309 research outputs found
Genome doubling enabled the expansion of yeast vesicle traffic pathways.
Vesicle budding and fusion in eukaryotes depend on a suite of protein types, such as Arfs, Rabs, coats and SNAREs. Distinct paralogs of these proteins act at distinct intracellular locations, suggesting a link between gene duplication and the expansion of vesicle traffic pathways. Genome doubling, a common source of paralogous genes in fungi, provides an ideal setting in which to explore this link. Here we trace the fates of paralog doublets derived from the 100-Ma-old hybridization event that gave rise to the whole genome duplication clade of budding yeast. We find that paralog doublets involved in specific vesicle traffic functions and pathways are convergently retained across the entire clade. Vesicle coats and adaptors involved in secretory and early-endocytic pathways are retained as doublets, at rates several-fold higher than expected by chance. Proteins involved in later endocytic steps and intra-Golgi traffic, including the entire set of multi-subunit and coiled-coil tethers, have reverted to singletons. These patterns demonstrate that selection has acted to expand and diversify the yeast vesicle traffic apparatus, across species and time
On the Archaeal Origins of Eukaryotes and the Challenges of Inferring Phenotype from Genotype
If eukaryotes arose through a merger between archaea and bacteria, what did the first true eukaryotic cell look like? A major step toward an answer came with the discovery of Lokiarchaeum, an archaeon whose genome encodes small GTPases related to those used by eukaryotes to regulate membrane traffic. Although 'Loki' cells have yet to be seen, their existence has prompted the suggestion that the archaeal ancestor of eukaryotes engulfed the future mitochondrion by phagocytosis. We propose instead that the archaeal ancestor was a relatively simple cell, and that eukaryotic cellular organization arose as the result of a gradual transfer of bacterial genes and membranes driven by an ever-closer symbiotic partnership between a bacterium and an archaeon
A stochastic spectral analysis of transcriptional regulatory cascades
The past decade has seen great advances in our understanding of the role of
noise in gene regulation and the physical limits to signaling in biological
networks. Here we introduce the spectral method for computation of the joint
probability distribution over all species in a biological network. The spectral
method exploits the natural eigenfunctions of the master equation of
birth-death processes to solve for the joint distribution of modules within the
network, which then inform each other and facilitate calculation of the entire
joint distribution. We illustrate the method on a ubiquitous case in nature:
linear regulatory cascades. The efficiency of the method makes possible
numerical optimization of the input and regulatory parameters, revealing design
properties of, e.g., the most informative cascades. We find, for threshold
regulation, that a cascade of strong regulations converts a unimodal input to a
bimodal output, that multimodal inputs are no more informative than bimodal
inputs, and that a chain of up-regulations outperforms a chain of
down-regulations. We anticipate that this numerical approach may be useful for
modeling noise in a variety of small network topologies in biology
Finding undetected protein associations in cell signaling by belief propagation
External information propagates in the cell mainly through signaling cascades
and transcriptional activation, allowing it to react to a wide spectrum of
environmental changes. High throughput experiments identify numerous molecular
components of such cascades that may, however, interact through unknown
partners. Some of them may be detected using data coming from the integration
of a protein-protein interaction network and mRNA expression profiles. This
inference problem can be mapped onto the problem of finding appropriate optimal
connected subgraphs of a network defined by these datasets. The optimization
procedure turns out to be computationally intractable in general. Here we
present a new distributed algorithm for this task, inspired from statistical
physics, and apply this scheme to alpha factor and drug perturbations data in
yeast. We identify the role of the COS8 protein, a member of a gene family of
previously unknown function, and validate the results by genetic experiments.
The algorithm we present is specially suited for very large datasets, can run
in parallel, and can be adapted to other problems in systems biology. On
renowned benchmarks it outperforms other algorithms in the field.Comment: 6 pages, 3 figures, 1 table, Supporting Informatio
Identification of direct residue contacts in protein-protein interaction by message passing
Understanding the molecular determinants of specificity in protein-protein
interaction is an outstanding challenge of postgenome biology. The availability
of large protein databases generated from sequences of hundreds of bacterial
genomes enables various statistical approaches to this problem. In this context
covariance-based methods have been used to identify correlation between amino
acid positions in interacting proteins. However, these methods have an
important shortcoming, in that they cannot distinguish between directly and
indirectly correlated residues. We developed a method that combines covariance
analysis with global inference analysis, adopted from use in statistical
physics. Applied to a set of >2,500 representatives of the bacterial
two-component signal transduction system, the combination of covariance with
global inference successfully and robustly identified residue pairs that are
proximal in space without resorting to ad hoc tuning parameters, both for
heterointeractions between sensor kinase (SK) and response regulator (RR)
proteins and for homointeractions between RR proteins. The spectacular success
of this approach illustrates the effectiveness of the global inference approach
in identifying direct interaction based on sequence information alone. We
expect this method to be applicable soon to interaction surfaces between
proteins present in only 1 copy per genome as the number of sequenced genomes
continues to expand. Use of this method could significantly increase the
potential targets for therapeutic intervention, shed light on the mechanism of
protein-protein interaction, and establish the foundation for the accurate
prediction of interacting protein partners.Comment: Supplementary information available on
http://www.pnas.org/content/106/1/67.abstrac
Noise Characteristics of Feed Forward Loops
A prominent feature of gene transcription regulatory networks is the presence
in large numbers of motifs, i.e, patterns of interconnection, in the networks.
