Int. School for Advanced Studies Molecular and Statistical Biophysics
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Dendrodendritic Inhibition and Simulated Odor Responses in a Detailed Olfactory Bulb Network Model
In the olfactory bulb, both the spatial distribution and the temporal structure of neuronal activity appear to be important for processing odor information, but it is currently impossible to measure both of these simultaneously with high resolution and in all layers of the bulb. We have developed a biologically realistic model of the mammalian olfactory bulb, incorporating the mitral and granule cells and the dendrodendritic synapses between them, which allows us to observe the network behavior in detail. The cell models were based on previously published work. The attributes of the synapses were obtained from the literature. The pattern of synaptic connections was based on the limited experimental data in the literature on the statistics of connections between neurons in the bulb. The results of simulation experiments with electrical stimulation agree closely in most details with published experimental data. This gives confidence that the model is capturing features of network interactions in the real olfactory bulb. The model predicts that the time course of dendrodendritic inhibition is dependent on the network connectivity as well as on the intrinsic parameters of the synapses. In response to simulated odor stimulation, strongly activated mitral cells tend to suppress neighboring cells, the mitral cells readily synchronize their firing, and increasing the stimulus intensity increases the degree of synchronization. Preliminary experiments suggest that slow temporal changes in the degree of synchronization are more useful in distinguishing between very similar odorants than is the spatial distribution of mean firing rate.
Generic allometric models including height best estimate forest biomass and carbon stocks in Indonesia
The choice of an appropriate allometric model is a critical step in reducing uncertainties in forest biomass stock estimates. With large greenhouse gases emissions due to deforestation, a systematic assessment and comparison of the models available in Indonesia is crucial for accurate assessments of forest carbon stocks and implementing REDD+ projects. In the present study, we compared the ability of two regional and two generic (pantropical) allometric models to estimate biomass at both tree and plot levels. We showed that regional models had lower performance in estimating tree biomass, with greater bias (-31 to 8 %) and higher AIC (177 to 204), compared to generic models (bias: -2 to 2 %; AIC: 57 to 67). At the plot level, the regional models underestimated biomass stocks by 0 – 40 % compared to the best generic model. The error in plot biomass stocks associated to models relying solely upon DBH ranged between -5 to +15 %. The integration of tree height estimated regionally resulted in an overestimate of 5 – 10 % in unmanaged forests. Despite the difficulty to accurately assess tree heights in tropical forests, integrating all or part of them in biomass assessment can reduce uncertainties.
Improving prokaryotic transposable elements identification using a combination of de novo and profile HMM methods
Background Insertion Sequences (ISs) and their non-autonomous derivatives (MITEs) are essential components of prokaryotic genomes inducing duplication, deletion, rearrangement or lateral gene transfers. Although ISs and MITEs are relatively simple and basic genetic elements, their detection remains a difficult task due to their remarkable sequence diversity. With the advent of high-throughput genome and metagenome sequencing technologies, the development of fast, reliable and sensitive methods of IS and MITEs detection become an important challenge. So far, almost all studies dealing with prokaryotic transposons have used classical BLAST-based detection methods against reference libraries. Here we introduce alternative methods of detection either taking advantages of the structural properties of the elements (de novo methods) or using an additional library-based methods using profile HMM searches. Results In this study, we have developed three different work flows dedicated to ISs and MITEs detection : the first two use de novo methods detecting either repeated sequences or presence of Inverted Repeats ; the third one use 28 in-house IS alignment profiles with HMM search methods. We have compared the respective performances of each methods using a reference dataset of 30 archaeal genomes in addition to simulated and real metagenomes. Compare to a BLAST-based method using ISFinder as library, de novo methods significantly improve IS and MITEs detection with the discovering of 30 news elements (+20%) in addition to the 141 multi-copies element already detected by the BLAST approach in the 30 archaeal genomes. Many of the new elements correspond to ISs belonging to unknown or highly divergent families. The total number of MITEs have even doubled with the discovering of elements displaying very limited sequence similarities with their respective autonomous partners (mainly in the Inverted Repeats of the elements). Concerning metagenomes, with the exception of short reads data (<300bp) where both techniques seem equally limited, profile HMM searches considerably ameliorate the detection of transposase encoding genes (up to +50%) generating low level of false positives compare to BLAST-based methods. Conclusion Compare to classical BLAST-based methods, the sensitivity of de novo and profile HMM methods developed in this study allow a better and more reliable detection of transposons in prokaryotic genomes and metagenomes. We believed that future studies implying ISs and MITEs identification in genomic data should combine at least one de novo and one library-based method, with optimal results obtain by running the two de novo methods in addition to a library-based search. For metagenomic data, profile HMM search should be favored, a BLAST-based step is only useful to the final annotation into groups and families.
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