scholarly journals Habitat variability does not generally promote metabolic network modularity in flies and mammals

2015 ◽  
Author(s):  
Kazuhiro Takemoto
Keyword(s):  
Gene Duplication ◽  
Metabolic Network ◽  
Biochemical Networks ◽  
General Mechanism ◽  
Biological Organization ◽  
Habitat Variability ◽  
Research Fields ◽  
Wide Range ◽  
Network Modularity ◽  
Enzymatic Networks

The evolution of species habitat range is an important topic over a wide range of research fields. In higher organisms, habitat range evolution is generally associated with genetic events such as gene duplication. However, the specific factors that determine habitat variability remain unclear at higher levels of biological organization (e.g., biochemical networks). One widely accepted hypothesis developed from both theoretical and empirical analyses is that habitat variability promotes network modularity; however, this relationship has not yet been directly tested in higher organisms. Therefore, I investigated the relationship between habitat variability and metabolic network modularity using compound and enzymatic networks in flies and mammals. Contrary to expectation, there was no clear positive correlation between habitat variability and network modularity. As an exception, the network modularity increased with habitat variability in the enzymatic networks of flies. However, the observed association was likely an artifact, and the frequency of gene duplication appears to be the main factor contributing to network modularity. These findings raise the question of whether or not there is a general mechanism for habitat range expansion at a higher level (i.e., above the gene scale). This study suggests that the currently widely accepted hypothesis for habitat variability should be reconsidered.

scholarly journals Hierarchical Harmonization of Atom-Resolved Metabolic Re-actions Across Metabolic Databases

2021 ◽  
Author(s):  
Huan Jin ◽  
Hunter N.B. Moseley
Keyword(s):  
Metabolic Network ◽  
Enzyme Commission ◽  
Detection Algorithm ◽  
Limiting Factor ◽  
Research Fields ◽  
Wide Range ◽  
Metabolic Reactions ◽  
Metabolic Models ◽  
Atom Level

Metabolic models have been proven to be useful tools in system biology and have been suc-cessfully applied to various research fields in a wide range of organisms. A relatively complete metabolic network is a prerequisite for deriving reliable metabolic models. The first step in con-structing metabolic network is to harmonize compounds and reactions across different metabolic databases. However, effectively integrating data from various sources still remains a big chal-lenge. Incomplete and inconsistent atomistic details in compound representations across data-bases is a very important limiting factor. Here, we optimized a subgraph isomorphism detection algorithm to validate generic compound pairs. Moreover, we defined a set of harmonization re-lationship types between compounds to deal with inconsistent chemical details while successfully capturing atom-level characteristics, enabling a more complete enabling compound harmoniza-tion across metabolic databases. In total, 15,704 compound pairs across KEGG (Kyoto Encyclo-pedia of Genes and Genomes) and MetaCyc databases were detected. Furthermore, utilizing the classification of compound pairs and EC (Enzyme Commission) numbers of reactions, we estab-lished hierarchical relationships between metabolic reactions, enabling the harmonization of 3,856 reaction pairs. In addition, we created and used atom-specific identifiers to evaluate the con-sistency of atom mappings within and between harmonized reactions, detecting some con-sistency issues between the reaction and compound descriptions in these metabolic databases.

scholarly journals Hierarchical Harmonization of Atom-Resolved Metabolic Reactions across Metabolic Databases

Metabolites ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 431
Author(s):  
Huan Jin ◽  
Hunter N. B. Moseley
Keyword(s):  
Metabolic Network ◽  
Detection Algorithm ◽  
Limiting Factor ◽  
Research Fields ◽  
Relationship Types ◽  
Wide Range ◽  
Metabolic Reactions ◽  
Metabolic Models ◽  
Atom Level

