The physicist's guide to one of biotechnology's hottest new topics: CRISPR-Cas

Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (Cas) constitute a multi-functional, constantly evolving immune system in bacteria and archaea cells. A heritable, molecular memory is generated of phage, plasmids, or other mobile genetic elements that attempt to attack the cell. This memory is used to recognize and interfere with subsequent invasions from the same genetic elements. This versatile prokaryotic tool has also been used to advance applications in biotechnology. Here we review a large body of CRISPR-Cas research to explore themes of evolution and selection, population dynamics, horizontal gene transfer, specific and cross-reactive interactions, cost and regulation, non-immunological CRISPR functions that boost host cell robustness, as well as applicable mechanisms for efficient and specific genetic engineering. We offer future directions that can be addressed by the physics community. Physical understanding of the CRISPR-Cas system will advance uses in biotechnology, such as developing cell lines and animal models, cell labeling and information storage, combatting antibiotic resistance, and human therapeutics.Comment: 75 pages, 15 figures, Physical Biology (2018

Software agents & human behavior

People make important decisions in emergencies. Often these decisions involve high stakes in terms of lives and property. Bhopal disaster (1984), Piper Alpha disaster (1988), Montara blowout (2009), and explosion on Deepwater Horizon (2010) are a few examples among many industrial incidents. In these incidents, those who were in-charge took critical decisions under various ental stressors such as time, fatigue, and panic. This thesis presents an application of naturalistic decision-making (NDM), which is a recent decision-making theory inspired by experts making decisions in real emergencies. This study develops an intelligent agent model that can be programed to make human-like decisions in emergencies. The agent model has three major components: (1) A spatial learning module, which the agent uses to learn escape routes that are designated routes in a facility for emergency evacuation, (2) a situation recognition module, which is used to recognize or distinguish among evolving emergency situations, and (3) a decision-support module, which exploits modules in (1) and (2), and implements an NDM based decision-logic for producing human-like decisions in emergencies. The spatial learning module comprises a generalized stochastic Petri net-based model of spatial learning. The model classifies routes into five classes based on landmarks, which are objects with salient spatial features. These classes deal with the question of how difficult a landmark turns out to be when an agent observes it the first time during a route traversal. An extension to the spatial learning model is also proposed where the question of how successive route traversals may impact retention of a route in the agent’s memory is investigated. The situation awareness module uses Markov logic network (MLN) to define different offshore emergency situations using First-order Logic (FOL) rules. The purpose of this module is to give the agent the necessary experience of dealing with emergencies. The potential of this module lies in the fact that different training samples can be used to produce agents having different experience or capability to deal with an emergency situation. To demonstrate this fact, two agents were developed and trained using two different sets of empirical observations. The two are found to be different in recognizing the prepare-to-abandon-platform alarm (PAPA ), and similar to each other in recognition of an emergency using other cues. Finally, the decision-support module is proposed as a union of spatial-learning module, situation awareness module, and NDM based decision-logic. The NDM-based decision-logic is inspired by Klein’s (1998) recognition primed decision-making (RPDM) model. The agent’s attitudes related to decision-making as per the RPDM are represented in the form of belief, desire, and intention (BDI). The decision-logic involves recognition of situations based on experience (as proposed in situation-recognition module), and recognition of situations based on classification, where ontological classification is used to guide the agent in cases where the agent’s experience about confronting a situation is inadequate. At the planning stage, the decision-logic exploits the agent’s spatial knowledge (as proposed in spatial-learning module) about the layout of the environment to make adjustments in the course of actions relevant to a decision that has already been made as a by-product of situation recognition. The proposed agent model has potential to be used to improve virtual training environment’s fidelity by adding agents that exhibit human-like intelligence in performing tasks related to emergency evacuation. Notwithstanding, the potential to exploit the basis provided here, in the form of an agent representing human fallibility, should not be ignored for fields like human reliability analysis

