Harmonic balance surrogate-based immunity modeling of a nonlinear analog circuit

A novel harmonic balance surrogate-based technique to create fast and accurate behavioral models predicting, in the early design stage, the performance of nonlinear analog devices during immunity tests is presented. The obtained immunity model hides the real netlist, reduces the simulation time, and avoids expensive and time-consuming measurements after tape-out, while still providing high accuracy. The model can easily be integrated into a circuit simulator together with additional subcircuits, e.g., board and package models, as such allowing to efficiently reproduce complete immunity test setups during the early design stage and without disclosing any intellectual property. The novel method is validated by means of application to an industrial case study, being an automotive voltage regulator, clearly showing the technique's capabilities and practical advantages

Computational analysis of a plant receptor interaction network

Trabajo fin de máster en Bioinformática y Biología ComputacionalIn all organisms, complex protein-protein interactions (PPI) networks control major biological functions yet studying their structural features presents a major analytical challenge. In plants, leucine-rich-repeat receptor kinases (LRR-RKs) are key in sensing and transmitting non-self as well as self-signals from the cell surface. As such, LRR-RKs have both developmental and immune functions that allow plants to make the most of their environments. In the model organism in plant molecular biology, Arabidopsis thaliana, most LRR-RKs are still represented by biochemically and genetically uncharacterized receptors. To fix this an LRR-based Cell Surface Interaction (CSI LRR ) network was obtained in 2018, a protein-protein interaction network of the extracellular domain of 170 LRR-RKs that contains 567 bidirectional interactions. Several network analyses have been performed with CSI LRR . However, these analyses have so far not considered the spatial and temporal expression of its proteins. Neither has it been characterized in detail the role of the extracellular domain (ECD) size in the network structure. Because of that, the objective of the present work is to continue with more in depth analyses with the CSI LRR network. This would provide important insights that will facilitate LRR-RKs function characterization. The first aim of this work is to test out the fit of the CSI LRR network to a scale-free topology. To accomplish that, the degree distribution of the CSI LRR network was compared with the degree distribution of the known network models of scale-free and random. Additionally, three network attack algorithms were implemented and applied to these two network models and the CSI LRR network to compare their behavior. However, since the CSI LRR interaction data comes from an in vitro screening, there is no direct evidence whether its protein-protein interactions occur inside the plant cells. To gain insight on how the network composition changes depending on the transcriptional regulation, the interaction data of the CSI LRR was integrated with 4 different RNA-Seq datasets related with the network biological functions. To automatize this task a Python script was written. Furthermore, it was evaluated the role of the LRR-RKs in the network structure depending on the size of their extracellular domain (large or small). For that, centrality parameters were measured, and size-targeted attacks performed. Finally, gene regulatory information was integrated into the CSI LRR to classify the different network proteins according to the function of the transcription factors that regulate its expression. The results were that CSI LRR fits a power law degree distribution and approximates a scale- free topology. Moreover, CSI LRR displays high resistance to random attacks and reduced resistance to hub/bottleneck-directed attacks, similarly to scale-free network model. Also, the integration of CSI LRR interaction data and RNA-Seq data suggests that the transcriptional regulation of the network is more relevant for developmental programs than for defense responses. Another result was that the LRR-RKs with a small ECD size have a major role in the maintenance of the CSI LRR integrity. Lastly, it was hypothesized that the integration of CSI LRR interaction data with predicted gene regulatory networks could shed light upon the functioning of growth-immunity signaling crosstalk

MicroRNA regulation of CD8+ T cell responses.

MicroRNAs (miRNAs) are a class of short noncoding RNAs that play critical roles in the regulation of a broad range of biological processes. Like transcription factors, miRNAs exert their effects by modulating the expression of networks of genes that operate in common or convergent pathways. CD8+ T cells are critical agents of the adaptive immune system that provide protection from infection and cancer. Here, we review the important roles of miRNAs in the regulation of CD8+ T cell biology and provide perspectives on the broader emerging principles of miRNA function

Synaptic Noise Facilitates the Emergence of Self-Organized Criticality in the Caenorhabditis elegans Neuronal Network

