The effects of physiologically plausible connectivity structure on local and global dynamics in large scale brain models.

Functionally relevant large scale brain dynamics operates within the framework imposed by anatomical connectivity and time delays due to finite transmission speeds. To gain insight on the reliability and comparability of large scale brain network simulations, we investigate the effects of variations in the anatomical connectivity. Two different sets of detailed global connectivity structures are explored, the first extracted from the CoCoMac database and rescaled to the spatial extent of the human brain, the second derived from white-matter tractography applied to diffusion spectrum imaging (DSI) for a human subject. We use the combination of graph theoretical measures of the connection matrices and numerical simulations to explicate the importance of both connectivity strength and delays in shaping dynamic behaviour. Our results demonstrate that the brain dynamics derived from the CoCoMac database are more complex and biologically more realistic than the one based on the DSI database. We propose that the reason for this difference is the absence of directed weights in the DSI connectivity matrix.

Native homing endonucleases can target conserved genes in humans and in animal models.

In recent years, both homing endonucleases (HEases) and zinc-finger nucleases (ZFNs) have been engineered and selected for the targeting of desired human loci for gene therapy. However, enzyme engineering is lengthy and expensive and the off-target effect of the manufactured endonucleases is difficult to predict. Moreover, enzymes selected to cleave a human DNA locus may not cleave the homologous locus in the genome of animal models because of sequence divergence, thus hampering attempts to assess the in vivo efficacy and safety of any engineered enzyme prior to its application in human trials. Here, we show that naturally occurring HEases can be found, that cleave desirable human targets. Some of these enzymes are also shown to cleave the homologous sequence in the genome of animal models. In addition, the distribution of off-target effects may be more predictable for native HEases. Based on our experimental observations, we present the HomeBase algorithm, database and web server that allow a high-throughput computational search and assignment of HEases for the targeting of specific loci in the human and other genomes. We validate experimentally the predicted target specificity of candidate fungal, bacterial and archaeal HEases using cell free, yeast and archaeal assays.

The application of trend surface models to the analysis of time factors in Swiss cancer mortality.

To study different temporal components on cancer mortality (age, period and cohort) methods of graphic representation were applied to Swiss mortality data from 1950 to 1984. Maps using continuous slopes ("contour maps") and based on eight tones of grey according to the absolute distribution of rates were used to represent the surfaces defined by the matrix of various age-specific rates. Further, progressively more complex regression surface equations were defined, on the basis of two independent variables (age/cohort) and a dependent one (each age-specific mortality rate). General patterns of trends in cancer mortality were thus identified, permitting definition of important cohort (e.g., upwards for lung and other tobacco-related neoplasms, or downwards for stomach) or period (e.g., downwards for intestines or thyroid cancers) effects, besides the major underlying age component. For most cancer sites, even the lower order (1st to 3rd) models utilised provided excellent fitting, allowing immediate identification of the residuals (e.g., high or low mortality points) as well as estimates of first-order interactions between the three factors, although the parameters of the main effects remained still undetermined. Thus, the method should be essentially used as summary guide to illustrate and understand the general patterns of age, period and cohort effects in (cancer) mortality, although they cannot conceptually solve the inherent problem of identifiability of the three components.

Defining new criteria for selection of cell-based intestinal models using publicly available databases.

