Dynamical modeling and analysis of large cellular regulatory networks.

The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.

White matter maturation reshapes structural connectivity in the late developing human brain.

From toddler to late teenager, the macroscopic pattern of axonal projections in the human brain remains largely unchanged while undergoing dramatic functional modifications that lead to network refinement. These functional modifications are mediated by increasing myelination and changes in axonal diameter and synaptic density, as well as changes in neurochemical mediators. Here we explore the contribution of white matter maturation to the development of connectivity between ages 2 and 18 y using high b-value diffusion MRI tractography and connectivity analysis. We measured changes in connection efficacy as the inverse of the average diffusivity along a fiber tract. We observed significant refinement in specific metrics of network topology, including a significant increase in node strength and efficiency along with a decrease in clustering. Major structural modules and hubs were in place by 2 y of age, and they continued to strengthen their profile during subsequent development. Recording resting-state functional MRI from a subset of subjects, we confirmed a positive correlation between structural and functional connectivity, and in addition observed that this relationship strengthened with age. Continuously increasing integration and decreasing segregation of structural connectivity with age suggests that network refinement mediated by white matter maturation promotes increased global efficiency. In addition, the strengthening of the correlation between structural and functional connectivity with age suggests that white matter connectivity in combination with other factors, such as differential modulation of axonal diameter and myelin thickness, that are partially captured by inverse average diffusivity, play an increasingly important role in creating brain-wide coherence and synchrony.

Structural prediction of peptides bound to MHC class I.

An ab initio structure prediction approach adapted to the peptide-major histocompatibility complex (MHC) class I system is presented. Based on structure comparisons of a large set of peptide-MHC class I complexes, a molecular dynamics protocol is proposed using simulated annealing (SA) cycles to sample the conformational space of the peptide in its fixed MHC environment. A set of 14 peptide-human leukocyte antigen (HLA) A0201 and 27 peptide-non-HLA A0201 complexes for which X-ray structures are available is used to test the accuracy of the prediction method. For each complex, 1000 peptide conformers are obtained from the SA sampling. A graph theory clustering algorithm based on heavy atom root-mean-square deviation (RMSD) values is applied to the sampled conformers. The clusters are ranked using cluster size, mean effective or conformational free energies, with solvation free energies computed using Generalized Born MV 2 (GB-MV2) and Poisson-Boltzmann (PB) continuum models. The final conformation is chosen as the center of the best-ranked cluster. With conformational free energies, the overall prediction success is 83% using a 1.00 Angstroms crystal RMSD criterion for main-chain atoms, and 76% using a 1.50 Angstroms RMSD criterion for heavy atoms. The prediction success is even higher for the set of 14 peptide-HLA A0201 complexes: 100% of the peptides have main-chain RMSD values < or =1.00 Angstroms and 93% of the peptides have heavy atom RMSD values < or =1.50 Angstroms. This structure prediction method can be applied to complexes of natural or modified antigenic peptides in their MHC environment with the aim to perform rational structure-based optimizations of tumor vaccines.

Central and Cortical Gray Mater Segmentation of Magnetic Resonance Images of the Fetal Brain

Motivation. The study of human brain development in itsearly stage is today possible thanks to in vivo fetalmagnetic resonance imaging (MRI) techniques. Aquantitative analysis of fetal cortical surfacerepresents a new approach which can be used as a markerof the cerebral maturation (as gyration) and also forstudying central nervous system pathologies [1]. However,this quantitative approach is a major challenge forseveral reasons. First, movement of the fetus inside theamniotic cavity requires very fast MRI sequences tominimize motion artifacts, resulting in a poor spatialresolution and/or lower SNR. Second, due to the ongoingmyelination and cortical maturation, the appearance ofthe developing brain differs very much from thehomogenous tissue types found in adults. Third, due tolow resolution, fetal MR images considerably suffer ofpartial volume (PV) effect, sometimes in large areas.Today extensive efforts are made to deal with thereconstruction of high resolution 3D fetal volumes[2,3,4] to cope with intra-volume motion and low SNR.However, few studies exist related to the automatedsegmentation of MR fetal imaging. [5] and [6] work on thesegmentation of specific areas of the fetal brain such asposterior fossa, brainstem or germinal matrix. Firstattempt for automated brain tissue segmentation has beenpresented in [7] and in our previous work [8]. Bothmethods apply the Expectation-Maximization Markov RandomField (EM-MRF) framework but contrary to [7] we do notneed from any anatomical atlas prior. Data set &Methods. Prenatal MR imaging was performed with a 1-Tsystem (GE Medical Systems, Milwaukee) using single shotfast spin echo (ssFSE) sequences (TR 7000 ms, TE 180 ms,FOV 40 x 40 cm, slice thickness 5.4mm, in plane spatialresolution 1.09mm). Each fetus has 6 axial volumes(around 15 slices per volume), each of them acquired inabout 1 min. Each volume is shifted by 1 mm with respectto the previous one. Gestational age (GA) ranges from 29to 32 weeks. Mother is under sedation. Each volume ismanually segmented to extract fetal brain fromsurrounding maternal tissues. Then, in-homogeneityintensity correction is performed using [9] and linearintensity normalization is performed to have intensityvalues that range from 0 to 255. Note that due tointra-tissue variability of developing brain someintensity variability still remains. For each fetus, ahigh spatial resolution image of isotropic voxel size of1.09 mm is created applying [2] and using B-splines forthe scattered data interpolation [10] (see Fig. 1). Then,basal ganglia (BS) segmentation is performed on thissuper reconstructed volume. Active contour framework witha Level Set (LS) implementation is used. Our LS follows aslightly different formulation from well-known Chan-Vese[11] formulation. In our case, the LS evolves forcing themean of the inside of the curve to be the mean intensityof basal ganglia. Moreover, we add local spatial priorthrough a probabilistic map created by fitting anellipsoid onto the basal ganglia region. Some userinteraction is needed to set the mean intensity of BG(green dots in Fig. 2) and the initial fitting points forthe probabilistic prior map (blue points in Fig. 2). Oncebasal ganglia are removed from the image, brain tissuesegmentation is performed as described in [8]. Results.The case study presented here has 29 weeks of GA. Thehigh resolution reconstructed volume is presented in Fig.1. The steps of BG segmentation are shown in Fig. 2.Overlap in comparison with manual segmentation isquantified by the Dice similarity index (DSI) equal to0.829 (values above 0.7 are considered a very goodagreement). Such BG segmentation has been applied on 3other subjects ranging for 29 to 32 GA and the DSI hasbeen of 0.856, 0.794 and 0.785. Our segmentation of theinner (red and blue contours) and outer cortical surface(green contour) is presented in Fig. 3. Finally, torefine the results we include our WM segmentation in theFreesurfer software [12] and some manual corrections toobtain Fig.4. Discussion. Precise cortical surfaceextraction of fetal brain is needed for quantitativestudies of early human brain development. Our workcombines the well known statistical classificationframework with the active contour segmentation forcentral gray mater extraction. A main advantage of thepresented procedure for fetal brain surface extraction isthat we do not include any spatial prior coming fromanatomical atlases. The results presented here arepreliminary but promising. Our efforts are now in testingsuch approach on a wider range of gestational ages thatwe will include in the final version of this work andstudying as well its generalization to different scannersand different type of MRI sequences. References. [1]Guibaud, Prenatal Diagnosis 29(4) (2009). [2] Rousseau,Acad. Rad. 13(9), 2006, [3] Jiang, IEEE TMI 2007. [4]Warfield IADB, MICCAI 2009. [5] Claude, IEEE Trans. Bio.Eng. 51(4) (2004). [6] Habas, MICCAI (Pt. 1) 2008. [7]Bertelsen, ISMRM 2009 [8] Bach Cuadra, IADB, MICCAI 2009.[9] Styner, IEEE TMI 19(39 (2000). [10] Lee, IEEE Trans.Visual. And Comp. Graph. 3(3), 1997, [11] Chan, IEEETrans. Img. Proc, 10(2), 2001 [12] Freesurfer,http://surfer.nmr.mgh.harvard.edu.

Relocalization of telomeric Ku and SIR proteins in response to DNA strand breaks in yeast.

Telomeric TG-rich repeats and their associated proteins protect the termini of eukaryotic chromosomes from end-to-end fusions. Associated with the cap structure at yeast telomeres is a subtelomeric domain of heterochromatin, containing the silent information regulator (SIR) complex. The Ku70/80 heterodimer (yKu) is associated both with the chromosome end and with subtelomeric chromatin. Surprisingly, both yKu and the chromatin-associated Rap1 and SIR proteins are released from telomeres in a RAD9-dependent response to DNA damage. yKu is recruited rapidly to double-strand cuts, while low levels of SIR proteins are detected near cleavage sites at later time points. Consistently, yKu- or SIR-deficient strains are hypersensitive to DNA-damaging agents. The release of yKu from telomeric chromatin may allow efficient scanning of the genome for DNA strand breaks.

Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.

Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature.

Comparing Multilevel Clustering Methods on Weighted Graphs : The Case of Worldwide Air Passenger Traffic 2000-2004

Specific properties emerge from the structure of large networks, such as that of worldwide air traffic, including a highly hierarchical node structure and multi-level small world sub-groups that strongly influence future dynamics. We have developed clustering methods to understand the form of these structures, to identify structural properties, and to evaluate the effects of these properties. Graph clustering methods are often constructed from different components: a metric, a clustering index, and a modularity measure to assess the quality of a clustering method. To understand the impact of each of these components on the clustering method, we explore and compare different combinations. These different combinations are used to compare multilevel clustering methods to delineate the effects of geographical distance, hubs, network densities, and bridges on worldwide air passenger traffic. The ultimate goal of this methodological research is to demonstrate evidence of combined effects in the development of an air traffic network. In fact, the network can be divided into different levels of âeurooecohesionâeuro, which can be qualified and measured by comparative studies (Newman, 2002; Guimera et al., 2005; Sales-Pardo et al., 2007).

Structural Analysis of Networks

Network analysis naturally relies on graph theory and, more particularly, on the use of node and edge metrics to identify the salient properties in graphs. When building visual maps of networks, these metrics are turned into useful visual cues or are used interactively to filter out parts of a graph while querying it, for instance. Over the years, analysts from different application domains have designed metrics to serve specific needs. Network science is an inherently cross-disciplinary field, which leads to the publication of metrics with similar goals; different names and descriptions of their analytics often mask the similarity between two metrics that originated in different fields. Here, we study a set of graph metrics and compare their relative values and behaviors in an effort to survey their potential contributions to the spatial analysis of networks.

X-ray microtomography of Larger Foraminifera

X-ray microtomography has become a new tool in earth sciences to obtain non-destructive 3D-image data from geological objects in which variations in mineralogy, chemical composition and/or porosity create sufficient x-ray density contrasts.We present here first, preliminary results of an application to the external and internal morphology of Permian to Recent Larger Foraminifera. We use a SkyScan-1072 high-resolution desk-top micro-CT system. The system has a conical x-ray source with a spot size of about 5µm that runs at 20-100kV, 0-250µA, resulting in a maximal resolution of 5µm. X-ray transmission images are captured by a scintillator coupled via fibre optics to a 1024x1024 pixel 12-bit CCD. The object is placed between the x-ray source and the scintillator on a stub that rotates 360°around its vertical axis in steps as small as 0.24 degrees. Sample size is limited to 2 cm due to the absorption of geologic material for x-rays. The transmission images are back projected using a Feldkamp algorithm into a vertical stack of up to 1000 1Kx1K images that represent horizontal cuts of the object. This calculation takes 2 to several hours on a Double-Processor 2.4GHz PC. The stack of images (.bmp) can be visualized with any 3D-imaging software, used to produce cuts of Larger Foraminifera. Among other applications, the 3D-imaging software furnished by SkyScan can produce 3D-models by defining a threshold density value to distinguish "solid" from "void. Several models with variable threshold values and colors can be imbricated, rotated and cut together. The best results were obtained with microfossils devoid of chamber-filling cements (Permian, Eocene, Recent). However, even slight differences in cement mineralogy/composition can result in surprisingly good x-ray density contrasts.X-ray microtomography may develop into a powerful tool for larger microfossils with a complex internal structure, because it is non-destructive, requires no preparation of the specimens, and produces a true 3D-image data set. We will use these data sets in the future to produce cuts in any direction to compare them with arbitrary cuts of complex microfossils in thin sections. Many groups of benthic and planktonic foraminifera may become more easily determinable in thin section by this way.

Quantifying network properties in multi-electrode recordings: spatiotemporal characterization and inter-trial variation of evoked gamma oscillations in mouse somatosensory cortex in vitro.

