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Biology subjects

Daniels, B. C.

Publications and source records attributed to Daniels, B. C..

4 recordsLinked to original sources

Tuning regimes in ant foraging dynamics depend on the existence of bistability

Characterizing how behavior must be tuned to produce useful coordination is key to understanding the evolution and regulation of collective behavior. While computational models can answer this question for specific instances, recurring patterns in model dynamics hint at a more general means of classifying collective dynamics. Using ant foraging models as a foundational example, we investigate mechanisms that can produce symmetry-breaking transitions to bistability as a first basic classification of collective behavior. Collective transitions are functionally important: They lead to sudden changes in collective states, enhanced sensitivity to environmental inputs, and hysteresis. We use bifurcation theory to argue that the point at which discontinuous transitions merge at a continuous transition forms a codimension-2 bifurcation with universal properties, and that this point is functionally equivalent to the critical point of a phase diagram. We show how analogous bistable transitions appear across models of ant foraging with different mechanistic assumptions, and we explore how biologically relevant collective effects play out near the transition. This framework clarifies the difficulty of tuning collective behavior: locating a continuous transition typically requires tuning two parameters, while a discontinuous transition requires tuning only one. Finally, we explore conditions that degrade or destroy bistable transitions: heterogeneity blurs the transitions, while recruitment mechanisms that do not create a positive feedback loop do not display bistability at all.

systems biology↗

The coexistence of localized and distributed behavioral information in neural activity

The degree to which control of an animals behavior is localized within particular neurons or distributed over large populations is central to understanding mechanisms of decision-making in brains. A first step in answering this question comes from understanding the scales at which neural activity is predictive of behavior. Here, we demonstrate how information measures at the individual, pairwise, and larger group levels characterize the localization of predictive information. We demonstrate these tools using high-dimensional neural data related to nematode and macaque behavioral decisions. Intriguingly, in both examples we find that similar behavioral information coexists across scales: the same information can be extracted from small groups of individually informative neurons or larger groups of randomly chosen neurons that individually have little predictive power. Our results suggest that methods for causal inference may miss potential causal pathways if they are biased toward finding localized control mechanisms.

neuroscience↗

Identifying a developmental transition in honey bees using gene expression data

In many organisms, interactions among genes lead to multiple functional states, and changes to interactions can lead to transitions into new states. These transitions can be related to bifurcations (or critical points) in dynamical systems theory. Characterizing these collective transitions is a major challenge for systems biology. Here, we develop a statistical method for identifying bistability near a continuous transition directly from high-dimensional gene expression data. We apply the method to data from honey bees, where a known developmental transition occurs between bees performing tasks in the nest and leaving the nest to forage. Our method, which makes use of the expected shape of the distribution of gene expression levels near a transition, successfully identifies the emergence of bistability and links it to genes that are known to be involved in the behavioral transition. This proof of concept demonstrates that going beyond correlative analysis to infer the shape of gene expression distributions might be used more generally to identify collective transitions from gene expression data.

systems biology↗

Automated, predictive, and interpretable inference of C. elegans escape dynamics

The roundworm C. elegans exhibits robust escape behavior in response to rapidly rising temperature. The behavior lasts for a few seconds, shows history dependence, involves both sensory and motor systems, and is too complicated to model mechanistically using currently available knowledge. Instead we model the process phenomenologically, and we use the Sir Isaac dynamical inference platform to infer the model in a fully automated fashion directly from experimental data. The inferred model requires incorporation of an unobserved dynamical variable, and is biologically interpretable. The model makes accurate predictions about the dynamics of the worm behavior, and it can be used to characterize the functional logic of the dynamical system underlying the escape response. This work illustrates the power of modern artificial intelligence to aid in discovery of accurate and interpretable models of complex natural systems.

systems biology↗