20,788 research outputs found
Noise control and utility: From regulatory network to spatial patterning
Stochasticity (or noise) at cellular and molecular levels has been observed
extensively as a universal feature for living systems. However, how living
systems deal with noise while performing desirable biological functions remains
a major mystery. Regulatory network configurations, such as their topology and
timescale, are shown to be critical in attenuating noise, and noise is also
found to facilitate cell fate decision. Here we review major recent findings on
noise attenuation through regulatory control, the benefit of noise via
noise-induced cellular plasticity during developmental patterning, and
summarize key principles underlying noise control
Oscillations and temporal signalling in cells
The development of new techniques to quantitatively measure gene expression
in cells has shed light on a number of systems that display oscillations in
protein concentration. Here we review the different mechanisms which can
produce oscillations in gene expression or protein concentration, using a
framework of simple mathematical models. We focus on three eukaryotic genetic
regulatory networks which show "ultradian" oscillations, with time period of
the order of hours, and involve, respectively, proteins important for
development (Hes1), apoptosis (p53) and immune response (NFkB). We argue that
underlying all three is a common design consisting of a negative feedback loop
with time delay which is responsible for the oscillatory behaviour
Modeling Cell-to-Cell Communication Networks Using Response-Time Distributions.
Cell-to-cell communication networks have critical roles in coordinating diverse organismal processes, such as tissue development or immune cell response. However, compared with intracellular signal transduction networks, the function and engineering principles of cell-to-cell communication networks are far less understood. Major complications include: cells are themselves regulated by complex intracellular signaling networks; individual cells are heterogeneous; and output of any one cell can recursively become an additional input signal to other cells. Here, we make use of a framework that treats intracellular signal transduction networks as "black boxes" with characterized input-to-output response relationships. We study simple cell-to-cell communication circuit motifs and find conditions that generate bimodal responses in time, as well as mechanisms for independently controlling synchronization and delay of cell-population responses. We apply our modeling approach to explain otherwise puzzling data on cytokine secretion onset times in T cells. Our approach can be used to predict communication network structure using experimentally accessible input-to-output measurements and without detailed knowledge of intermediate steps
Partial differential equations for self-organization in cellular and developmental biology
Understanding the mechanisms governing and regulating the emergence of structure and heterogeneity within cellular systems, such as the developing embryo, represents a multiscale challenge typifying current integrative biology research, namely, explaining the macroscale behaviour of a system from microscale dynamics. This review will focus upon modelling how cell-based dynamics orchestrate the emergence of higher level structure. After surveying representative biological examples and the models used to describe them, we will assess how developments at the scale of molecular biology have impacted on current theoretical frameworks, and the new modelling opportunities that are emerging as a result. We shall restrict our survey of mathematical approaches to partial differential equations and the tools required for their analysis. We will discuss the gap between the modelling abstraction and biological reality, the challenges this presents and highlight some open problems in the field
Messenger RNA Fluctuations and Regulatory RNAs Shape the Dynamics of Negative Feedback Loop
Single cell experiments of simple regulatory networks can markedly differ
from cell population experiments. Such differences arise from stochastic events
in individual cells that are averaged out in cell populations. For instance,
while individual cells may show sustained oscillations in the concentrations of
some proteins, such oscillations may appear damped in the population average.
In this paper we investigate the role of RNA stochastic fluctuations as a
leading force to produce a sustained excitatory behavior at the single cell
level. Opposed to some previous models, we build a fully stochastic model of a
negative feedback loop that explicitly takes into account the RNA stochastic
dynamics. We find that messenger RNA random fluctuations can be amplified
during translation and produce sustained pulses of protein expression.
Motivated by the recent appreciation of the importance of non--coding
regulatory RNAs in post--transcription regulation, we also consider the
possibility that a regulatory RNA transcript could bind to the messenger RNA
and repress translation. Our findings show that the regulatory transcript helps
reduce gene expression variability both at the single cell level and at the
cell population level.Comment: 87.18.Vf --> Systems biology 87.10.Mn --> Stochastic models in
biological systems 87.18.Tt --> Noise in biological systems
http://www.ncbi.nlm.nih.gov/pubmed/20365787
http://www.weizmann.ac.il/complex/tlusty/papers/PhysRevE2010.pd
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