Avalanches with power-law distributed size parameters have been observed in neuronal networks. This observation might be a manifestation of the self-organized criticality (SOC). Yet, the physiological mechanicsm of this behavior is currently unknown. Describing synaptic noise as transmission failures mainly originating from the probabilistic nature of neurotransmitter release, this study investigates the potential of this noise as a mechanism for driving the functional architecture of the neuronal networks towards SOC. To this end, a simple finite state neuron model, with activity dependent and synapse specific failure probabilities, was built based on the known anatomical connectivity data of the nematode Ceanorhabditis elegans. Beginning from random values, it was observed that synaptic noise levels picked out a set of synapses and consequently an active subnetwork which generates power-law distributed neuronal avalanches. The findings of this study brings up the possibility that synaptic failures might be a component of physiological processes underlying SOC in neuronal networks

Causality, Information and Biological Computation: An algorithmic software approach to life, disease and the immune system

Biology has taken strong steps towards becoming a computer science aiming at reprogramming nature after the realisation that nature herself has reprogrammed organisms by harnessing the power of natural selection and the digital prescriptive nature of replicating DNA. Here we further unpack ideas related to computability, algorithmic information theory and software engineering, in the context of the extent to which biology can be (re)programmed, and with how we may go about doing so in a more systematic way with all the tools and concepts offered by theoretical computer science in a translation exercise from computing to molecular biology and back. These concepts provide a means to a hierarchical organization thereby blurring previously clear-cut lines between concepts like matter and life, or between tumour types that are otherwise taken as different and may not have however a different cause. This does not diminish the properties of life or make its components and functions less interesting. On the contrary, this approach makes for a more encompassing and integrated view of nature, one that subsumes observer and observed within the same system, and can generate new perspectives and tools with which to view complex diseases like cancer, approaching them afresh from a software-engineering viewpoint that casts evolution in the role of programmer, cells as computing machines, DNA and genes as instructions and computer programs, viruses as hacking devices, the immune system as a software debugging tool, and diseases as an information-theoretic battlefield where all these forces deploy. We show how information theory and algorithmic programming may explain fundamental mechanisms of life and death.Comment: 30 pages, 8 figures. Invited chapter contribution to Information and Causality: From Matter to Life. Sara I. Walker, Paul C.W. Davies and George Ellis (eds.), Cambridge University Pres

The re-emission spectrum of digital hardware subjected to EMI

The emission spectrum of digital hardware under the influence of external electromagnetic interference is shown to contain information about the interaction of the incident energy with the digital circuits in the system. The generation mechanism of the re-emission spectrum is reviewed, describing how nonlinear effects may be a precursor to the failure of the equipment under test. Measurements on a simple circuit are used to demonstrate how the characteristics of the re-emission spectrum may be correlated with changes to the digital waveform within the circuit. The technique is also applied to a piece of complex digital hardware where Similar, though more subtle, effects can be measured. It is shown that the re-emission spectrum can be used to detect the interaction of the interference with the digital devices at a level well below that which is able to cause static failures in the circuits. The utility of the technique as a diagnostic tool for immunity testing of digital hardware, by identifying which subsystems are being affected by external interference, is also demonstrated

Organization and evolution of synthetic idiotypic networks

We introduce a class of weighted graphs whose properties are meant to mimic the topological features of idiotypic networks, namely the interaction networks involving the B-core of the immune system. Each node is endowed with a bit-string representing the idiotypic specificity of the corresponding B cell and a proper distance between any couple of bit-strings provides the coupling strength between the two nodes. We show that a biased distribution of the entries in bit-strings can yield fringes in the (weighted) degree distribution, small-worlds features, and scaling laws, in agreement with experimental findings. We also investigate the role of ageing, thought of as a progressive increase in the degree of bias in bit-strings, and we show that it can possibly induce mild percolation phenomena, which are investigated too.Comment: 13 page

Immune cognition, social justice and asthma: structured stress and the developing immune system

We explore the implications of IR Cohen's work on immune cognition for understanding rising rates of asthma morbidity and mortality in the US. Immune cognition is conjoined with central nervous system cognition, and with the cognitive function of the embedding sociocultural networks by which individuals are acculturated and through which they work with others to meet challenges of threat and opportunity. Using a mathematical model, we find that externally- imposed patterns of 'structured stress' can, through their effect on a child's socioculture, become synergistic with the development of immune cognition, triggering the persistence of an atopic Th2 phenotype, a necessary precursor to asthma and other immune disease. Reversal of the rising tide of asthma and related chronic diseases in the US thus seems unlikely without a 21st Century version of the earlier Great Urban Reforms which ended the scourge of infectious diseases