Neuropeptides as potential modulators of behavioral transitions in the ant Cataglyphis nodus

AbstractDifferential gene expression has been associated with transitions between behavioral states for a wide variety of organisms and behaviors. Heterochrony, genetic toolkits, and predictable pathways underlying behavioral transitions have been hypothesized to explain the relationship between transcription and behavioral changes. Less studied is how variation in transcription is related to variation within a behavior, and if the genes that are associated with this variation are predictable. Here, we adopt an evolutionary systems biology perspective to address 2 hypotheses relating differential expression to changes within and between behavior. We predicted fewer genes will be associated with variation within a behavior than with transitions between states, and the genes underlying variation within a behavior will represent a narrower set of biological functions. We tested for associations with parenting variation within a state with a set of genes known a priori to be differentially expressed (DE) between parenting states in the burying beetle Nicrophorus vespilloides. As predicted, we found that far fewer genes are DE related to variation within parenting. Moreover, these were not randomly distributed among categories or pathways in the gene set we tested and primarily involved genes associated with neurotransmission. We suggest that this means candidate genes will be easier to identify for associations within a behavior, as descriptions of behavioral state may include more than a single phenotype.

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Are There Transitional Forms in the Fossil Record?

The Paleontological Society Papers ◽

10.1017/s108933260000053x ◽

1999 ◽

Vol 5 ◽

pp. 47-82

Author(s):

Kevin Padian ◽

Kenneth D. Angielczyk

Keyword(s):

Fossil Record ◽

Intelligent Design ◽

Cladistic Analysis ◽

Sampling Bias ◽

Erosion And Deposition ◽

Scientific Methods ◽

Incomplete Knowledge ◽

Behavioral Transitions ◽

History Of ◽

Transitional Forms

The record of the history of life, as preserved in the fossil record, is not complete for reasons related to erosion and deposition, preservation and sampling bias, and approaches to analysis of the information provided by fossils. Incomplete knowledge is not unique to paleontology; the record of extant humans is no better for many questions of human genealogy. The problem is not that there are no or few transitional fossils; it is rather that, given the incompleteness of the fossil record, it is unreasonable to expect to find transitions of forms rather than transitions of features. The use of cladistic analysis largely overcomes this problem methodologically, but does not itself improve the fossil record. However, when the characters of fossil and living taxa are analyzed cladistically, they can tell us not only the sequence of origination of clades, but also how functional, adaptational, physiological, and behavioral transitions took place. In this way, hypotheses about the origins of major groups and major adaptations can be tested by standard scientific methods. In contrast, notions of the ontology of these groups as explained by “Intelligent Design” are vacuous and untestable.

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Sketching the Power of Machine Learning to Decrypt a Neural Systems Model of Behavior

Brain Sciences ◽

10.3390/brainsci9030067 ◽

2019 ◽

Vol 9 (3) ◽

pp. 67 ◽

Cited By ~ 2

Author(s):

Monique Ernst ◽

Joshua Gowin ◽

Claudie Gaillard ◽

Ryan Philips ◽

Christian Grillon

Keyword(s):

Machine Learning ◽

Large Data ◽

Neural Systems ◽

Complex Processes ◽

Systems Model ◽

Motivated Behavior ◽

Behavioral Transitions ◽

The Neural Networks ◽

Ultimate Objective ◽

Brain Behavior

Uncovering brain-behavior mechanisms is the ultimate goal of neuroscience. A formidable amount of discoveries has been made in the past 50 years, but the very essence of brain-behavior mechanisms still escapes us. The recent exploitation of machine learning (ML) tools in neuroscience opens new avenues for illuminating these mechanisms. A key advantage of ML is to enable the treatment of large data, combing highly complex processes. This essay provides a glimpse of how ML tools could test a heuristic neural systems model of motivated behavior, the triadic neural systems model, which was designed to understand behavioral transitions in adolescence. This essay previews analytic strategies, using fictitious examples, to demonstrate the potential power of ML to decrypt the neural networks of motivated behavior, generically and across development. Of note, our intent is not to provide a tutorial for these analyses nor a pipeline. The ultimate objective is to relate, as simply as possible, how complex neuroscience constructs can benefit from ML methods for validation and further discovery. By extension, the present work provides a guide that can serve to query the mechanisms underlying the contributions of prefrontal circuits to emotion regulation. The target audience concerns mainly clinical neuroscientists. As a caveat, this broad approach leaves gaps, for which references to comprehensive publications are provided.

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