Biology subjects

Goodman, N.

Publications and source records attributed to Goodman, N..

4 recordsLinked to original sources

bioRxiv2020

TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data

Uncovering how transcription factors (TFs) regulate their targets at the DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) in normal and diseased states. RNA-seq has become a standard method to measure gene regulation using an established set of analysis steps. However, none of the currently available pipeline methods for interpreting ordered genomic data (in time or space) use time series models to assign cause and effect relationships within GRNs, are adaptive to diverse experimental designs, or enable user interpretation through a web-based platform. Furthermore, methods which integrate ordered RNA-seq data with transcription factor binding data are urgently needed. Here, we present TIMEOR (Trajectory Inference and Mechanism Exploration with Omics data in R), the first web-based and adaptive time series multi-omics pipeline method which infers the relationship between gene regulatory events across time. TIMEOR addresses the critical need for methods to predict causal regulatory mechanism networks between TFs from time series multi-omics data. We used TIMEOR to identify a new link between insulin stimulation and the circadian rhythm cycle. TIMEOR is available at https://github.com/ashleymaeconard/TIMEOR.git.

bioinformatics↗

bioRxiv2019

A conserved ATG2-GABARAP interaction is critical for phagophore closure

The intracellular trafficking pathway, macroautophagy, acts as a recycling and disposal service that can be upregulated during periods of stress, to maintain cellular homeostasis. An essential transition point in the pathway is the sealing of the immature phagophore to form an autophagosome, isolating unwanted cargo prior to lysosomal degradation. However, little mechanistic detail is known about phagophore closure. Human ATG2A and ATG2B proteins, through their interaction with WIPI proteins, are thought to be key players during phagophore closure. We have identified a highly-conserved motif driving the interaction between human ATG2 and GABARAP proteins that is in close proximity to the ATG2-WIPI4 interaction site. We show that the ATG2-GABARAP interaction mutants are unable to close phagophores resulting in blocked autophagy, similar to ATG2A/ATG2B double knock-out cells. In contrast, the ATG2-WIPI4 interaction mutant fully restored phagophore closure and autophagy flux, similar to wild type ATG2. Taken together, we provide new mechanistic insights to the requirements for ATG2 function at the phagophore and suggest that an ATG2-GABARAP interaction is essential for phagophore closure, whereas ATG2-WIPI4 interaction is dispensable.

cell biology↗

bioRxiv2017

Amortized Hypothesis Generation

Bayesian models of cognition posit that people compute probability distributions over hypotheses, possibly by constructing a sample-based approximation. Since people encounter many closely related distributions, a computationally efficient strategy is to selectively reuse computations - either the samples themselves or some summary statistic. We refer to these reuse strategies as amortized inference. In two experiments, we present evidence consistent with amortization. When sequentially answering two related queries about natural scenes, we show that answers to the second query vary systematically depending on the structure of the first query. Using a cognitive load manipulation, we find evidence that people cache summary statistics rather than raw sample sets. These results enrich our notions of how the brain approximates probabilistic inference.

animal behavior and cognition↗

bioRxiv2017

Beyond Reward Prediction Errors: Human Striatum Updates Rule Values During Learning

Humans naturally group the world into coherent categories defined by membership rules. Rules can be learned implicitly by building stimulus-response associations using reinforcement learning (RL) or by using explicit reasoning. We tested if the striatum, in which activation reliably scales with reward prediction error, would track prediction errors in a task that required explicit rule generation. Using functional magnetic resonance imaging during a categorization task, we show that striatal responses to feedback scale with a \"surprise\" signal derived from a Bayesian rule-learning model and are inconsistent with RL prediction error. We also find that striatum and caudal inferior frontal sulcus (cIFS) are involved in updating the likelihood of discriminative rules. We conclude that the striatum, in cooperation with the cIFS, is involved in updating the values assigned to categorization rules when people learn using explicit reasoning.

neuroscience↗