420 research outputs found
A quantitative comparison of sRNA-based and protein-based gene regulation
Small, non-coding RNAs (sRNAs) play important roles as genetic regulators in
prokaryotes. sRNAs act post-transcriptionally via complementary pairing with
target mRNAs to regulate protein expression. We use a quantitative approach to
compare and contrast sRNAs with conventional transcription factors (TFs) to
better understand the advantages of each form of regulation. In particular, we
calculate the steady-state behavior, noise properties, frequency-dependent gain
(amplification), and dynamical response to large input signals of both forms of
regulation. While the mean steady-state behavior of sRNA-regulated proteins
exhibits a distinctive tunable threshold-linear behavior, our analysis shows
that transcriptional bursting leads to significantly higher intrinsic noise in
sRNA-based regulation than in TF-based regulation in a large range of
expression levels and limits the ability of sRNAs to perform quantitative
signaling. Nonetheless, we find that sRNAs are better than TFs at filtering
noise in input signals. Additionally, we find that sRNAs allow cells to respond
rapidly to large changes in input signals. These features suggest a niche for
sRNAs in allowing cells to transition quickly yet reliably between distinct
states. This functional niche is consistent with the widespread appearance of
sRNAs in stress-response and quasi-developmental networks in prokaryotes.Comment: 26 pages, 8 figures; accepted for publication in Molecular Systems
Biolog
The switching dynamics of the bacterial flagellar motor
Many swimming bacteria are propelled by flagellar motors that stochastically
switch between the clockwise and counterclockwise rotation direction. While the
switching dynamics are one of the most important characteristics of flagellar
motors, the mechanisms that control switching are poorly understood. We present
a statistical-mechanical model of the flagellar rotary motor, which consists of
a number of stator proteins that drive the rotation of a ring of rotor
proteins, which in turn drives the rotation of a flagellar filament. At the
heart of our model is the assumption that the rotor protein complex can exist
in two conformational states corresponding to the two respective rotation
directions, and that switching between these states depends on interactions
with the stator proteins. This naturally couples the switching dynamics to the
rotation dynamics, making the switch sensitive to torque and speed. Another key
element of our model is that after a switching event, it takes time for the
load to build up, due to polymorphic transitions of the filament. Our model
predicts that this slow relaxation dynamics of the filament, in combination
with the load dependence of the switching frequency, leads to a characteristic
switching time, in agreement with recent observations.Comment: 7 pages, 6 figures, RevTeX
Kinetic Analysis of Discrete Path Sampling Stationary Point Databases
Analysing stationary point databases to extract phenomenological rate
constants can become time-consuming for systems with large potential energy
barriers. In the present contribution we analyse several different approaches
to this problem. First, we show how the original rate constant prescription
within the discrete path sampling approach can be rewritten in terms of
committor probabilities. Two alternative formulations are then derived in which
the steady-state assumption for intervening minima is removed, providing both a
more accurate kinetic analysis, and a measure of whether a two-state
description is appropriate. The first approach involves running additional
short kinetic Monte Carlo (KMC) trajectories, which are used to calculate
waiting times. Here we introduce `leapfrog' moves to second-neighbour minima,
which prevent the KMC trajectory oscillating between structures separated by
low barriers. In the second approach we successively remove minima from the
intervening set, renormalising the branching probabilities and waiting times to
preserve the mean first-passage times of interest. Regrouping the local minima
appropriately is also shown to speed up the kinetic analysis dramatically at
low temperatures. Applications are described where rates are extracted for
databases containing tens of thousands of stationary points, with effective
barriers that are several hundred times kT.Comment: 28 pages, 1 figure, 4 table
Colored extrinsic fluctuations and stochastic gene expression
