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 Scarcity of scale-free topology is universal across biochemical networks

2020 ◽  
Author(s):  
Harrison B. Smith ◽  
Hyunju Kim ◽  
Sara I. Walker
Keyword(s):  
Biochemical Networks ◽  
Coarse Grained ◽  
Technological Systems ◽  
Sharp Transition ◽  
Scale Free ◽  
Levels Of Organization ◽  
Scale Free Networks ◽  
Ecosystem Level ◽  
Different Levels ◽  
Organizing Principles

AbstractBiochemical reactions underlie all living processes. Like many biological and technological systems, their complex web of interactions is difficult to fully capture and quantify with simple mathematical objects. Nonetheless, a huge volume of research has suggested many real-world biological and technological systems – including biochemical systems – can be described rather simply as ‘scale-free’ networks, characterized by a power-law degree distribution. More recently, rigorous statistical analyses across a variety of systems have upended this view, suggesting truly scale-free networks may be rare. We provide a first application of these newer methods across two distinct levels of biological organization: analyzing a large ensemble of biochemical networks generated from the reactions encoded in 785 ecosystem-level metagenomes and 1082 individual-level genomes (representing all three domains of life). Our results confirm only a few percent of individual and ecosystem-level biochemical networks meet the criteria necessary to be anything more than super-weakly scale-free. Leveraging the simultaneous analysis of the multiple coarse-grained projections of biochemistry, we perform distinguishability tests across properties of individual and ecosystem-level biochemical networks to determine whether or not they share common structure, indicative of common generative mechanisms across levels. Our results indicate there is no sharp transition in the organization of biochemistry across distinct levels of the biological hierarchy - a result that holds across different network projections. This suggests the existence of common organizing principles operating across different levels of organization in biochemical networks, independent of the project chosen.Author SummaryFully characterizing living systems requires rigorous analysis of the complex webs of interactions governing living processes. Here we apply statistical approaches to analyze a large data set of biochemical networks across two levels of organization: individuals and ecosystems. We find that independent of level of organization, the standard ‘scale-free’ model is not a good description of the data. Interestingly, there is no sharp transition in the shape of degree distributions for biochemical networks when comparing those of individuals to ecosystems. This suggests the existence of common organizing principles operating across different levels of biochemical organization that are revealed across different network projections.

scholarly journals Methods in field chronobiology

2017 ◽  
Vol 372 (1734) ◽  
pp. 20160247 ◽  
Author(s):  
Davide M. Dominoni ◽  
Susanne Åkesson ◽  
Raymond Klaassen ◽  
Kamiel Spoelstra ◽  
Martin Bulla
Keyword(s):  
Technological Development ◽  
Monitoring Systems ◽  
Migratory Restlessness ◽  
Biological Organization ◽  
Rapid Improvement ◽  
Free Living ◽  
The Future ◽  
In The Wild ◽  
Temporal Aspect ◽  
Different Levels

Chronobiological research has seen a continuous development of novel approaches and techniques to measure rhythmicity at different levels of biological organization from locomotor activity (e.g. migratory restlessness) to physiology (e.g. temperature and hormone rhythms, and relatively recently also in genes, proteins and metabolites). However, the methodological advancements in this field have been mostly and sometimes exclusively used only in indoor laboratory settings. In parallel, there has been an unprecedented and rapid improvement in our ability to track animals and their behaviour in the wild. However, while the spatial analysis of tracking data is widespread, its temporal aspect is largely unexplored. Here, we review the tools that are available or have potential to record rhythms in the wild animals with emphasis on currently overlooked approaches and monitoring systems. We then demonstrate, in three question-driven case studies, how the integration of traditional and newer approaches can help answer novel chronobiological questions in free-living animals. Finally, we highlight unresolved issues in field chronobiology that may benefit from technological development in the future. As most of the studies in the field are descriptive, the future challenge lies in applying the diverse technologies to experimental set-ups in the wild. This article is part of the themed issue ‘Wild clocks: integrating chronobiology and ecology to understand timekeeping in free-living animals’.

scholarly journals Universal scaling across biochemical networks on Earth

2019 ◽  
Vol 5 (1) ◽  
pp. eaau0149 ◽  
Author(s):  
Hyunju Kim ◽  
Harrison B. Smith ◽  
Cole Mathis ◽  
Jason Raymond ◽  
Sara I. Walker
Keyword(s):  
Scaling Laws ◽  
Network Size ◽  
Biochemical Networks ◽  
Biochemical Network ◽  
Reaction Networks ◽  
Biochemical Reaction Networks ◽  
Global Database ◽  
Universal Scaling ◽  
Planetary Scale ◽  
Domains Of Life

The application of network science to biology has advanced our understanding of the metabolism of individual organisms and the organization of ecosystems but has scarcely been applied to life at a planetary scale. To characterize planetary-scale biochemistry, we constructed biochemical networks using a global database of 28,146 annotated genomes and metagenomes and 8658 cataloged biochemical reactions. We uncover scaling laws governing biochemical diversity and network structure shared across levels of organization from individuals to ecosystems, to the biosphere as a whole. Comparing real biochemical reaction networks to random reaction networks reveals that the observed biological scaling is not a product of chemistry alone but instead emerges due to the particular structure of selected reactions commonly participating in living processes. We show that the topology of biochemical networks for the three domains of life is quantitatively distinguishable, with >80% accuracy in predicting evolutionary domain based on biochemical network size and average topology. Together, our results point to a deeper level of organization in biochemical networks than what has been understood so far.

Establishing Causality between Pollution and Effects at Different Levels of Biological Organization: The VALIMAR Project

2003 ◽  
Vol 9 (1) ◽  
pp. 171-194 ◽  
Author(s):  
Rita Triebskorn ◽  
Stefan Adam ◽  
Anja Behrens ◽  
Stefanie Beier ◽  
Jürgen Böhmer ◽  
...  
Keyword(s):  
Biological Organization ◽  
Different Levels

scholarly journals The influence of environmental factors on water mite assemblages (Acari, Hydrachnidia) in a small lowland river: an analysis at different levels of organization of the environment

Limnology ◽  
2017 ◽  
Vol 18 (3) ◽  
pp. 333-343 ◽  
Author(s):  
Andrzej Zawal ◽  
Robert Stryjecki ◽  
Edyta Stępień ◽  
Edyta Buczyńska ◽  
Paweł Buczyński ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Lowland River ◽  
Water Mite ◽  
Levels Of Organization ◽  
Different Levels

Interrelating Concepts from Genetics and Evolution: Why are Cod Shrinking?

2018 ◽  
Vol 80 (2) ◽  
pp. 132-138 ◽  
Author(s):  
Janina Jördens ◽  
Roman Asshoff ◽  
Harald Kullmann ◽  
Marcus Hammann
Keyword(s):  
Evolutionary Change ◽  
Allele Frequencies ◽  
Biological Organization ◽  
Classroom Simulation ◽  
Different Levels

Understanding evolutionary change requires an integrated understanding of genetics and evolution, as well as interrelating concepts from different levels of biological organization. However, students' knowledge about genetics and evolution often remains compartmentalized, and students struggle with thinking across levels. Thus, we present a classroom simulation of how selection affects both phenotypes and genotypes, which helps students distinguish between different levels of biological organization (i.e., phenotype and genotype) and track changes in phenotypes to changes in allele frequencies.

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