ترغب بنشر مسار تعليمي؟ اضغط هنا

Fluctuations in network dynamics: SMAR1 can trigger apoptosis

74   0   0.0 ( 0 )
 نشر من قبل R.K. Brojen Singh
 تاريخ النشر 2015
  مجال البحث علم الأحياء
والبحث باللغة English




اسأل ChatGPT حول البحث

SMAR1 is a sensitive signaling molecule in p53 regulatory network which can drive p53 network dynamics to three distinct states, namely, stabilized (two), damped and sustain oscillation states. In the interaction of p53 network with SMAR1, p53 network sees SMAR1 as a sub-network with its new complexes formed by SMAR1, where SMAR1 is the central node, and fluctuations in SMAR1 concentration is propagated as a stress signal throughout the network. Excess stress induced by SMAR1 can drive p53 network dynamics to amplitude death scenario which corresponds to apoptotic state. The permutation entropy calculated for normal state is minimum indicating self-organized behavior, whereas for apoptotic state, the value is maximum showing breakdown of self-organization. We also show that the regulation of SMAR1 togather with other signaling molecules p300 and HDAC1 in the p53 regulatory network can be engineered to extend the range of stress such that the system can be save from apoptosis.



قيم البحث

اقرأ أيضاً

Cellular processes do not follow deterministic rules; even in identical environments genetically identical cells can make random choices leading to different phenotypes. This randomness originates from fluctuations present in the biomolecular interac tion networks. Most previous work has been focused on the intrinsic noise (IN) of these networks. Yet, especially for high-copy-number biomolecules, extrinsic or environmental noise (EN) has been experimentally shown to dominate the variation. Here, we develop an analytical formalism that allows for calculation of the effect of EN on gene-expression motifs. We introduce a method for modeling bounded EN as an auxiliary species in the master equation. The method is fully generic and is not limited to systems with small EN magnitudes. We focus our study on motifs that can be viewed as the building blocks of genetic switches: a nonregulated gene, a self-inhibiting gene, and a self-promoting gene. The role of the EN properties (magnitude, correlation time, and distribution) on the statistics of interest are systematically investigated, and the effect of fluctuations in different reaction rates is compared. Due to its analytical nature, our formalism can be used to quantify the effect of EN on the dynamics of biochemical networks and can also be used to improve the interpretation of data from single-cell gene-expression experiments.
Most complex networks serve as conduits for various dynamical processes, ranging from mass transfer by chemical reactions in the cell to packet transfer on the Internet. We collected data on the time dependent activity of five natural and technologic al networks, finding that for each the coupling of the flux fluctuations with the total flux on individual nodes obeys a unique scaling law. We show that the observed scaling can explain the competition between the systems internal collective dynamics and changes in the external environment, allowing us to predict the relevant scaling exponents.
During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here we review the links between disordered proteins and the associated networks, and describe the consequences of local, mesoscopic and glo bal network disorder on changes in protein structure and dynamics. We introduce a new classification of protein networks into cumulus-type, i.e., those similar to puffy (white) clouds, and stratus-type, i.e., those similar to flat, dense (dark) low-lying clouds, and relate these network types to protein disorder dynamics and to differences in energy transmission processes. In the first class, there is limited overlap between the modules, which implies higher rigidity of the individual units; there the conformational changes can be described by an energy transfer mechanism. In the second class, the topology presents a compact structure with significant overlap between the modules; there the conformational changes can be described by multi-trajectories; that is, multiple highly populated pathways. We further propose that disordered protein regions evolved to help other protein segments reach rarely visited but functionally-related states. We also show the role of disorder in spatial games of amino acids; highlight the effects of intrinsically disordered proteins (IDPs) on cellular networks and list some possible studies linking protein disorder and protein structure networks.
148 - Thimo Rohlf , Chris Winkler 2008
Genetic regulation is a key component in development, but a clear understanding of the structure and dynamics of genetic networks is not yet at hand. In this work we investigate these properties within an artificial genome model originally introduced by Reil. We analyze statistical properties of randomly generated genomes both on the sequence- and network level, and show that this model correctly predicts the frequency of genes in genomes as found in experimental data. Using an evolutionary algorithm based on stabilizing selection for a phenotype, we show that robustness against single base mutations, as well as against random changes in initial network states that mimic stochastic fluctuations in environmental conditions, can emerge in parallel. Evolved genomes exhibit characteristic patterns on both sequence and network level.
Motivation: Measurements of gene expression over time enable the reconstruction of transcriptional networks. However, Bayesian networks and many other current reconstruction methods rely on assumptions that conflict with the differential equations th at describe transcriptional kinetics. Practical approximations of kinetic models would enable inferring causal relationships between genes from expression data of microarray, tag-based and conventional platforms, but conclusions are sensitive to the assumptions made. Results: The representation of a sufficiently large portion of genome enables computation of an upper bound on how much confidence one may place in influences between genes on the basis of expression data. Information about which genes encode transcription factors is not necessary but may be incorporated if available. The methodology is generalized to cover cases in which expression measurements are missing for many of the genes that might control the transcription of the genes of interest. The assumption that the gene expression level is roughly proportional to the rate of translation led to better empirical performance than did either the assumption that the gene expression level is roughly proportional to the protein level or the Bayesian model average of both assumptions. Availability: http://www.oisb.ca points to R code implementing the methods (R Development Core Team 2004). Supplementary information: http://www.davidbickel.com
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا