peter R King, Jos´ e S Andrade Jr, Sergey V Buldyrev, Nikolay Dokholyan, Youngki Lee, Shlomo Havlin, and H Eugene Stanley. Predicting oil recovery using perco- lation.Physica A: Statistical mechanics and its Appli- cations, 266(1-4):107–114, 1999

    1999

  5. [11]

    Spatial networks.Phys Rep, 499(1- 3):1–101, 2011

    Marc Barth´ elemy. Spatial networks.Phys Rep, 499(1- 3):1–101, 2011

    2011

  6. [12]

    A structural transition in physical net- works.Nature, 563(7733):676–680, 2018

    Nima Dehmamy, Soodabeh Milanlouei, and Albert- L´ aszl´ o Barab´ asi. A structural transition in physical net- works.Nature, 563(7733):676–680, 2018

    2018

  7. [13]

    Impact of physicality on net- work structure.Nat Phys, 20(1):142–149, 2024

    M´ arton P´ osfai, Bal´ azs Szegedy, Iva Baˇ ci´ c, Luka Blago- jevi´ c, Mikl´ os Ab´ ert, J´ anos Kert´ esz, L´ aszl´ o Lov´ asz, and Albert-L´ aszl´ o Barab´ asi. Impact of physicality on net- work structure.Nat Phys, 20(1):142–149, 2024

    2024

  8. [14]

    Three-dimensional shape and connectivity of physical networks.Sci Rep, 14(1):16874, 2024

    Luka Blagojevi´ c and M´ arton P´ osfai. Three-dimensional shape and connectivity of physical networks.Sci Rep, 14(1):16874, 2024

    2024

  9. [15]

    Emergence of the mitochondrial reticulum from fission and fusion dynam- ics.PloS Comput Biol, 8(10):e1002745, 2012

    Valerii M Sukhorukov, Daniel Dikov, Andreas S Re- ichert, and Michael Meyer-Hermann. Emergence of the mitochondrial reticulum from fission and fusion dynam- ics.PloS Comput Biol, 8(10):e1002745, 2012

    2012

  10. [16]

    Morphology and mechanics of fungal mycelium.Sci Rep, 7(1):13070, 2017

    Mohammad Retaful Islam, Gregory Tudryn, R Bucinell, L Schadler, and RC Picu. Morphology and mechanics of fungal mycelium.Sci Rep, 7(1):13070, 2017

    2017

  11. [17]

    A connectome and analysis of the adult drosophila central brain.elife, 9:e57443, 2020

    Louis K Scheffer, C Shan Xu, Michal Januszewski, Zhiyuan Lu, Shin-ya Takemura, Kenneth J Hayworth, Gary B Huang, Kazunori Shinomiya, Jeremy Maitlin- Shepard, Stuart Berg, et al. A connectome and analysis of the adult drosophila central brain.elife, 9:e57443, 2020

    2020

  12. [18]

    3d numerical reconstruction 12 of well-connected porous structure of rock using fractal algorithms.Comput Method Appl M, 279:212–226, 2014

    Yang Ju, Jiangtao Zheng, Marcelo Epstein, Les Sudak, Jinbo Wang, and Xi Zhao. 3d numerical reconstruction 12 of well-connected porous structure of rock using fractal algorithms.Comput Method Appl M, 279:212–226, 2014

    2014

  13. [19]

    Amer- ican Mathematical Soc., 1997

    Fan RK Chung.Spectral graph theory, volume 92. Amer- ican Mathematical Soc., 1997

    1997

  14. [20]

    Random walks and diffusion on networks.Phys Rep, 716:1–58, 2017

    Naoki Masuda, Mason A Porter, and Renaud Lam- biotte. Random walks and diffusion on networks.Phys Rep, 716:1–58, 2017

    2017

  15. [21]

    Physical net- works as network-of-networks.Nat Commun, 15(1): 4882, 2024

    G´ abor Pete, ´Ad´ am Tim´ ar, Sigurdur¨Orn Stef´ ansson, Ivan Bonamassa, and M´ arton P´ osfai. Physical net- works as network-of-networks.Nat Commun, 15(1): 4882, 2024

    2024

  16. [22]

    What is the function of mitochondrial networks? a theo- retical assessment of hypotheses and proposal for future research.Bioessays, 37(6):687–700, 2015

    Hanne Hoitzing, Iain G Johnston, and Nick S Jones. What is the function of mitochondrial networks? a theo- retical assessment of hypotheses and proposal for future research.Bioessays, 37(6):687–700, 2015

    2015

  17. [23]

    A comparison prin- ciple for random walk on dynamical percolation.Ann Probab, 48(6):2952–2987, 2020

    Jonathan Hermon and Perla Sousi. A comparison prin- ciple for random walk on dynamical percolation.Ann Probab, 48(6):2952–2987, 2020

    2020

  18. [25]

