Search arXiv⌕ 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

Accelerating reaction network identification via frequency-domain analysis

The identification of intracellular reaction networks from single-cell data lies at the heart of many biological studies, as it can unravel key mechanisms in living organisms and provide insights for their rational engineering. However, the stochastic and nonlinear nature of these reacting systems poses significant challenges for this identification problem, making the state-of-the-art methods (e.g., particle-filtering-based approaches) computationally demanding. This paper reports a noteworthy numerical observation that for certain systems, the Fourier transform can render the likelihood of the measurement data approximately Gaussian, therefore resulting in a substantially accelerated Bayesian inference algorithm. A gene transcription example is presented to illustrate this finding and demonstrate the efficiency of {the proposed Fourier-based method}.

q-bio.MN↗

Can a self-sustaining chemical network emerge due to just a few catalysts?

Understanding how self-sustaining chemical reaction networks can emerge from simple molecules is an important question in chemical approaches to the origin of life. Reflexively autocatalytic and food-generated (RAF) sets provide a mathematical framework for investigating this question. This line of research goes back to the early work of Kauffman on autocatalytic reaction networks and was subsequently developed through the probabilistic framework of Mossel and Steel. Further contributions by Hordijk, Steel, and collaborators extended the theory to more heterogeneous forms of catalysis, including reaction-dependent probabilities or power-law distributions of catalytic activity. We introduce a stochastic model that combines heterogeneous molecular catalytic activity with reaction-dependent molecule-reaction compatibility. By assigning random catalytic weights to molecules and incorporating compatibility through an explicit formulation of catalysis probabilities, our model provides a concrete and flexible setting that captures both sources of heterogeneity while remaining mathematically tractable. We establish novel and general criteria for RAF formation and nonformation. We further demonstrate that classical phase-transition criteria need not remain valid in the presence of reaction-dependent catalysis. For heavy-tailed catalytic weights, we identify and rigorously quantify an additional phase transition. In models with finite-mean catalytic weights, RAF formation and nonformation are governed by a catalytic intensity parameter. For infinite-mean weights, however, we show that RAFs may emerge with probability tending to one even when the catalytic intensity parameter tends to zero.

q-bio.MN↗

Modeling Gene Expression Noise: From Transcriptional Dynamics to Cell Growth and Division

Gene expression is inherently noisy, leading to substantial cell-to-cell variation in mRNA and protein levels. The telegraph model and the simpler bursty model provide good fits to single-cell count distributions and have been used extensively to understand transcriptional kinetics and estimate the kinetic parameters. However, these models all describe a static cell. Here, we review models of stochastic gene expression in growing and dividing cells which aim to understand how a cell's own life history, from its birth, through copying its genome, to its division, leaves a distinct fluctuation signature on molecule numbers. We discuss the biochemical and biophysical processes captured by these models, their analytical tractability, the concept of cyclo-stationary behavior, when this is well approximated by the steady-state of static models and how the distributions of molecular numbers measured under these conditions sensitively depend on the experimental setup. This is followed by a discussion of how these models are fitted to single-cell measurements, how biological variability can be separated from measurement noise, and which computational advances make this feasible for thousands of genes. We finish by highlighting the open problems in the field.

q-bio.MN↗