A prototype for a conversational companion for reminiscing about images

This work was funded by the COMPANIONS project sponsored by the European Commission as part of the Information Society Technologies (IST) programme under EC grant number IST-FP6-034434. Companions demonstrators can be seen at: http://www.dcs.shef.ac.uk/∼roberta/companions/Web/.This paper describes an initial prototype of the Companions project (www.companions-project.org): the Senior Companion (SC), designed to be a platform to display novel approaches to: (1) The use of Information Extraction (IE) techniques to extract the content of incoming dialogue utterances after an ASR phase. (2) The conversion of the input to RDF form to allow the generation of new facts from existing ones, under the control of a Dialogue Manager (DM), that also has access to stored knowledge and knowledge accessed in real time from the web, all in RDF form. (3) A DM expressed as a stack and network virtual machine that models mixed initiative in dialogue control. (4) A tuned dialogue act detector based on corpus evidence. The prototype platform was evaluated, and we describe this; it is also designed to support more extensive forms of emotion detection carried by both speech and lexical content, as well as extended forms of machine learning. We describe preliminary studies and results for these, in particular a novel approach to enabling reinforcement learning for open dialogue systems through the detection of emotion in the speech signal and its deployment as a form of a learned DM, at a higher level than the DM virtual machine and able to direct the SC’s responses to a more emotionally appropriate part of its repertoire. © 2010 Elsevier Ltd. All rights reserved.peer-reviewe

The role of diffusion and membrane topography in the initiation of high affinity IgE receptor signaling

The high affinity IgE receptor (FcεRI) plays a primary role in the pathogenesis of allergic disease and shares significant similarities with the two other multichain immune recognition receptor family members, the B-cell receptor and T-cell receptor. A wealth of information exists in all three of these receptor systems with regard to the signaling cascades occurring subsequent to receptor activation. It is also known that all three require binding of multivalent antigen to initiate signaling. However, very little is known about the precise mechanism by which multivalent antigen binding initializes downstream signaling. It has long been known that, in response to antigen binding, FcεRI reorganizes into large aggregates on the cell surface and that the receptor transitions from freely diffusing to highly immobile. The extent of aggregation and immobilization appears to correlate strongly with the extent of cellular activation, as measured by release of pre-formed mediators of allergic inflammation from intracellular granules. These observations have fueled speculation that immobilization of FcεRI may be the primary driver behind signal initiation. However, technical limitations related to the challenges of imaging highly dynamic, nanometer scale phenomena in living cells has precluded detailed examination of these processes. Here, we describe the development of novel live cell imaging techniques and quantum dot (QD) based probes to address the role played by receptor dynamics in FcεRI signaling. Using multi-color single QD tracking, we rigorously quantified the diffusion of FcεRI in the absence of multivalent antigen and discovered a novel role for the actin cytoskeleton in modulating the diffusion of transmembrane proteins on micron length scales. We developed a real-time assay to monitor the kinetics of antigen-induced immobilization of FcεRI and report that this process is influenced by the actin cytoskeleton and heavily dependent on multivalent antigen concentration. We describe the relationship between immobilization, clustering and signal intiation and demonstrate that immobilization is not required for robust signaling. We also show that antigen-induced aggregation and internalization of FcεRI is not dependent on downstream signaling. From these data, we propose that the size of receptor clusters alone dictates the mobility, signaling competence, and internalization of FcεRI

Evaluating systems of systems against mission requirements

This thesis investigates the nature of systems problems and the need for an open viewpoint to explain a system by viewing it as part of a larger whole and explaining its role in terms of that larger whole. The problem this research investigates is wicked and hence is unique in each instance. Therefore, an empirical proof would only hold for that particular instantiation of the problem, not the problem as a whole. After exposing some of the limitations of traditional systems engineering to this type of problem it is clear that a new approach is needed. The approach taken in the thesis is model driven and it is the architecture of this approach that is the stable artefact rather than the artefacts of a particular solution. The approach developed in this research has been demonstrated to be practicable. Specifically, this research has developed and demonstrated a novel approach for a decision support system that can be used to analyse a system of systems as part of a larger whole from both open and closed viewpoints in order to support the decision of which systems to use to conduct a particular military mission. Such planning decisions are wicked due to the uncertain and unique nature of military missions. Critical rationalism was used to validate the model driven approach and to falsify a parametric approach representative of traditional systems engineering through historical case studies. The main issue found with the parametric approach was the entanglement of functionality with the individual systems selected to implement the system of systems. The advantage of the model driven approach is that it separates functionality from implementation and uses model transformation for systems specification. Thus, although wicked problems do not have an exhaustively describable set of potential solutions this thesis has shown that they are not unapproachable