Stochasticity is both exploited and controlled by cells. Although the intrinsic stochasticity inherent in biochemistry is relatively well understood, cellular variation, or ‘noise', is predominantly generated by interactions of the system of interest with other stochastic systems in the cell or its environment. Such extrinsic fluctuations are nonspecific, affecting many system components, and have a substantial lifetime, comparable to the cell cycle (they are ‘colored'). Here, we extend the standard stochastic simulation algorithm to include extrinsic fluctuations. We show that these fluctuations affect mean protein numbers and intrinsic noise, can speed up typical network response times, and can explain trends in high-throughput measurements of variation. If extrinsic fluctuations in two components of the network are correlated, they may combine constructively (amplifying each other) or destructively (attenuating each other). Consequently, we predict that incoherent feedforward loops attenuate stochasticity, while coherent feedforwards amplify it. Our results demonstrate that both the timescales of extrinsic fluctuations and their nonspecificity substantially affect the function and performance of biochemical networks
Nonlocal observables and lightcone-averaging in relativistic thermodynamics
The unification of relativity and thermodynamics has been a subject of
considerable debate over the last 100 years. The reasons for this are twofold:
(i) Thermodynamic variables are nonlocal quantities and, thus, single out a
preferred class of hyperplanes in spacetime. (ii) There exist different,
seemingly equally plausible ways of defining heat and work in relativistic
systems. These ambiguities led, for example, to various proposals for the
Lorentz transformation law of temperature. Traditional 'isochronous'
formulations of relativistic thermodynamics are neither theoretically
satisfactory nor experimentally feasible. Here, we demonstrate how these
deficiencies can be resolved by defining thermodynamic quantities with respect
to the backward-lightcone of an observation event. This approach yields novel,
testable predictions and allows for a straightforward-extension of
thermodynamics to General Relativity. Our theoretical considerations are
illustrated through three-dimensional relativistic many-body simulations.Comment: typos in Eqs. (12) and (14) corrected, minor additions in the tex
Lyapunov exponent of the random frequency oscillator: cumulant expansion approach
We consider a one-dimensional harmonic oscillator with a random frequency,
focusing on both the standard and the generalized Lyapunov exponents,
and respectively. We discuss the numerical difficulties that
arise in the numerical calculation of in the case of strong
intermittency. When the frequency corresponds to a Ornstein-Uhlenbeck process,
we compute analytically by using a cumulant expansion including
up to the fourth order. Connections with the problem of finding an analytical
estimate for the largest Lyapunov exponent of a many-body system with smooth
interactions are discussed.Comment: 6 pages, 4 figures, to appear in J. Phys. Conf. Series - LAWNP0
Noise control for molecular computing
Synthetic biology is a growing interdisciplinary field, with far-reaching applications, which aims to design biochemical systems that behave in a desired manner. With the advancement of strand-displacement DNA computing, a large class of abstract biochemical networks may be physically realized using DNA molecules. Methods for systematic design of the abstract systems with prescribed behaviors have been predominantly developed at the (less-detailed) deterministic level. However, stochastic effects, neglected at the deterministic level, are increasingly found to play an important role in biochemistry. In such circumstances, methods for controlling the intrinsic noise in the system are necessary for a successful network design at the (more-detailed) stochastic level. To bridge the gap, the noise-control algorithm for designing biochemical networks is developed in this paper. The algorithm structurally modifies any given reaction network under mass-action kinetics, in such a way that (i) controllable state-dependent noise is introduced into the stochastic dynamics, while (ii) the deterministic dynamics are preserved. The capabilities of the algorithm are demonstrated on a production-decay reaction system, and on an exotic system displaying bistability. For the production-decay system, it is shown that the algorithm may be used to redesign the network to achieve noise-induced multistability. For the exotic system, the algorithm is used to redesign the network to control the stochastic switching, and achieve noise-induced oscillations
A Universal Lifetime Distribution for Multi-Species Systems
Lifetime distributions of social entities, such as enterprises, products, and
media contents, are one of the fundamental statistics characterizing the social
dynamics. To investigate the lifetime distribution of mutually interacting
systems, simple models having a rule for additions and deletions of entities
are investigated. We found a quite universal lifetime distribution for various
kinds of inter-entity interactions, and it is well fitted by a
stretched-exponential function with an exponent close to 1/2. We propose a
"modified Red-Queen" hypothesis to explain this distribution. We also review
empirical studies on the lifetime distribution of social entities, and
discussed the applicability of the model.Comment: 10 pages, 6 figures, Proceedings of Social Modeling and Simulations +
Econophysics Colloquium 201
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