[
International Worm Meeting,
2017]
A brain activity can be measured as a large number of neural activities using simultaneous optical monitoring, which is described as a time series vector data of many neural activities. Behavior, the final output of neural activities, however, is still poorly described, often just with one or a few parameters chosen subjectively. This huge asymmetry in data of neural activity and behavior is caused by the difficulty in analysis of behavior: Which aspects of a behavior such as speed, direction, their duration and changing rate, are related to animal's brain activities like sensory perception, memory, and decision-making? Behavioral analyses using machine learning have been reported to classify animal's behavioral states or behavioral patterns (e.g., Brown et al., PNAS 2013), although they have not yet provided clues to understand the causal relationship between stimuli and behavioral response. To explore new methods for a comprehensive analysis of animals' behavioral response to environmental stimuli in collaboration with data science, we applied 3 classic and cutting-edge machine learning methods-decision tree, deep neural network (DNN), and pattern mining-on worms' repulsive odor learning (Kimura et al., J Neurosci 2010). (1) In decision tree analysis, researchers specify a number of features, and the algorithm chooses the ones that effectively classify two groups. We found that naive worms stop straight migration upon sensing a slight increase in the repulsive odor concentration (dC/dt > 0), although learned worms do not respond to it and continue the migration, suggesting that they ignore the "yellow signal". In addition, multiple mutant strains were classified based on the characteristic patterns of component change. (See Yamazaki et al. this meeting.) (2) Using DNN for time series data analysis, we found that, although the speed of naive worms is relatively constant, that of learned worms changed more vigorously in windows of tens of seconds even though the average speeds were the same. The result may suggest that the learned worms avoid the odor using "accelerator and brake" more effectively. (See Maekawa et al. this meeting.) (3) In discriminative pattern mining, we represented odor concentration change and behavioral responses per second as 30 combinations, and found prominently discriminative behavior patterns between wild-type and mutant worms from an extremely large number of possible patterns by using a new sparse estimation technique. In summary, each machine learning method efficiently provided novel and critical knowledge that cannot be obtained from traditional analyses. Such objective comprehensive study of behavioral response, or "behavioromics," allows us to concentrate on key features of behavior. Currently, we intend to apply the above methods for the behavioral analysis of other animals, such as long-travelling sea birds, disease model mice, and humans.
[
International Worm Meeting,
2009]
FOG-3 TOB/BTG family protein is essential for sperm fate specification in C. elegans (Chen et al. 2000) and the primary C. elegans ERK/MAPK, known as MPK-1, has also been implicated in sperm fate specification (Lee et al. 2007). We have obtained several lines of genetic evidence that suggest a key role for MPK-1 in controlling the sperm fate. To learn how MPK-1 might function in this important cell fate decision, we examined the FOG-3 amino acid sequence and found a potential MAPK-docking site and four predicted MAPK phosphorylation sites (i.e S/TP). We then test the idea that MPK-1 might control the sperm fate by FOG-3 phosphorylation. In vitro, we have found that FOG-3 can be directly phosphorylated by murine ERK/MAP kinase; in vivo, we have generated a battery of FOG-3 transgenes and found that FOG-3 phosphorylation is critical for sperm fate specification. Importantly mammalian Tob is also a substrate of MAPK (Maekawa et al. 2002; Suzuki et al. 2002). We suggest therefore that the C. elegans germline may use an ancient regulatory cassette for control of the sperm/oocyte decision. References: Lee et al. (2007) Multiple functions and dynamic activation of MPK-1 extracellular signal-regulated kinase signaling in Caenorhabditis elegans germline development. Genetics 177, 2039-62. Maekawa et al. (2002) Identification of the anti-proliferative protein Tob as a MAPK substrate. J Biol Chem 277, 37783-7. Suzuki et al. (2002) Phosphorylation of three regulatory serines of Tob by Erk1 and Erk2 is required for Ras-mediated cell proliferation and transformation. Genes Dev 16, 1356-70. Chen et al. (2000) A novel member of the Tob family of proteins controls sexual fate in Caenorhabditis elegans germ cells. Dev Biol 217, 77-90.