Quantification and Minimization of Crosstalk Sensitivity in Networks

Crosstalk is defined as the set of unwanted interactions among the different entities of a network. Crosstalk is present in various degrees in every system where information is transmitted through a means that is accessible by all the individual units of the network. Using concepts from graph theory, we introduce a quantifiable measure for sensitivity to crosstalk, and analytically derive the structure of the networks in which it is minimized. It is shown that networks with an inhomogeneous degree distribution are more robust to crosstalk than corresponding homogeneous networks. We provide a method to construct the graph with the minimum possible sensitivity to crosstalk, given its order and size. Finally, for networks with a fixed degree sequence, we present an algorithm to find the optimal interconnection structure among their vertices

Quantification of signaling networks

Studies in living system in the past several decades have generated qualitative understanding of the molecular interactions resulting in large networks. These networks were essentially deciphered by breaking the components of a cell through a reductionist approach. Biological networks comprising of interactions between genes, proteins and metabolites co-ordinate in the regulation of cellular processes. However, understanding the cellular function also requires quantitative information including network dynamics, which results due to an inherent design principle embedded in the network. Interactions within the network are well organized to form a definite regulatory structure, which in turn exhibits different emergent properties. The property of the network helps the cell to achieve the desired phenotypic state in a controlled manner. The dynamics of the network or the relationship between network structure and cellular behavior cannot be understood intuitively from the interaction map of the network. Computational methods can now be employed to study these networks at system level. The field of systems biology looks at integrating the interaction maps obtained through molecular biological approach. Various studies at the system level have been reported for pathways namely chemotactic response in bacteria, cell cycle and osmotic signaling in yeast, growth factor stimulated signaling pathways in mammals. This review focuses on understanding signaling networks with the help of mathematical models

Frequency-dependent AVO inversion applied to physically based models for seismic attenuation

Seismic inversion of amplitude versus offset (AVO) data in viscoelastic media can potentially provide high-resolution subsurface models of seismic velocities and attenuation from offset/angle seismic gathers. P- and S-wave quality factors (Q), whose inverse represent a measure of attenuation, depend on reservoir rock and pore fluid properties, in particular, saturation, permeability, porosity, fluid viscosity and lithology; however, these quality factors are rarely taken into account in seismic AVO inversion. For this reason, in this work, we aim to integrate quality factors derived from physically based models in AVO inversion by proposing a gradient descent optimization-based inversion technique to predict the unknown model properties (P- and S-wave velocities, the related quality factors and density). The proposed inversion minimizes the non-linear least-squares misfit with the observed data. The optimal solution is iteratively obtained by optimizing the data misfit using a second-order limited-memory quasi-Newton technique. The forward model is performed in the frequency–frequency-angle domain based on a convolution of broad-band signals and a linearized viscoelastic frequency-dependent AVO (FAVO) equation. The optimization includes the adjoint-state-based gradients with the Lagrangian formulation to improve the efficiency of the non-linear seismic FAVO inversion process. The inversion is tested on synthetic seismic data, in 1-D and 2-D, with and without noise. The sensitivity for seismic quality factors is evaluated using various rock physics models for seismic attenuation and quality factors. The results demonstrate that the proposed inversion method reliably retrieves the unknown elastic and an-elastic properties with good convergence and accuracy. The stability of the inverse solution especially seismic quality factors estimation relies on the noise level of the seismic data. We further investigate the uncertainty of the solution as a function of the variability of the initial models.Frequency-dependent AVO inversion applied to physically based models for seismic attenuationpublishedVersio

