872 research outputs found
Aquatic invertebrate fauna of western Tasmania
Although there are close associations with the south-western fauna , there are nevertheless some aquatic invertebrates found predominantly in western Tasmania, and some biological problems best studied by reference to such animals; examples from two families of crustacea and two insect orders are discussed in this paper .
Western Tasmania may be regarded as the freshwater crayfish ( Parastacidae) centre of the State , all four Tasmanian genera being found there . Each genus is discussed in turn ; the habitats utilised are considered and adaptation to these habitats is illustrated by particular reference to burrow systems , calcium distribution in the exoskeleton and where possible , breeding patterns . Data is also provided on distribut i on and
associated zoogeographical questions are briefly considered .
Two genera of the fami ly Koonungidae ( Syncarida) are found in the west -
Micraspides and Koonunga . Their habitats �nd distribution are discus s ed and att ent ion
is drawn to the need for more ecological data in order to formulate answers to the
zoogeographical issues invo lved .
The Odonata exemplify an insect order with g enerally good dispersal power s . Al l
dragonfly species found in west ern Tasmania, t ogether with their nymphal habitats, are
co llated and discussed. The mo st interest ing speci es are the co ld- c l imat e adapted
species , especial ly archai cs such as Archipetalia auriculata and Synthemiopsis
gomphomacromioides. At t ention is drawn to the probable importance of diapaus e as an
adaptat ion to cold montane habitat s . Three component s are recogni sed in the odonatan
fauna , - cosmopo l i tan groups , anci ent Aus tralasian groups , and very anc i ent Gondwanal and re lict s .
The Plecoptera is an order demonstrat ing poor vagi l i ty and only 6 of 31 Tasmani an
species are shared with mainl and Aus tral i a . The maj ori ty of Tasmanian speci es are
cold c l imat e adapted forms and cons equent ly west ern wat ers have a part i cularly conspi cuous s tone fly fauna . Data on the habitats and di stribut ion of a l l speci es found
in west ern Tasmania is co l l at ed and discussed.
The influence of man on aquat ic environments in west ern Tasmania is bri efly cons idered . Environmental modi fication result s from mining act ivities , hydro -el ectri c
deve l opmen t , fore s try practices and from the introduction of exotic speci es such a
The fidelity of dynamic signaling by noisy biomolecular networks
This is the final version of the article. Available from Public Library of Science via the DOI in this record.Cells live in changing, dynamic environments. To understand cellular decision-making, we must therefore understand how fluctuating inputs are processed by noisy biomolecular networks. Here we present a general methodology for analyzing the fidelity with which different statistics of a fluctuating input are represented, or encoded, in the output of a signaling system over time. We identify two orthogonal sources of error that corrupt perfect representation of the signal: dynamical error, which occurs when the network responds on average to other features of the input trajectory as well as to the signal of interest, and mechanistic error, which occurs because biochemical reactions comprising the signaling mechanism are stochastic. Trade-offs between these two errors can determine the system's fidelity. By developing mathematical approaches to derive dynamics conditional on input trajectories we can show, for example, that increased biochemical noise (mechanistic error) can improve fidelity and that both negative and positive feedback degrade fidelity, for standard models of genetic autoregulation. For a group of cells, the fidelity of the collective output exceeds that of an individual cell and negative feedback then typically becomes beneficial. We can also predict the dynamic signal for which a given system has highest fidelity and, conversely, how to modify the network design to maximize fidelity for a given dynamic signal. Our approach is general, has applications to both systems and synthetic biology, and will help underpin studies of cellular behavior in natural, dynamic environments.We acknowledge support from a Medical Research Council and Engineering and Physical Sciences Council funded Fellowship in Biomedical Informatics (CGB) and a Scottish Universities Life Sciences Alliance chair in Systems Biology (PSS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
The role of input noise in transcriptional regulation
Even under constant external conditions, the expression levels of genes
fluctuate. Much emphasis has been placed on the components of this noise that
are due to randomness in transcription and translation; here we analyze the
role of noise associated with the inputs to transcriptional regulation, the
random arrival and binding of transcription factors to their target sites along
the genome. This noise sets a fundamental physical limit to the reliability of
genetic control, and has clear signatures, but we show that these are easily
obscured by experimental limitations and even by conventional methods for
plotting the variance vs. mean expression level. We argue that simple, global
models of noise dominated by transcription and translation are inconsistent
with the embedding of gene expression in a network of regulatory interactions.
