Reverse Engineering Gene Networks with ANN: Variability in Network Inference Algorithms

Motivation :Reconstructing the topology of a gene regulatory network is one of the key tasks in systems biology. Despite of the wide variety of proposed methods, very little work has been dedicated to the assessment of their stability properties. Here we present a methodical comparison of the performance of a novel method (RegnANN) for gene network inference based on multilayer perceptrons with three reference algorithms (ARACNE, CLR, KELLER), focussing our analysis on the prediction variability induced by both the network intrinsic structure and the available data. Results: The extensive evaluation on both synthetic data and a selection of gene modules of "Escherichia coli" indicates that all the algorithms suffer of instability and variability issues with regards to the reconstruction of the topology of the network. This instability makes objectively very hard the task of establishing which method performs best. Nevertheless, RegnANN shows MCC scores that compare very favorably with all the other inference methods tested. Availability: The software for the RegnANN inference algorithm is distributed under GPL3 and it is available at the corresponding author home page (http://mpba.fbk.eu/grimaldi/regnann-supmat

Mouse Embryonic Retina Delivers Information Controlling Cortical Neurogenesis

The relative contribution of extrinsic and intrinsic mechanisms to cortical development is an intensely debated issue and an outstanding question in neurobiology. Currently, the emerging view is that interplay between intrinsic genetic mechanisms and extrinsic information shape different stages of cortical development [1]. Yet, whereas the intrinsic program of early neocortical developmental events has been at least in part decoded [2], the exact nature and impact of extrinsic signaling are still elusive and controversial. We found that in the mouse developing visual system, acute pharmacological inhibition of spontaneous retinal activity (retinal waves-RWs) during embryonic stages increase the rate of corticogenesis (cell cycle withdrawal). Furthermore, early perturbation of retinal spontaneous activity leads to changes of cortical layer structure at a later time point. These data suggest that mouse embryonic retina delivers long-distance information capable of modulating cell genesis in the developing visual cortex and that spontaneous activity is the candidate long-distance acting extrinsic cue mediating this process. In addition, these data may support spontaneous activity to be a general signal coordinating neurogenesis in other developing sensory pathways or areas of the central nervous system

A novel parametric approach to mine gene regulatory relationship from microarray datasets

<p>Abstract</p> <p>Background</p> <p>Microarray has been widely used to measure the gene expression level on the genome scale in the current decade. Many algorithms have been developed to reconstruct gene regulatory networks based on microarray data. Unfortunately, most of these models and algorithms focus on global properties of the expression of genes in regulatory networks. And few of them are able to offer intuitive parameters. We wonder whether some simple but basic characteristics of microarray datasets can be found to identify the potential gene regulatory relationship.</p> <p>Results</p> <p>Based on expression correlation, expression level variation and vectors derived from microarray expression levels, we first introduced several novel parameters to measure the characters of regulating gene pairs. Subsequently, we used the naïve Bayesian network to integrate these features as well as the functional co-annotation between transcription factors and their target genes. Then, based on the character of time-delay from the expression profile, we were able to predict the existence and direction of the regulatory relationship respectively.</p> <p>Conclusions</p> <p>Several novel parameters have been proposed and integrated to identify the regulatory relationship. This new model is proved to be of higher efficacy than that of individual features. It is believed that our parametric approach can serve as a fast approach for regulatory relationship mining.</p

From Retinal Waves to Activity-Dependent Retinogeniculate Map Development

A neural model is described of how spontaneous retinal waves are formed in infant mammals, and how these waves organize activity-dependent development of a topographic map in the lateral geniculate nucleus, with connections from each eye segregated into separate anatomical layers. The model simulates the spontaneous behavior of starburst amacrine cells and retinal ganglion cells during the production of retinal waves during the first few weeks of mammalian postnatal development. It proposes how excitatory and inhibitory mechanisms within individual cells, such as Ca2+-activated K+ channels, and cAMP currents and signaling cascades, can modulate the spatiotemporal dynamics of waves, notably by controlling the after-hyperpolarization currents of starburst amacrine cells. Given the critical role of the geniculate map in the development of visual cortex, these results provide a foundation for analyzing the temporal dynamics whereby the visual cortex itself develops

