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A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing
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Quantifying biochemical reaction rates from static population variability within incompletely observed complex

Timon Wittenstein1,2, Nava Leibovich1, Andreas Hilfinger1,3,4,5

  • 1Department of Physics, University of Toronto, Ontario, Canada.

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This study introduces a new method to quantify biochemical reaction rates in complex cellular systems. It uses incompletely specified mechanistic models to analyze covariability data, making complex biological systems easier to study.

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Area of Science:

  • Systems Biology
  • Biochemistry
  • Computational Biology

Background:

  • Quantifying biochemical reaction rates in complex cellular processes is a significant challenge in systems biology.
  • High-throughput single-cell data provide snapshots of population variability but analyzing complex, non-linear systems is difficult.
  • Simultaneous observation of all components and temporal tracking are often not feasible.

Purpose of the Study:

  • To develop a novel approach for quantifying reaction rates in complex biological systems.
  • To overcome the limitations of traditional descriptive statistical models.
  • To enable the analysis of systems with stochastic and non-linear interactions using incomplete data.

Main Methods:

  • Utilized incompletely specified mechanistic models.
  • Translated qualitative interaction knowledge into reaction rate functions.
  • Employed covariability data between pairs of components.
  • Transformed a globally intractable problem into a sequence of solvable inference problems.

Main Results:

  • Demonstrated that incompletely specified mechanistic models can effectively quantify reaction rates.
  • Showcased the translation of qualitative interaction knowledge into quantitative reaction rate functions.
  • Successfully used covariability data from incomplete snapshots to infer network dynamics.

Conclusions:

  • The proposed method offers a promising solution for quantifying complex interaction networks.
  • This approach facilitates the analysis of stochastic fluctuations in biological systems.
  • It enables the quantification of complex systems from incomplete observational data.