Implementing a Space-Aware Stochastic Simulator on Low-Power Architectures: A Systems Biology Case Study

2017 ◽  
Author(s):  
Lucia Morganti ◽  
Elena Corni ◽  
Andrea Ferraro ◽  
Daniele Cesini ◽  
Daniele D'Agostino ◽  
...  
Keyword(s):  
Systems Biology ◽  
Low Power ◽  
Stochastic Simulator ◽  
Low Power Architectures

Revisiting HyperDimensional Learning for FPGA and Low-Power Architectures

2021 ◽  
Author(s):  
Mohsen Imani ◽  
Zhuowen Zou ◽  
Samuel Bosch ◽  
Sanjay Anantha Rao ◽  
Sahand Salamat ◽  
...  
Keyword(s):  
Low Power ◽  
Low Power Architectures

Systems biology of persistent infection: tuberculosis as a case study

2008 ◽  
Vol 6 (7) ◽  
pp. 520-528 ◽  
Author(s):  
Douglas Young ◽  
Jaroslav Stark ◽  
Denise Kirschner
Keyword(s):  
Systems Biology ◽  
Persistent Infection

scholarly journals Estimating cellular pathways from an ensemble of heterogeneous data sources

2014 ◽  
Author(s):  
Alexander Franks ◽  
Florian Markowetz ◽  
Edoardo Airoldi
Keyword(s):  
Systems Biology ◽  
Expert Knowledge ◽  
Mapk Pathway ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Pheromone Response ◽  
Specific Strategy ◽  
Heterogeneous Data Sources ◽  
Cellular Pathways

Building better models of cellular pathways is one of the major challenges of systems biology and functional genomics. There is a need for methods to build on established expert knowledge and reconcile it with results of high-throughput studies. Moreover, the available data sources are heterogeneous and need to be combined in a way specific for the part of the pathway in which they are most informative. Here, we present a compartment specific strategy to integrate edge, node and path data for the refinement of a network hypothesis. Specifically, we use a local-move Gibbs sampler for refining pathway hypotheses from a compendium of heterogeneous data sources, including novel methodology for integrating protein attributes. We demonstrate the utility of this approach in a case study of the pheromone response MAPK pathway in the yeast S. cerevisiae.

Multimethod optimization in the cloud: A case-study in systems biology modelling

2018 ◽  
Vol 30 (12) ◽  
pp. e4488 ◽  
Author(s):  
Patricia González ◽  
David R. Penas ◽  
Xoan C. Pardo ◽  
Julio R. Banga ◽  
Ramón Doallo
Keyword(s):  
Systems Biology ◽  
Multimethod Optimization

Detecting Electronic Initiators Using Electromagnetic Emissions

2014 ◽  
pp. 69-99
Author(s):  
Colin Stagner ◽  
Sarah Seguin ◽  
Steve Grant ◽  
Daryl Beetner
Keyword(s):  
Low Power ◽  
Normal Operation ◽  
Radio Receivers ◽  
Novel Technique ◽  
Different Types ◽  
Radio Signals ◽  
Electromagnetic Emissions ◽  
Weak Stimulation ◽  
Explosive Device

The accurate and timely discovery of radio receivers can assist in the detection of radio-controlled explosives. By detecting radio receivers, it is possible to indirectly infer the presence of an explosive device. Radio receivers unintentionally emit low-power radio signals during normal operation. By using a weak stimulation signal, it is possible to inject a known signal into these unintended emissions. This process is known as stimulated emissions. Unlike chemical traces, these stimulated emissions can propagate through walls and air-tight containers. The following case study discusses methods for detecting and locating two different types of radio receivers. Functional stimulated emissions detectors are constructed, and their performance is analyzed. Stimulated emissions are capable of detecting super-regenerative receivers at distances of at least one hundred meters and accurately locating superheterodyne receivers at distances of at least fifty meters. These results demonstrate a novel technique for detecting potential explosive threats at stand-off detection distances.

Agent-Based Modelling in Multicellular Systems Biology

2020 ◽  
pp. 369-389
Author(s):  
Sara Montagna ◽  
Andrea Omicini
Keyword(s):  
Developmental Biology ◽  
Drosophila Melanogaster ◽  
Systems Biology ◽  
Computational Models ◽  
The Self ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Multi Agent

This chapter aims at discussing the content of multi-agent based simulation (MABS) applied to computational biology i.e., to modelling and simulating biological systems by means of computational models, methodologies, and frameworks. In particular, the adoption of agent-based modelling (ABM) in the field of multicellular systems biology is explored, focussing on the challenging scenarios of developmental biology. After motivating why agent-based abstractions are critical in representing multicellular systems behaviour, MABS is discussed as the source of the most natural and appropriate mechanism for analysing the self-organising behaviour of systems of cells. As a case study, an application of MABS to the development of Drosophila Melanogaster is finally presented, which exploits the ALCHEMIST platform for agent-based simulation.

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