    Temporal networks

    Petter Holme and Jari Saram¨ aki. Temporal networks. Phys Rep, 519(3):97–125, 2012

    2012

  19. [26]

    System of mobile agents to model social net- works.Phys Rev Lett, 96(8):088702, 2006

    Marta C Gonz´ alez, Pedro G Lind, and Hans J Her- rmann. System of mobile agents to model social net- works.Phys Rev Lett, 96(8):088702, 2006

    2006

  20. [27]

    Fractal continuum model for tracer trans- port in a porous medium.Physical Review E-Statistical, Nonlinear, and Soft Matter Physics, 88(6):063004, 2013

    EC Herrera-Hern´ andez, M Coronado, and H Hern´ andez- Coronado. Fractal continuum model for tracer trans- port in a porous medium.Physical Review E-Statistical, Nonlinear, and Soft Matter Physics, 88(6):063004, 2013

    2013

  21. [28]

    Endoplasmic reticulum network heterogene- ity guides diffusive transport and kinetics.Biophys J, 122(15):3191–3205, 2023

    Zubenelgenubi C Scott, Katherine Koning, Molly Van- derwerp, Lorna Cohen, Laura M Westrate, and Elena F Koslover. Endoplasmic reticulum network heterogene- ity guides diffusive transport and kinetics.Biophys J, 122(15):3191–3205, 2023

    2023

  22. [29]

    Luminal transport through intact endoplas- mic reticulum limits the magnitude of localized ca2+ signals.P Natl Acad Sci, 121(13):e2312172121, 2024

    C´ ecile C Crapart, Zubenelgenubi C Scott, Tasuku Konno, Aman Sharma, Pierre Parutto, David MD Bai- ley, Laura M Westrate, Edward Avezov, and Elena F Koslover. Luminal transport through intact endoplas- mic reticulum limits the magnitude of localized ca2+ signals.P Natl Acad S...

    2024

  23. [30]

    Deterministic early endoso- mal maturations emerge from a stochastic trigger-and- convert mechanism.Nat Commun, 14(1):4652, 2023

    Harrison M York, Kunaal Joshi, Charles S Wright, Laura Z Kreplin, Samuel J Rodgers, Ullhas K Moor- thi, Hetvi Gandhi, Abhishek Patil, Christina A Mitchell, Srividya Iyer-Biswas, et al. Deterministic early endoso- mal maturations emerge from a stochastic trigger-and- convert me...

    2023

  24. [31]

    Spatiotemporal analysis of ax- onal autophagosome–lysosome dynamics reveals limited fusion events and slow maturation.Mol Biol Cell, 33 (13):ar123, 2022

    Sydney E Cason, Saurabh S Mogre, Erika LF Holzbaur, and Elena F Koslover. Spatiotemporal analysis of ax- onal autophagosome–lysosome dynamics reveals limited fusion events and slow maturation.Mol Biol Cell, 33 (13):ar123, 2022

    2022

  25. [32]

    MitoTNT: Mitochondrial temporal network tracking for 4D live-cell fluorescence microscopy data.PLoS Comput Biol, 19(4):e1011060, 2023

    Zichen Wang, Parth Natekar, Challana Tea, Sharon Tamir, Hiroyuki Hakozaki, and Johannes Sch¨ oneberg. MitoTNT: Mitochondrial temporal network tracking for 4D live-cell fluorescence microscopy data.PLoS Comput Biol, 19(4):e1011060, 2023

    2023

  26. [33]

    Mitochondrial fission and fusion dynamics generate effi- cient, robust, and evenly distributed network topologies in budding yeast cells.Cell Syst, 10(3):287–297, 2020

    Matheus P Viana, Aidan I Brown, Irina A Mueller, Claire Goul, Elena F Koslover, and Susanne M Rafelski. Mitochondrial fission and fusion dynamics generate effi- cient, robust, and evenly distributed network topologies in budding yeast cells.Cell Syst, 10(3):287–297, 2020

    2020

  27. [34]

    Percolation and criticality in a mitochondrial network

    Miguel A Aon, Sonia Cortassa, and Brian O’Rourke. Percolation and criticality in a mitochondrial network. P Natl Acad Sci, 101(13):4447–4452, 2004

    2004

  28. [35]

    Network analysis of arabidopsis mitochondrial dynamics reveals a resolved tradeoff be- tween physical distribution and social connectivity.Cell Syst, 12(5):419–431, 2021

    Joanna M Chustecki, Daniel J Gibbs, George W Bassel, and Iain G Johnston. Network analysis of arabidopsis mitochondrial dynamics reveals a resolved tradeoff be- tween physical distribution and social connectivity.Cell Syst, 12(5):419–431, 2021

    2021

  29. [36]

    Mitochondrial dynamics–fusion, fission, movement, and mitophagy–in neurodegenerative diseases.Hum Mol Genet, 18(R2): R169–R176, 2009