Analysis of recent experiments on transcriptional control in the early
Drosophila embryo shows that these results are quantitatively consistent with
the predicted signatures of input noise, and we discuss the experiments needed
to test the importance of input noise more generally.Comment: 11 pages, 5 figures minor correction
The Goldbeter-Koshland switch in the first-order region and its response to dynamic disorder
In their classical work (Proc. Natl. Acad. Sci. USA, 1981, 78:6840-6844),
Goldbeter and Koshland mathematically analyzed a reversible covalent
modification system which is highly sensitive to the concentration of
effectors. Its signal-response curve appears sigmoidal, constituting a
biochemical switch. However, the switch behavior only emerges in the
"zero-order region", i.e. when the signal molecule concentration is much lower
than that of the substrate it modifies. In this work we showed that the
switching behavior can also occur under comparable concentrations of signals
and substrates, provided that the signal molecules catalyze the modification
reaction in cooperation. We also studied the effect of dynamic disorders on the
proposed biochemical switch, in which the enzymatic reaction rates, instead of
constant, appear as stochastic functions of time. We showed that the system is
robust to dynamic disorder at bulk concentration. But if the dynamic disorder
is quasi-static, large fluctuations of the switch response behavior may be
observed at low concentrations. Such fluctuation is relevant to many biological
functions. It can be reduced by either increasing the conformation
interconversion rate of the protein, or correlating the enzymatic reaction
rates in the network.Comment: 23 pages, 4 figures, accepted by PLOS ON
Effect of promoter architecture on the cell-to-cell variability in gene expression
According to recent experimental evidence, the architecture of a promoter,
defined as the number, strength and regulatory role of the operators that
control the promoter, plays a major role in determining the level of
cell-to-cell variability in gene expression. These quantitative experiments
call for a corresponding modeling effort that addresses the question of how
changes in promoter architecture affect noise in gene expression in a
systematic rather than case-by-case fashion. In this article, we make such a
systematic investigation, based on a simple microscopic model of gene
regulation that incorporates stochastic effects. In particular, we show how
operator strength and operator multiplicity affect this variability. We examine
different modes of transcription factor binding to complex promoters
(cooperative, independent, simultaneous) and how each of these affects the
level of variability in transcription product from cell-to-cell. We propose
that direct comparison between in vivo single-cell experiments and theoretical
predictions for the moments of the probability distribution of mRNA number per
cell can discriminate between different kinetic models of gene regulation.Comment: 35 pages, 6 figures, Submitte
A Genome-Wide Analysis of Promoter-Mediated Phenotypic Noise in Escherichia coli
Gene expression is subject to random perturbations that lead to fluctuations in the rate of protein production. As a consequence, for any given protein, genetically identical organisms living in a constant environment will contain different amounts of that particular protein, resulting in different phenotypes. This phenomenon is known as “phenotypic noise.” In bacterial systems, previous studies have shown that, for specific genes, both transcriptional and translational processes affect phenotypic noise. Here, we focus on how the promoter regions of genes affect noise and ask whether levels of promoter-mediated noise are correlated with genes' functional attributes, using data for over 60% of all promoters in Escherichia coli. We find that essential genes and genes with a high degree of evolutionary conservation have promoters that confer low levels of noise. We also find that the level of noise cannot be attributed to the evolutionary time that different genes have spent in the genome of E. coli. In contrast to previous results in eukaryotes, we find no association between promoter-mediated noise and gene expression plasticity. These results are consistent with the hypothesis that, in bacteria, natural selection can act to reduce gene expression noise and that some of this noise is controlled through the sequence of the promoter region alon
Kinetic modelling of competition and depletion of shared miRNAs by competing endogenous RNAs
Non-conding RNAs play a key role in the post-transcriptional regulation of
mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact
with their target RNAs through protein-mediated, sequence-specific binding,
giving rise to extended and highly heterogeneous miRNA-RNA interaction
networks. Within such networks, competition to bind miRNAs can generate an
effective positive coupling between their targets. Competing endogenous RNAs
(ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk.
