5,417 research outputs found
Computational Investigations on Polymerase Actions in Gene Transcription and Replication Combining Physical Modeling and Atomistic Simulations
Polymerases are protein enzymes that move along nucleic acid chains and
catalyze template-based polymerization reactions during gene transcription and
replication. The polymerases also substantially improve transcription or
replication fidelity through the non-equilibrium enzymatic cycles. We briefly
review computational efforts that have been made toward understanding
mechano-chemical coupling and fidelity control mechanisms of the polymerase
elongation. The polymerases are regarded as molecular information motors during
the elongation process. It requires a full spectrum of computational approaches
from multiple time and length scales to understand the full polymerase
functional cycle. We keep away from quantum mechanics based approaches to the
polymerase catalysis due to abundant former surveys, while address only
statistical physics modeling approach and all-atom molecular dynamics
simulation approach. We organize this review around our own modeling and
simulation practices on a single-subunit T7 RNA polymerase, and summarize
commensurate studies on structurally similar DNA polymerases. For multi-subunit
RNA polymerases that have been intensively studied in recent years, we leave
detailed discussions on the simulation achievements to other computational
chemical surveys, while only introduce very recently published representative
studies, including our own preliminary work on structure-based modeling on
yeast RNA polymerase II. In the end, we quickly go through kinetic modeling on
elongation pauses and backtracking activities. We emphasize the fluctuation and
control mechanisms of the polymerase actions, highlight the non-equilibrium
physical nature of the system, and try to bring some perspectives toward
understanding replication and transcription regulation from single molecular
details to a genome-wide scale
Ribosome traffic on mRNAs maps to gene ontology : genome-wide quantification of translation initiation rates and polysome size regulation
Peer reviewedPublisher PD
Mechanical and Systems Biology of Cancer
Mechanics and biochemical signaling are both often deregulated in cancer,
leading to cancer cell phenotypes that exhibit increased invasiveness,
proliferation, and survival. The dynamics and interactions of cytoskeletal
components control basic mechanical properties, such as cell tension,
stiffness, and engagement with the extracellular environment, which can lead to
extracellular matrix remodeling. Intracellular mechanics can alter signaling
and transcription factors, impacting cell decision making. Additionally,
signaling from soluble and mechanical factors in the extracellular environment,
such as substrate stiffness and ligand density, can modulate cytoskeletal
dynamics. Computational models closely integrated with experimental support,
incorporating cancer-specific parameters, can provide quantitative assessments
and serve as predictive tools toward dissecting the feedback between signaling
and mechanics and across multiple scales and domains in tumor progression.Comment: 18 pages, 3 figure
Information transfer in signaling pathways : a study using coupled simulated and experimental data
Background: The topology of signaling cascades has been studied in quite some detail. However, how information is processed exactly is still relatively unknown. Since quite diverse information has to be transported by one and the same signaling cascade (e.g. in case of different agonists), it is clear that the underlying mechanism is more complex than a simple binary switch which relies on the
mere presence or absence of a particular species. Therefore, finding means to analyze the information transferred will help in deciphering how information is processed exactly in the cell. Using the information-theoretic measure transfer entropy, we studied the properties of information transfer in an example case, namely calcium signaling under different cellular
conditions. Transfer entropy is an asymmetric and dynamic measure of the dependence of two (nonlinear) stochastic processes. We used calcium signaling since it is a well-studied example of complex cellular signaling. It has been suggested that specific information is encoded in the
amplitude, frequency and waveform of the oscillatory Ca2+-signal.
Results: We set up a computational framework to study information transfer, e.g. for calcium
signaling at different levels of activation and different particle numbers in the system. We stochastically coupled simulated and experimentally measured calcium signals to simulated target proteins and used kernel density methods to estimate the transfer entropy from these bivariate
time series. We found that, most of the time, the transfer entropy increases with increasing particle numbers. In systems with only few particles, faithful information transfer is hampered by random fluctuations. The transfer entropy also seems to be slightly correlated to the complexity (spiking, bursting or irregular oscillations) of the signal. Finally, we discuss a number of peculiarities of our approach in detail.
