Biology subjects

Wild, B.

Publications and source records attributed to Wild, B..

6 recordsLinked to original sources

bioRxiv2025

Biologically Guided Variational Inference for Interpretable Multimodal Single-Cell Integration and Mechanistic Discovery

Multi-omics technologies allow detailed characterization of cell types and states across omics layers as well as chemical and genetic perturbations. Variational autoencoders have become a cornerstone of single-cell data integration; however, they are often implemented as black-box models, requiring post hoc interpretation using known markers, pathways, or regulators to make sense of their latent representations. NetworkVI fundamentally flips this paradigm by incorporating biological knowledge directly into the model architecture. By embedding co-regulation networks derived from topologically associated domains and structured ontologies such as the Gene Ontology (GO), NetworkVI introduces a biologically-informed inductive bias that promotes the preservation of meaningful variation during integration while enforcing interpretability at both the gene and GO levels. NetworkVI achieves state-of-the-art data integration, modality imputation, and cell label transfer across bimodal and trimodal datasets. Beyond integration, here we show that NetworkVI facilitates ontology-guided hypothesis generation by exploiting established associations between genes, structured cellular programs, and regulatory domains to interpretably model cellular identities. Furthermore, decomposition of GO activation spaces resolves lineage-specific functional states within immune cell types, including quiescent, inflammatory, and transitional monocyte subpopulations, that are invisible to transcriptomic clustering. NetworkVI prioritizes GO-term programs associated with immunosenescence, consistent with age-associated immune dysregulation and reveals candidate immune evasion mechanisms consistent with CD58 loss in a Perturb-CITE-seq melanoma dataset.

bioinformatics↗

bioRxiv2024

Collective flow of circadian clock information in honeybee colonies

Honeybee colonies exhibit a collective circadian rhythm reflecting the periodic dynamics of the environment. Thousands of workers, including those engaged in in-hive tasks, must synchronize in various processes that may be rhythmic, such as nectar inflows, or non-rhythmic, such as brood care but it remains unknown how those different rhythms are integrated into a colony-level circadian rhythm. Using an AI-driven automated tracking system, we obtained uninterrupted long-term tracking of all individuals in two honeybee colonies. We demonstrate that circadian rhythmicity is present across all age groups and that this rhythm is entrained into all individuals, however, with peak activity shifting by up to 2 hours in workers furthest from the entrance. Extensive data analysis and an agent-based model suggest that mechanical interactions between individuals facilitate the transfer of movement speed, and hence Zeitgeber information. Finally, we show that this speed transfer leads to a collective slow wave of activity that initiates at the nest entrance, spreading throughout the nest. This simple mechanism, workers bumping into each other, enables colonies to entrain their rhythm to the daily cycle of the external environment and, because of the spatial organization of the nest, activates different groups of workers sequentially. The speed transfer interactions demonstrate a tightly-tuned mechanism that underlines the elegant self-organization of the superorganism.

animal behavior and cognition↗

bioRxiv2023

Honey bee drones are synchronously hyperactive inside the nest

1Eusocial insects operate as an integrated collective with tasks allocated among individuals. This applies also to reproduction, through coordinated mating flights between male and female reproductives. While in some species male sexuals take only a single mating flight and never return, in the Western honey bee, Apis mellifera, the male sexuals (drones) live in the colony throughout their lives. Prior research has focused almost exclusively on drone behavior outside of the nest (mating flights), while ignoring the majority of their life, which is spent inside the nest. To understand the in-nest behavior of drones across their lives, we used the BeesBook tracking system to track 192 individually-marked drones continuously for over 20 days, to examine how drones moved and spent time in the nest. In agreement with previous work, we find that drones spend most of their time immobile at the nest periphery. However, we also observe that drones have periods of in-nest hyperactivity, during which they become the most active individuals in the entire colony. This in-nest hyperactivity develops in drones after age 7 days, occurs daily in the afternoon, and coincides with drones taking outside trips. We find strong synchronization across the drones in the start/end of activity, such that the drones in the colony exhibit a "shared activation period". The duration of the shared activation period depends on the weather; when conditions are suitable for mating flights, the activation period is extended. At the individual-level, we see that the activation order changes from day to day, suggesting that both the external influence of weather conditions, as well as exchange of social information, influences individual activation. Using an accumulation-to-threshold model of drone activation, we show that simulations using social information match experimental observations. These results provide new insight into the in-nest behavior of drones, and how their behavior reflects their role as the male gametes of the colony.

