ORCID Profile
0000-0002-0777-0425
Current Organisation
Tokyo Institute of Technology
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Publisher: Elsevier BV
Date: 07-2019
DOI: 10.1016/J.PLREV.2018.05.002
Abstract: Physarum polycephalum, a single-celled, multinucleate slime mould, is a seemingly simple organism, yet it exhibits quasi-intelligent behaviour during extension, foraging, and as it adapts to dynamic environments. For these reasons, Physarum is an attractive target for modelling with the underlying goal to uncover the physiological mechanisms behind the exhibited quasi-intelligence and/or to devise novel algorithms for solving complex computational problems. The recent increase in modelling studies on Physarum has prompted us to review the latest developments in this field in the context of modelling and computing alike. Specifically, we cover models based on (i) morphology, (ii) taxis, and (iii) positive feedback dynamics found in top-down and bottom-up modelling techniques. We also survey the application of each of these core features of Physarum to solving difficult computational problems with real-world applications. Finally, we highlight some open problems in the field and present directions for future research.
Publisher: Oxford University Press (OUP)
Date: 27-03-2019
Publisher: American Association for the Advancement of Science (AAAS)
Date: 28-04-2023
Abstract: The model used by White et al . ( 1 ) to explore life-history optimization of metabolic scaling has limited ability to capture observed combinations of growth and reproduction, including those of the domestic chicken. The analyses and interpretations may change substantially with realistic parameters. The model’s biological and thermodynamic realism needs further exploration and justification before being applied to life-history optimization studies.
Publisher: Oxford University Press (OUP)
Date: 05-2019
Publisher: Wiley
Date: 02-06-2021
Abstract: The use of traits is growing in ecology and bio ersity informatics, with initiatives to collate trait data and integrate it into bio ersity databases. A need to develop better predictive capacity for how species respond to environmental change has in part motivated this focus. Functional traits are of most interest—those with a defined link to in idual survival, development, growth and reproduction. Non‐trivial challenges arise immediately in deciding which functional traits to prioritise and how to characterise them. Here we discuss the advantages of a theoretical perspective for defining functional traits in the context of dynamical systems models of energy and mass exchange that link organisms to their environments. We argue that the theoretical frameworks upon which such models are built (biophysical ecology, metabolic theory) provide clear criteria to decide upon functional trait definitions, measurement requirements and associated metadata, via their mathematical connection to model parameters and state variables, and thus to system performance (survival, development, growth and reproduction). We distinguish ‘descriptive’ traits from ‘functional’ traits by iding the latter into four classes—parameter, model, threshold and estimation—according to whether they are model parameters, define model structure, are threshold state variables or can be used to estimate model parameters. We develop a decision tree for this classification and illustrate it in the context of mammalian heat exchange but emphasise the scheme's generality to any kind of organism. We show how a theoretical perspective may change how we prioritise traits for collection and databasing in ways that are not necessarily more difficult to achieve, especially with new technologies, and provide clear guidance for requisite metadata. The use of theoretically driven criteria for prioritising the collection of functional trait data will maximise the generality, quality and consistency of trait databases for comparative analyses. Such databases will simultaneously facilitate the development of integrated predictive modelling frameworks across multiple organisational scales from in iduals to ecosystems. A free Plain Language Summary can be found within the Supporting Information of this article.
Publisher: Elsevier BV
Date: 07-2019
DOI: 10.1016/J.PLREV.2019.07.002
Abstract: We look at a recent expansion of Physarum research from inspiring biomimetic algorithms to serving as a model organism in the evolutionary study of perception, memory, learning, and decision making.
Publisher: Wiley
Date: 30-12-2022
DOI: 10.1111/GCB.16557
Abstract: A core challenge in global change biology is to predict how species will respond to future environmental change and to manage these responses. To make such predictions and management actions robust to novel futures, we need to accurately characterize how organisms experience their environments and the biological mechanisms by which they respond. All organisms are thermodynamically connected to their environments through the exchange of heat and water at fine spatial and temporal scales and this exchange can be captured with biophysical models. Although mechanistic models based on biophysical ecology have a long history of development and application, their use in global change biology remains limited despite their enormous promise and increasingly accessible software. We contend that greater understanding and training in the theory and methods of biophysical ecology is vital to expand their application. Our review shows how biophysical models can be implemented to understand and predict climate change impacts on species' behavior, phenology, survival, distribution, and abundance. It also illustrates the types of outputs that can be generated, and the data inputs required for different implementations. Ex les range from simple calculations of body temperature at a particular site and time, to more complex analyses of species' distribution limits based on projected energy and water balances, accounting for behavior and phenology. We outline challenges that currently limit the widespread application of biophysical models relating to data availability, training, and the lack of common software ecosystems. We also discuss progress and future developments that could allow these models to be applied to many species across large spatial extents and timeframes. Finally, we highlight how biophysical models are uniquely suited to solve global change biology problems that involve predicting and interpreting responses to environmental variability and extremes, multiple or shifting constraints, and novel abiotic or biotic environments.
No related grants have been discovered for Marko Jusup.