BACKGROUND: The criteria for choosing relevant cell lines among a vast panel of available intestinal-derived lines exhibiting a wide range of functional properties are still ill-defined. The objective of this study was, therefore, to establish objective criteria for choosing relevant cell lines to assess their appropriateness as tumor models as well as for drug absorption studies. RESULTS: We made use of publicly available expression signatures and cell based functional assays to delineate differences between various intestinal colon carcinoma cell lines and normal intestinal epithelium. We have compared a panel of intestinal cell lines with patient-derived normal and tumor epithelium and classified them according to traits relating to oncogenic pathway activity, epithelial-mesenchymal transition (EMT) and stemness, migratory properties, proliferative activity, transporter expression profiles and chemosensitivity. For example, SW480 represent an EMT-high, migratory phenotype and scored highest in terms of signatures associated to worse overall survival and higher risk of recurrence based on patient derived databases. On the other hand, differentiated HT29 and T84 cells showed gene expression patterns closest to tumor bulk derived cells. Regarding drug absorption, we confirmed that differentiated Caco-2 cells are the model of choice for active uptake studies in the small intestine. Regarding chemosensitivity we were unable to confirm a recently proposed association of chemo-resistance with EMT traits. However, a novel signature was identified through mining of NCI60 GI50 values that allowed to rank the panel of intestinal cell lines according to their drug responsiveness to commonly used chemotherapeutics. CONCLUSIONS: This study presents a straightforward strategy to exploit publicly available gene expression data to guide the choice of cell-based models. While this approach does not overcome the major limitations of such models, introducing a rank order of selected features may allow selecting model cell lines that are more adapted and pertinent to the addressed biological question.

Ispol'zovanie metodov teorii vosstanovleniia v modeliakh optimal'nogo dobyvaniia pishchi [Restoration theory in models of optimal feeding behavior]

The method of stochastic dynamic programming is widely used in ecology of behavior, but has some imperfections because of use of temporal limits. The authors presented an alternative approach based on the methods of the theory of restoration. Suggested method uses cumulative energy reserves per time unit as a criterium, that leads to stationary cycles in the area of states. This approach allows to study the optimal feeding by analytic methods.

Local and global error models to improve uncertainty quantification

In groundwater applications, Monte Carlo methods are employed to model the uncertainty on geological parameters. However, their brute-force application becomes computationally prohibitive for highly detailed geological descriptions, complex physical processes, and a large number of realizations. The Distance Kernel Method (DKM) overcomes this issue by clustering the realizations in a multidimensional space based on the flow responses obtained by means of an approximate (computationally cheaper) model; then, the uncertainty is estimated from the exact responses that are computed only for one representative realization per cluster (the medoid). Usually, DKM is employed to decrease the size of the sample of realizations that are considered to estimate the uncertainty. We propose to use the information from the approximate responses for uncertainty quantification. The subset of exact solutions provided by DKM is then employed to construct an error model and correct the potential bias of the approximate model. Two error models are devised that both employ the difference between approximate and exact medoid solutions, but differ in the way medoid errors are interpolated to correct the whole set of realizations. The Local Error Model rests upon the clustering defined by DKM and can be seen as a natural way to account for intra-cluster variability; the Global Error Model employs a linear interpolation of all medoid errors regardless of the cluster to which the single realization belongs. These error models are evaluated for an idealized pollution problem in which the uncertainty of the breakthrough curve needs to be estimated. For this numerical test case, we demonstrate that the error models improve the uncertainty quantification provided by the DKM algorithm and are effective in correcting the bias of the estimate computed solely from the MsFV results. The framework presented here is not specific to the methods considered and can be applied to other combinations of approximate models and techniques to select a subset of realizations

