7,356 research outputs found
Kinetic approaches to lactose operon induction and bimodality
The quasi-equilibrium approximation is acceptable when molecular interactions
are fast enough compared to circuit dynamics, but is no longer allowed when
cellular activities are governed by rare events. A typical example is the
lactose operon (lac), one of the most famous paradigms of transcription
regulation, for which several theories still coexist to describe its behaviors.
The lac system is generally analyzed by using equilibrium constants,
contradicting single-event hypotheses long suggested by Novick and Weiner
(1957). Enzyme induction as an all-or-none phenomenon. Proc. Natl. Acad. Sci.
USA 43, 553-566) and recently refined in the study of (Choi et al., 2008. A
stochastic single-molecule event triggers phenotype switching of a bacterial
cell. Science 322, 442-446). In the present report, a lac repressor
(LacI)-mediated DNA immunoprecipitation experiment reveals that the natural
LacI-lac DNA complex built in vivo is extremely tight and long-lived compared
to the time scale of lac expression dynamics, which could functionally
disconnect the abortive expression bursts and forbid using the standard modes
of lac bistability. As alternatives, purely kinetic mechanisms are examined for
their capacity to restrict induction through: (i) widely scattered derepression
related to the arrival time variance of a predominantly backward asymmetric
random walk and (ii) an induction threshold arising in a single window of
derepression without recourse to nonlinear multimeric binding and Hill
functions. Considering the complete disengagement of the lac repressor from the
lac promoter as the probabilistic consequence of a transient stepwise
mechanism, is sufficient to explain the sigmoidal lac responses as functions of
time and of inducer concentration. This sigmoidal shape can be misleadingly
interpreted as a phenomenon of equilibrium cooperativity classically used to
explain bistability, but which has been reported to be weak in this system
Stochastic Modeling for the Expression of a Gene Regulated by Competing Transcription Factors
It is widely accepted that gene expression regulation is a stochastic event. The common approach for its computer simulation requires detailed information on the interactions of individual molecules, which is often not available for the analyses of biological experiments. As an alternative approach, we employed a more intuitive model to simulate the experimental result, the Markov-chain model, in which a gene is regulated by activators and repressors, which bind the same site in a mutually exclusive manner. Our stochastic simulation in the presence of both activators and repressors predicted a Hill-coefficient of the dose-response curve closer to the experimentally observed value than the calculated value based on the simple additive effects of activators alone and repressors alone. The simulation also reproduced the heterogeneity of gene expression levels among individual cells observed by Fluorescence Activated Cell Sorting analysis. Therefore, our approach may help to apply stochastic simulations to broader experimental data
Bioengineering models of cell signaling
Strategies for rationally manipulating cell behavior in cell-based technologies and molecular therapeutics and understanding effects of environmental agents on physiological systems may be derived from a mechanistic understanding of underlying signaling mechanisms that regulate cell functions. Three crucial attributes of signal transduction necessitate modeling approaches for analyzing these systems: an ever-expanding plethora of signaling molecules and interactions, a highly interconnected biochemical scheme, and concurrent biophysical regulation. Because signal flow is tightly regulated with positive and negative feedbacks and is bidirectional with commands traveling both from outside-in and inside-out, dynamic models that couple biophysical and biochemical elements are required to consider information processing both during transient and steady-state conditions. Unique mathematical frameworks will be needed to obtain an integrated perspective on these complex systems, which operate over wide length and time scales. These may involve a two-level hierarchical approach wherein the overall signaling network is modeled in terms of effective "circuit" or "algorithm" modules, and then each module is correspondingly modeled with more detailed incorporation of its actual underlying biochemical/biophysical molecular interactions
Implications of Rewiring Bacterial Quorum Sensing
Bacteria employ quorum sensing, a form of cell-cell communication, to sense changes in population density and regulate gene expression accordingly. This work investigated the rewiring of one quorum-sensing module, the lux circuit from the marine bacterium Vibrio fischeri. Steady-state experiments demonstrate that rewiring the network architecture of this module can yield graded, threshold, and bistable gene expression as predicted by a mathematical model. The experiments also show that the native lux operon is most consistent with a threshold, as opposed to a bistable, response. Each of the rewired networks yielded functional population sensors at biologically relevant conditions, suggesting that this operon is particularly robust. These findings (i) permit prediction of the behaviors of quorum-sensing operons in bacterial pathogens and (ii) facilitate forward engineering of synthetic gene circuits
Mesoscopic Biochemical Basis of Isogenetic Inheritance and Canalization: Stochasticity, Nonlinearity, and Emergent Landscape
Biochemical reaction systems in mesoscopic volume, under sustained
environmental chemical gradient(s), can have multiple stochastic attractors.