Metabolic models have been proven to be useful tools in system biology and have been successfully applied to various research fields in a wide range of organisms. A relatively complete metabolic network is a prerequisite for deriving reliable metabolic models. The first step in constructing metabolic network is to harmonize compounds and reactions across different metabolic databases. However, effectively integrating data from various sources still remains a big challenge. Incomplete and inconsistent atomistic details in compound representations across databases is a very important limiting factor. Here, we optimized a subgraph isomorphism detection algorithm to validate generic compound pairs. Moreover, we defined a set of harmonization relationship types between compounds to deal with inconsistent chemical details while successfully capturing atom-level characteristics, enabling a more complete enabling compound harmonization across metabolic databases. In total, 15,704 compound pairs across KEGG (Kyoto Encyclopedia of Genes and Genomes) and MetaCyc databases were detected. Furthermore, utilizing the classification of compound pairs and EC (Enzyme Commission) numbers of reactions, we established hierarchical relationships between metabolic reactions, enabling the harmonization of 3856 reaction pairs. In addition, we created and used atom-specific identifiers to evaluate the consistency of atom mappings within and between harmonized reactions, detecting some consistency issues between the reaction and compound descriptions in these metabolic databases.

scholarly journals Methods Used and Application of the Mouse Grimace Scale in Biomedical Research 10 Years On: A Scoping Review

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 673
Author(s):  
Alexandra L. Whittaker ◽  
Yifan Liu ◽  
Timothy H. Barker
Keyword(s):  
Animal Models ◽  
Mouse Models ◽  
Biomedical Research ◽  
Scoping Review ◽  
Facial Features ◽  
Inclusion Criteria ◽  
Academic Literature ◽  
Research Fields ◽  
Neuroscience Research ◽  
Wide Range

The Mouse Grimace Scale (MGS) was developed 10 years ago as a method for assessing pain through the characterisation of changes in five facial features or action units. The strength of the technique is that it is proposed to be a measure of spontaneous or non-evoked pain. The time is opportune to map all of the research into the MGS, with a particular focus on the methods used and the technique’s utility across a range of mouse models. A comprehensive scoping review of the academic literature was performed. A total of 48 articles met our inclusion criteria and were included in this review. The MGS has been employed mainly in the evaluation of acute pain, particularly in the pain and neuroscience research fields. There has, however, been use of the technique in a wide range of fields, and based on limited study it does appear to have utility for pain assessment across a spectrum of animal models. Use of the method allows the detection of pain of a longer duration, up to a month post initial insult. There has been less use of the technique using real-time methods and this is an area in need of further research.

scholarly journals Scarcity of scale-free topology is universal across biochemical networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Harrison B. Smith ◽  
Hyunju Kim ◽  
Sara I. Walker
Keyword(s):  
Large Body ◽  
Biochemical Networks ◽  
Biological Organization ◽  
Scale Free ◽  
Levels Of Organization ◽  
Common Structure ◽  
Domains Of Life ◽  
Ecosystem Level ◽  
Different Levels ◽  
Organizing Principles

AbstractBiochemical reactions underlie the functioning of all life. Like many examples of biology or technology, the complex set of interactions among molecules within cells and ecosystems poses a challenge for quantification within simple mathematical objects. A large body of research has indicated many real-world biological and technological systems, including biochemistry, can be described by power-law relationships between the numbers of nodes and edges, often described as “scale-free”. Recently, new statistical analyses have revealed true scale-free networks are rare. We provide a first application of these methods to data sampled from across two distinct levels of biological organization: individuals and ecosystems. We analyze a large ensemble of biochemical networks including networks generated from data of 785 metagenomes and 1082 genomes (sampled from the three domains of life). The results confirm no more than a few biochemical networks are any more than super-weakly scale-free. Additionally, we test the distinguishability of individual and ecosystem-level biochemical networks and show there is no sharp transition in the structure of biochemical networks across these levels of organization moving from individuals to ecosystems. This result holds across different network projections. Our results indicate that while biochemical networks are not scale-free, they nonetheless exhibit common structure across different levels of organization, independent of the projection chosen, suggestive of shared organizing principles across all biochemical networks.

scholarly journals Cycle and flow trusses in directed networks

2016 ◽  
Vol 3 (11) ◽  
pp. 160270 ◽  
Author(s):  
Taro Takaguchi ◽  
Yuichi Yoshida
Keyword(s):  
Truss Structures ◽  
Directed Network ◽  
Unified Framework ◽  
Directed Networks ◽  
Research Fields ◽  
Wide Range ◽  
Definition Of ◽  
Information Finding ◽  
Empirical Networks ◽  
Module Structures

