Engineering topics

Gu, Pengfei

Publications and source records attributed to Gu, Pengfei.

3 records

DOE OSTI2024

Current and future directions in network biology

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

NeRVI: Compressive neural representation of visualization images for communicating volume visualization results

We present NeRVI, a new deep-learning approach that compresses a large collection of visualization images generated from time-varying data for communicating volume visualization results. Based on an image-based implicit neural representation, our approach represents tens of thousands of high-resolution rendering images parametrized by different parameters via a hybrid model of multilayer perceptrons and convolutional neural networks. Here, our model predicts images and corresponding masks, and the masks are utilized for loss computation and network training to capture fine structural details and small components. In conjunction with model quantization and weight encoding, NeRVI yields highly compact compressive neural representations while preserving the image fidelity well. We demonstrate the effectiveness of NeRVI with isosurface rendering and direct volume rendering images generated from multiple data sets and compare NeRVI with other state-of-the-art deep learning-based (InSituNet, SIREN, NeRF, and NeRV) methods. Quantitative and qualitative results show that NeRVI provides an alternative solution that augments domain scientists' ability to manage, represent, and communicate scientific visualization output.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2018

Developing a Cas9-Based Tool to Engineer Native Plasmids in Synechocystis sp. PCC 6803

The oxygenic photosynthetic bacterium Synechocystis sp. PCC 6803 (S6803) is a model cyanobacterium widely used for fundamental research and biotechnology applications. Due to its polyploidy, existing methods for genome engineering of S6803 require multiple rounds of selection to modify all genome copies, which is time consuming and inefficient. In this study, we engineered the Cas9 tool for onestep, segregationfree genome engineering. We further used our Cas9 tool to delete three of seven S6803 native plasmids. Our results show that all three smallsize native plasmids, but not the largesize native plasmids, can be deleted with this tool. To further facilitate heterologous gene expression in S6803, a shuttle vector based on the native plasmid pCC5.2 was created. The shuttle vector can be introduced into Cas9containing S6803 in one step without requiring segregation and can be stably maintained without antibiotic pressure for at least 30 days. Moreover, genes encoded on the shuttle vector remain functional after 30 days of continuous cultivation without selective pressure. Thus, this study provides a set of new tools for rapid modification of the S6803 genome and for stable expression of heterologous genes, potentially facilitating both fundamental research and biotechnology applications using S6803.

biotechnology↗