Using multi-level Petri nets models to simulate microbiota resistance to antibiotics

The spread of antibiotic resistance is a growing problem known to be caused by antibiotic usage itself. This problem can be analyzed at different levels. Antibiotic administration policies and practices affect the societal system, which is made by human individuals and by their relations. Individuals developing resistance interact with each other and with the environment while receiving antibiotic treatments moving the problem at a different level of analysis. Each individual can be further see as a meta-organism together with his associated microbiotas, which prove to have a prominent role in the resistance spreading dynamics. Eventually, in each microbiota, population dynamics and vertical or horizontal transfer events implement cellular and molecular mechanisms for resistance spreading and possibly for its prevention. Using the Nets-within-nets formalism, in this work we model the relation between different antibiotic administration protocols and resistance spread dynamics both at the human population and at the single microbiota level

A New miRNA Motif Protects Pathways’ Expression in Gene Regulatory Networks

The continuing discovery of new functions and classes of small non-coding RNAs is suggesting the presence of regulatory mechanisms far more complex than the ones identified so far. In our computational analysis of a large set of public available databases, we found statistical evidence of an inter-pathway regulatory motif, not previously described, that reveals a new protective role miRNAs may play in the successful activation of a pathway. This paper reports the main outcomes of this analysis

ReNE: A Cytoscape Plugin for Regulatory Network Enhancement

One of the biggest challenges in the study of biological regulatory mechanisms is the integration, modeling, and analysis of the complex interactions which take place in biological networks. Despite post transcriptional regulatory elements (i.e., miRNAs) are widely investigated in current research, their usage and visualization in biological networks is very limited. Regulatory networks are commonly limited to gene entities. To integrate networks with post transcriptional regulatory data, researchers are therefore forced to manually resort to specific third party databases. In this context, we introduce ReNE, a Cytoscape 3.x plugin designed to automatically enrich a standard gene-based regulatory network with more detailed transcriptional, post transcriptional, and translational data, resulting in an enhanced network that more precisely models the actual biological regulatory mechanisms. ReNE can automatically import a network layout from the Reactome or KEGG repositories, or work with custom pathways described using a standard OWL/XML data format that the Cytoscape import procedure accepts. Moreover, ReNE allows researchers to merge multiple pathways coming from different sources. The merged network structure is normalized to guarantee a consistent and uniform description of the network nodes and edges and to enrich all integrated data with additional annotations retrieved from genome-wide databases like NCBI, thus producing a pathway fully manageable through the Cytoscape environment. The normalized network is then analyzed to include missing transcription factors, miRNAs, and proteins. The resulting enhanced network is still a fully functional Cytoscape network where each regulatory element (transcription factor, miRNA, gene, protein) and regulatory mechanism (up-regulation/down-regulation) is clearly visually identifiable, thus enabling a better visual understanding of its role and the effect in the network behavior. The enhanced network produced by ReNE is exportable in multiple formats for further analysis via third party applications. ReNE can be freely installed from the Cytoscape App Store (http://apps.cytoscape.org/apps/rene) and the full source code is freely available for download through a SVN repository accessible at http://www.sysbio.polito.it/tools_svn/Bi​oInformatics/Rene/releases/. ReNE enhances a network by only integrating data from public repositories, without any inference or prediction. The reliability of the introduced interactions only depends on the reliability of the source data, which is out of control of ReNe developers

A Functional Verification based Fault Injection Environment

Fault injection is needed for different purposes such as analyzing the reaction of a system in a faulty environment or validating fault-detection and/or fault-correction techniques. In this paper we propose a simulation-based fault injection tool able to work at different abstraction levels and with user-defined fault models. By exploiting the facilities provided by a functional verification environment it allows to speed up the entire fault injection process: from the creation of the workload to the analysis of the results of injection campaigns. Moreover, the adoption of techniques to optimize the fault list significantly reduces the simulation time. Being the tool targeted to the validation of dependable systems, it includes a way to extract information from the Failure Mode and Effect Analysis and to correlate fault injection results with estimates

Influence of intrafamilial abuse in children's change of values towards their parents

