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Energy constrains to increasing complexity in the biosphere.

ABSTRACT: Thirty years ago, the systems ecologist Howard T. Odum introduced the concept of transformity, which is a thermodynamic measure of quality within the trial and error evolutionary dynamics of ecosystems, namely an indicator of rank in the hierarchical system structure of the biosphere. Based on a global database of individual processes and whole economies, this paper extends, refines, and updates Odum's idea, demonstrating the strength of the postulated relation. In particular, an inverse linear logarithmic relationship is shown to hold between resource quantity (exergy) and quality (emergy), which is the result of an overall energetic efficiency characteristic of energy transformation processes of the biosphere. This relation extends from natural renewable energy sources to human information (including global internet data flows) and know-how embedded in national economies, thus identifying a consistent theory of hierarchical organization of the biosphere grounded in energetics and ultimately setting constraints to illusions of unlimited growth.

SUBMITTER: Liu G

PROVIDER: S-EPMC8529171 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Energy constrains to increasing complexity in the biosphere.

Liu Gengyuan G Yang Zhifeng Z Giannetti Biagio F BF Casazza Marco M Agostinho Feni F Pan Jiamin J Yan Ningyu N Hao Yan Y Zhang Lixiao L Almeida Cecilia M V B CMVB Gonella Francesco F Ulgiati Sergio S Brown Mark T MT

Innovation (Cambridge (Mass.)) 20210927 4

Thirty years ago, the systems ecologist Howard T. Odum introduced the concept of transformity, which is a thermodynamic measure of quality within the trial and error evolutionary dynamics of ecosystems, namely an indicator of rank in the hierarchical system structure of the biosphere. Based on a global database of individual processes and whole economies, this paper extends, refines, and updates Odum's idea, demonstrating the strength of the postulated relation. In particular, an inverse linear ...[more]

PMID: 34712999

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Project description:BackgroundIn microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes.ResultsIn this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes.ConclusionsWe show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events.

Dataset Information

Energy constrains to increasing complexity in the biosphere.

Publications

Energy constrains to increasing complexity in the biosphere.

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