Role of DNA binding sites and slow unbinding kinetics in titration-based oscillators

Eukaryotic transcription factors in the NF-κB family are central components of an extensive genetic network that activates cellular responses to inflammation and to a host of other external stressors. This network consists of feedback loops that involve the inhibitor IκBα, numerous downstream functional targets, and still more numerous binding sites that do not appear to be directly functional. Under steady stimulation, the regulatory network of NF-κB becomes oscillatory, and temporal patterns of NF-κB pulses appear to govern the patterns of downstream gene expression needed for immune response. Understanding how the information from external stress passes to oscillatory signals and is then ultimately relayed to gene expression is a general issue in systems biology. Recently, in vitro kinetic experiments as well as molecular simulations suggest that active stripping of NF-κB by IκBα from its binding sites can modify the traditional systems biology view of NF-κB/IκBα gene circuits. In this work, we revise the commonly adopted minimal model of the NF-κB regulatory network to account for the presence of the large number of binding sites for NF-κB along with dissociation from these sites that may proceed either by passive unbinding or by active molecular stripping. We identify regimes where the kinetics of target and decoy unbinding and molecular stripping enter a dynamic tug of war that may either compensate each other or amplify nuclear NF-κB activity, leading to distinct oscillatory patterns. Our finding that decoys and stripping play a key role in shaping the NF-κB oscillations suggests strategies to control NF-κB responses by introducing artificial decoys therapeutically.

Section: Discussionmentioning

confidence: 99%

Molecular stripping, targets and decoys as modulators of oscillations in the NF-κB/IκBα/DNA genetic network

Wang

Potoyan

Wolynes

2016

“…The second model is called a repressor-titration circuit (RTC) because X is a transcriptional repressor [ 15 ] ( figure 1 b ). This model differs from the ATC in two ways.…”

Section: Mathematical Frameworkmentioning

confidence: 99%

“…The PDMP framework will now be applied to more sophisticated models of the ATC. A previous model of the ATC [ 15 ], which we call the KB model, showed that multiple binding sites lengthened the period and improved coherence of stochastic cycling. Using the PDMP, we will show that multiple binding sites per se are insufficient to improve coherence.…”

Section: Analyses Of More Detailed Mechanistic Modelsmentioning

confidence: 99%

“…Below, we describe PDMP analysis of the KB model [ 15 ] for the ATC shown in figure 5 a . Beginning with the master equation governing the KB model, we performed the system-size expansion presented in § 2.4 and arrived at the following PDMP: We used the same variables and parameter set in figure 6 of the original paper [ 15 ]. The state variables, [ r A ], [ r I ], [ A ], [ A 2 ], [ I ], [ AI ] and G are the concentrations of the activator mRNA, inhibitor mRNA, monomeric activators, homodimeric activators, inhibitors and heterodimers.…”

Section: Analyses Of More Detailed Mechanistic Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Efficient analysis of stochastic gene dynamics in the non-adiabatic regime using piecewise deterministic Markov processes

Lin

Buchler

2018

Self Cite

Single-cell experiments show that gene expression is stochastic and bursty, a feature that can emerge from slow switching between promoter states with different activities. In addition to slow chromatin and/or DNA looping dynamics, one source of long-lived promoter states is the slow binding and unbinding kinetics of transcription factors to promoters, i.e. the non-adiabatic binding regime. Here, we introduce a simple analytical framework, known as a piecewise deterministic Markov process (PDMP), that accurately describes the stochastic dynamics of gene expression in the non-adiabatic regime. We illustrate the utility of the PDMP on a non-trivial dynamical system by analysing the properties of a titration-based oscillator in the non-adiabatic limit. We first show how to transform the underlying chemical master equation into a PDMP where the slow transitions between promoter states are stochastic, but whose rates depend upon the faster deterministic dynamics of the transcription factors regulated by these promoters. We show that the PDMP accurately describes the observed periods of stochastic cycles in activator and repressor-based titration oscillators. We then generalize our PDMP analysis to more complicated versions of titration-based oscillators to explain how multiple binding sites lengthen the period and improve coherence. Last, we show how noise-induced oscillation previously observed in a titration-based oscillator arises from non-adiabatic and discrete binding events at the promoter site.

“…In the synthetic biology context designing ways to enhance coherence or reducing the dichotomous noise of promoter binding is one of the main challenges for modular design of functional circuits [14]. So far this problem of engineering modular circuits has been combatted in a brute force fashion by increasing the cooperative association of proteins with DN A or by adding more copies of the promoter sites [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Stochastic resonances in a distributed genetic broadcasting system: the NF κ B/I κ B paradigm

Wang

Potoyan

Wolynes

2018

Gene regulatory networks must relay information from extracellular signals to downstream genes in an efficient, timely and coherent manner. Many complex functional tasks such as the immune response require system-wide broadcasting of information not to one but to many genes carrying out distinct functions whose dynamical binding and unbinding characteristics are widely distributed. In such broadcasting networks, the intended target sites are also often dwarfed in number by the even more numerous non-functional binding sites. Taking the genetic regulatory network of NFB as an exemplary system we explore the impact of having numerous distributed sites on the stochastic dynamics of oscillatory broadcasting genetic networks pointing out how resonances in binding cycles control the network's specificity and performance. We also show that active kinetic regulation of binding and unbinding through molecular stripping of DNA bound transcription factors can lead to a higher coherence of gene-co-expression and synchronous clearance.