One such motif is the feed forward loop (FFL) consisting of three genes X, Y
and Z. The protein product of x of X controls the synthesis of protein product
y of Y. Proteins x and y jointly regulate the synthesis of z proteins from the
gene Z. The FFLs, depending on the nature of the regulating interactions, can
be of eight different types which can again be classified into two categories:
coherent and incoherent. In this paper, we study the noise characteristics of
FFLs using the Langevin formalism and the Monte Carlo simulation technique
based on the Gillespie algorithm. We calculate the variances around the mean
protein levels in the steady states of the FFLs and find that, in the case of
coherent FFLs, the most abundant FFL, namely, the Type-1 coherent FFL, is the
least noisy. This is however not so in the case of incoherent FFLs. The results
suggest possible relationships between noise, functionality and abundance.Comment: 17 page
Strong negative self regulation of Prokaryotic transcription factors increases the intrinsic noise of protein expression
Background
Many prokaryotic transcription factors repress their own transcription. It is often asserted that such regulation enables a cell to homeostatically maintain protein abundance. We explore the role of negative self regulation of transcription in regulating the variability of protein abundance using a variety of stochastic modeling techniques.
Results
We undertake a novel analysis of a classic model for negative self regulation. We demonstrate that, with standard approximations, protein variance relative to its mean should be independent of repressor strength in a physiological range. Consequently, in that range, the coefficient of variation would increase with repressor strength. However, stochastic computer simulations demonstrate that there is a greater increase in noise associated with strong repressors than predicted by theory. The discrepancies between the mathematical analysis and computer simulations arise because with strong repressors the approximation that leads to Michaelis-Menten-like hyperbolic repression terms ceases to be valid. Because we observe that strong negative feedback increases variability and so is unlikely to be a mechanism for noise control, we suggest instead that negative feedback is evolutionarily favoured because it allows the cell to minimize mRNA usage. To test this, we used in silico evolution to demonstrate that while negative feedback can achieve only a modest improvement in protein noise reduction compared with the unregulated system, it can achieve good improvement in protein response times and very substantial improvement in reducing mRNA levels.
Conclusions
Strong negative self regulation of transcription may not always be a mechanism for homeostatic control of protein abundance, but instead might be evolutionarily favoured as a mechanism to limit the use of mRNA. The use of hyperbolic terms derived from quasi-steady-state approximation should also be avoided in the analysis of stochastic models with strong repressors
Genetic noise control via protein oligomerization
Gene expression in a cell entails random reaction events occurring over
disparate time scales. Thus, molecular noise that often results in phenotypic
and population-dynamic consequences sets a fundamental limit to biochemical
signaling. While there have been numerous studies correlating the architecture
of cellular reaction networks with noise tolerance, only a limited effort has
been made to understand the dynamic role of protein-protein interactions. Here
we have developed a fully stochastic model for the positive feedback control of
a single gene, as well as a pair of genes (toggle switch), integrating
quantitative results from previous in vivo and in vitro studies. We find that
the overall noise-level is reduced and the frequency content of the noise is
dramatically shifted to the physiologically irrelevant high-frequency regime in
the presence of protein dimerization. This is independent of the choice of
monomer or dimer as transcription factor and persists throughout the multiple
model topologies considered. For the toggle switch, we additionally find that
the presence of a protein dimer, either homodimer or heterodimer, may
significantly reduce its random switching rate. Hence, the dimer promotes the
robust function of bistable switches by preventing the uninduced (induced)
state from randomly being induced (uninduced). The specific binding between
regulatory proteins provides a buffer that may prevent the propagation of
fluctuations in genetic activity. The capacity of the buffer is a non-monotonic
function of association-dissociation rates. Since the protein oligomerization
per se does not require extra protein components to be expressed, it provides a
basis for the rapid control of intrinsic or extrinsic noise
Cost and Capacity of Signaling in the Escherichia coli Protein Reaction Network
In systems biology new ways are required to analyze the large amount of
existing data on regulation of cellular processes. Recent work can be roughly
classified into either dynamical models of well-described subsystems, or
coarse-grained descriptions of the topology of the molecular networks at the
scale of the whole organism. In order to bridge these two disparate approaches
one needs to develop simplified descriptions of dynamics and topological
measures which address the propagation of signals in molecular networks. Here,
we consider the directed network of protein regulation in E. coli,
characterizing its modularity in terms of its potential to transmit signals. We
demonstrate that the simplest measure based on identifying sub-networks of
strong components, within which each node could send a signal to every other
node, indeed partitions the network into functional modules. We then suggest
measures to quantify the cost and spread associated with sending a signal
between any particular pair of proteins. Thereby, we address the signalling
specificity within and between modules, and show that in the regulation of
E.coli there is a systematic reduction of the cost and spread for signals
traveling over more than two intermediate reactions.Comment: 21 pages, 6 figure
On-the-fly Uniformization of Time-Inhomogeneous Infinite Markov Population Models
This paper presents an on-the-fly uniformization technique for the analysis
of time-inhomogeneous Markov population models. This technique is applicable to
models with infinite state spaces and unbounded rates, which are, for instance,
encountered in the realm of biochemical reaction networks. To deal with the
infinite state space, we dynamically maintain a finite subset of the states
where most of the probability mass is located. This approach yields an
underapproximation of the original, infinite system. We present experimental
results to show the applicability of our technique
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