IST Austria Thesis

In the thesis we focus on the interplay of the biophysics and evolution of gene regulation. We start by addressing how the type of prokaryotic gene regulation – activation and repression – affects spurious binding to DNA, also known as transcriptional crosstalk. We propose that regulatory interference caused by excess regulatory proteins in the dense cellular medium – global crosstalk – could be a factor in determining which type of gene regulatory network is evolutionarily preferred. Next,we use a normative approach in eukaryotic gene regulation to describe minimal non-equilibrium enhancer models that optimize so-called regulatory phenotypes. We find a class of models that differ from standard thermodynamic equilibrium models by a single parameter that notably increases the regulatory performance. Next chapter addresses the question of genotype-phenotype-fitness maps of higher dimensional phenotypes. We show that our biophysically realistic approach allows us to understand how the mechanisms of promoter function constrain genotypephenotype maps, and how they affect the evolutionary trajectories of promoters. In the last chapter we ask whether the intrinsic instability of gene duplication and amplification provides a generic alternative to canonical gene regulation. Using mathematical modeling, we show that amplifications can tune gene expression in many environments, including those where transcription factor-based schemes are hard to evolve or maintain

A novel yeast hybrid modeling framework integrating Boolean and enzyme-constrained networks enables exploration of the interplay between signaling and metabolism

The interplay between nutrient-induced signaling and metabolism plays an important role in maintaining homeostasis and its malfunction has been implicated in many different human diseases such as obesity, type 2 diabetes, cancer, and neurological disorders. Therefore, unraveling the role of nutrients as signaling molecules and metabolites together with their interconnectivity may provide a deeper understanding of how these conditions occur. Both signaling and metabolism have been extensively studied using various systems biology approaches. However, they are mainly studied individually and in addition, current models lack both the complexity of the dynamics and the effects of the crosstalk in the signaling system. To gain a better understanding of the interconnectivity between nutrient signaling and metabolism in yeast cells, we developed a hybrid model, combining a Boolean module, describing the main pathways of glucose and nitrogen signaling, and an enzyme-constrained model accounting for the central carbon metabolism of Saccharomyces cerevisiae, using a regulatory network as a link. The resulting hybrid model was able to capture a diverse utalization of isoenzymes and to our knowledge outperforms constraint-based models in the prediction of individual enzymes for both respiratory and mixed metabolism. The model showed that during fermentation, enzyme utilization has a major contribution in governing protein allocation, while in low glucose conditions robustness and control are prioritized. In addition, the model was capable of reproducing the regulatory effects that are associated with the Crabtree effect and glucose repression, as well as regulatory effects associated with lifespan increase during caloric restriction. Overall, we show that our hybrid model provides a comprehensive framework for the study of the non-trivial effects of the interplay between signaling and metabolism, suggesting connections between the Snf1 signaling pathways and processes that have been related to chronological lifespan of yeast cells

Extremal Properties of Complex Networks

We describe the structure of connected graphs with the minimum and maximum average distance, radius, diameter, betweenness centrality, efficiency and resistance distance, given their order and size. We find tight bounds on these graph qualities for any arbitrary number of nodes and edges and analytically derive the form and properties of such networks

Real-Time MEG Source Localization Using Regional Clustering

With its millisecond temporal resolution, Magnetoencephalography (MEG) is well suited for real-time monitoring of brain activity. Real-time feedback allows the adaption of the experiment to the subject’s reaction and increases time efficiency by shortening acquisition and off-line analysis. Two formidable challenges exist in real-time analysis: the low signal-to-noise ratio (SNR) and the limited time available for computations. Since the low SNR reduces the number of distinguishable sources, we propose an approach which downsizes the source space based on a cortical atlas and allows to discern the sources in the presence of noise. Each cortical region is represented by a small set of dipoles, which is obtained by a clustering algorithm. Using this approach, we adapted dynamic statistical parametric mapping for real-time source localization. In terms of point spread and crosstalk between regions the proposed clustering technique performs better than selecting spatially evenly distributed dipoles. We conducted real-time source localization on MEG data from an auditory experiment. The results demonstrate that the proposed real-time method localizes sources reliably in the superior temporal gyrus. We conclude that real-time source estimation based on MEG is a feasible, useful addition to the standard on-line processing methods, and enables feedback based on neural activity during the measurements.Deutsche Forschungsgemeinschaft (grant Ba 4858/1-1)National Institutes of Health (U.S.) (grants 5R01EB009048 and 2P41EB015896)Universitätsschule Jena (J21)German Academic Exchange Servic