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• W4207055448 abstract "Problem: Systems biology is essentially based on the assumption that the complexity of a system can be described by almost generic models to predict the behavior of many other similar systems. To this end, inductive (data-intensive) or deductive (mechanistic) models are currently being developed either to discover patterns and identify plausible correlations from past events, or to connect different causal relationships of interacting elements to construct predictive models. The use of any mathematical approach, or combination of mathematical approaches, presupposes the existence of constant and observable universal causal principles for all biological systems. However, there are currently no tools to assess the robustness of adoption over these universal causal principles, even considering that organisms not only respond to inherent processes and environmental stimuli across multiple scales, but also integrate information across and within these scales, introducing a degree of uncontrollable uncertainty. Methodology: To this end, we have developed a method to better evaluate these causal processes by evaluating the information contained in the dynamic trajectories, using concepts of geometric information theory and persistent homology to analyze patterns in time series, so that recognizing the persistence of these patterns over different time periods leads to the evaluation of stable causal relationships. Since we are assessing causal relationships from the patterns in the time series, we are here going beyond mere feature engineering. With this measure, and together with the evaluation of persistent entropy in the trajectories in relation to different individual systems, we have developed a method called the Φ-S Diagram as a measure of complexity to better recognize when organisms follow mechanistic pathways or respond autonomously and individually, which is a limitation for both the inductive and deductive modeling methods. Results: We calculated the Φ-S Diagram using a deterministic data set available in the UCI repository for reference to test the interpretability of the method, as well as health data available in the same repository that includes the physiological response to movement measured in individuals outside of laboratory conditions. We were able to confirm the mechanistic character in both data sets. Although the current data show that the cardiac response to exercise could be explained in a mechanistic way, we have also discovered that some individuals have a more autonomous than aleatoric response, implying that there are persistent individual variations that limit the universal observability of the system, suggesting that such individuals need an empirical rather than a systemic approach. This proof of concept presents a step towards a clearer framework for the theoretical representation of complex biological systems." @default.
• W4207055448 created "2022-01-26" @default.
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• W4207055448 date "2022-01-23" @default.
• W4207055448 modified "2023-09-25" @default.
• W4207055448 title "A unified Method for assessing the Observability of Dynamic Complex Systems in Biology" @default.
• W4207055448 doi "https://doi.org/10.1101/2022.01.21.477230" @default.
• W4207055448 hasPublicationYear "2022" @default.
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