8,657 research outputs found
Linear approaches to intramolecular Förster Resonance Energy Transfer probe measurements for quantitative modeling
Numerous unimolecular, genetically-encoded Forster Resonance Energy Transfer (FRET) probes for monitoring biochemical activities in live cells have been developed over the past decade. As these probes allow for collection of high frequency, spatially resolved data on signaling events in live cells and tissues, they are an attractive technology for obtaining data to develop quantitative, mathematical models of spatiotemporal signaling dynamics. However, to be useful for such purposes the observed FRET from such probes should be related to a biological quantity of interest through a defined mathematical relationship, which is straightforward when this relationship is linear, and can be difficult otherwise. First, we show that only in rare circumstances is the observed FRET linearly proportional to a biochemical activity. Therefore in most cases FRET measurements should only be compared either to explicitly modeled probes or to concentrations of products of the biochemical activity, but not to activities themselves. Importantly, we find that FRET measured by standard intensity-based, ratiometric methods is inherently non-linear with respect to the fraction of probes undergoing FRET. Alternatively, we find that quantifying FRET either via (1) fluorescence lifetime imaging (FLIM) or (2) ratiometric methods where the donor emission intensity is divided by the directly-excited acceptor emission intensity (denoted R<sub>alt</sub>) is linear with respect to the fraction of probes undergoing FRET. This linearity property allows one to calculate the fraction of active probes based on the FRET measurement. Thus, our results suggest that either FLIM or ratiometric methods based on R<sub>alt</sub> are the preferred techniques for obtaining quantitative data from FRET probe experiments for mathematical modeling purpose
Modeling Evolution of Crosstalk in Noisy Signal Transduction Networks
Signal transduction networks can form highly interconnected systems within
cells due to network crosstalk, the sharing of input signals between multiple
downstream responses. To better understand the evolutionary design principles
underlying such networks, we study the evolution of crosstalk and the emergence
of specificity for two parallel signaling pathways that arise via gene
duplication and are subsequently allowed to diverge. We focus on a sequence
based evolutionary algorithm and evolve the network based on two physically
motivated fitness functions related to information transmission. Surprisingly,
we find that the two fitness functions lead to very different evolutionary
outcomes, one with a high degree of crosstalk and the other without.Comment: 18 Pages, 16 Figure
Receptor crosstalk improves concentration sensing of multiple ligands
Cells need to reliably sense external ligand concentrations to achieve
various biological functions such as chemotaxis or signaling. The molecular
recognition of ligands by surface receptors is degenerate in many systems
leading to crosstalk between different receptors. Crosstalk is often thought of
as a deviation from optimal specific recognition, as the binding of non-cognate
ligands can interfere with the detection of the receptor's cognate ligand,
possibly leading to a false triggering of a downstream signaling pathway. Here
we quantify the optimal precision of sensing the concentrations of multiple
ligands by a collection of promiscuous receptors. We demonstrate that crosstalk
can improve precision in concentration sensing and discrimination tasks. To
achieve superior precision, the additional information about ligand
concentrations contained in short binding events of the non-cognate ligand
should be exploited. We present a proofreading scheme to realize an approximate
estimation of multiple ligand concentrations that reaches a precision close to
the derived optimal bounds. Our results help rationalize the observed ubiquity
of receptor crosstalk in molecular sensing
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
Gut microbiota as a trigger of accelerated directional adaptive evolution. Acquisition of herbivory in the context of extracellular vesicles, microRNAs and inter-kingdom crosstalk
According to a traditional view, the specific diet in vertebrates is one of the key factors
structuring the composition of the gut microbiota. In this interpretation, the microbiota
assumes a subordinate position, where the larger host shapes, through evolution
and its fitness, the taxonomical composition of the hosted microbiota. The present
contribution shows how the evolution of herbivory, framed within the new concept of
holobiont, the possibility of inter-kingdom crosstalk and its epigenetic effects, could
pave the way to a completely reversed interpretation: instead of being passively shaped,
the microbiota can mold and shape the general host body structure to increase its
fitness. Central elements to consider in this context are the inter-kingdom crosstalk, the
possibility of transporting RNAs through nanovesicles in feces from parents to offspring,
and the activation of epigenetic processes passed on vertically from generation to
generation. The new hypothesis is that the gut microbiota could play a great role in
the macroevolutionary dynamics of herbivorous vertebrates, causing directly through
host-microbiota dialog of epigenetic nature (i.e., methylation, histone acetylation, etc.),
major changes in the organisms phenotype. The vertical exchange of the same microbial
communities from parents to offspring, the interaction of these microbes with fairly
uniform genotypes, and the socially restricted groups where these processes take
place, could all explain the reasons why herbivory has appeared several time (and
independently) during the evolution of vertebrates. The new interpretation could also
represent a key factor in understanding the convergent evolution of analogous body
structures in very distant lineages
Signal Processing during Developmental Multicellular Patterning
Developing design strategies for tissue engineering and regenerative medicine is limited by our nascent understanding of how cell populations self-organize into multicellular structures on synthetic scaffolds. Mechanistic insights can be gleaned from the quantitative analysis of biomolecular signals that drive multicellular patterning during the natural processes of embryonic and adult development. This review describes three critical layers of signal processing that govern multicellular patterning: spatiotemporal presentation of extracellular cues, intracellular signaling networks that mediate crosstalk among extracellular cues, and finally, intranuclear signal integration at the level of transcriptional regulation. At every level in this hierarchy, the quantitative attributes of signals have a profound impact on patterning. We discuss how experiments and mathematical models are being used to uncover these quantitative features and their impact on multicellular phenotype
Intrinsic noise of microRNA-regulated genes and the ceRNA hypothesis
MicroRNAs are small noncoding RNAs that regulate genes post-transciptionally
by binding and degrading target eukaryotic mRNAs. We use a quantitative model
to study gene regulation by inhibitory microRNAs and compare it to gene
regulation by prokaryotic small non-coding RNAs (sRNAs). Our model uses a
combination of analytic techniques as well as computational simulations to
calculate the mean-expression and noise profiles of genes regulated by both
microRNAs and sRNAs. We find that despite very different molecular machinery
and modes of action (catalytic vs stoichiometric), the mean expression levels
and noise profiles of microRNA-regulated genes are almost identical to genes
regulated by prokaryotic sRNAs. This behavior is extremely robust and persists
across a wide range of biologically relevant parameters. We extend our model to
study crosstalk between multiple mRNAs that are regulated by a single microRNA
and show that noise is a sensitive measure of microRNA-mediated interaction
between mRNAs. We conclude by discussing possible experimental strategies for
uncovering the microRNA-mRNA interactions and testing the competing endogenous
RNA (ceRNA) hypothesis.Comment: 32 pages, 11 figure
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