Information Flow and Complexity in Large-Scale Metabolic Systems

1985 ◽  
pp. 55-67 ◽  
Author(s):  
Michael Kohn ◽  
Samuel Bedrosian
Keyword(s):  
Information Flow ◽  
Large Scale ◽  
Metabolic Systems

Fast Data Processing for Large-Scale SOA and Event-Based Systems

2015 ◽  
Vol 5 (4) ◽  
pp. 54-77
Author(s):  
Marcel Tilly ◽  
Stephan Reiff-Marganiec
Keyword(s):  
Data Processing ◽  
Information Flow ◽  
Mobile Networks ◽  
Large Scale ◽  
New Concepts ◽  
Combining Data ◽  
Access To Data ◽  
Processing Techniques ◽  
Event Based ◽  
The Internet Of Things

The deluge of intelligent objects that are providing continuous access to data and services on one hand and the demand of developers and consumers to handle these data on the other hand require us to think about new communication paradigms and middleware. In hyper-scale systems, such as in the Internet of Things, large scale sensor networks or even mobile networks, one emerging requirement is to process, procure, and provide information with almost zero latency. This work is introducing new concepts for a middleware to enable fast communication by limiting information flow with filtering concepts using policy obligations and combining data processing techniques adopted from complex event processing.

scholarly journals DMPy: a Python package for automated mathematical model construction of large-scale metabolic systems

2018 ◽  
Vol 12 (1) ◽  
Author(s):  
Robert W. Smith ◽  
Rik P. van Rosmalen ◽  
Vitor A. P. Martins dos Santos ◽  
Christian Fleck
Keyword(s):  
Mathematical Model ◽  
Large Scale ◽  
Model Construction ◽  
Metabolic Systems ◽  
Python Package

scholarly journals Large-Scale Information Flow in Conscious and Unconscious States: an ECoG Study in Monkeys

PLoS ONE ◽  
2013 ◽  
Vol 8 (11) ◽  
pp. e80845 ◽  
Author(s):  
Toru Yanagawa ◽  
Zenas C. Chao ◽  
Naomi Hasegawa ◽  
Naotaka Fujii
Keyword(s):  
Information Flow ◽  
Large Scale

scholarly journals Information flow, cell types and stereotypy in a full olfactory connectome

2020 ◽  
Author(s):  
Philipp Schlegel ◽  
Alexander Shakeel Bates ◽  
Tomke Stürner ◽  
Sridhar R. Jagannathan ◽  
Nikolas Drummond ◽  
...  
Keyword(s):  
Information Flow ◽  
Olfactory System ◽  
Mushroom Body ◽  
Antennal Lobe ◽  
Large Scale ◽  
Cell Types ◽  
Neuronal Morphology ◽  
Striking Similarity ◽  
Data Set ◽  
Lateral Horn

AbstractThe hemibrain connectome (Scheffer et al., 2020) provides large scale connectivity and morphology information for the majority of the central brain of Drosophila melanogaster. Using this data set, we provide a complete description of the most complex olfactory system studied at synaptic resolution to date, covering all first, second and third-order neurons of the olfactory system associated with the antennal lobe and lateral horn (mushroom body neurons are described in a parallel paper, (Li et al., 2020)). We develop a generally applicable strategy to extract information flow and layered organisation from synaptic resolution connectome graphs, mapping olfactory input to descending interneurons. This identifies a range of motifs including highly lateralised circuits in the antennal lobe and patterns of convergence downstream of the mushroom body and lateral horn. We also leverage a second data set (FAFB, (Zheng et al., 2018)) to provide a first quantitative assessment of inter- versus intra-individual stereotypy. Complete reconstruction of select developmental lineages in two brains (three brain hemispheres) reveals striking similarity in neuronal morphology across brains for >170 cell types. Within and across brains, connectivity correlates with morphology. Notably, neurons of the same morphological type show similar connection variability within one brain as across brains; this property should enable a rigorous quantitative approach to cell typing.

Data-driven rational biosynthesis design: from molecules to cell factories

2019 ◽  
Vol 21 (4) ◽  
pp. 1238-1248
Author(s):  
Fu Chen ◽  
Le Yuan ◽  
Shaozhen Ding ◽  
Yu Tian ◽  
Qian-Nan Hu
Keyword(s):  
Large Scale ◽  
Protein Domain ◽  
Data Driven ◽  
Cell Factory ◽  
Dna Assembly ◽  
Metabolic Reaction ◽  
Cell Factories ◽  
Metabolic Systems ◽  
One Stop ◽  
Target Molecules

Abstract A proliferation of chemical, reaction and enzyme databases, new computational methods and software tools for data-driven rational biosynthesis design have emerged in recent years. With the coming of the era of big data, particularly in the bio-medical field, data-driven rational biosynthesis design could potentially be useful to construct target-oriented chassis organisms. Engineering the complicated metabolic systems of chassis organisms to biosynthesize target molecules from inexpensive biomass is the main goal of cell factory design. The process of data-driven cell factory design could be divided into several parts: (1) target molecule selection; (2) metabolic reaction and pathway design; (3) prediction of novel enzymes based on protein domain and structure transformation of biosynthetic reactions; (4) construction of large-scale DNA for metabolic pathways; and (5) DNA assembly methods and visualization tools. The construction of a one-stop cell factory system could achieve automated design from the molecule level to the chassis level. In this article, we outline data-driven rational biosynthesis design steps and provide an overview of related tools in individual steps.

Local and large-scale beta oscillatory dysfunction in males with mild traumatic brain injury

2020 ◽  
Vol 124 (6) ◽  
pp. 1948-1958
Author(s):  
Jing Zhang ◽  
Kristina Safar ◽  
Zahra Emami ◽  
George M. Ibrahim ◽  
Shannon E. Scratch ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Basal Ganglia ◽  
Mild Traumatic Brain Injury ◽  
Information Flow ◽  
Large Scale ◽  
Neural Oscillations ◽  
Beta Activity ◽  
Regional Power ◽  
Flow Through

Mild traumatic brain injury (mTBI) disrupts the dynamic repertoire of neural oscillations, but so far beta activity has not been studied. In mTBI, we found reductions in frontal beta and large-scale beta networks, indicative of thalamocortical dysconnectivity and disrupted information flow through cortico-basal ganglia-thalamic circuits. Relatively, connectivity more accurately classifies individual mTBI cases compared with regional power. We show the relevance of beta oscillations in mTBI and the reliability of these markers in classification.

scholarly journals Simulink-Based Analysis for Coupled Metabolic Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Shinq-Jen Wu ◽  
Cheng-Tao Wu
Keyword(s):  
Stability Analysis ◽  
Dynamic Simulation ◽  
Large Scale ◽  
Dynamic Behaviour ◽  
Canonical Systems ◽  
Metabolic Systems ◽  
Good Potential ◽  
Set Up ◽  
The Stability ◽  
Interactive Behaviour

Stability analysis and dynamic simulation are important for researchers to capture the performance and the properties of underling systems. S-systems have good potential for characterizing dynamic interactive behaviour of large scale metabolic and genetic systems. It is important to develop a platform to achieve timely dynamic behaviour of S-systems to various situations. In this study, we first set up the respective block diagrams of S-systems for module-based simulation. We then derive reasonable theorems to examine the stability of S-systems and find out what kinds of environmental situations will make systems stable. Three canonical systems are used to examine the results which are carried out in the Matlab/Simulink environments.

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