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CellTarget: a convex optimisation approach to discover cellular objectives.

Mariana Monteiro1, James Morrissey2,3, Cleo Kontoravdi2

  • 1The Sargent Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London, UK. m.monteiro21@imperial.ac.uk.

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Summary

CellTarget infers cellular objectives from experimental flux data, overcoming limitations in mammalian metabolic modeling. This algorithm improves flux predictions by optimizing objectives against network constraints.

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Area of Science:

  • Systems Biology
  • Synthetic Biology
  • Metabolic Engineering

Background:

  • Metabolic network models are crucial for studying cellular metabolism across diverse organisms.
  • Mammalian metabolic models face challenges due to limited intracellular flux data and underdetermined systems.
  • Unlike microbial cells, mammalian cells lack clear growth-maximization objectives, complicating model parameterization.

Purpose of the Study:

  • To develop an algorithm, CellTarget, for inferring cellular objectives directly from experimental flux data.
  • To address the limitations of objective specification in mammalian metabolic modeling.
  • To improve the accuracy of metabolic network models for mammalian cells.

Main Methods:

  • CellTarget integrates convex Flux Balance Analysis (FBA) with an error-minimization module.
  • The algorithm iteratively adjusts objective coefficients to match predicted and measured fluxes.
  • Implicit differentiation enables backpropagation for joint optimization of objectives and flux predictions.

Main Results:

  • CellTarget accurately predicts fluxes across various Chinese Hamster Ovary (CHO) cell lines and conditions.
  • The algorithm outperforms a benchmark inverse FBA method in predictive accuracy.
  • Cellular objective non-identifiability was observed, with network constraints being primary drivers of predictive accuracy.

Conclusions:

  • CellTarget provides a robust method for inferring cellular objectives in mammalian metabolic models.
  • The findings highlight the significant role of network constraints over objective specificity in model predictions.
  • This work advances the application of metabolic modeling in systems and synthetic biology for mammalian systems.