Literature DB >> 31299332

On signalling and estimation limits for molecular birth-processes.

Kris V Parag1.   

Abstract

Understanding and uncovering the mechanisms or motifs that molecular networks employ to regulate noise is a key problem in cell biology. As it is often difficult to obtain direct and detailed insight into these mechanisms, many studies instead focus on assessing the best precision attainable on the signalling pathways that compose these networks. Molecules signal one another over such pathways to solve noise regulating estimation and control problems. Quantifying the maximum precision of these solutions delimits what is achievable and allows hypotheses about underlying motifs to be tested without requiring detailed biological knowledge. The pathway capacity, which defines the maximum rate of transmitting information along it, is a widely used proxy for precision. Here it is shown, for estimation problems involving elementary yet biologically relevant birth-process networks, that capacity can be surprisingly misleading. A time-optimal signalling motif, called birth-following, is derived and proven to better the precision expected from the capacity, provided the maximum signalling rate constraint is large and the mean one above a certain threshold. When the maximum constraint is relaxed, perfect estimation is predicted by the capacity. However, the true achievable precision is found highly variable and sensitive to the mean constraint. Since the same capacity can map to different combinations of rate constraints, it can only equivocally measure precision. Deciphering the rate constraints on a signalling pathway may therefore be more important than computing its capacity.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Keywords:  Birth-processes; Cellular signalling; Information theoretic bounds; Intrinsic noise; Molecular estimation; Queueing theory

Mesh:

Year:  2019        PMID: 31299332     DOI: 10.1016/j.jtbi.2019.07.007

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  2 in total

1.  Using information theory to optimise epidemic models for real-time prediction and estimation.

Authors:  Kris V Parag; Christl A Donnelly
Journal:  PLoS Comput Biol       Date:  2020-07-01       Impact factor: 4.475

2.  An exact method for quantifying the reliability of end-of-epidemic declarations in real time.

Authors:  Kris V Parag; Christl A Donnelly; Rahul Jha; Robin N Thompson
Journal:  PLoS Comput Biol       Date:  2020-11-30       Impact factor: 4.475

  2 in total

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