Conclusion: This study presents the first application of transfer entropy to biochemical signaling pathways. We could quantify the information transferred from simulated/experimentally measured calcium signals to a target enzyme under different cellular conditions. Our approach, comprising stochastic coupling and using the information-theoretic measure transfer entropy, could also be a valuable tool for the analysis of other signaling pathways
Information transfer in signaling pathways : a study using coupled simulated and experimental data
Background: The topology of signaling cascades has been studied in quite some detail. However, how information is processed exactly is still relatively unknown. Since quite diverse information has to be transported by one and the same signaling cascade (e.g. in case of different agonists), it is clear that the underlying mechanism is more complex than a simple binary switch which relies on the
mere presence or absence of a particular species. Therefore, finding means to analyze the information transferred will help in deciphering how information is processed exactly in the cell. Using the information-theoretic measure transfer entropy, we studied the properties of information transfer in an example case, namely calcium signaling under different cellular
conditions. Transfer entropy is an asymmetric and dynamic measure of the dependence of two (nonlinear) stochastic processes. We used calcium signaling since it is a well-studied example of complex cellular signaling. It has been suggested that specific information is encoded in the
amplitude, frequency and waveform of the oscillatory Ca2+-signal.
Results: We set up a computational framework to study information transfer, e.g. for calcium
signaling at different levels of activation and different particle numbers in the system. We stochastically coupled simulated and experimentally measured calcium signals to simulated target proteins and used kernel density methods to estimate the transfer entropy from these bivariate
time series. We found that, most of the time, the transfer entropy increases with increasing particle numbers. In systems with only few particles, faithful information transfer is hampered by random fluctuations. The transfer entropy also seems to be slightly correlated to the complexity (spiking, bursting or irregular oscillations) of the signal. Finally, we discuss a number of peculiarities of our approach in detail.
Conclusion: This study presents the first application of transfer entropy to biochemical signaling pathways. We could quantify the information transferred from simulated/experimentally measured calcium signals to a target enzyme under different cellular conditions. Our approach, comprising stochastic coupling and using the information-theoretic measure transfer entropy, could also be a valuable tool for the analysis of other signaling pathways
Differential growth of wrinkled biofilms
Biofilms are antibiotic-resistant bacterial aggregates that grow on moist
surfaces and can trigger hospital-acquired infections. They provide a classical
example in biology where the dynamics of cellular communities may be observed
and studied. Gene expression regulates cell division and differentiation, which
affect the biofilm architecture. Mechanical and chemical processes shape the
resulting structure. We gain insight into the interplay between cellular and
mechanical processes during biofilm development on air-agar interfaces by means
of a hybrid model. Cellular behavior is governed by stochastic rules informed
by a cascade of concentration fields for nutrients, waste and autoinducers.
Cellular differentiation and death alter the structure and the mechanical
properties of the biofilm, which is deformed according to Foppl-Von Karman
equations informed by cellular processes and the interaction with the
substratum. Stiffness gradients due to growth and swelling produce wrinkle
branching. We are able to reproduce wrinkled structures often formed by
biofilms on air-agar interfaces, as well as spatial distributions of
differentiated cells commonly observed with B. subtilis.Comment: 19 pages, 13 figure
Elucidating regulatory mechanisms downstream of a signaling pathway using informative experiments
Signaling cascades are triggered by environmental stimulation and propagate the signal to regulate transcription. Systematic reconstruction of the underlying regulatory mechanisms requires pathway-targeted, informative experimental data. However, practical experimental design approaches are still in their infancy. Here, we propose a framework that iterates design of experiments and identification of regulatory relationships downstream of a given pathway. The experimental design component, called MEED, aims to minimize the amount of laboratory effort required in this process. To avoid ambiguity in the identification of regulatory relationships, the choice of experiments maximizes diversity between expression profiles of genes regulated through different mechanisms. The framework takes advantage of expert knowledge about the pathways under study, formalized in a predictive logical model. By considering model-predicted dependencies between experiments, MEED is able to suggest a whole set of experiments that can be carried out simultaneously. Our framework was applied to investigate interconnected signaling pathways in yeast. In comparison with other approaches, MEED suggested the most informative experiments for unambiguous identification of transcriptional regulation in this system
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