The socialization that parents and society exercise on children instills in them a set of values towards parents. Some of these values are not lying, feeling affection for the parents, and wanting to have contact with them. In this work, we attempt to determine whether these values change in the face of intrafamilial abuse. To that end, an incidental sample was used, consisting of 2730 minors aged between 6 to 18 years, who had never suffered abuse. They were asked to put themselves in the place of the main character of a story. The story varied depending on the conditions to be studied: observation and direct suffering or account of the abuse by another, type of abuse (physical or psychological), who perpetrated the abuse (custodian or non-custodial), and who received it (the other custodian or the minor). The results show that, as a rule, children lie to conceal both parents' abusive behavior; they love their parents and want to have contact with them, even in the presence of abuse. Notwithstanding that in the presence of abuse by one of their parents, children still love them and want to have contact with both parents, a significant number of children, however, stop loving them or want to have contact with the abusive parent. These results undermine what is defended by theories like PAS with no scientific evidence, and underline the need to use scientific procedures to test the reliability of minors’ testimony based on the idea that children tell the truth.Die von Eltern und Gesellschaft initiierten Sozialisationsprozesse erziehen Kindern eine Reihe von Werten im Umgang mit ihren Eltern an. Einige dieser Werte sind beispielsweise: nicht zu lügen, eine Zuneigung zu den Eltern zu verspüren und Kontakt zu ihnen haben zu wollen. In dieser wissenschaftlichen Arbeit wird versucht zu untersuchen, ob sich diese Werte bei innerfamilialem Missbrauch verändern. Zu diesem Zweck wurde eine Stichprobe von 2730 Minderjährigen im Alter zwischen 6 und 18 Jahren, die noch nie missbraucht wurden, herangezogen. Sie wurden gebeten, sich in die Hauptfigur einer Geschichte hineinzuversetzen. Die Geschichte variierte je nach den zu untersuchenden Bedingungen: Beobachtung und direktes Erleiden oder Erzählung des Missbrauchs durch andere, Art des Missbrauchs (physisch oder psychisch), wer den Missbrauch begangen hat (erziehungsberechtigter oder nicht erziehungsberechtigter Elternteil) und wer dem Missbrauch ausgesetzt war (der andere Elternteil oder das Kind). Die Ergebnisse zeigen, dass Kinder in der Regel lügen, wenn es darum geht das missbräuchliche Verhalten von einem der beiden Elternteile zu verbergen. Sie lieben ihre Eltern und möchten Kontakt zu ihnen haben, selbst im Falle von Missbrauch. Ungeachtet dessen, das einige Kinder bei Misshandlung durch einen ihrer Elternteile, beide Eltern immer noch lieben und Kontakt zu ihnen haben wollen, hört eine signifikante Anzahl von Kinder auf, den missbrauchenden Elternteil zu lieben oder Kontakt zu ihm haben zu wollen. Diese Ergebnisse falsifizieren, was von Theorien wie PAS ohne wissenschaftliche Beweise behauptet wird und unterstreichen die Notwendigkeit der Verwendung wissenschaftlicher Verfahren, die auf der Idee fußen, dass Kinder die Wahrheit sagen, zur zuverlässigen Untersuchung der Zeugenaussagen von Kindern

ATPG for Dynamic Burn-In Test in Full-Scan Circuits

Yield and reliability are two key factors affecting costs and profits in the semiconductor industry. Stress testing is a technique based on the application of higher than usual levels of stress to speed up the deterioration of electronic devices and increase yield and reliability. One of the standard industrial approaches for stress testing is high temperature burn-in. This work proposes a full-scan circuit ATPG for dynamic burn-in. The goal of the proposed ATPG approach is to generate test patterns able to force transitions into each node of a full scan circuit to guarantee a uniform distribution of the stress during the dynamic burn-in tes

Reducing the Complexity of Complex Gene Coexpression Networks by Coupling Multiweighted Labeling with Topological Analysis

