Pathway to the PiezoElectronic Transduction Logic Device

The information age challenges computer technology to process an exponentially increasing computational load on a limited energy budget - a requirement that demands an exponential reduction in energy per operation. In digital logic circuits, the switching energy of present FET devices is intimately connected with the switching voltage, and can no longer be lowered sufficiently, limiting the ability of current technology to address the challenge. Quantum computing offers a leap forward in capability, but a clear advantage requires algorithms presently developed for only a small set of applications. Therefore, a new, general purpose, classical technology based on a different paradigm is needed to meet the ever increasing demand for data processing.Comment: in Nano Letters (2015

Complementary Symmetry Nanowire Logic Circuits: Experimental Demonstrations and in Silico Optimizations

Complementary symmetry (CS) Boolean logic utilizes both p- and n-type field-effect transistors (FETs) so that an input logic voltage signal will turn one or more p- or n-type FETs on, while turning an equal number of n- or p-type FETs off. The voltage powering the circuit is prevented from having a direct pathway to ground, making the circuit energy efficient. CS circuits are thus attractive for nanowire logic, although they are challenging to implement. CS logic requires a relatively large number of FETs per logic gate, the output logic levels must be fully restored to the input logic voltage level, and the logic gates must exhibit high gain and robust noise margins. We report on CS logic circuits constructed from arrays of 16 nm wide silicon nanowires. Gates up to a complexity of an XOR gate (6 p-FETs and 6 n-FETs) containing multiple nanowires per transistor exhibit signal restoration and can drive other logic gates, implying that large scale logic can be implemented using nanowires. In silico modeling of CS inverters, using experimentally derived look-up tables of individual FET properties, is utilized to provide feedback for optimizing the device fabrication process. Based upon this feedback, CS inverters with a gain approaching 50 and robust noise margins are demonstrated. Single nanowire-based logic gates are also demonstrated, but are found to exhibit significant device-to-device fluctuations

Predicting future state for adaptive clinical pathway management

Clinical decision support systems are assisting physicians in providing care to patients. However, in the context of clinical pathway management such systems are rather limited as they only take the current state of the patient into account and ignore the possible evolvement of that state in the future. In the past decade, the availability of big data in the healthcare domain did open a new era for clinical decision support. Machine learning technologies are now widely used in the clinical domain, nevertheless, mostly as a tool for disease prediction. A tool that not only predicts future states, but also enables adaptive clinical pathway management based on these predictions is still in need. This paper introduces weighted state transition logic, a logic to model state changes based on actions planned in clinical pathways. Weighted state transition logic extends linear logic by taking weights -- numerical values indicating the quality of an action or an entire clinical pathway -- into account. It allows us to predict the future states of a patient and it enables adaptive clinical pathway management based on these predictions. We provide an implementation of weighted state transition logic using semantic web technologies, which makes it easy to integrate semantic data and rules as background knowledge. Executed by a semantic reasoner, it is possible to generate a clinical pathway towards a target state, as well as to detect potential conflicts in the future when multiple pathways are coexisting. The transitions from the current state to the predicted future state are traceable, which builds trust from human users on the generated pathway

Analysis of signalling pathways using the prism model checker

We describe a new modelling and analysis approach for signal transduction networks in the presence of incomplete data. We illustrate the approach with an example, the RKIP inhibited ERK pathway [1]. Our models are based on high level descriptions of continuous time Markov chains: reactions are modelled as synchronous processes and concentrations are modelled by discrete, abstract quantities. The main advantage of our approach is that using a (continuous time) stochastic logic and the PRISM model checker, we can perform quantitative analysis of queries such as if a concentration reaches a certain level, will it remain at that level thereafter? We also perform standard simulations and compare our results with a traditional ordinary differential equation model. An interesting result is that for the example pathway, only a small number of discrete data values is required to render the simulations practically indistinguishable