Linking the structural connectivity of brain circuits to their cooperative dynamics and emergent functions is a central aim of neuroscience research. Graph theory has recently been applied to study the structure-function relationship of networks, where dynamical similarity of different nodes has been turned into a "static" functional connection. However, the capability of the brain to adapt, learn and process external stimuli requires a constant dynamical functional rewiring between circuitries and cell assemblies. Hence, we must capture the changes of network functional connectivity over time. Multi-electrode array data present a unique challenge within this framework. We study the dynamics of gamma oscillations in acute slices of the somatosensory cortex from juvenile mice recorded by planar multi-electrode arrays. Bursts of gamma oscillatory activity lasting a few hundred milliseconds could be initiated only by brief trains of electrical stimulations applied at the deepest cortical layers and simultaneously delivered at multiple locations. Local field potentials were used to study the spatio-temporal properties and the instantaneous synchronization profile of the gamma oscillatory activity, combined with current source density (CSD) analysis. Pair-wise differences in the oscillation phase were used to determine the presence of instantaneous synchronization between the different sites of the circuitry during the oscillatory period. Despite variation in the duration of the oscillatory response over successive trials, they showed a constant average power, suggesting that the rate of expenditure of energy during the gamma bursts is consistent across repeated stimulations. Within each gamma burst, the functional connectivity map reflected the columnar organization of the neocortex. Over successive trials, an apparently random rearrangement of the functional connectivity was observed, with a more stable columnar than horizontal organization. This work reveals new features of evoked gamma oscillations in developing cortex.

Real-time antifungal susceptibility testing of Mucor spp., Fusarium spp. and Scedosporium spp. by isothermal microcalorimetry

Objective: Aspergillus species are the main pathogens causing invasive fungal infections but the prevalence of other mould species is rising. Resistance to antifungals among these new emerging pathogens presents a challenge for managing of infections. Conventional susceptibility testing of non-Aspergillus species is laborious and often difficult to interpret. We evaluated a new method for real-time susceptibility testing of moulds based on their of growth-related heat production.Methods: Laboratory and clinical strains of Mucor spp. (n = 4), Scedoporium spp. (n = 4) and Fusarium spp. (n = 5) were used. Conventional MIC was determined by microbroth dilution. Isothermal microcalorimetry was performed at 37 C using Sabouraud dextrose broth (SDB) inoculated with 104 spores/ml (determined by microscopical enumeration). SDB without antifungals was used for evaluation of growth characteristics. Detection time was defined as heat flow exceeding 10 lW. For susceptibility testing serial dilutions of amphotericin B, voriconazole, posaconazole and caspofungin were used. The minimal heat inhibitory concentration (MHIC) was defined as the lowest antifungal concentration, inhbiting 50% of the heat produced by the growth control at 48 h or at 24 h for Mucor spp. Susceptibility tests were performed in duplicate.Results: Tested mould genera had distinctive heat flow profiles with a median detection time (range) of 3.4 h (1.9-4.1 h) for Mucor spp, 11.0 h (7.1-13.7 h) for Fusarium spp and 29.3 h (27.4-33.0 h) for Scedosporium spp. Graph shows heat flow (in duplicate) of one representative strain from each genus (dashed line marks detection limit). Species belonging to the same genus showed similar heat production profiles. Table shows MHIC and MIC ranges for tested moulds and antifungals.Conclusions: Microcalorimetry allowed rapid detection of growth of slow-growing species, such as Fusarium spp. and Scedosporium spp. Moreover, microcalorimetry offers a new approach for antifungal susceptibility testing of moulds, correlating with conventional MIC values. Interpretation of calorimetric susceptibility data is easy and real-time data on the effect of different antifungals on the growth of the moulds is additionally obtained. This method may be used for investigation of different mechanisms of action of antifungals, new substances and drug-drug combinations.

Winter habitat selection by two sympatric forest grouse in western Switzerland: implications for conservation

Two endangered tetraonids, the capercaillie (Tetrao urogallus) and the hazel grouse (Bonasa bonasia rupestris), are sympatric throughout part of their distribution range in central Europe. Precise information on their specific habitat requirements is needed if the coexistence of both species in exploited forests is to be maintained. We quantified winter habitat selection for both species in the upper part (1100-1600 m) of the Jura mountains (Switzerland). No preference for altitude or exposure could be detected. Capercaillie preferred open forests (including grazed forests) with a sparse canopy dominated by spruce (Picea abies) and fir (Abies alba), and avoided dense undercanopy and understorey, especially when dominated by spruce and beech (Fagus sylvatica). By contrast, hazel grouse preferred feeding sites with a dense understorey of rowan (Sorbus aucuparia), willow (Salix sp.), beech and spruce. These preferences can be related to the feeding habits and predator avoidance behaviour of both species. Coexistence thus requires a mosaic distribution of habitat types, with a matrix of open forests (30% canopy cover) where fir is favoured, and understorey kept sparse (20%). Group-cuts of mature trees should allow regeneration patches, where a dense understorey (50% cover) should provide suitable habitats for hazel grouse

Continuous monitoring of cerebrovascular pressure reactivity in patients with head injury.

