Biology subjects

Dang, W.

Publications and source records attributed to Dang, W..

3 recordsLinked to original sources

bioRxiv2020

Protein-protein interaction probability landscapes for yeast replicative aging

We proposed a novel probability landscape approach to map the systems-level profile changes of gene networks during replicative aging in Saccharomyces cerevisiae. This approach enabled us to apply quasi-potentials, the negative logarithm of the probabilities, to calibrate the elevation of the landscapes with young cells as a reference state. Our approach detected opposite landscape changes based on protein abundances from transcript levels, especially for intra-essential gene interactions. We showed that essential proteins play different roles from hub proteins on the age-dependent landscapes. We verified that hub proteins tend to avoid other hub proteins, but essential proteins are attractive to other essential proteins. Overall, we showed that the probability landscape is promising for inferring network profile change during aging and that the essential hub proteins may play an important role in the uncoupling between protein and transcript levels during replicative aging.

systems biology↗

bioRxiv2020

Emergence of Non-Linear Mixed Selectivity in Prefrontal Cortex after Training

Neurons in the prefrontal cortex (PFC) are typically activated by different cognitive tasks, and also by different stimuli and abstract variables within these tasks. A single neurons selectivity for a given stimulus dimension often changes depending on its context, a phenomenon known as nonlinear mixed selectivity (NMS). It has previously been hypothesized that NMS emerges as a result of training to perform tasks in different contexts. We tested this hypothesis directly by examining the neuronal responses of different PFC areas before and after monkeys were trained to perform different working memory tasks involving visual stimulus locations and/or shapes. We found that training induces a modest increase in the proportion of PFC neurons with NMS exclusively for spatial working memory, but not shape working memory tasks, with area 9/46 undergoing the most significant increase in NMS cell proportion. We also found that increased working memory task complexity, in the form of simultaneously storing location and shape combinations, does not increase the degree of NMS for stimulus shape with other task variables. Lastly, in contrast to the previous studies, we did not find evidence that NMS is predictive of task performance. Our results thus provide critical insights on the representation of stimuli and task information in neuronal populations, which may pave the way to a greater understanding of neural selectivity in working memory. SIGNIFICANCE STATEMENTHow multiple types of information are represented in working memory remains a complex computational problem. It has been hypothesized that nonlinear mixed selectivity allows neurons to efficiently encode multiple stimuli in different contexts, after subjects have been trained in complex tasks. Our analysis of prefrontal recordings obtained before and after training monkeys to perform working memory tasks only partially agreed with this prediction, in that nonlinear mixed selectivity emerged for spatial but not shape information, and mostly in mid-dorsal PFC. Nonlinear mixed selectivity also displayed little modulation across either task complexity or correct performance. These results point to other mechanisms, in addition to nonlinear mixed selectivity, to represent complex information about stimulus and context in neuronal activity.

neuroscience↗

bioRxiv2019

Complementary Performances of Convolutional and Capsule Neural Networks on Classifying Microfluidic Images of Dividing Yeast Cells

Microfluidic-based assays have become effective high-throughput approaches to examining replicative aging of budding yeast cells. Deep learning may offer an efficient way to analyze a large number of images collected from microfluidic experiments. Here, we compare three deep learning architectures to classify microfluidic time-lapsed images of dividing yeast cells into categories that represent different stages in the yeast replicative aging process. We found that convolutional neural networks outperformed capsule networks in terms of accuracy, precision, and recall. The capsule networks had the most robust performance at detecting one specific category of cell images. An ensemble of three best-fitted single-architecture models achieves the highest overall accuracy, precision, and recall due to complementary performances. In addition, extending classification classes and augmentation of the training dataset can improve the predictions of the biological categories in our study. This work lays a useful framework for sophisticated deep-learning processing of microfluidics-based assays of yeast replicative aging.

bioinformatics↗