A quantitative understanding of organism-level behaviour requires predictive models that can capture the richness of behavioural phenotypes, yet are simple enough to connect with underlying mechanistic processes. Here, we investigate the motile behaviour of nematodes at the level of their translational motion on surfaces driven by undulatory propulsion. We broadly sample the nematode behavioural repertoire by measuring motile trajectories of the canonical laboratory strain Caenorhabditis elegans N2 as well as wild strains and distant species. We focus on trajectory dynamics over time scales spanning the transition from ballistic (straight) to diffusive (random) movement and find that salient features of the motility statistics are captured by a random walk model with independent dynamics in the speed, bearing and reversal events. We show that the model parameters vary among species in a correlated, low-dimensional manner suggestive of a common mode of behavioural control and a trade-off between exploration and exploitation. The distribution of phenotypes along this primary mode of variation reveals that not only the mean but also the variance varies considerably across strains, suggesting that these nematode lineages employ contrasting ‘bet-hedging’ strategies for foraging.

A quantitative understanding of organism-level behavior requires predictive models that can capture the richness of behavioral phenotypes, yet are simple enough to connect with underlying mechanistic processes. Here we investigate the motile behavior of nematodes at the level of their translational motion on surfaces driven by undulatory propulsion. We broadly sample the nematode behavioral repertoire by measuring motile trajectories of the canonical lab strain C. elegans N2 as well as wild strains and distant species. We focus on trajectory dynamics over timescales spanning the transition from ballistic (straight) to diffusive (random) movement and find that salient features of the motility statistics are captured by a random walk model with independent dynamics in the speed, bearing and reversal events. We show that the model parameters vary among species in a correlated, low-dimensional manner suggestive of a common mode of behavioral control and a trade-off between exploration and exploitation. The distribution of phenotypes along this primary mode of variation reveals that not only the mean but also the variance varies considerably across strains, suggesting that these nematode lineages employ contrasting "bet-hedging" strategies for foraging.

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While measurement advances now allow extensive surveys of gene activity (large numbers of genes across many samples), interpretation of these data is often confounded by noise -expression counts can differ strongly across samples due to variation of both biological and experimental origin. Complimentary to perturbation approaches, we extract functionally related groups of genes by analyzing the standing variation within a sampled population. To distinguish biologically meaningful patterns from uninterpretable noise, we focus on correlated variation and develop a novel density-based clustering approach that takes advantage of a percolation transition generically arising in random, uncorrelated data. We apply our approach to two contrasting RNA sequencing data sets that sample individual variation -across single cells of fission yeast and whole animals of C. elegans worms -and demonstrate robust applicability and versatility in revealing correlated gene clusters of diverse biological origin, including cell cycle phase, development/reproduction, tissue-specific functions, and feeding history. Our technique exploits generic features of noisy high-dimensional data and is applicable, beyond gene expression, to feature-rich data that sample population-level variability in the presence of noise. (180/250) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 standing variation | RNAseq | clustering | random networks | criticality A cornerstone of experimental biology is the perturbation-1 response paradigm, in which targeted manipulations are care-2 fully designed to yield functional and mechanistic insights.3With the recent advent of high-throughput techniques, how-4 ever, the analysis of naturally occurring patterns of variation is 5 emerging as a powerful complementary approach, and has been 6 successfully applied to a variety of problems including protein 7 structure-function mappings (1), gene-network prediction (2), 8 transgenerational memory (3), and aging (4). 9For studies of gene regulatory interactions, a key high-10 throughput technology is RNA sequencing (RNAseq), which 11 allows transcription-level profiling of gene expression on a 12 genome-wide scale. RNAseq experiments conforming to the 13 perturbation-response paradigm -differential analysis of gene 14 expression between manipulated and control conditions -have 15 already transformed our understanding of a wide range of bio-16 logical processes (5-7). With advances in single-cell techniques, 17 RNAseq studies increasingly exploit, beyond perturbation-18 response, information carried by natural variation across in-19 dividuals within unperturbed populations. A major success 20 has been in classifying cells within a heterogeneous population 21 into distinct cell types according to transcriptomic differences 22 (8-13). 23In this study, we address the complementary challenge 24 of identifying the underlying regulatory relationships among 25 genes from the standing variation in expression across sampled 26 individuals. Rather than seeking to fully infer the unde...

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