OBJECT: Cerebrovascular pressure reactivity is the ability of cerebral vessels to respond to changes in transmural pressure. A cerebrovascular pressure reactivity index (PRx) can be determined as the moving correlation coefficient between mean intracranial pressure (ICP) and mean arterial blood pressure. METHODS: The authors analyzed a database consisting of 398 patients with head injuries who underwent continuous monitoring of cerebrovascular pressure reactivity. In 298 patients, the PRx was compared with a transcranial Doppler ultrasonography assessment of cerebrovascular autoregulation (the mean index [Mx]), in 17 patients with the PET-assessed static rate of autoregulation, and in 22 patients with the cerebral metabolic rate for O(2). Patient outcome was assessed 6 months after injury. RESULTS: There was a positive and significant association between the PRx and Mx (R(2) = 0.36, p < 0.001) and with the static rate of autoregulation (R(2) = 0.31, p = 0.02). A PRx > 0.35 was associated with a high mortality rate (> 50%). The PRx showed significant deterioration in refractory intracranial hypertension, was correlated with outcome, and was able to differentiate patients with good outcome, moderate disability, severe disability, and death. The graph of PRx compared with cerebral perfusion pressure (CPP) indicated a U-shaped curve, suggesting that too low and too high CPP was associated with a disturbance in pressure reactivity. Such an optimal CPP was confirmed in individual cases and a greater difference between current and optimal CPP was associated with worse outcome (for patients who, on average, were treated below optimal CPP [R(2) = 0.53, p < 0.001] and for patients whose mean CPP was above optimal CPP [R(2) = -0.40, p < 0.05]). Following decompressive craniectomy, pressure reactivity initially worsened (median -0.03 [interquartile range -0.13 to 0.06] to 0.14 [interquartile range 0.12-0.22]; p < 0.01) and improved in the later postoperative course. After therapeutic hypothermia, in 17 (70.8%) of 24 patients in whom rewarming exceeded the brain temperature threshold of 37 degrees C, ICP remained stable, but the average PRx increased to 0.32 (p < 0.0001), indicating significant derangement in cerebrovascular reactivity. CONCLUSIONS: The PRx is a secondary index derived from changes in ICP and arterial blood pressure and can be used as a surrogate marker of cerebrovascular impairment. In view of an autoregulation-guided CPP therapy, a continuous determination of a PRx is feasible, but its value has to be evaluated in a prospective controlled trial.

Evaluation of two long synthetic merozoite surface protein 2 peptides as malaria vaccine candidates.

Merozoite surface protein 2 (MSP2) is a promising vaccine candidate against Plasmodium falciparum blood stages. A recombinant 3D7 form of MSP2 was a subunit of Combination B, a blood stage vaccine tested in the field in Papua New Guinea. A selective effect in favour of the allelic family not represented by the vaccine argued for a MSP2 vaccine consisting of both dimorphic variants. An alternative approach to recombinant manufacture of vaccines is the production of long synthetic peptides (LSP). LSP exceeding a length of well over 100 amino acids can now be routinely synthesized. Synthetic production of vaccine antigens cuts the often time-consuming steps of protein expression and purification short. This considerably reduces the time for a candidate to reach the phase of clinical trials. Here we present the evaluation of two long synthetic peptides representing both allelic families of MSP2 as potential vaccine candidates. The constructs were well recognized by human immune sera from different locations and different age groups. Furthermore, peptide-specific antibodies in human immune sera were associated with protection from clinical malaria. The synthetic fragments share major antigenic properties with native MSP2. Immunization of mice with these antigens yielded high titre antibody responses and monoclonal antibodies recognized parasite-derived MSP2. Our results justify taking these candidate poly-peptides into further vaccine development.

Additive functions in boolean models of gene regulatory network modules.

Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate.