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Limitations of this model as applied to the predictions of cell lifetime and voltage are discussed.
For biological circuits, we need to produce quantitative predictions of cell behavior for a given genotype as consequence of the different molecular interactions.
Ultimately these advances should lead to predictions of cell behavior, which at present are measured empirically and inserted into macroscopic cell models.
Our approach is based on minimization of a toxicity cost function, a mathematical model that allows calculation of the cumulative cytotoxicity during a candidate CPA addition or removal procedure using predictions of cell membrane transport.
The technique described here gives predictions of cell size and/or monolith length distributions such that the conversion efficiency is spatially uniform across the monolith for the general case of a non-uniform flow distribution.
No significant difference in the predictions of cell volume excursion during CPA addition was observed when using either the K-K model or the two-parameter model and it was hence advised to adopt the simple two-parameter model in the evaluation.
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Considerable success of the computational modeling approach used in the present work points to the possibility of precise computational predictions of cell-level effects of such therapeutic interventions, which could facilitate their rational development.
In contrast, predictions of cell-wide translational activity are possible from such datasets with higher accuracy, and current datasets predict a production rate of about 13,000 proteins per haploid cell per second under fast growth conditions.
Obenauer, J. C., Cantley, L. C. & Yaffe, M. B. Scansite 2.0: proteome-wide prediction of cell signaling interactions using short sequence motifs.
Obenauer, J. C., Cantley, L. C. & Yaffe, M. B. Scansite 2.0: Proteome-wide prediction of cell signaling interactions using short sequence motifs.
Such a behavior is in contrast to the prediction of cell model simulations.
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