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Pietrosemoli N.,Rice University | Pietrosemoli N.,National Center for Biotechnology | Lopez D.,Computational System Biology Group | Pazos F.,Computational Systems Biology Group
IEEE Signal Processing Magazine | Year: 2012

If the genomic era was characterized by the massive determination of genomic sequences, the so-called postgenomic era is, among other things, characterized by a lack of methods for obtaining functionally relevant information from these raw sequences. As the number of known protein sequences grows exponentially, it is impossible to experimentally determine their biological functions and the particular regions of these proteins responsible for such functions. For this reason, computational methods that are able to process this genomic information for extracting protein functional features are sought after. © 2012 IEEE. Source

Frey O.,ETH Zurich | Frey O.,Bio Engineering Laboratory | Rudolf F.,Computational Systems Biology Group | Schmidt G.W.,ETH Zurich | And 3 more authors.
Analytical Chemistry | Year: 2015

Optical long-term observation of individual cells, combined with modern data analysis tools, allows for a detailed study of cell-to-cell variability, heredity, and differentiation. We developed a microfluidic device featuring facile cell loading, simple and robust operation, and which is amenable to high-resolution life-cell imaging. Different cell strains can be grown in parallel in the device under constant or changing media perfusion without cross-talk between the cell ensembles. The culturing chamber has been optimized for use with nonadherent cells, such as Saccharomyces cerevisiae, and enables controlled colony growth over multiple generations under aerobic or anaerobic conditions. Small changes in the layout will make the device also useable with bacteria or mammalian cells. The platform can be readily set up in every laboratory with minimal additional requirements and can be operated without technology training. © 2015 American Chemical Society. Source

Boer H.M.T.,Animal Breeding and Genomics Center | Boer H.M.T.,Wageningen University | Apri M.,Wageningen University | Molenaar J.,Wageningen University | And 3 more authors.
Journal of Dairy Science | Year: 2012

The complex interplay of physiological factors that underlies fertility in dairy cows was investigated using a mechanistic mathematical model of the dynamics of the bovine estrous cycle. The model simulates the processes of follicle and corpus luteum development and its relations with key hormones that interact to control these processes. Several factors may perturb the regular oscillatory behavior of a normal estrous cycle, and such perturbations are likely the effect of simultaneous changes in multiple parameters. The objective of this paper was to investigate how multiple parameter perturbation changes the behavior of the estrous cycle model, so as to identify biological mechanisms that could play a role in the development of cystic ovaries. Cystic ovaries are a common reason for reproductive failure in dairy cows, but much about the causes of this disorder remains unknown. We investigated in which region of the parameter space the model predicts a normal cycle, and when a progesterone pattern occurred with delayed ovulation (indicating a cystic follicle) or delayed luteolysis (indicating a persistent corpus luteum). Perturbation of the initial values for all parameters simultaneously showed 2 specific parameter configurations leading to delayed ovulation or delayed luteolysis immediately. The most important parameter changes in these 2 configurations involve the regulation of corpus luteum functioning, luteolytic signals, and GnRH synthesis, suggesting that these mechanisms are likely involved in the development of cystic ovaries. In the multidimensional parameter space, areas exist in which the parameter configurations resulted in normal cycles. These areas may be separated by areas in which irregular cycle patterns occurred. These irregular patterns thus mark the transition from one stable (normal) situation to another. Interestingly, within a series, there were some cycles with delayed ovulation and some with delayed luteolysis in these patterns. This could represent a situation of resumption of normal cyclicity (e.g., after parturition). In conclusion, the method of parameter perturbation used in the present study is an effective tool to find parameter configurations that lead to progesterone profiles associated with delayed ovulation and delayed luteolysis. Thereby, the model helps to generate hypotheses regarding the underlying cause of the development of cystic ovaries, which could be investigated in future experiments. © 2012 American Dairy Science Association. Source

Buldu J.M.,Rey Juan Carlos University | Buldu J.M.,Center for Biomedical Technology | Bajo R.,Complutense University of Madrid | Maestu F.,Complutense University of Madrid | And 12 more authors.
PLoS ONE | Year: 2011

Whether the balance between integration and segregation of information in the brain is damaged in Mild Cognitive Impairment (MCI) subjects is still a matter of debate. Here we characterize the functional network architecture of MCI subjects by means of complex networks analysis. Magnetoencephalograms (MEG) time series obtained during a memory task were evaluated by synchronization likelihood (SL), to quantify the statistical dependence between MEG signals and to obtain the functional networks. Graphs from MCI subjects show an enhancement of the strength of connections, together with an increase in the outreach parameter, suggesting that memory processing in MCI subjects is associated with higher energy expenditure and a tendency toward random structure, which breaks the balance between integration and segregation. All features are reproduced by an evolutionary network model that simulates the degenerative process of a healthy functional network to that associated with MCI. Due to the high rate of conversion from MCI to Alzheimer Disease (AD), these results show that the analysis of functional networks could be an appropriate tool for the early detection of both MCI and AD. © 2011 Buldú et al. Source

Buldu J.M.,Rey Juan Carlos University | Buldu J.M.,Center for Biomedical Technology | Papo D.,Computational Systems Biology Group | Pineda J.A.,Center for Biomedical Engineering | And 3 more authors.
IFAC Proceedings Volumes (IFAC-PapersOnline) | Year: 2012

This contribution reviews the current state of the art comprising the application of Complex Networks Theory to the analysis of functional brain networks. We briefly overview the main advances in this field during the last decade and we explain how graph analysis has increased our knowledge about how the brain behaves when performing a specific task or how it fails when a certain pathology arises. We also show the limitations of this kind of analysis, which have been a source of confusion and misunderstanding when interpreting the results obtained. Finally, we discuss possible future directions for the years to come. © 2012 IFAC. Source

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