Role of the inflammasome in murine experimental models of arthritis

Summary : The purpose of this study was to investigate the role of the inflammasome in human and experimental murine models (such as ΑΙΑ and K/BxN) of rheumatoid arthritis (RA)RA, affecting 1% of the population is the most frequent inflammatory disease characterized by synovial hyperplasia and cartilage and bone erosion, leading to joint destruction. In general, women are 3 times more affected by RA suggesting a role of estrogen in this disease. The inflammasome is a multiproteic complex triggering the activation of caspase-1 leading to the activation of IL-1 β, an important pro-inflammatory cytokine implicated in arthritis. The inflammasome has been implicated in several inflammatory diseases and particularly in gout. To highlight a possible role of the inflammasome in murine arthritis, we obtained ASC, caspase-1 and NALP3 +/+ and -/- littermate mice to perform ΑΙΑ and K/BxN arthritis. NALP3 -/- and caspase-1 -/- mice were as arthritic as wild type littermate mice in both ΑΙΑ and K/BxN models implicating that the NALP3 inflammasome is not involved in experimental arthritis. By contrast, ΑΙΑ severity was significantly diminished in ASC- deficient male and female mice, and in the K/BxN model, in ASC-deficient female mice. These results were supported by histological scoring and acute phase protein serum amyloid A (SAA) levels that were equivalent between NALP+/+ and NALP3-/- mice and diminished in ASC -/- mice. In ΑΙΑ and K/BxN murine experimental models, we observed a sexdependent phenotype. We studied the role of estradiol in both the ALA and the K/BxN models. Castrated female or male ASC -/- mice that received estradiol had a decreased arthritis severity. This implies a protective role of estrogen in the absence of ASC. In the ΑΙΑ model, proliferation assay were performed using splenocytes from mBSA- immunized ASC +/+ and -/- mice. The mBSA-induced proliferation was significantly lower in ASC-/- splenocytes. Moreover the CD3-specific proliferation of purified splenic Τ cells was significantly lower in ASC-/- cells. Finally, Τ cells from ASC-/- mice produced significantly decreased levels of IFN-gamma associated with increased levels of IL-10. These results imply a possible role of ASC in the TCR-signaling pathway and Τ cell cytokine production. In parallel the expression of the different inflammasome components were analyzed in biopsies from rheumatoid arthritis (RA) and osteoarthritis (OA) patiens. The expression of the 14 different NALPs, their effector protein ASC, and caspase-1 and -5 was readily measurable by RT-PCR in a similar proportion in RA and OA synovial samples, with the exception of NALP-5 and NALP-13, which weren't found in samples from either disease. The corresponding NALP1, -3, -12 and ASC proteins were expressed at similar levels in both OA and RA biopsies, as determined by immunohistochemistry and Western-blot analysis. By contrast, caspase-1 levels were significantly enhanced in RA synovial tissues compared to those from OA patients. NALP-1, -2, -3, -10, -12 and -14, as well as ASC, caspase-1, and -5 were detected in RNA from unstimulated and stimulated RA synoviocytes. In FLS, only ASC and caspase-1 were expressed at the protein level. NALP1, 3 and 12 were not detected. However, upon stimulation, no secreted IL-Ιβ was detectable in either RA or in OA synoviocytes culture medium. Résumé : Le but de ce projet était d'étudier le rôle de l'inflammasome dans des modèles expérimentaux d'arthrite tels que les modèles ΑΙΑ et K/BxN ainsi que dans la polyarthrite humaine (RA). La polyarthrite est une maladie inflammatoire très fréquente avec 1 % de la population affectée et touche 3 fois plus les femmes que les hommes, suggérant un rôle des hormones sexuelles dans cette pathologie. L'inflammasome est un complexe multiprotéique qui permet l'activation de la caspase-1, une cystéine protéase qui va ensuite cliver et activer rinterleukine-ΐβ (IL-Ιβ). L'inflammasome a été impliqué ces dernières années dans de nombreuses maladies inflammatoires notamment dans la goutte. Pour mettre en évidence un éventuel rôle de l'inflammasome dans l'arthrite expérimentale nous avons obtenu des souris déficientes pour certains des composants de l'inflammasome tels que ASC, NALP3 et caspase-1. Les souris NALP3 déficientes et caspase-1 déficientes sont aussi arthritiques que les souris wild type correspondantes que ce soit dans le modèle ΑΙΑ ou K/BxN. Par contre les souris mâles et femelles ASC-déficientes sont moins arthritiques que les souris +/+ correspondantes dans le modèle ΑΙΑ. Dans le modèle KRN, le même phénotype (diminution de la sévérité de l'arthrite) est observé uniquement chez les femelles ASC-/- Ce phénotype est corrélé avec l'histologie ainsi qu'avec le dosage du serum amyloid A (SAA) qui reflète l'inflammation systémique et qui est diminué chez les souris ASC-déficientes. Nous avons ensuite étudié le rôle de Γ estradiol (une des formes active des estrogènes) dans les modèles K/BxN et ΑΙΑ. Les souris castrées maies ou femelles déficientes pour ASC ayant reçu de l'estradiol ont une arthrite moins sévère ce qui implique que les estradiol ont un effet protecteur en l'absence de ASC. Dans le modèle ΑΙΑ, nous nous sommes aussi intéressés à la réponse immune. Des tests de prolifération ont été effectués sur des splénocytes en présence de mBSA (qui est l'antigène utilisé dans le modèle ΑΙΑ). Les splénocytes ASC -/- ont une proliferation qui est diminuée en présence de l'antigène. De plus la proliferation de cellules Τ spléniques purifiées en présence d'anti-CD3 est diminuée chez les cellules Τ ASC-/-. Ces résultats nous indiquent une éventuelle implication de ASC dans la signalisation par le récépteur des cellules T. En parallèle l'expression des différents composants de l'inflammasome a été analysée dans des biopsies de patients atteints de polyarthrite rhumatoide (RA) et d'arthrose (OA). L'expression des 14 différents NALPs, de l'adaptateur ASC, ainsi que des caspase-1 et -5 était similaires dans les échantillons RA et OA, à l'exception de NALP5 et 13 qui n'étaient pas détéctables. L'expression protéique de NALP1, 3, 12 et ASC effectuée par Western blot et immunohistochimie était similaire dans les biopsies RA et OA. Par contre la quantité de la caspase-1 mesurée par ELISA était augmentée de façon significative dans les extraits protéiques de biopsies RA. NALP-1, -2. -3, -10, -12, and -14 ainsi que ASC, caspase-1 et -5 étaient exprimés de façon similaire par les synoviocytes RA non stimulés et stimulés. Dans les synoviocytes seuls ASC et caspase-1 étaient détéctable au niveau protéique. NALP-1, -3 et -12 n'était pas détéctables. Cependant après stimulation il n'y avait d'IL-Ιβ sécrété que ce soit dans les surnageants de cultures de synoviocytes RA ou OA.

SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools.

BACKGROUND: Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing. RESULTS: We present the Systems Biology Markup Language (SBML) Qualitative Models Package ("qual"), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models. CONCLUSIONS: SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks.

Pattern recognition ecological niche models fit to presence-only and presence-absence data

1. Identifying the boundary of a species' niche from observational and environmental data is a common problem in ecology and conservation biology and a variety of techniques have been developed or applied to model niches and predict distributions. Here, we examine the performance of some pattern-recognition methods as ecological niche models (ENMs). Particularly, one-class pattern recognition is a flexible and seldom used methodology for modelling ecological niches and distributions from presence-only data. The development of one-class methods that perform comparably to two-class methods (for presence/absence data) would remove modelling decisions about sampling pseudo-absences or background data points when absence points are unavailable. 2. We studied nine methods for one-class classification and seven methods for two-class classification (five common to both), all primarily used in pattern recognition and therefore not common in species distribution and ecological niche modelling, across a set of 106 mountain plant species for which presence-absence data was available. We assessed accuracy using standard metrics and compared trade-offs in omission and commission errors between classification groups as well as effects of prevalence and spatial autocorrelation on accuracy. 3. One-class models fit to presence-only data were comparable to two-class models fit to presence-absence data when performance was evaluated with a measure weighting omission and commission errors equally. One-class models were superior for reducing omission errors (i.e. yielding higher sensitivity), and two-classes models were superior for reducing commission errors (i.e. yielding higher specificity). For these methods, spatial autocorrelation was only influential when prevalence was low. 4. These results differ from previous efforts to evaluate alternative modelling approaches to build ENM and are particularly noteworthy because data are from exhaustively sampled populations minimizing false absence records. Accurate, transferable models of species' ecological niches and distributions are needed to advance ecological research and are crucial for effective environmental planning and conservation; the pattern-recognition approaches studied here show good potential for future modelling studies. This study also provides an introduction to promising methods for ecological modelling inherited from the pattern-recognition discipline.