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Abstract Quantifying the inheritance of regulatory networks among proteins during asymmetric cell division remains a challenge due to the complexity of these systems and the lack of robust mathematical definitions for inheritance. We propose a novel statistical framework called ODEinherit to measure how much a mother cell’s regulatory network explains its daughter’s trajectories, addressing this gap. Using time-lapse microscopy, we tracked the expression dynamics of six proteins across 85 dividingS. cerevisiaecells, observed over eight hours at 12-minute intervals. Our framework employs a two-step approach. First, we estimate an ordinary differential equation (ODE) system for each cell to characterize protein interactions, introducing novel adjustments for non-oscillatory time series and leveraging multi-cell data. Second, we assess inheritance by clustering cells based on cycling markers and quantifying how well a mother’s regulatory network predicts her daughter’s. Preliminary findings suggest stage-dependent differences in inheritance rates, paving the way for applications in cellular stress response and cell-fate prediction studies across generations.
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A new approach for extracting information from protein dynamics
Abstract Increased ability to predict protein structures is moving research focus towards understanding protein dynamics. A promising approach is to represent protein dynamics through networks and take advantage of well‐developed methods from network science. Most studies build protein dynamics networks from correlation measures, an approach that only works under very specific conditions, instead of the more robust inverse approach. Thus, we apply the inverse approach to the dynamics of protein dihedral angles, a system of internal coordinates, to avoid structural alignment. Using the well‐characterized adhesion protein, FimH, we show that our method identifies networks that are physically interpretable, robust, and relevant to the allosteric pathway sites. We further use our approach to detect dynamical differences, despite structural similarity, for Siglec‐8 in the immune system, and the SARS‐CoV‐2 spike protein. Our study demonstrates that using the inverse approach to extract a network from protein dynamics yields important biophysical insights.
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- Award ID(s):
- 2034584
- PAR ID:
- 10443431
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Proteins: Structure, Function, and Bioinformatics
- Volume:
- 91
- Issue:
- 2
- ISSN:
- 0887-3585
- Page Range / eLocation ID:
- p. 183-195
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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