    Hsiuchen Chen and David C Chan. Mitochondrial dynamics–fusion, fission, movement, and mitophagy–in neurodegenerative diseases.Hum Mol Genet, 18(R2): R169–R176, 2009

    2009

  30. [37]

    Disturbed mitochondrial dynam- ics and neurodegenerative disorders.Nat Rev Neurol, 11(1):11–24, 2015

    Florence Burt´ e, Valerio Carelli, Patrick F Chinnery, and Patrick Yu-Wai-Man. Disturbed mitochondrial dynam- ics and neurodegenerative disorders.Nat Rev Neurol, 11(1):11–24, 2015

    2015

  31. [38]

    Mitochondria dysfunction in the pathogenesis of alzheimer’s disease: Recent ad- vances.Mol Neurodegener, 15:1–22, 2020

    Wenzhang Wang, Fanpeng Zhao, Xiaopin Ma, George Perry, and Xiongwei Zhu. Mitochondria dysfunction in the pathogenesis of alzheimer’s disease: Recent ad- vances.Mol Neurodegener, 15:1–22, 2020

    2020

  32. [39]

    Mitochondrial dysfunction in parkinson’s disease: new mechanistic insights and therapeutic perspectives.Curr Neurol Neurosci, 18:1–11, 2018

    Jin-Sung Park, Ryan L Davis, and Carolyn M Sue. Mitochondrial dysfunction in parkinson’s disease: new mechanistic insights and therapeutic perspectives.Curr Neurol Neurosci, 18:1–11, 2018

    2018

  33. [40]

    The role of mitochondria in neurodegenera- tive diseases.J Neurol, 258:1763–1774, 2011

    Massimiliano Filosto, Mauro Scarpelli, Maria Sofia Cotelli, Valentina Vielmi, Alice Todeschini, Valeria Gre- gorelli, Paola Tonin, Giuliano Tomelleri, and Alessandro Padovani. The role of mitochondria in neurodegenera- tive diseases.J Neurol, 258:1763–1774, 2011

    2011

  34. [41]

    Metformin restores the mitochondrial network and reverses mitochondrial dysfunction in down syndrome cells.Hum Mol Genet, 26(6):1056–1069, 2017

    Antonella Izzo, Maria Nitti, Nunzia Mollo, Simona Paladino, Claudio Procaccini, Deriggio Faicchia, Gae- tano Cal ` ı, Rita Genesio, Ferdinando Bonfiglio, Rita Ci- catiello, et al. Metformin restores the mitochondrial network and reverses mitochondrial dysfunction in down syndr...

    2017

  35. [42]

    Mitochondrial dynam- ics in the regulation of nutrient utilization and energy expenditure.Cell Metab, 17(4):491–506, 2013

    Marc Liesa and Orian S Shirihai. Mitochondrial dynam- ics in the regulation of nutrient utilization and energy expenditure.Cell Metab, 17(4):491–506, 2013

    2013

  36. [43]

    Tubular network formation protects mitochondria from autophagosomal degradation during nutrient starvation.P Natl Acad Sci, 108(25):10190–10195, 2011

    Angelika S Rambold, Brenda Kostelecky, Natalie Elia, and Jennifer Lippincott-Schwartz. Tubular network formation protects mitochondria from autophagosomal degradation during nutrient starvation.P Natl Acad Sci, 108(25):10190–10195, 2011

    2011

  37. [44]

    Using miter for 3d anal- ysis of mitochondrial morphology and er contacts.Cell Reports Methods, 4(1), 2024

    Therese Kichuk, Satyen Dhamankar, Saurabh Malani, William A Hofstadter, Scott A Wegner, Ileana M Cristea, and Jos´ e L Avalos. Using miter for 3d anal- ysis of mitochondrial morphology and er contacts.Cell Reports Methods, 4(1), 2024

    2024

  38. [45]

    Mitochondrial dynamics and apoptosis.Gene Dev, 22 (12):1577–1590, 2008

    Der-Fen Suen, Kristi L Norris, and Richard J Youle. Mitochondrial dynamics and apoptosis.Gene Dev, 22 (12):1577–1590, 2008

    2008

  39. [46]

    Thapsigargin in- duces biphasic fragmentation of mitochondria through calcium-mediated mitochondrial fission and apoptosis

    Jennifer R Hom, Jennifer S Gewandter, Limor Michael, Shey-Shing Sheu, and Yisang Yoon. Thapsigargin in- duces biphasic fragmentation of mitochondria through calcium-mediated mitochondrial fission and apoptosis. J Cell Physiol, 212(2):498–508, 2007

    2007

  40. [47]

    Mitochondrial dynamics and inheritance during cell division, develop- ment and disease.Nat Rev Mol Cell Bio, 15(10):634– 646, 2014

    Prashant Mishra and David C Chan. Mitochondrial dynamics and inheritance during cell division, develop- ment and disease.Nat Rev Mol Cell Bio, 15(10):634– 646, 2014