When we represent real-world systems as networks, the directions of links often convey valuable information. Finding module structures that respect link directions is one of the most important tasks for analysing directed networks. Although many notions of a directed module have been proposed, no consensus has been reached. This lack of consensus results partly because there might exist distinct types of modules in a single directed network, whereas most previous studies focused on an independent criterion for modules. To address this issue, we propose a generic notion of the so-called truss structures in directed networks. Our definition of truss is able to extract two distinct types of trusses, named the cycle truss and the flow truss, from a unified framework. By applying the method for finding trusses to empirical networks obtained from a wide range of research fields, we find that most real networks contain both cycle and flow trusses. In addition, the abundance of (and the overlap between) the two types of trusses may be useful to characterize module structures in a wide variety of empirical networks. Our findings shed light on the importance of simultaneously considering different types of modules in directed networks.

scholarly journals Stem–loop formation drives RNA folding in mechanical unzipping experiments

2022 ◽  
Vol 119 (3) ◽  
pp. e2025575119
Author(s):  
Paolo Rissone ◽  
Cristiano V. Bizarro ◽  
Felix Ritort
Keyword(s):  
Optical Tweezers ◽  
Rna Structure ◽  
Nearest Neighbor ◽  
Rna Folding ◽  
General Mechanism ◽  
Loop Formation ◽  
Stem Loop ◽  
Wide Range ◽  
Irreversible Effects ◽  
And Function

Accurate knowledge of RNA hybridization is essential for understanding RNA structure and function. Here we mechanically unzip and rezip a 2-kbp RNA hairpin and derive the 10 nearest-neighbor base pair (NNBP) RNA free energies in sodium and magnesium with 0.1 kcal/mol precision using optical tweezers. Notably, force–distance curves (FDCs) exhibit strong irreversible effects with hysteresis and several intermediates, precluding the extraction of the NNBP energies with currently available methods. The combination of a suitable RNA synthesis with a tailored pulling protocol allowed us to obtain the fully reversible FDCs necessary to derive the NNBP energies. We demonstrate the equivalence of sodium and magnesium free-energy salt corrections at the level of individual NNBP. To characterize the irreversibility of the unzipping–rezipping process, we introduce a barrier energy landscape of the stem–loop structures forming along the complementary strands, which compete against the formation of the native hairpin. This landscape correlates with the hysteresis observed along the FDCs. RNA sequence analysis shows that base stacking and base pairing stabilize the stem–loops that kinetically trap the long-lived intermediates observed in the FDC. Stem–loops formation appears as a general mechanism to explain a wide range of behaviors observed in RNA folding.

scholarly journals Size matters: Size Dependency of Gold Nanoparticles Interacting with Model Membranes

2019 ◽  
Author(s):  
Claudia Contini ◽  
James W. Hindley ◽  
Tom Macdonald ◽  
Joseph Barritt ◽  
Oscar Ces ◽  
...  
Keyword(s):  
Gold Nanoparticles ◽  
Lipid Bilayers ◽  
Lipid Membrane ◽  
Rapid Development ◽  
Membrane System ◽  
Size Dependent ◽  
Research Fields ◽  
Large Unilamellar Vesicles ◽  
Wide Range ◽  
Experimental Characterisation

<p><b>The rapid development of nanomaterials has led to an increase in the number and variety of engineered nanomaterials (ENMs) in the environment. Gold nanoparticles (AuNPs) are an example of a commonly studied ENM whose highly tailorable properties have generated significant interest through a wide range of research fields. In the present work, we report the first qualitative as well as quantitative experimental characterisation of the AuNP-membrane interaction. We investigate the interactions between citrate-stabilised AuNPs (diameters 5, 10, 25, 35, 50, 60 nm) and large unilamellar vesicles (LUVs) acting as a model membrane system. LUVs were prepared in two different formulations using 1-palmitoyl-2-oleoyl-glycero-3-phosphocholine (POPC) and 1,2-dileoyl-sn-glycero-3-phosphocholine (DOPC). Our results show that the interaction between AuNPs and LUVs is size dependent; in particular, we reveal the existence of two AuNP’s critical diameters which determine the fate of AuNPs in contact with a lipid membrane. The results provide a new understanding of the size dependent interaction between AuNPs and lipid bilayers of direct relevance to nanotoxicology and to the design of NP vectors.</b></p>

Sign in / Sign up

Export Citation Format

Share Document