Seeking legitimacy through CSR: Institutional Pressures and Corporate Responses of Multinationals in Sri Lanka

Arguably, the corporate social responsibility (CSR) practices of multinational enterprises (MNEs) are influenced by a wide range of both internal and external factors. Perhaps most critical among the exogenous forces operating on MNEs are those exerted by state and other key institutional actors in host countries. Crucially, academic research conducted to date offers little data about how MNEs use their CSR activities to strategically manage their relationship with those actors in order to gain legitimisation advantages in host countries. This paper addresses that gap by exploring interactions between external institutional pressures and firm-level CSR activities, which take the form of community initiatives, to examine how MNEs develop their legitimacy-seeking policies and practices. In focusing on a developing country, Sri Lanka, this paper provides valuable insights into how MNEs instrumentally utilise community initiatives in a country where relationship-building with governmental and other powerful non-governmental actors can be vitally important for the long-term viability of the business. Drawing on neo-institutional theory and CSR literature, this paper examines and contributes to the embryonic but emerging debate about the instrumental and political implications of CSR. The evidence presented and discussed here reveals the extent to which, and the reasons why, MNEs engage in complex legitimacy-seeking relationships with Sri Lankan institutions

Antioxidant activity and hepatoprotective potential of agaro-oligosaccharides in vitro and in vivo

BACKGROUND: Agaro-oligosaccharides derived from red seaweed polysaccharide have been reported to possess antioxidant activity. In order to assess the live protective effects of agar-oligosaccharides, we did both in vitro and in vivo studies based on own-made agaro-oligosaccharides, and the structural information of this oligosaccharide was also determined. METHOD: Structure of agaro-oligosaccharides prepared with acid hydrolysis on agar was confirmed by matrix-assisted ultraviolet laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS) and NMR. The antioxidant effect of agaro-oligosaccharides on intracellular reactive oxygen species (ROS) was assessed by 2', 7'-dichlorofluorescin diacetate. Carbon tetrachloride was used to induce liver injury, some index including SOD, GSH-Px, MDA, AST, ALT were examined to determine the hepatoprotective effect of agaro-oligosaccharides. RESULTS: Agaro-oligosaccharides we got were composed of odd polymerizations with molecular weights ranged from 500 to 2500. Results from intracellular test indicated that agaro-oligosaccharides could significantly scavenge the level of oxidants in the hepatocytes, more beneficially, also associated with the improvement of cell viability In vivo studies of the antioxidant effects on tissue peroxidative damage induced by carbon tetrachloride in rat model indicated that agaro-oligosaccharides could elevate the activity of superoxide dismutase (SOD), glutathione peroxidase (GSH-Px) and decrease the level of malondialdehyde (MDA), glutamate oxaloacetate transaminase (AST), glutamic pyruvic transaminase (ALT) significantly. At 400 mg/kg, MDA level reduced 44 % and 21 % in liver and heart, SOD and GSH-Px increased to highest in liver and serum, while ALT level decreased 22.16 % in serum. CONCLUSION: Overall, the results of the present study indicate that agaro-oligosaccharides can exert their in vitro and in vivo hepatoprotective effect through scavenging oxidative damage induced by ROS

DREAM4: Combining Genetic and Dynamic Information to Identify Biological Networks and Dynamical Models

Current technologies have lead to the availability of multiple genomic data types in sufficient quantity and quality to serve as a basis for automatic global network inference. Accordingly, there are currently a large variety of network inference methods that learn regulatory networks to varying degrees of detail. These methods have different strengths and weaknesses and thus can be complementary. However, combining different methods in a mutually reinforcing manner remains a challenge.We investigate how three scalable methods can be combined into a useful network inference pipeline. The first is a novel t-test-based method that relies on a comprehensive steady-state knock-out dataset to rank regulatory interactions. The remaining two are previously published mutual information and ordinary differential equation based methods (tlCLR and Inferelator 1.0, respectively) that use both time-series and steady-state data to rank regulatory interactions; the latter has the added advantage of also inferring dynamic models of gene regulation which can be used to predict the system's response to new perturbations.Our t-test based method proved powerful at ranking regulatory interactions, tying for first out of methods in the DREAM4 100-gene in-silico network inference challenge. We demonstrate complementarity between this method and the two methods that take advantage of time-series data by combining the three into a pipeline whose ability to rank regulatory interactions is markedly improved compared to either method alone. Moreover, the pipeline is able to accurately predict the response of the system to new conditions (in this case new double knock-out genetic perturbations). Our evaluation of the performance of multiple methods for network inference suggests avenues for future methods development and provides simple considerations for genomic experimental design. Our code is publicly available at http://err.bio.nyu.edu/inferelator/

