Searcharxiv⌕ Search

arXiv · q-bio/0501002

Nonlinear Protein Degradation and the Function of Genetic Circuits

Abstract

The functions of most genetic circuits require sufficient degrees of cooperativity in the circuit components. While mechanisms of cooperativity have been studied most extensively in the context of transcriptional initiation control, cooperativity from other processes involved in the operation of the circuits can also play important roles. In this study, we examine a simple kinetic source of cooperativity stemming from the nonlinear degradation of multimeric proteins. Ample experimental evidence suggests that protein subunits can degrade less rapidly when associated in multimeric complexes, an effect we refer to as cooperative stability. For dimeric transcription factors, this effect leads to a concentration-dependence in the degradation rate because monomers, which are predominant at low concentrations, will be more rapidly degraded. Thus cooperative stability can effectively widen the accessible range of protein levels in vivo. Through theoretical analysis of two exemplary genetic circuits in bacteria, we show that such an increased range is important for the robust operation of genetic circuits as well as their evolvability. Our calculations demonstrate that a few-fold difference between the degradation rate of monomers and dimers can already enhance the function of these circuits substantially. These results suggest that cooperative stability needs to be considered explicitly and characterized quantitatively in any systematic experimental or theoretical study of gene circuits.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Nicolas E. Buchler, Ulrich Gerland, Terence Hwa. 2005-01-03. Nonlinear Protein Degradation and the Function of Genetic Circuits. https://doi.org/10.1073/pnas.0409553102

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

GINsim 3.1: Qualitative modelling and dynamical analysis of logical regulatory networks

GINsim is a software for the definition and dynamical analysis of qualitative (Boolean and multi-valued) models of biological regulatory networks. Building on its modular architecture, based on the MDDlib decision-diagram and bioLQM model-manipulation libraries, GINsim version 3.1 introduces new functionalities aimed at the analysis of large logical models: the symbolic computation of trap-spaces, the Booleanisation of multilevel models and the reversal of model dynamics. GINsim 3.1 also extends the estimation of attractor reachability (Monte Carlo and Avatar algorithms), the computation of stable states, as well as model reduction functionalities. Analyses can be applied to perturbed models, and scripted, in particular from the CoLoMoTo interactive notebook. We illustrate the main functionalities of GINsim on a reference multilevel model of regulatory network that control the mammalian cell cycle, including the computation of its stable states and cyclic attractor, the assessment of their reachability, and the identification of the regulatory circuits driving multistability and oscillations. GINsim is freely available under GPL-3.0 at https://ginsim.github.io/, runs on any platform supporting Java, and is interoperable with the other tools of the CoLoMoTo community. Its website provides installation instructions and reference documentation, as well as a repository of Boolean and multilevel logical models.

q-bio.MN↗

Synergy and Complementarity: The Generative Basis of Chemical Organizations

Identifying structural features ensuring the persistence of a reaction network is fundamental for understanding the evolution and complexification of biological entities. By defining organizations as subsets of species that are closed and self-maintaining, Chemical Organization Theory (COT) shows that organizations enable filtering out the regions of the phase space where attractors can exist. Despite various theoretical explorations current methods to compute the organizations face unclear combinatorial challenges. In this article, we first perform a systematic study of such combinatorial challenges and identify a criteria of productive novelty and irreducibility that separate relevant from redundant combinations. Second, we identify the minimal building blocks that shall be combined to build organizations, called elementary reaction closures (ERC), and characterize them as a hierarchy. Third, we show that all persistent modules can be generated as combination of ERCs only, and operationalize two properties among ERCs, synergy and complementarity, that define a sufficient criteria to build such generators in a minimal way. Fourth we show that every relevant persistent module can be built from such minimal sequences of ERCs. We next show that the synergies and complementarities we use to generate persistent modules not only scale radically slower than usual combinatorial methods. We quantify these structures across $438$ biological reaction networks from the BioModels and BiGG databases. The proportion of fundamental synergies and complementarities among all ERC pairs shrinks as networks grow even as raw counts increase, confirming that larger biological networks achieve persistence through a progressively smaller and more selective sets. We do not prescribe algorithms to compute organizations, discuss how algorithms based on our results could permit applying COT.

q-bio.MN↗

Transmission and filtering of stochastic rhythms in bursty gene expression

How stochastic rhythms in upstream regulatory signals are transmitted to downstream gene expression is a fundamental question in systems biology. Although experiments have shown that molecular degradation strongly influences downstream rhythmic responses, a general quantitative theory explaining how degradation, stochastic bursting, and upstream timing variability jointly govern rhythm transmission remains lacking. Here we develop a general analytical framework based on a martingale-based orthogonal decomposition, applicable to arbitrarily complex upstream dynamics, including memory effects. We derive exact autocorrelation and power-spectrum transmission laws, establish an oscillation-efficiency contraction principle, and identify a critical degradation rate that provides a necessary and sufficient condition for stochastic rhythm transmission. An explicit rhythm-transmission relation reveals that increasing burst frequency and modulation depth promotes rhythm transmission, whereas greater variability in upstream cycle duration suppresses it. In particular, increasing burst size leaves oscillation efficiency unchanged. The predicted degradation threshold correctly separates all ten pulsing and nonpulsing p53 target proteins reported in previous experiments. These results provide a theoretical foundation for understanding how cells selectively preserve or integrate rhythmic regulatory signals.

q-bio.MN↗