Collective behavior and emergent phenomena often arise in complex adaptive biological systems. That is, biological systems in which there are interactions among a number of individuals that somehow result in population level behavior or activity. The understanding of this collective behavior is not easily reducible to the behavior of any particular individual. Understanding the mechanisms that underlie the processes of complex biological systems is important to developments in engineering, technology, and advancing basic knowledge in the science of biological systems generally.
Additionally, the scientific study of collective behavior and emergent phenomenon in biological systems presents challenging problems and their solutions will lead to greater understanding of complex adaptive systems. Some of the challenges of the field are dealing with autocorrelation, the dynamical nature of relevant interaction networks, and the broad range in the sizes of data sets. Furthermore, there is a significant gulf between researchers with training in the collection of data and researchers with training in developing theoretical tools that are most appropriate for analyzing the data or modeling the mechanisms that underlie systems that can be studied experimentally.
In this Research Topics, we aim to present results arising out of a recent workshop held at the Statistical and Applied Mathematical Sciences Institute. In addition, we welcome contributions outside the workshop addressing data-driven modeling of collective behavior and emergent phenomena in biology.
The workshop was an interdisciplinary collaborative meeting for researchers and students with research interests in collective behavior and emergent phenomena in biology and its applications. The scope of the workshop was to provide an environment in which experimentalists with expertise in data collection and classical statistical techniques can work directly with theoreticians with expertise in the construction and application of current mathematical, statistical and computational models.
The outcome of the workshop are projects that combine biological data with methods of mathematical or statistical modeling to reach a new understanding of some particular biological systems. The projects address the following topics
1. Movements and interaction patterns within bumblebee nests
2. Identifying social rank stability across time in dominance hierarchies using rank-based overlap
3. Modeling heterogeneity in the dynamics of neural decisions
4. Machine learning of ant task classification and dynamic communities
5. Mechanisms underlying the recovery of group cohesion in a semi-free ranging herd of domestic goats
6. Pattern of information transfer in acellular slime mold
7. Collective pulsing behavior in xeniid corals
We encourage manuscripts addressing similar themes in the broad field of biology to complement this collection.
Collective behavior and emergent phenomena often arise in complex adaptive biological systems. That is, biological systems in which there are interactions among a number of individuals that somehow result in population level behavior or activity. The understanding of this collective behavior is not easily reducible to the behavior of any particular individual. Understanding the mechanisms that underlie the processes of complex biological systems is important to developments in engineering, technology, and advancing basic knowledge in the science of biological systems generally.
Additionally, the scientific study of collective behavior and emergent phenomenon in biological systems presents challenging problems and their solutions will lead to greater understanding of complex adaptive systems. Some of the challenges of the field are dealing with autocorrelation, the dynamical nature of relevant interaction networks, and the broad range in the sizes of data sets. Furthermore, there is a significant gulf between researchers with training in the collection of data and researchers with training in developing theoretical tools that are most appropriate for analyzing the data or modeling the mechanisms that underlie systems that can be studied experimentally.
In this Research Topics, we aim to present results arising out of a recent workshop held at the Statistical and Applied Mathematical Sciences Institute. In addition, we welcome contributions outside the workshop addressing data-driven modeling of collective behavior and emergent phenomena in biology.
The workshop was an interdisciplinary collaborative meeting for researchers and students with research interests in collective behavior and emergent phenomena in biology and its applications. The scope of the workshop was to provide an environment in which experimentalists with expertise in data collection and classical statistical techniques can work directly with theoreticians with expertise in the construction and application of current mathematical, statistical and computational models.
The outcome of the workshop are projects that combine biological data with methods of mathematical or statistical modeling to reach a new understanding of some particular biological systems. The projects address the following topics
1. Movements and interaction patterns within bumblebee nests
2. Identifying social rank stability across time in dominance hierarchies using rank-based overlap
3. Modeling heterogeneity in the dynamics of neural decisions
4. Machine learning of ant task classification and dynamic communities
5. Mechanisms underlying the recovery of group cohesion in a semi-free ranging herd of domestic goats
6. Pattern of information transfer in acellular slime mold
7. Collective pulsing behavior in xeniid corals
We encourage manuscripts addressing similar themes in the broad field of biology to complement this collection.
