Vascular remodelling of an arterio-venous blood vessel network during solid tumour growth

We formulate a theoretical model to analyze the vascular remodelling process of an arterio-venous vessel network during solid tumour growth. The model incorporates a hierarchically organized initial vasculature comprising arteries, veins and capillaries, and involves sprouting angiogenesis, vessel cooption, dilation and regression as well as tumour cell proliferation and death. The emerging tumour vasculature is non-hierarchical, compartmentalized into well-characterized zones and transports efficiently an injected drug-bolus. It displays a complex geometry with necrotic zones and “hot spots” of increased vascular density and blood flow of varying size. The corresponding cluster size distribution is algebraic, reminiscent of a self-organized critical state. The intra-tumour vascular-density fluctuations correlate with pressure drops in the initial vasculature suggesting a physical mechanism underlying hot spot formation.     

NetMHCpan,a method for MHC class I binding prediction beyond humans

Binding of peptides to major histocompatibility complex (MHC) molecules is the single most selective step in the recognition of pathogens by the cellular immune system. The human MHC genomic region (called HLA) is extremely polymorphic comprising several thousand alleles, each encoding a distinct MHC molecule. The potentially unique specificity of the majority of HLA alleles that have been identified to date remains uncharacterized. Likewise, only a limited number of chimpanzee and rhesus macaque MHC class I molecules have been characterized experimentally. Here, we present NetMHCpan-2.0, a method that generates quantitative predictions of the affinity of any peptide–MHC class I interaction. NetMHCpan-2.0 has been trained on the hitherto largest set of quantitative MHC binding data available, covering HLA-A and HLA-B, as well as chimpanzee, rhesus macaque, gorilla, and mouse MHC class I molecules. We show that the NetMHCpan-2.0 method can accurately predict binding to uncharacterized HLA molecules, including HLA-C and HLA-G. Moreover, NetMHCpan-2.0 is demonstrated to accurately predict peptide binding to chimpanzee and macaque MHC class I molecules. The power of NetMHCpan-2.0 to guide immunologists in interpreting cellular immune responses in large out-bred populations is demonstrated. Further, we used NetMHCpan-2.0 to predict potential binding peptides for the pig MHC class I molecule SLA-1*0401. Ninety-three percent of the predicted peptides were demonstrated to bind stronger than 500 nM. The high performance of NetMHCpan-2.0 for non-human primates documents the method’s ability to provide broad allelic coverage also beyond human MHC molecules. The method is available at . Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Data-Based Analysis of Winner-Loser Models of Hierarchy Formation in Animals

We review winner-loser models, the currently popular explanation for the occurrence of linear dominance hierarchies, via a three-part approach. (1) We isolate the two most significant components of the mathematical formulation of three of the most widely-cited models and rigorously evaluate the components’ predictions against data collected on hierarchy formation in groups of hens. (2) We evaluate the experimental support in the literature for the basic assumptions contained in winner-loser models. (3) We apply new techniques to the hen data to uncover several behavioral dynamics of hierarchy formation not previously described. The mathematical formulations of these models do not show satisfactory agreement with the hen data, and key model assumptions have either little or no conclusive support from experimental findings in the literature. In agreement with the latest experimental results concerning social cognition, the new behavioral dynamics of hierarchy formation discovered in the hen data suggest that members of groups are intensely aware both of their own interactions as well as interactions occurring among other members of their group. We suggest that more adequate models of hierarchy formation should be based upon behavioral dynamics that reflect more sophisticated levels of social cognition.     

Stability and structural constraints of random brain networks with excitatory and inhibitory neural populations

The stability of brain networks with randomly connected excitatory and inhibitory neural populations is investigated using a simplified physiological model of brain electrical activity. Neural populations are randomly assigned to be excitatory or inhibitory and the stability of a brain network is determined by the spectrum of the network’s matrix of connection strengths. The probability that a network is stable is determined from its spectral density which is numerically determined and is approximated by a spectral distribution recently derived by Rajan and Abbott. The probability that a brain network is stable is maximum when the total connection strength into a population is approximately zero and is shown to depend on the arrangement of the excitatory and inhibitory connections and the parameters of the network. The maximum excitatory and inhibitory input into a structure allowed by stability occurs when the net input equals zero and, in contrast to networks with randomly distributed excitatory and inhibitory connections, substantially increases as the number of connections increases. Networks with the largest excitatory and inhibitory input allowed by stability have multiple marginally stable modes, are highly responsive and adaptable to external stimuli, have the same total input into each structure with minimal variance in the excitatory and inhibitory connection strengths, and have a wide range of flexible, adaptable, and complex behavior.     

Prostate cancer regulatory networks

Although the timing with which common epithelial malignancies arise and become established remains a matter of debate, it is clear that by the time they are detected these tumors harbor hundreds of deregulated, aberrantly expressed or mutated genes. This enormous complexity poses formidable challenges to identify gene pathways that are drivers of tumorigenesis, potentially suitable for therapeutic intervention. An alternative approach is to consider cancer pathways as interconnected networks, and search for potential nodal proteins capable of connecting multiple signaling networks of tumor maintenance. We have modeled this approach in advanced prostate cancer, a condition with current limited therapeutic options. We propose that the integration of three signaling networks, including chaperone‐mediated mitochondrial homeostasis, integrin‐dependent cell signaling, and Runx2‐regulated gene expression in the metastatic bone microenvironment plays a critical role in prostate cancer maintenance, and offers novel options for molecular therapy. J. Cell. Biochem. 107: 845–852, 2009. © 2009 Wiley‐Liss, Inc.     

Accurate intraocular pressure prediction from applanation response data using genetic algorithm and neural networks

The fact that Goldmann applanation tonometry does not accurately account for individual corneal elastic stiffness often leads to inaccuracy in the measurement of intraocular pressure (IOP). IOP should account not only for the effect of central corneal thickness (CCT) but should also account for other corneal biomechanical factors. A computational method for accurate and reliable determination of IOP is investigated with a modified applanation tonometer in this paper. The proposed method uses a combined genetic algorithm/neural network procedure to match the clinically measured applanation force-displacement history with that obtained from a nonlinear finite element simulation of applanation. An additional advantage of the proposed method is that it also provides the ability to determine CCT and material properties of the cornea from the same applanation response data. The performance of the proposed method has been demonstrated through a parametric study and via comparison with a well known clinical case. The proposed method is also shown to be computationally efficient, which is an important practical consideration for clinical application.     

Global Versus Local Centrality in Evolution of Yeast Protein Network

It is shown here that in the yeast protein interaction network the global centrality measure (betweenness) depends on the protein evolutionary age (i.e., on historic contingency) more weakly than the local centrality measure (degree). This phenomenon is not observed in mutational duplication-and-divergence models. The network domains responsible for this difference deal with DNA/RNA information processing, regulation, and cell cycle. A selection vector can operate in these domains, which integrates the network activity and thus compensates for the process of mutational divergence. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.