Undirected gene coexpression networks obtained from experimental expression data coupled with efficient computational procedures are increasingly used to identify potentially relevant biological information (e.g., biomarkers) for a particular disease. However, coexpression networks built from experimental expression data are in general large highly connected networks with an elevated number of false-positive interactions (nodes and edges). In order to infer relevant information, the network must be properly filtered and its complexity reduced. Given the complexity and the multivariate nature of the information contained in the network, this requires the development and application of efficient feature selection algorithms to be able to exploit the topological characteristics of the network to identify relevant nodes and edges. This paper proposes an efficient multivariate filtering designed to analyze the topological properties of a coexpression network in order to identify potential relevant genes for a given disease. The algorithm has been tested on three datasets for three well known and studied diseases: acute myeloid leukemia, breast cancer, and diffuse large B-cell lymphoma. Results have been validated resorting to bibliographic data automatically mined using the ProteinQuest literature mining tool

Using Boolean Networks to Model Post-transcriptional Regulation in Gene Regulatory Networks

In Gene Regulatory Networks research there is a considerable lack of tech- niques and tools to understand these networks from a System Biology point of view. The typical biological approach is to reconstruct a particular network from expression patterns, then try to validate its dynamics by simulation, use simulation to analyze its reactions to some perturbations, and finally go back “in vitro” to validate the simulation results. Nevertheless, when the goal is to understand the high-level, general mechanisms that allow these networks to work or to be stable under mild perturbations, this type of approach has shown very strong limitations. In this work we want to better understand the role of miRNA as a stabilizing mechanism in gene regulatory networks. Boolean networks have been recently used to better understand the struc- tural and dynamical properties of regulatory networks. Attractors and ergodic sets have been easily correlated with many of the typical biological cell behav- iors (cancer, differentiation, pluripotential, ...). The most widely used model are nevertheless very simple, and work under too strict constraints. We are defining an enhanced model based on Boolean Networks but also able to take into account post-transcriptional regulation and possibly be extended to other regulatory mechanisms (e.g. ceRNA) that have been already proven crucial in vivo. The final goal is to try to understand if the wide number of miRNA targets constitutes a structural network-stability mechanism used to make the network immune to „regulatory“ noise. To achieve this result we evolve the modified Boolean networks for high or low sensitivity to perturbations, and then analyze the resulting networks to understand if specific structural patterns containing miRNA-like post-transcriptional regulatory elements can be correlated with the network stability

An agent-based simulation framework for complex systems

In this abstract we present a new approach to the simulation of complex systems as biological interaction networks, chemical reactions, ecosystems, etc. It aims at overcoming previously proposed analytical approaches that, because of several computational challenges, could not handle systems of realistic com- plexity. The proposed model is based on a set of agents interacting through a shared environment. Each agent functions independently from the others, and its be- havior is driven only by its current status and the “content” of the surrounding environment. The environment is the only “data repository” and does not store the value of variables, but only their presence and concentration. Each agent performs 3 main functions: 1. it samples the environment at random locations 2. based on the distribution of the sampled data and a proper Transfer Func- tion, it computes the rate at which the output values are generated 3. it writes the output “products” at random locations. The environment is modeled as a Really Random Access Memory (R2AM). Data is written and sampled at random memory locations. Each memory location represent an atomic sample (a molecule, a chemical compound, a protein, an ion, . . . ). Presence and concentration of these samples are what constitutes the environment data set. The environment can be sensitive to external stimuli (e.g., pH, Temperature, ...) and can include topological information to allow its partitioning (e.g. between nucleus and cytoplasm in a cell) and the modeling of sample “movements” within the environment. The proposed approach is easily scalable in both complexity and computa- tional costs. Each module could implement a very simple object as a single chemical reaction or a very complex process as a gene translation into a pro- tein. At the same time, from the hardware point of view, the complexity of the objects implementing a single agent can range from a single software process to a dedicated computer or hardware platform

Automatic March Tests Generations for Static Linked Faults in SRAMs

Static linked faults are considered an interesting class of memory faults. Their capability of influencing the behavior of other faults causes the hiding of the fault effect and makes test algorithm design a very complex task. A large number of March tests with different fault coverage have been published and some methodologies have been presented to automatically generate March tests. In this paper we present an approach to automatically generate March tests for static linked faults. The proposed approach generates better test algorithms then previous, by reducing the test lengt