    2014

  41. [48]

    A hyper- fused mitochondrial state achieved at g1–s regulates cy- clin e buildup and entry into s phase.P Natl Acad Sci, 13 106(29):11960–11965, 2009

    Kasturi Mitra, Christian Wunder, Badrinath Roysam, Gang Lin, and Jennifer Lippincott-Schwartz. A hyper- fused mitochondrial state achieved at g1–s regulates cy- clin e buildup and entry into s phase.P Natl Acad Sci, 13 106(29):11960–11965, 2009

    2009

  42. [49]

    Collective mitochondrial dynamics resolve conflicting cellular ten- sions: From plants to general principles

    Joanna M Chustecki and Iain G Johnston. Collective mitochondrial dynamics resolve conflicting cellular ten- sions: From plants to general principles. InSeminars in cell & developmental biology, volume 156, pages 253–

  43. [50]

    Miro1-mediated mitochondrial positioning shapes intracellular energy gradients required for cell migration.Mol Biol Cell, 28(16):2159–2169, 2017

    Max-Hinderk Schuler, Agnieszka Lewandowska, Giuseppe Di Caprio, Wesley Skillern, Srigokul Upad- hyayula, Tom Kirchhausen, Janet M Shaw, and Brian Cunniff. Miro1-mediated mitochondrial positioning shapes intracellular energy gradients required for cell migration.Mol Biol Cell, ...

    2017

  44. [51]

    Estimating physical conditions supporting gradients of atp concen- tration in the eukaryotic cell.Biophys J, 2025

    Rajneesh Kumar and Iain G Johnston. Estimating physical conditions supporting gradients of atp concen- tration in the eukaryotic cell.Biophys J, 2025

    2025

  45. [52]

    The functional universe of membrane contact sites.Nat Rev Mol Cell Bio, 21(1):7–24, 2020

    William A Prinz, Alexandre Toulmay, and Tamas Balla. The functional universe of membrane contact sites.Nat Rev Mol Cell Bio, 21(1):7–24, 2020

    2020

  46. [53]

    The machineries, regulation and cellular functions of mito- chondrial calcium.Nat Rev Mol Cell Bio, 19(11):713– 730, 2018

    Carlotta Giorgi, Saverio Marchi, and Paolo Pinton. The machineries, regulation and cellular functions of mito- chondrial calcium.Nat Rev Mol Cell Bio, 19(11):713– 730, 2018

    2018

  47. [54]

    Mitochondrial sig- nal transduction.Cell Metab, 34(11):1620–1653, 2022

    Martin Picard and Orian S Shirihai. Mitochondrial sig- nal transduction.Cell Metab, 34(11):1620–1653, 2022

    2022

  48. [55]

    Mitochondrial heterogeneity in metabolic diseases.Biology, 10(9):927, 2021

    Jennifer Ngo, Corey Osto, Frankie Villalobos, and Orian S Shirihai. Mitochondrial heterogeneity in metabolic diseases.Biology, 10(9):927, 2021

    2021

  49. [56]

    Cellular atp demand creates metabolically distinct subpopulations of mitochondria.Nature, 635 (8039):746–754, 2024

    Keun Woo Ryu, Tak Shun Fung, Daphne C Baker, Michelle Saoi, Jinsung Park, Christopher A Febres- Aldana, Rania G Aly, Ruobing Cui, Anurag Sharma, Yi Fu, et al. Cellular atp demand creates metabolically distinct subpopulations of mitochondria.Nature, 635 (8039):746–754, 2024

    2024

  50. [57]

    Mitochondrial fusion is required for mtdna stability in skeletal muscle and tolerance of mtdna mutations.Cell, 141(2):280–289, 2010

    Hsiuchen Chen, Marc Vermulst, Yun E Wang, Anne Chomyn, Tomas A Prolla, J Michael McCaffery, and David C Chan. Mitochondrial fusion is required for mtdna stability in skeletal muscle and tolerance of mtdna mutations.Cell, 141(2):280–289, 2010

    2010

  51. [58]

    Evolution of the mitochondrial fusion–fission cycle and its role in aging

    Axel Kowald and Tom BL Kirkwood. Evolution of the mitochondrial fusion–fission cycle and its role in aging. P Natl Acad Sci, 108(25):10237–10242, 2011

    2011

  52. [59]

    Konstantinos Giannakis, Joanna M Chustecki, and Iain G Johnston. Exchange on dynamic encounter net- works allows plant mitochondria to collect complete sets of mitochondrial dna products despite their incomplete genomes.Quantitative Plant Biology, 3:e18, 2022

    2022

  53. [60]

    Distinct fission signa- tures predict mitochondrial degradation or biogenesis

    Tatjana Kleele, Timo Rey, Julius Winter, Sofia Za- ganelli, Dora Mahecic, H´ el` ene Perreten Lambert, Francesco Paolo Ruberto, Mohamed Nemir, Timothy Wai, Thierry Pedrazzini, et al. Distinct fission signa- tures predict mitochondrial degradation or biogenesis. Nature, 593(785...

    2021

  54. [61]

    Optimal dynamics for quality control in spa- tially distributed mitochondrial networks.Plos Comput Biol, 9(7):e1003108, 2013

    Pinkesh K Patel, Orian Shirihai, and Kerwyn Casey Huang. Optimal dynamics for quality control in spa- tially distributed mitochondrial networks.Plos Comput Biol, 9(7):e1003108, 2013

    2013

  55. [62]

    Selective degradation of mitochondria by mi- tophagy.Arch Biochem Biophys, 462(2):245–253, 2007

    Insil Kim, Sara Rodriguez-Enriquez, and John J Lemas- ters. Selective degradation of mitochondria by mi- tophagy.Arch Biochem Biophys, 462(2):245–253, 2007

    2007

  56. [63]

    Fis- sion and selective fusion govern mitochondrial segrega- tion and elimination by autophagy.Embo J, 27(2):433– 446, 2008

    Gilad Twig, Alvaro Elorza, Anthony JA Molina, Hibo Mohamed, Jakob D Wikstrom, Gil Walzer, Linsey Stiles, Sarah E Haigh, Steve Katz, Guy Las, et al. Fis- sion and selective fusion govern mitochondrial segrega- tion and elimination by autophagy.Embo J, 27(2):433– 446, 2008

    2008

  57. [64]

    Increased production of reactive oxygen species in hy- perglycemic conditions requires dynamic change of mi- tochondrial morphology.P Natl Acad Sci, 103(8):2653– 2658, 2006

    Tianzheng Yu, James L Robotham, and Yisang Yoon. Increased production of reactive oxygen species in hy- perglycemic conditions requires dynamic change of mi- tochondrial morphology.P Natl Acad Sci, 103(8):2653– 2658, 2006

    2006

  58. [65]

    Mitochondrial filaments and clusters as intracellular power-transmitting cables

    Vladimir P Skulachev. Mitochondrial filaments and clusters as intracellular power-transmitting cables. Trends Biochem Sci, 26(1):23–29, 2001

    2001

  59. [66]

    Impact of global structure on diffusive ex- ploration of organelle networks.Sci Rep, 10(1):4984, 2020

    Aidan I Brown, Laura M Westrate, and Elena F Koslover. Impact of global structure on diffusive ex- ploration of organelle networks.Sci Rep, 10(1):4984, 2020

    2020

  60. [68]

    Cambridge University Press, 2010

    Pavel L Krapivsky, Sidney Redner, and Eli Ben-Naim.A kinetic view of statistical physics. Cambridge University Press, 2010

    2010

  61. [69]

    Topological properties of diffusion limited aggregation and cluster-cluster aggregation.Journal of Physics A: Mathematical and General, 17(18):L975, 1984

    Paul Meakin, I Majid, S Havlin, and H Eugene Stanley. Topological properties of diffusion limited aggregation and cluster-cluster aggregation.Journal of Physics A: Mathematical and General, 17(18):L975, 1984

    1984

  62. [70]

    Mitochondrial pro- tein heterogeneity stems from the stochastic nature of co-translational protein targeting in cell senescence.Nat Commun, 15(1):8274, 2024

    Abdul Haseeb Khan, Xuefang Gu, Rutvik J Patel, Prabha Chuphal, Matheus P Viana, Aidan I Brown, Brian M Zid, and Tatsuhisa Tsuboi. Mitochondrial pro- tein heterogeneity stems from the stochastic nature of co-translational protein targeting in cell senescence.Nat Commun, 15(1):8...

    2024

  63. [71]

    Mitochondrial structure, dynamics, and physiology: light microscopy to disen- tangle the network.Annu Rev Cell Dev Bi, 40, 2024

    Juan C Landoni, Tatjana Kleele, Julius Winter, Willi Stepp, and Suliana Manley. Mitochondrial structure, dynamics, and physiology: light microscopy to disen- tangle the network.Annu Rev Cell Dev Bi, 40, 2024

    2024

  64. [72]

    Imaging interor- ganelle contacts and local calcium dynamics at the er- mitochondrial interface.Mol Cell, 39(1):121–132, 2010

    Gy¨ orgy Csord´ as, P´ eter V´ arnai, T¨ unde Golen´ ar, Swati Roy, George Purkins, Timothy G Schneider, Tam´ as Balla, and Gy¨ orgy Hajn´ oczky. Imaging interor- ganelle contacts and local calcium dynamics at the er- mitochondrial interface.Mol Cell, 39(1):121–132, 2010

    2010

  65. [73]

    Cambridge university press, 2002

    Randall J LeVeque.Finite volume methods for hyper- bolic problems, volume 31. Cambridge university press, 2002

    2002

  66. [74]

    Anomalous diffusion induced by cristae geometry in the inner mitochondrial membrane.PLoS One, 4(2):e4604, 2009

    Valerii M Sukhorukov and J¨ urgen Bereiter-Hahn. Anomalous diffusion induced by cristae geometry in the inner mitochondrial membrane.PLoS One, 4(2):e4604, 2009

    2009

  67. [75]

    Cristae-dependent quality control of the mitochondrial genome.Science advances, 7(36): eabi8886, 2021

    Christopher Jakubke, Rodaria Roussou, Andreas Maiser, Christina Schug, Felix Thoma, Raven Bunk, David H¨ orl, Heinrich Leonhardt, Peter Walter, Till Klecker, et al. Cristae-dependent quality control of the mitochondrial genome.Science advances, 7(36): eabi8886, 2021

    2021

  68. [76]

    Solute diffusion is hindered in the mitochondrial matrix

    Cindy EJ Dieteren, Stan CAM Gielen, Leo GJ Nijt- mans, Jan AM Smeitink, Herman G Swarts, Roland Brock, Peter HGM Willems, and Werner JH Koopman. Solute diffusion is hindered in the mitochondrial matrix. P Natl Acad Sci, 108(21):8657–8662, 2011

    2011

  69. [77]

    Heike Rampelt, Ralf M Zerbes, Martin van der Laan, and Nikolaus Pfanner. Role of the mitochondrial con- tact site and cristae organizing system in membrane ar- chitecture and dynamics.Biochimica et Biophysica Acta (BBA)-Molecular Cell Research, 1864(4):737–746, 2017

    2017

  70. [78]

    Determination of protein mobility in mito- chondrial membranes of living cells.Biochimica et Biophysica Acta (BBA)-Biomembranes, 1798(11):2022– 2032, 2010

    Valerii M Sukhorukov, Daniel Dikov, Karin Busch, Valentina Strecker, Ilka Wittig, and J¨ urgen Bereiter- 14 Hahn. Determination of protein mobility in mito- chondrial membranes of living cells.Biochimica et Biophysica Acta (BBA)-Biomembranes, 1798(11):2022– 2032, 2010

    2022

  71. [79]

    Inherited complex i deficiency is associated with faster protein diffusion in the matrix of moving mitochondria.Am J Physiol-cell Ph, 294(5):C1124–C1132, 2008

    Werner JH Koopman, Felix Distelmaier, Mark A Hink, Sjoerd Verkaart, Mietske Wijers, Jack Fransen, Jan AM Smeitink, and Peter HGM Willems. Inherited complex i deficiency is associated with faster protein diffusion in the matrix of moving mitochondria.Am J Physiol-cell Ph, 294(5...

    2008

  72. [80]

    Mitochondrial ca2+ dynamics reveals limited intramitochondrial ca2+ diffusion.Biophys J, 88(1):698–714, 2005

    Akos A Gerencser and Vera Adam-Vizi. Mitochondrial ca2+ dynamics reveals limited intramitochondrial ca2+ diffusion.Biophys J, 88(1):698–714, 2005

    2005

  73. [81]

    Extending the analogy between in- tracellular motion in mammalian cells and glassy dy- namics.Soft Matter, 19(14):2529–2538, 2023

    Beatrice Corci, Oscar Hooiveld, Amalia M Dolga, and Christoffer ˚Aberg. Extending the analogy between in- tracellular motion in mammalian cells and glassy dy- namics.Soft Matter, 19(14):2529–2538, 2023

    2023

  74. [82]

    Mitochondrial network size scaling in budding yeast.Science, 338(6108):822–824, 2012

    Susanne M Rafelski, Matheus P Viana, Yi Zhang, Yee- Hung M Chan, Kurt S Thorn, Phoebe Yam, Jennifer C Fung, Hao Li, Luciano da F Costa, and Wallace F Mar- shall. Mitochondrial network size scaling in budding yeast.Science, 338(6108):822–824, 2012

    2012

  75. [83]

    Regulation of mitochondrial matrix volume.Am J Physiol-cell Ph, 292(1):C157–C163, 2007

    Allen Kaasik, Dzhamilja Safiulina, Alexander Zharkovsky, and Vladimir Veksler. Regulation of mitochondrial matrix volume.Am J Physiol-cell Ph, 292(1):C157–C163, 2007

    2007

  76. [84]

    Super-resolution microscopy of mitochondria.Curr Opin Chem Biol, 20: 9–15, 2014

    Stefan Jakobs and Christian A Wurm. Super-resolution microscopy of mitochondria.Curr Opin Chem Biol, 20: 9–15, 2014

    2014

  77. [85]

    Gilad Twig, Xingguo Liu, Marc Liesa, Jakob D Wik- strom, Anthony JA Molina, Guy Las, Gal Yaniv, Gy¨ orgy Hajn´ oczky, and Orian S Shirihai. Biophysical properties of mitochondrial fusion events in pancreatic β-cells and cardiac cells unravel potential control mech- anisms of i...

    2010

  78. [86]

    Fission and fusion machineries converge at er contact sites to regulate mitochondrial morphology.J Cell Biol, 219(4): e201911122, 2020

    Robert G Abrisch, Samantha C Gumbin, Brett Taylor Wisniewski, Laura L Lackner, and Gia K Voeltz. Fission and fusion machineries converge at er contact sites to regulate mitochondrial morphology.J Cell Biol, 219(4): e201911122, 2020

    2020

  79. [87]

    CRC press, 2018

    Dietrich Stauffer and Ammon Aharony.Introduction to percolation theory. CRC press, 2018

    2018

  80. [88]

    Application of fractal concepts to polymer statistics and to anomalous transport in ran- domly porous media.J Stat Phys, 36(5):843–860, 1984

    H Eugene Stanley. Application of fractal concepts to polymer statistics and to anomalous transport in ran- domly porous media.J Stat Phys, 36(5):843–860, 1984

    1984

  81. [89]

    Fractal analysis of permeabil- ities for porous media.Aiche J, 50(1):46–57, 2004

    Boming Yu and Wei Liu. Fractal analysis of permeabil- ities for porous media.Aiche J, 50(1):46–57, 2004

    2004

  82. [90]

    Defining dimension of a complex network

    O Shanker. Defining dimension of a complex network. Mod Phys Lett B, 21(06):321–326, 2007

    2007

  83. [91]

    The chemical distance distribution in percolation clusters

    S Havlin, B Trus, GH Weiss, and D Ben-Avraham. The chemical distance distribution in percolation clusters. Journal of Physics A: Mathematical and General, 18 (5):L247, 1985

    1985

  84. [92]

    Structural and dynamical properties of the percolation backbone in two and three dimensions

    Markus Porto, Armin Bunde, Shlomo Havlin, and H Ed- uardo Roman. Structural and dynamical properties of the percolation backbone in two and three dimensions. Phys Rev E, 56(2):1667, 1997

    1997

  85. [93]

    Cambridge university press, 2000

    Daniel Ben-Avraham and Shlomo Havlin.Diffusion and reactions in fractals and disordered systems. Cambridge university press, 2000

    2000

  86. [94]

    Flow phenomena in rocks: from continuum models to fractals, percolation, cellular au- tomata, and simulated annealing.Rev Mod Phys, 65(4): 1393, 1993

    Muhammad Sahimi. Flow phenomena in rocks: from continuum models to fractals, percolation, cellular au- tomata, and simulated annealing.Rev Mod Phys, 65(4): 1393, 1993

    1993

  87. [95]

    Tortuosity in porous media: a critical review.Soil Sci Soc Am J, 77(5):1461–1477, 2013

    Behzad Ghanbarian, Allen G Hunt, Robert P Ewing, and Muhammad Sahimi. Tortuosity in porous media: a critical review.Soil Sci Soc Am J, 77(5):1461–1477, 2013

    2013

  88. [96]

    Tor- tuosity of porous media: Image analysis and physical simulation.Earth-sci Rev, 212:103439, 2021

    Jinlong Fu, Hywel R Thomas, and Chenfeng Li. Tor- tuosity of porous media: Image analysis and physical simulation.Earth-sci Rev, 212:103439, 2021

    2021

  89. [97]

    Cambridge University Press, 2001

    Sidney Redner.A Guide to First-Passage Processes. Cambridge University Press, 2001

    2001

  90. [98]

    Courier Corporation, 2013

    Terrell L Hill.Free energy transduction and biochemical cycle kinetics. Courier Corporation, 2013

    2013

  91. [99]

    Effects of temperature on the rates of diffusion-controlled reactions.T Faraday Soc, 63:1712– 1719, 1967

    SR Logan. Effects of temperature on the rates of diffusion-controlled reactions.T Faraday Soc, 63:1712– 1719, 1967

    1967

  92. [100]

    Asymmetric mitochondrial trafficking maintains net- work morphology by balancing perinuclear biogenesis

    Julius Winter, Juan C Landoni, Keaton Holt, Sheda Ben Nejma, Emine Berna Durmus, Tat- jana Kleele, Elena F Koslover, and Suliana Manley. Asymmetric mitochondrial trafficking maintains net- work morphology by balancing perinuclear biogenesis. bioRxiv, pages 2025–12, 2025

    2025

  93. [101]

    Encounter times of intermittently running particles.arXiv preprint arXiv:2512.13973, 2025

    Lizzy Teryoshin, Mario Hidalgo-Soria, and Elena F Koslover. Encounter times of intermittently running particles.arXiv preprint arXiv:2512.13973, 2025

    2025

  94. [102]

    Physics of chemoreception.Biophys J, 20(2):193–219, 1977

    Howard C Berg and Edward M Purcell. Physics of chemoreception.Biophys J, 20(2):193–219, 1977

    1977

  95. [103]

    Frequent fu- sion and fission of plant mitochondria with unequal nu- cleoid distribution.P Natl Acad Sci, 101(20):7805–7808, 2004

    Shin-ichi Arimura, Junko Yamamoto, Gen Paul Aida, Mikio Nakazono, and Nobuhiro Tsutsumi. Frequent fu- sion and fission of plant mitochondria with unequal nu- cleoid distribution.P Natl Acad Sci, 101(20):7805–7808, 2004

    2004

  96. [104]

    Quantifying mitochondrial content in living cells

    Matheus Palhares Viana, Swee Lim, and Susanne M Rafelski. Quantifying mitochondrial content in living cells. InMethods in cell biology, volume 125, pages 77–

  97. [105]

    Mitochondrial network complexity emerges from fission/fusion dynamics.Sci Rep, 8(1):1–10, 2018

    Nahuel Zamponi, Emiliano Zamponi, Sergio A Cannas, Orlando V Billoni, Pablo R Helguera, and Dante R Chialvo. Mitochondrial network complexity emerges from fission/fusion dynamics.Sci Rep, 8(1):1–10, 2018

    2018

  98. [106]

    Mitochon- drial networks through the lens of mathematics.Phys Biol, 20(5):051001, 2023

    Greyson R Lewis and Wallace F Marshall. Mitochon- drial networks through the lens of mathematics.Phys Biol, 20(5):051001, 2023

    2023

  99. [107]

    Integrity of the yeast mitochondrial genome, but not its distribution and in- heritance, relies on mitochondrial fission and fusion.P Natl Acad Sci, 112(9):E947–E956, 2015

    Christof Osman, Thomas R Noriega, Voytek Okreglak, Jennifer C Fung, and Peter Walter. Integrity of the yeast mitochondrial genome, but not its distribution and in- heritance, relies on mitochondrial fission and fusion.P Natl Acad Sci, 112(9):E947–E956, 2015

    2015

  100. [108]

    Dynamic tubulation of mito- chondria drives mitochondrial network formation.Cell Res, 25(10):1108–1120, 2015

    Chong Wang, Wanqing Du, Qian Peter Su, Mingli Zhu, Peiyuan Feng, Ying Li, Yichen Zhou, Na Mi, Yueyao Zhu, Dong Jiang, et al. Dynamic tubulation of mito- chondria drives mitochondrial network formation.Cell Res, 25(10):1108–1120, 2015

    2015

  101. [109]

    Optimality of spatially inhomo- geneous search strategies.Phys Rev Lett, 117(6):068101, 2016

    Karsten Schwarz, Yannick Schr¨ oder, Bin Qu, Markus Hoth, and Heiko Rieger. Optimality of spatially inhomo- geneous search strategies.Phys Rev Lett, 117(6):068101, 2016

    2016

  102. [110]

    Mitochondrial mrna localization is gov- erned by translation kinetics and spatial transport.Plos Comput Biol, 18(8):e1010413, 2022

    Ximena G Arceo, Elena F Koslover, Brian M Zid, and Aidan I Brown. Mitochondrial mrna localization is gov- erned by translation kinetics and spatial transport.Plos Comput Biol, 18(8):e1010413, 2022

    2022

  103. [111]

    Inter-mitochondrial com- plementation: mitochondria-specific system preventing 15 mice from expression of disease phenotypes by mutant mtdna.Nat Med, 7(8):934–940, 2001

    Kazuto Nakada, Kimiko Inoue, Tomoko Ono, Ko- toyo Isobe, Atsuo Ogura, Yu-Ichi Goto, Ikuya Non- aka, and Jun-Ichi Hayashi. Inter-mitochondrial com- plementation: mitochondria-specific system preventing 15 mice from expression of disease phenotypes by mutant mtdna.Nat Med, 7(8):...

    2001

  104. [112]

    Calcium transport and signaling in mitochon- dria.Comprehensive physiology, 7(2):623–634, 2017

    Roberto Bravo-Sagua, Valentina Parra, Camila L´ opez- Crisosto, Paula D ´ ıaz, Andrew FG Quest, and Sergio La- vandero. Calcium transport and signaling in mitochon- dria.Comprehensive physiology, 7(2):623–634, 2017

    2017

  105. [113]

    Network dif- fusion model of progression predicts longitudinal pat- terns of atrophy and metabolism in alzheimer’s disease

    Ashish Raj, Eve LoCastro, Amy Kuceyeski, Duygu To- sun, Norman Relkin, and Michael Weiner. Network dif- fusion model of progression predicts longitudinal pat- terns of atrophy and metabolism in alzheimer’s disease. Cell Rep, 10(3):359–369, 2015

    2015

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