We revisit the modeling of the diauxic growth of a pure microorganism on two distinct sugars which was first described by Monod. Most available models are deterministic and make the assumption that all cells of the microbial ecosystem behave homogeneously with respect to both sugars, all consuming the first one and then switching to the second when the first is exhausted. We propose here a stochastic model which describes what is called "metabolic heterogeneity". It allows to consider small populations as in microfluidics as well as large populations where billions of individuals coexist in the medium in a batch or chemostat. We highlight the link between the stochastic model and the deterministic behavior in real large cultures using a large population approximation. Then the influence of model parameter values on model dynamics is studied, notably with respect to the lag-phase observed in real systems depending on the sugars on which the microorganism grows. It is shown that both metabolic parameters as well as initial conditions play a crucial role on system dynamics.
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http://dx.doi.org/10.3934/mbe.2020276 | DOI Listing |
Neural Comput
January 2025
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200437, China
Spiking neural networks (SNNs) have attracted significant interest in the development of brain-inspired computing systems due to their energy efficiency and similarities to biological information processing. In contrast to continuous-valued artificial neural networks, which produce results in a single step, SNNs require multiple steps during inference to achieve a desired accuracy level, resulting in a burden in real-time response and energy efficiency. Inspired by the tradeoff between speed and accuracy in human and animal decision-making processes, which exhibit correlations among reaction times, task complexity, and decision confidence, an inquiry emerges regarding how an SNN model can benefit by implementing these attributes.
View Article and Find Full Text PDFNeural Comput
January 2025
Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, BT48 7JL Derry-Londonderry, Northern Ireland, U.K.
Decision formation in perceptual decision making involves sensory evidence accumulation instantiated by the temporal integration of an internal decision variable toward some decision criterion or threshold, as described by sequential sampling theoretical models. The decision variable can be represented in the form of experimentally observable neural activities. Hence, elucidating the appropriate theoretical model becomes crucial to understanding the mechanisms underlying perceptual decision formation.
View Article and Find Full Text PDFJ Math Biol
January 2025
Laboratory of Mathematics and Complex Systems, Ministry of Education, School of Mathematical Sciences, Beijing Normal University, Beijing, People's Republic of China.
Networked evolutionary game theory is a well-established framework for modeling the evolution of social behavior in structured populations. Most of the existing studies in this field have focused on 2-strategy games on heterogeneous networks or n-strategy games on regular networks. In this paper, we consider n-strategy games on arbitrary networks under the pairwise comparison updating rule.
View Article and Find Full Text PDFJAMA Pediatr
December 2024
Centre for Translational Medicine, Semmelweis University, Budapest, Hungary.
Importance: Intraventricular hemorrhage (IVH) has been described to typically occur during the early hours of life (HOL); however, the exact time of onset is still unknown.
Objective: To investigate the temporal distribution of IVH reported in very preterm neonates.
Data Sources: PubMed, Embase, Cochrane Library, and Web of Science were searched on May 9, 2024.
Alzheimers Dement
December 2024
Bonn-Aachen International Center for IT (b-it), Bonn, Germany.
Background: Alzheimer's Disease (AD) is associated with sleep disturbances. Moreover, individuals with sleep disturbances have been reported to have a higher risk for developing AD. The measurement of sleep behavior therefore opens the opportunity for a potential digital biomarker of AD.
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