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In mechanical engineering, the stability of systems under various forces is critical for designing durable and efficient structures. One fundamental way to explore these concepts is by analyzing systems like two rods connected at a pivot point, O, with a torsional spring of spring constant k at the pivot point. This system is similar in appearance to a scissor jack used to change tires on a car. In this case, the arms of the linkage (equivalent to the rods in this system) are entirely vertical,...
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Body:The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...
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Eigenvalue sensitivity-based analysis for evaluation of biological network stability versus disturbances.

Maryam Gholampour1, Ali Khaki Sedigh1, Mohammad Ghassem Mahjani2

  • 1Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran.

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|October 31, 2021
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Summary

This study introduces a new method, the random sensitivity index matrix (RSIM), to identify critical edges affecting biological network stability. RSIM helps pinpoint influential network components for better systems understanding.

Keywords:
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Area of Science:

  • Systems Biology
  • Network Science
  • Computational Biology

Background:

  • Complex biological systems are often represented as networks.
  • Understanding network stability is crucial for biological insights.
  • Identifying influential network components is a key challenge.

Purpose of the Study:

  • To develop a novel stochastic strategy for identifying influential edges in biological networks.
  • To propose a new criterion, the random sensitivity index matrix (RSIM), for evaluating edge influence on network stability.
  • To compare the contribution of edges to network instability under varying disturbance levels.

Main Methods:

  • Utilized network principles and control-theory basics, including Jacobian and eigenvalue sensitivity analysis.
  • Developed the random sensitivity index matrix (RSIM) based on Monte Carlo algorithms.
  • Applied RSIM to evaluate eigenvalue sensitivity of edges under stochastic disturbances.

Main Results:

  • RSIM effectively identifies sensitive edges within biological networks.
  • The method's results remained consistent across different percentages of stochastic disturbances.
  • Simulations on the lactose operon and MAPK pathways validated the proposed method's performance.

Conclusions:

  • The developed stochastic strategy and RSIM provide a robust approach for analyzing biological network stability.
  • RSIM is a valuable tool for pinpointing critical edges that impact network dynamics.
  • The findings offer new insights into the stability of complex biological systems.