11,790 research outputs found
A software tool for network topology analysis under a Metabolic Engineering perspective
In this work, we present a software application that runs as a plug-in over the OptFlux Metabolic Engineering platform allowing the topological analysis of metabolic networks. The major aim of this tool is to allow the interconnection between phenotype simulation tasks (using algorithms such as Flux Balance Analysis) and topological analysis of the same networks. The provided methods include node degree and degree distributions, shortest path analysis, clustering coefficients and several node rankers (betweenness and closeness centrality, hubs and authorities, etc).
Also, it allows the creation of sub-networks through severalfilters, including some based on the results of phenotype simulation.(undefined
Model evaluation for glycolytic oscillations in yeast biotransformations of xenobiotics
Anaerobic glycolysis in yeast perturbed by the reduction of xenobiotic
ketones is studied numerically in two models which possess the same topology
but different levels of complexity. By comparing both models' predictions for
concentrations and fluxes as well as steady or oscillatory temporal behavior we
answer the question what phenomena require what kind of minimum model
abstraction. While mean concentrations and fluxes are predicted in agreement by
both models we observe different domains of oscillatory behavior in parameter
space. Generic properties of the glycolytic response to ketones are discussed
Contrasting Views of Complexity and Their Implications For Network-Centric Infrastructures
There exists a widely recognized need to better understand
and manage complex “systems of systems,” ranging from
biology, ecology, and medicine to network-centric technologies.
This is motivating the search for universal laws of highly evolved
systems and driving demand for new mathematics and methods
that are consistent, integrative, and predictive. However, the theoretical
frameworks available today are not merely fragmented
but sometimes contradictory and incompatible. We argue that
complexity arises in highly evolved biological and technological
systems primarily to provide mechanisms to create robustness.
However, this complexity itself can be a source of new fragility,
leading to “robust yet fragile” tradeoffs in system design. We
focus on the role of robustness and architecture in networked
infrastructures, and we highlight recent advances in the theory
of distributed control driven by network technologies. This view
of complexity in highly organized technological and biological systems
is fundamentally different from the dominant perspective in
the mainstream sciences, which downplays function, constraints,
and tradeoffs, and tends to minimize the role of organization and
design
Eco‐Holonic 4.0 Circular Business Model to Conceptualize Sustainable Value Chain Towards Digital Transition
The purpose of this paper is to conceptualize a circular business model based on an Eco-Holonic Architecture, through the integration of circular economy and holonic principles. A conceptual model is developed to manage the complexity of integrating circular economy principles, digital transformation, and tools and frameworks for sustainability into business models. The proposed architecture is multilevel and multiscale in order to achieve the instantiation of the sustainable value chain in any territory. The architecture promotes the incorporation of circular economy and holonic principles into new circular business models. This integrated perspective of business model can support the design and upgrade of the manufacturing companies in their respective industrial sectors. The conceptual model proposed is based on activity theory that considers the interactions between technical and social systems and allows the mitigation of the metabolic rift that exists between natural and social metabolism. This study contributes to the existing literature on circular economy, circular business models and activity theory by considering holonic paradigm concerns, which have not been explored yet. This research also offers a unique holonic architecture of circular business model by considering different levels, relationships, dynamism and contextualization (territory) aspects
Engineering simulations for cancer systems biology
Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions
Proceedings of Abstracts Engineering and Computer Science Research Conference 2019
© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care
SWIM: A computational tool to unveiling crucial nodes in complex biological networks
SWItchMiner (SWIM) is a wizard-like software implementation of a procedure, previously described, able to extract information contained in complex networks. Specifically, SWIM allows unearthing the existence of a new class of hubs, called "fight-club hubs", characterized by a marked negative correlation with their first nearest neighbors. Among them, a special subset of genes, called "switch genes", appears to be characterized by an unusual pattern of intra- and inter-module connections that confers them a crucial topological role, interestingly mirrored by the evidence of their clinic-biological relevance. Here, we applied SWIM to a large panel of cancer datasets from The Cancer Genome Atlas, in order to highlight switch genes that could be critically associated with the drastic changes in the physiological state of cells or tissues induced by the cancer development. We discovered that switch genes are found in all cancers we studied and they encompass protein coding genes and non-coding RNAs, recovering many known key cancer players but also many new potential biomarkers not yet characterized in cancer context. Furthermore, SWIM is amenable to detect switch genes in different organisms and cell conditions, with the potential to uncover important players in biologically relevant scenarios, including but not limited to human cancer
MuxViz: A Tool for Multilayer Analysis and Visualization of Networks
Multilayer relationships among entities and information about entities must
be accompanied by the means to analyze, visualize, and obtain insights from
such data. We present open-source software (muxViz) that contains a collection
of algorithms for the analysis of multilayer networks, which are an important
way to represent a large variety of complex systems throughout science and
engineering. We demonstrate the ability of muxViz to analyze and interactively
visualize multilayer data using empirical genetic, neuronal, and transportation
networks. Our software is available at https://github.com/manlius/muxViz.Comment: 18 pages, 10 figures (text of the accepted manuscript
Parameter estimation for Boolean models of biological networks
Boolean networks have long been used as models of molecular networks and play
an increasingly important role in systems biology. This paper describes a
software package, Polynome, offered as a web service, that helps users
construct Boolean network models based on experimental data and biological
input. The key feature is a discrete analog of parameter estimation for
continuous models. With only experimental data as input, the software can be
used as a tool for reverse-engineering of Boolean network models from
experimental time course data.Comment: Web interface of the software is available at
http://polymath.vbi.vt.edu/polynome
- …