Albeit potentially weak, ceRNA interactions can occur both dynamically,
affecting e.g. the regulatory clock, and at stationarity, in which case ceRNA
networks as a whole can be implicated in the composition of the cell's
proteome. Many features of ceRNA interactions, including the conditions under
which they become significant, can be unraveled by mathematical and in silico
models. We review the understanding of the ceRNA effect obtained within such
frameworks, focusing on the methods employed to quantify it, its role in the
processing of gene expression noise, and how network topology can determine its
reach.Comment: review article, 29 pages, 7 figure
Phenotypic Variation and Bistable Switching in Bacteria
Microbial research generally focuses on clonal populations. However, bacterial cells with identical genotypes frequently display different phenotypes under identical conditions. This microbial cell individuality is receiving increasing attention in the literature because of its impact on cellular differentiation, survival under selective conditions, and the interaction of pathogens with their hosts. It is becoming clear that stochasticity in gene expression in conjunction with the architecture of the gene network that underlies the cellular processes can generate phenotypic variation. An important regulatory mechanism is the so-called positive feedback, in which a system reinforces its own response, for instance by stimulating the production of an activator. Bistability is an interesting and relevant phenomenon, in which two distinct subpopulations of cells showing discrete levels of gene expression coexist in a single culture. In this chapter, we address techniques and approaches used to establish phenotypic variation, and relate three well-characterized examples of bistability to the molecular mechanisms that govern these processes, with a focus on positive feedback.
Trade-Offs and Constraints in Allosteric Sensing
Sensing extracellular changes initiates signal transduction and is the first stage of cellular decision-making. Yet relatively little is known about why one form of sensing biochemistry has been selected over another. To gain insight into this question, we studied the sensing characteristics of one of the biochemically simplest of sensors: the allosteric transcription factor. Such proteins, common in microbes, directly transduce the detection of a sensed molecule to changes in gene regulation. Using the Monod-Wyman-Changeux model, we determined six sensing characteristics – the dynamic range, the Hill number, the intrinsic noise, the information transfer capacity, the static gain, and the mean response time – as a function of the biochemical parameters of individual sensors and of the number of sensors. We found that specifying one characteristic strongly constrains others. For example, a high dynamic range implies a high Hill number and a high capacity, and vice versa. Perhaps surprisingly, these constraints are so strong that most of the space of characteristics is inaccessible given biophysically plausible ranges of parameter values. Within our approximations, we can calculate the probability distribution of the numbers of input molecules that maximizes information transfer and show that a population of one hundred allosteric transcription factors can in principle distinguish between more than four bands of input concentrations. Our results imply that allosteric sensors are unlikely to have been selected for high performance in one sensing characteristic but for a compromise in the performance of many
Signal duration and the time scale dependence of signal integration in biochemical pathways
Signal duration (e.g. the time scales over which an active signaling
intermediate persists) is a key regulator of biological decisions in myriad
contexts such as cell growth, proliferation, and developmental lineage
commitments. Accompanying differences in signal duration are numerous
downstream biological processes that require multiple steps of biochemical
regulation. Here, we present an analysis that investigates how simple
biochemical motifs that involve multiple stages of regulation can be
constructed to differentially process signals that persist at different time
scales. We compute the dynamic gain within these networks and resulting power
spectra to better understand how biochemical networks can integrate signals at
different time scales. We identify topological features of these networks that
allow for different frequency dependent signal processing properties. Our
studies suggest design principles for why signal duration in connection with
multiple steps of downstream regulation is a ubiquitous control motif in
biochemical systems.Comment: 27 pages, 4 figure
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