Discriminating different classes of biological networks by analyzing the graphs spectra distribution

The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e.g., normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a "fingerprint". Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the "uncertainty" of a random graph and the Kullback-Leibler and Jensen-Shannon divergences between graph spectra to compare networks. We also introduce general methods for model selection and network model parameter estimation, as well as a statistical procedure to test the nullity of divergence between two classes of complex networks. Finally, we demonstrate the usefulness of the proposed methods by applying them on (1) protein-protein interaction networks of different species and (2) on networks derived from children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and typically developing children. We conclude that scale-free networks best describe all the protein-protein interactions. Also, we show that our proposed measures succeeded in the identification of topological changes in the network while other commonly used measures (number of edges, clustering coefficient, average path length) failed

Associations between serum lipids and hepatitis C antiviral treatment efficacy

Approximately one half of patients who undergo antiviral therapy for chronic hepatitis C virus (HCV) genotype 1 infection do not respond to treatment. African Americans (AAs) are less responsive to treatment than Caucasian Americans (CAs), but the reasons for this disparity are largely unknown. Recent studies suggest that serum lipids may be associated with treatment response. The aims of this study were to evaluate baseline and changes in serum lipids during therapy, determine whether serum lipids are associated with virological response, and assess whether these measures explain the racial difference in efficacy. The study participants were from Virahep-C, a prospective study of treatment-naÏve patients with genotype 1 HCV infection who received peginterferon (PEG-IN) alfa-2a plus ribavirin therapy for up to 48 weeks. Fasting serum lipids were analyzed at baseline and during and after therapy in 160 AAs and 170 CAs. A relative risk (RR) model was employed to evaluate characteristics associated with sustained virological response (SVR). Antiviral therapy was associated with changes in serum lipids during and after antiviral therapy, with the changes differing by race and the amount of PEG-IFN taken. Baseline lipid measures independently associated with higher rates of SVR were lower triglyceride and higher low-density lipoprotein cholesterol, with an interaction between high-density lipoprotein cholesterol (HDLc) and gender. Lipid measures did not contribute significantly to an explanation of the racial difference in SVR. Conclusion: Serum lipids are associated with SVR, although these paramaters did not explain the racial difference in treatment response. The results of this study are compatible with proposed biological mechanisms of HCV entry, replication, and secretion, and may underscore new potential therapeutic targets for HCV eradication. (Hepatology 2010)Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78060/1/23796_ftp.pd

Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing

Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded. Methods: We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables, we derive a new variational annealing method that searches the configuration of the binary variables maximizing the marginal likelihood. Results: For the performance evaluation, we use time series data from two topologically similar synthetic networks, and confirm that our proposed approach estimates commonly existing regulations as well as changes on regulations with higher coverage and precision than other existing approaches in almost all the experimental settings. For a real data application, our proposed approach is applied to time series data from normal Human lung cells and Human lung cells treated by stimulating EGF-receptors and dosing an anticancer drug termed Gefitinib. In the treated lung cells, a cancer cell condition is simulated by the stimulation of EGF-receptors, but the effect would be counteracted due to the selective inhibition of EGF-receptors by Gefitinib. However, gene expression profiles are actually different between the conditions, and the genes related to the identified changes are considered as possible off-targets of Gefitinib. Conclusions: From the synthetically generated time series data, our proposed approach can identify changes on regulations more accurately than existing methods. By applying the proposed approach to the time series data on normal and treated Human lung cells, candidates of off-target genes of Gefitinib are found. According to the published clinical information, one of the genes can be related to a factor of interstitial pneumonia, which is known as a side effect of Gefitinib

Spin states of zigzag-edged Mobius graphene nanoribbons from first principles

Mobius graphene nanoribbons have only one edge topologically. How the magnetic structures, previously associated with the two edges of zigzag-edged flat nanoribbons or cyclic nanorings, would change for their Mobius counterparts is an intriguing question. Using spin-polarized density functional theory, we shed light on this question. We examine spin states of zigzag-edged Mobius graphene nanoribbons (ZMGNRs) with different widths and lengths. We find a triplet ground state for a Mobius cyclacene, while the corresponding two-edged cyclacene has an open-shell singlet ground state. For wider ZMGNRs, the total magnetization of the ground state is found to increase with the ribbon length. For example, a quintet ground state is found for a ZMGNR. Local magnetic moments on the edge carbon atoms form domains of majority and minor spins along the edge. Spins at the domain boundaries are found to be frustrated. Our findings show that the Mobius topology (i.e., only one edge) causes ZMGNRs to favor one spin over the other, leading to a ground state with non-zero total magnetization.Comment: 17 pages, 4 figure