Stacking the odds for Golgi cisternal maturation

  1. Somya Mani
  2. Mukund Thattai  Is a corresponding author
  1. Tata Institute of Fundamental Research, India
4 figures

Figures

Figure 1 with 1 supplement
Boolean dynamics of compartments and vesicles: examples of maturation chains.

(A,B,C) A system with N = 2 molecular labels X and Y. (D,E,F) A system with N = 3 molecular labels X, Y and Z. (A,D) Left: the C×V budding matrix G:Gij = 1 means compartment type i (row) buds out vesicle type …

https://doi.org/10.7554/eLife.16231.002
Figure 1—figure supplement 1
Combining vesicular and non-vesicular transport.

Example of a system with N=9 molecular label types. The Rab GTPase (R) shuttles between the cytoplasm and the membrane. The GDF (D: GDI displacement factor) recruits the Rab GTPase to the correct …

https://doi.org/10.7554/eLife.16231.003
Figure 2 with 2 supplements
Homeostatic vesicle traffic networks.

These data relate to the 14,809 homeostatic networks for N=7 molecular label types. (AC) Examples of homeostatic networks (compartment and vesicle compositions are omitted for clarity) for budding …

https://doi.org/10.7554/eLife.16231.005
Figure 2—figure supplement 1
Statistical sampling of networks.

(A,B) For systems with N=4,5,6,7 molecular label types, we show the histogram of (A) the number of compartments and (B) and the number maturation chains. There is almost no variation when going from N=6 to N=7 label types. (C,D,E) Properties of systems for N=7  label types and A=1,2,3,4,5 adaptor/coatomer types; A sets the limit on the number of distinct vesicles a compartment can generate. We generate random budding and fusion matrices using two parameters (Materials and methods: Sampling homeostatic vesicle traffic networks): g is the loading propensity of molecular labels onto vesicles; f is the fusion propensity between vesicles and compartments. For each combination of parameters we generate ten sets of matrices G and F. For each instance of G and F we run the vesicle traffic dynamics starting from a random initial condition. We store all homeostatic states resulting from this procedure, and track the average value of any property across them. (C) Probability of reaching a homeostatic steady state from a random initial condition. (D) Number of compartments. (E) Fraction of compartments involved in a maturation process. Left column: the relevant quantity as a heat map for fixed g and f, but any A. Middle and right columns: the relevant quantity for each value of A, as a function of fixed f (middle) or fixed g (right). The adaptor/coatomer number A weakly influences the number of compartments, but strongly influences the fraction of compartments undergoing maturation. The gray, brown and blue square icons show the parameters used to generate of the networks from Figure 2A–C.

https://doi.org/10.7554/eLife.16231.006
Figure 2—figure supplement 2
Properties of homeostatic networks.

These data relate to the 14,809 homeostatic networks for N=7 molecular label types. (A) The homeostatic networks generated by our sampling procedure can break into subsets of compartments connected …

https://doi.org/10.7554/eLife.16231.007
Figure 3 with 1 supplement
Frequent vesicle traffic motifs.

These data relate to the 14,809 homeostatic networks for N=7 molecular label types. (A,B) Insets indicate the number of homeostatic networks that contain at least one copy of the motif. Histograms …

https://doi.org/10.7554/eLife.16231.008
Figure 3—figure supplement 1
Vesicle traffic motif statistics.

The number of networks in which each connected 3-compartment motif occurs vs. the motif index (Materials and methods: Vesicle traffic motifs). Motifs are sorted along the x-axis, from most to least …

https://doi.org/10.7554/eLife.16231.009
Figure 4 with 1 supplement
Anatomy of a cisternal maturation chain.

(A) A compartment created at the first timepoint (source, thick brown arrow) matures at each successive timepoint (inlet, thick blue arrows). This process can terminate at a compartment with fixed …

https://doi.org/10.7554/eLife.16231.010
Figure 4—figure supplement 1
Spatial optimization of networks.

We optimize the spatial locations of compartments such that all parts of a cell are close to at least one compartment, while the distance travelled by vesicles between compartments is minimized …

https://doi.org/10.7554/eLife.16231.011

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