Two distinct mechanisms are known for their origins: () Stochastic
single-molecule events, such as gene expression, with slow gene on-off
dynamics; and () nonlinear networks with feedbacks. These two mechanisms
yield different volume dependence for the sojourn time of an attractor. As in
the classic Arrhenius theory for temperature dependent transition rates, a
landscape perspective provides a natural framework for the system's behavior.
However, due to the nonequilibrium nature of the open chemical systems, the
landscape, and the attractors it represents, are all themselves {\em emergent
properties} of complex, mesoscopic dynamics. In terms of the landscape, we show
a generalization of Kramers' approach is possible to provide a rate theory. The
emergence of attractors is a form of self-organization in the mesoscopic
system; stochastic attractors in biochemical systems such as gene regulation
and cellular signaling are naturally inheritable via cell division.
Delbr\"{u}ck-Gillespie's mesoscopic reaction system theory, therefore, provides
a biochemical basis for spontaneous isogenetic switching and canalization.Comment: 24 pages, 6 figure
Emergence of switch-like behavior in a large family of simple biochemical networks
Bistability plays a central role in the gene regulatory networks (GRNs)
controlling many essential biological functions, including cellular
differentiation and cell cycle control. However, establishing the network
topologies that can exhibit bistability remains a challenge, in part due to the
exceedingly large variety of GRNs that exist for even a small number of
components. We begin to address this problem by employing chemical reaction
network theory in a comprehensive in silico survey to determine the capacity
for bistability of more than 40,000 simple networks that can be formed by two
transcription factor-coding genes and their associated proteins (assuming only
the most elementary biochemical processes). We find that there exist reaction
rate constants leading to bistability in ~90% of these GRN models, including
several circuits that do not contain any of the TF cooperativity commonly
associated with bistable systems, and the majority of which could only be
identified as bistable through an original subnetwork-based analysis. A
topological sorting of the two-gene family of networks based on the presence or
absence of biochemical reactions reveals eleven minimal bistable networks
(i.e., bistable networks that do not contain within them a smaller bistable
subnetwork). The large number of previously unknown bistable network topologies
suggests that the capacity for switch-like behavior in GRNs arises with
relative ease and is not easily lost through network evolution. To highlight
the relevance of the systematic application of CRNT to bistable network
identification in real biological systems, we integrated publicly available
protein-protein interaction, protein-DNA interaction, and gene expression data
from Saccharomyces cerevisiae, and identified several GRNs predicted to behave
in a bistable fashion.Comment: accepted to PLoS Computational Biolog
Gene autoregulation via intronic microRNAs and its functions
Background: MicroRNAs, post-transcriptional repressors of gene expression,
play a pivotal role in gene regulatory networks. They are involved in core
cellular processes and their dysregulation is associated to a broad range of
human diseases. This paper focus on a minimal microRNA-mediated regulatory
circuit, in which a protein-coding gene (host gene) is targeted by a microRNA
located inside one of its introns. Results: Autoregulation via intronic
microRNAs is widespread in the human regulatory network, as confirmed by our
bioinformatic analysis, and can perform several regulatory tasks despite its
simple topology. Our analysis, based on analytical calculations and
simulations, indicates that this circuitry alters the dynamics of the host gene
expression, can induce complex responses implementing adaptation and Weber's
law, and efficiently filters fluctuations propagating from the upstream network
to the host gene. A fine-tuning of the circuit parameters can optimize each of
these functions. Interestingly, they are all related to gene expression
homeostasis, in agreement with the increasing evidence suggesting a role of
microRNA regulation in conferring robustness to biological processes. In
addition to model analysis, we present a list of bioinformatically predicted
candidate circuits in human for future experimental tests. Conclusions: The
results presented here suggest a potentially relevant functional role for
negative self-regulation via intronic microRNAs, in particular as a homeostatic
control mechanism of gene expression. Moreover, the map of circuit functions in
terms of experimentally measurable parameters, resulting from our analysis, can
be a useful guideline for possible applications in synthetic biology.Comment: 29 pages and 7 figures in the main text, 18 pages of Supporting
Informatio
Protein synthesis driven by dynamical stochastic transcription
In this manuscript we propose a mathematical framework to couple
transcription and translation in which mRNA production is described by a set of
master equations while the dynamics of protein density is governed by a random
differential equation. The coupling between the two processes is given by a
stochastic perturbation whose statistics satisfies the master equations. In
this approach, from the knowledge of the analytical time dependent distribution
of mRNA number, we are able to calculate the dynamics of the probability
density of the protein population.Comment: 20 pages, 3 figure
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