A model checking approach to the parameter estimation of biochemical pathways

Model checking has historically been an important tool to verify models of a wide variety of systems. Typically a model has to exhibit certain properties to be classed ‘acceptable’. In this work we use model checking in a new setting; parameter estimation. We characterise the desired behaviour of a model in a temporal logic property and alter the model to make it conform to the property (determined through model checking). We have implemented a computational system called MC2(GA) which pairs a model checker with a genetic algorithm. To drive parameter estimation, the fitness of set of parameters in a model is the inverse of the distance between its actual behaviour and the desired behaviour. The model checker used is the simulation-based Monte Carlo Model Checker for Probabilistic Linear-time Temporal Logic with numerical constraints, MC2(PLTLc). Numerical constraints as well as the overall probability of the behaviour expressed in temporal logic are used to minimise the behavioural distance. We define the theory underlying our parameter estimation approach in both the stochastic and continuous worlds. We apply our approach to biochemical systems and present an illustrative example where we estimate the kinetic rate constants in a continuous model of a signalling pathway

Analysis of signalling pathways using continuous time Markov chains

We describe a quantitative modelling and analysis approach for signal transduction networks. We illustrate the approach with an example, the RKIP inhibited ERK pathway [CSK+03]. Our models are high level descriptions of continuous time Markov chains: proteins are modelled by synchronous processes and reactions by transitions. Concentrations are modelled by discrete, abstract quantities. The main advantage of our approach is that using a (continuous time) stochastic logic and the PRISM model checker, we can perform quantitative analysis such as what is the probability that if a concentration reaches a certain level, it will remain at that level thereafter? or how does varying a given reaction rate affect that probability? We also perform standard simulations and compare our results with a traditional ordinary differential equation model. An interesting result is that for the example pathway, only a small number of discrete data values is required to render the simulations practically indistinguishable

A two-dimensional outcome pathway model for research for development (R4D) programs

The CGIAR Challenge Program on Water and Food (CPWF) is a research for development program that focuses on improving the lives and environment of stakeholders through improved water management in the agriculture and fisheries sectors. To successfully meet the program goal, a project’s outcome pathway must be designed in such a way that makes the project theories of change explicit. The project must make explicit the cause and effect logic, by which its research will help achieve developmental outcomes. In the first phase of the CPWF, projects designed their outcome pathways as a linear logic model starting from inputs to activities to outputs and then outcomes. Outputs and outcomes are linked by intermediate outcomes from the scaling-up and scaling–out processes. This model was found to be an oversimplification. Thus, in Phase 2, CPWF revised the outcome pathway into a two-dimensional logic model, with institutional scale as the second dimension. This logic model was developed through a participatory impact pathway analysis as part of the ex-post impact assessment of the project, Coastal Resource Management for Improving Livelihoods. The two-dimensional outcome pathway model consists of interdependent outcome pathways on at least three scale levels: farm, community, and an enabling environment that affects both. The model describes how project research will influence behavior of actors at the three scales and how these pathways support each other. The Basin Development Challenge Programs in CPWF Phase 2 use this framework to plan for widescale and sustainable adoption of technologies

Physical limits on cooperative protein-DNA binding and the kinetics of combinatorial transcription regulation

Much of the complexity observed in gene regulation originates from cooperative protein-DNA binding. While studies of the target search of proteins for their specific binding sites on the DNA have revealed design principles for the quantitative characteristics of protein-DNA interactions, no such principles are known for the cooperative interactions between DNA-binding proteins. We consider a simple theoretical model for two interacting transcription factor (TF) species, searching for and binding to two adjacent target sites hidden in the genomic background. We study the kinetic competition of a dimer search pathway and a monomer search pathway, as well as the steady-state regulation function mediated by the two TFs over a broad range of TF-TF interaction strengths. Using a transcriptional AND-logic as exemplary functional context, we identify the functionally desirable regime for the interaction. We find that both weak and very strong TF-TF interactions are favorable, albeit with different characteristics. However, there is also an unfavorable regime of intermediate interactions where the genetic response is prohibitively slow.Comment: manuscript and supplementary material combined into a single document; to be published in Biophysical Journa