animal behavior and cognition↗

bioRxiv2021

Learning to embed lifetime social behavior from interaction dynamics

Interactions of individuals in complex social systems give rise to emergent behaviors at the group level. Identifying the functional role that individuals take in the group at a specific time facilitates understanding the dynamics of these emergent processes. An individuals behavior at a given time can be partially inferred by common factors, such as age, but internal and external factors also substantially influence behavior, making it difficult to disentangle common development from individuality. Here we show that such dependencies on common factors can be used as an implicit bias to learn a temporally consistent representation of a functional role from social interaction networks. Using a unique dataset containing lifetime trajectories of multiple generations of individually-marked honey bees in two colonies, we propose a new temporal matrix factorization model that jointly learns the average developmental path and structured variations of individuals in the social network over their entire lives. Our method yields inherently interpretable embeddings that are biologically relevant and consistent over time, allowing one to compare individuals functional roles regardless of when or in which colony they lived. Our method provides a quantitative framework for understanding behavioral heterogeneity in complex social systems, and is applicable to fields such as behavioral biology, social sciences, neuroscience, and information science. Author summaryGroup-level emergent behaviors are the result of interactions between individual group members. To understand these social dynamics, one must objectively measure the function of an individual in their group at any given time. Ideally, one would also like to compare individuals from different groups, for example, to measure how specific environmental conditions or other external factors influence group behavior. Unfortunately, such an objective measure is hard to obtain because the group and its dynamics constantly change, making it challenging to define an individuals role in the group as a function of its actions and interactions. We propose a principled approach to model individuals in complex social systems by considering that function often depends, at least partially, on common factors such as age. The model learns a meaningful and interpretable descriptor for all individuals, and can be used to understand how complex social systems function and the emergence of group behavior.

animal behavior and cognition↗

bioRxiv2021

The dominant axes of lifetime behavioral variation in honey bees

Insect colonies are decentralized systems that employ task allocation, whereby individuals undertake different roles to fulfil colony needs, such as honey bee "nurses", "nest workers", and "foragers". However, the extent to which individuals can be well-classified by discrete "roles", how they change behavior from day-to-day, over entire lifetimes, and with environmental conditions, is poorly understood. Using long-term automated tracking of over 4,200 individually-identified bees Apis mellifera, we use behavioral metrics to quantify and compare behavior. We show that individuals exhibit behavioral variation along two dominant axes that represent nest substrate use and movement within the nest. Across lifetimes, we find that individuals differ in foraging onset, and that certain bees exhibit lifelong consistencies in their movement patterns. Furthermore, we examine a period of sudden nectar availability where the honey stores tripled over 6 days, and see that the colony exhibits a distributed shift in activity that did not require a large-scale colony reorganization. Our quantitative approach shows how collective units differ over days and lifetimes, and how sources of variation and variability contribute to the colonys robust yet flexible response.

animal behavior and cognition↗

bioRxiv2020

Social networks predict the life and death of honey bees

In complex societies, individuals roles are reflected by interactions with other conspecifics. Honey bees (Apis mellifera) generally change tasks as they age, but developmental trajectories of individuals can vary drastically due to physiological and environmental factors. We introduce a succinct descriptor of an individuals social network that can be obtained without interfering with the colony. This network age accurately predicts task allocation, survival, activity patterns, and future behavior. We analyze developmental trajectories of multiple cohorts of individuals in a natural setting and identify distinct developmental pathways and critical life changes. Our findings suggest a high stability in task allocation on an individual level. We show that our method is versatile and can extract different properties from social networks, opening up a broad range of future studies. Our approach highlights the relationship of social interactions and individual traits, and provides a scalable technique for understanding how complex social systems function.

animal behavior and cognition↗