Controlling the Response: Predictive Modeling of a Highly Central, Pathogen-Targeted Core Response Module in Macrophage Activation

We have investigated macrophage activation using computational analyses of a compendium of transcriptomic data covering responses to agonists of the TLR pathway, Salmonella infection, and manufactured amorphous silica nanoparticle exposure. We inferred regulatory relationship networks using this compendium and discovered that genes with high betweenness centrality, so-called bottlenecks, code for proteins targeted by pathogens. Furthermore, combining a novel set of bioinformatics tools, topological analysis with analysis of differentially expressed genes under the different stimuli, we identified a conserved core response module that is differentially expressed in response to all studied conditions. This module occupies a highly central position in the inferred network and is also enriched in genes preferentially targeted by pathogens. The module includes cytokines, interferon induced genes such as Ifit1 and 2, effectors of inflammation, Cox1 and Oas1 and Oasl2, and transcription factors including AP1, Egr1 and 2 and Mafb. Predictive modeling using a reverse-engineering approach reveals dynamic differences between the responses to each stimulus and predicts the regulatory influences directing this module. We speculate that this module may be an early checkpoint for progression to apoptosis and/or inflammation during macrophage activation

Towards probabilistic analysis of human-system integration in automated driving

According to the Automated Driving Roadmap ERTRAC 17, only vehicles of Level 5 may not need human interference. The current adaptive cruise control system or more advanced automated driving solutions below Level 5 require, therefore, that a human driver takes over, if an extraordinary situation occurs. A critical safety problem may be caused by the very short time span available to the driver. It has been recently demonstrated, mostly in application to the aerospace domain, how probabilistic analytical modeling (PAM) could effectively complement computational simulation techniques in various human-in-the-loop (HITL) related missions and off-normal situations, when the reliability of the equipment (instrumentation), both hard- and software, and the performance of the human contribute jointly to their likely outcome. Our objective is to extend this approach, with appropriate modifications, to safety analyses of automated driving applications

Extraordinary automated driving situations: probabilistic analytical modeling of Human-Systems-Integration (HSI) and the role of trust

Automated driving (AD) specifications require that the human driver takes over, if an off-normal situation, such as a possible collision occurs. A critical problem is due in this case to the short time available to the driver to intervene and to take appropriate actions. It has been recently demonstrated, mostly in application to the aerospace domain, how Probabilistic Analytical Modeling (PAM) could effectively complement computer simulations in various Human-System-Integration (HSI) related missions and situations, when the system’s reliability and the human’s performance contribute jointly to the never-zero probability of failure. The convolution model is brought in application to a situation, when an obstacle is suddenly detected in front of the moving vehicle, and the only possible way to avoid collision is by using brakes to decelerate the vehicle. The role and significance of the driver’s trust towards the system are also addressed and briefly discussed

Extraordinary automated driving situations : probabilistic analytical modeling of Human-Systems-Integration (HSI) and the role of trust

Automated driving (AD) specifications require that the human driver takes over, if an off-normal situation, such as a possible collision occurs. A critical problem is due in this case to the short time available to the driver to intervene and to take appropriate actions. It has been recently demonstrated, mostly in application to the aerospace domain, how Probabilistic Analytical Modeling (PAM) could effectively complement computer simulations in various Human-System-Integration (HSI) related missions and situations, when the system’s reliability and the human’s performance contribute jointly to the never-zero probability of failure. The convolution model is brought in application to a situation, when an obstacle is suddenly detected in front of the moving vehicle, and the only possible way to avoid collision is by using brakes to decelerate the vehicle. The role and significance of the driver’s trust towards the system are also addressed and briefly discussed