OptCircuit: An optimization based method for computational design of genetic circuits

<p>Abstract</p> <p>Background</p> <p>Recent years has witnessed an increasing number of studies on constructing simple synthetic genetic circuits that exhibit desired properties such as oscillatory behavior, inducer specific activation/repression, etc. It has been widely acknowledged that that task of building circuits to meet multiple inducer-specific requirements is a challenging one. This is because of the incomplete description of component interactions compounded by the fact that the number of ways in which one can chose and interconnect components, increases exponentially with the number of components.</p> <p>Results</p> <p>In this paper we introduce OptCircuit, an optimization based framework that automatically identifies the circuit components from a list and connectivity that brings about the desired functionality. Multiple literature sources are used to compile a comprehensive compilation of kinetic descriptions of promoter-protein pairs. The dynamics that govern the interactions between the elements of the genetic circuit are currently modeled using deterministic ordinary differential equations but the framework is general enough to accommodate stochastic simulations. The desired circuit response is abstracted as the maximization/minimization of an appropriately constructed objective function. Computational results for a toggle switch example demonstrate the ability of the framework to generate the complete list of circuit designs of varying complexity that exhibit the desired response. Designs identified for a genetic decoder highlight the ability of OptCircuit to suggest circuit configurations that go beyond the ones compatible with digital logic-based design principles. Finally, the results obtained from the concentration band detector example demonstrate the ability of OptCircuit to design circuits whose responses are contingent on the level of external inducer as well as pinpoint parameters for modification to rectify an existing (non-functional) biological circuit and restore functionality.</p> <p>Conclusion</p> <p>Our results demonstrate that OptCircuit framework can serve as a design platform to aid in the construction and finetuning of integrated biological circuits.</p

Automatic Design of Synthetic Gene Circuits through Mixed Integer Non-linear Programming

Automatic design of synthetic gene circuits poses a significant challenge to synthetic biology, primarily due to the complexity of biological systems, and the lack of rigorous optimization methods that can cope with the combinatorial explosion as the number of biological parts increases. Current optimization methods for synthetic gene design rely on heuristic algorithms that are usually not deterministic, deliver sub-optimal solutions, and provide no guaranties on convergence or error bounds. Here, we introduce an optimization framework for the problem of part selection in synthetic gene circuits that is based on mixed integer non-linear programming (MINLP), which is a deterministic method that finds the globally optimal solution and guarantees convergence in finite time. Given a synthetic gene circuit, a library of characterized parts, and user-defined constraints, our method can find the optimal selection of parts that satisfy the constraints and best approximates the objective function given by the user. We evaluated the proposed method in the design of three synthetic circuits (a toggle switch, a transcriptional cascade, and a band detector), with both experimentally constructed and synthetic promoter libraries. Scalability and robustness analysis shows that the proposed framework scales well with the library size and the solution space. The work described here is a step towards a unifying, realistic framework for the automated design of biological circuits

Performance of the CMS Cathode Strip Chambers with Cosmic Rays

The Cathode Strip Chambers (CSCs) constitute the primary muon tracking device in the CMS endcaps. Their performance has been evaluated using data taken during a cosmic ray run in fall 2008. Measured noise levels are low, with the number of noisy channels well below 1%. Coordinate resolution was measured for all types of chambers, and fall in the range 47 microns to 243 microns. The efficiencies for local charged track triggers, for hit and for segments reconstruction were measured, and are above 99%. The timing resolution per layer is approximately 5 ns