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1. From Bench to Keyboard and Back Again: A Brief History of Lambda Phage Modeling

   https://doi.org/10.1146/annurev-biophys-082020-063558  

   Cortes, Michael G.; Lin, Yiruo; Zeng, Lanying; Balázsi, Gábor (May 2021, Annual Review of Biophysics)

   Cellular decision making is the process whereby cells choose one developmental pathway from multiple possible ones, either spontaneously or due to environmental stimuli. Examples in various cell types suggest an almost inexhaustible plethora of underlying molecular mechanisms. In general, cellular decisions rely on the gene regulatory network, which integrates external signals to drive cell fate choice. The search for general principles of such a process benefits from appropriate biological model systems that reveal how and why certain gene regulatory mechanisms drive specific cellular decisions according to ecological context and evolutionary outcomes. In this article, we review the historical and ongoing development of the phage lambda lysis–lysogeny decision as a model system to investigate all aspects of cellular decision making. The unique generality, simplicity, and richness of phage lambda decision making render it a constant source ofmathematical modeling–aided inspiration across all of biology. We discuss the origins and progress of quantitative phage lambda modeling from the 1950s until today, as well as its possible future directions. We provide examples of how modeling enabled methods and theory development, leading to new biological insights by revealing gaps in the theory and pinpointing areas requiring further experimental investigation. Overall, we highlight the utility of theoretical approaches both as predictive tools, to forecast the outcome of novel experiments, and as explanatory tools, to elucidate the natural processes underlying experimental data. 

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2. Single-cell classification using graph convolutional networks

   https://doi.org/10.1186/s12859-021-04278-2  

   Wang, Tianyu; Bai, Jun; Nabavi, Sheida (July 2021, BMC Bioinformatics)

   Abstract BackgroundAnalyzing single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the identification of cell types. With the availability of a huge amount of single cell sequencing data and discovering more and more cell types, classifying cells into known cell types has become a priority nowadays. Several methods have been introduced to classify cells utilizing gene expression data. However, incorporating biological gene interaction networks has been proved valuable in cell classification procedures. ResultsIn this study, we propose a multimodal end-to-end deep learning model, named sigGCN, for cell classification that combines a graph convolutional network (GCN) and a neural network to exploit gene interaction networks. We used standard classification metrics to evaluate the performance of the proposed method on the within-dataset classification and the cross-dataset classification. We compared the performance of the proposed method with those of the existing cell classification tools and traditional machine learning classification methods. ConclusionsResults indicate that the proposed method outperforms other commonly used methods in terms of classification accuracy and F1 scores. This study shows that the integration of prior knowledge about gene interactions with gene expressions using GCN methodologies can extract effective features improving the performance of cell classification. 

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3. Systems and synthetic biology approaches in understanding biological oscillators

   https://doi.org/10.1007/s40484-017-0120-7  

   Li, Zhengda; Yang, Qiong (March 2018, Quantitative Biology)

   BackgroundSelf‐sustained oscillations are a ubiquitous and vital phenomenon in living systems. From primitive single‐cellular bacteria to the most sophisticated organisms, periodicities have been observed in a broad spectrum of biological processes such as neuron firing, heart beats, cell cycles, circadian rhythms, etc. Defects in these oscillators can cause diseases from insomnia to cancer. Elucidating their fundamental mechanisms is of great significance to diseases, and yet challenging, due to the complexity and diversity of these oscillators. ResultsApproaches in quantitative systems biology and synthetic biology have been most effective by simplifying the systems to contain only the most essential regulators. Here, we will review major progress that has been made in understanding biological oscillators using these approaches. The quantitative systems biology approach allows for identification of the essential components of an oscillator in an endogenous system. The synthetic biology approach makes use of the knowledge to design the simplest,de novooscillators in both live cells and cell‐free systems. These synthetic oscillators are tractable to further detailed analysis and manipulations. ConclusionWith the recent development of biological and computational tools, both approaches have made significant achievements. 

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4. Interfacial Tissue Regeneration with Bone

   https://doi.org/10.1007/s11914-024-00859-1  

   Steltzer, Stephanie S.; Abraham, Adam C.; Killian, Megan L. (February 2024, Current Osteoporosis Reports)

   Abstract Purpose of ReviewInterfacial tissue exists throughout the body at cartilage-to-bone (osteochondral interface) and tendon-to-bone (enthesis) interfaces. Healing of interfacial tissues is a current challenge in regenerative approaches because the interface plays a critical role in stabilizing and distributing the mechanical stress between soft tissues (e.g., cartilage and tendon) and bone. The purpose of this review is to identify new directions in the field of interfacial tissue development and physiology that can guide future regenerative strategies for improving post-injury healing. Recent FindingsCues from interfacial tissue development may guide regeneration including biological cues such as cell phenotype and growth factor signaling; structural cues such as extracellular matrix (ECM) deposition, ECM, and cell alignment; and mechanical cues such as compression, tension, shear, and the stiffness of the cellular microenvironment. SummaryIn this review, we explore new discoveries in the field of interfacial biology related to ECM remodeling, cellular metabolism, and fate. Based on emergent findings across multiple disciplines, we lay out a framework for future innovations in the design of engineered strategies for interface regeneration. Many of the key mechanisms essential for interfacial tissue development and adaptation have high potential for improving outcomes in the clinic. 

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5. Emerging applications at the interface of DNA nanotechnology and cellular membranes: Perspectives from biology, engineering, and physics

   https://doi.org/10.1063/5.0027022  

   Wang, Weitao; Arias, D_Sebastian; Deserno, Markus; Ren, Xi; Taylor, Rebecca_E (December 2020, APL Bioengineering)

   DNA nanotechnology has proven exceptionally apt at probing and manipulating biological environments as it can create nanostructures of almost arbitrary shape that permit countless types of modifications, all while being inherently biocompatible. Emergent areas of particular interest are applications involving cellular membranes, but to fully explore the range of possibilities requires interdisciplinary knowledge of DNA nanotechnology, cell and membrane biology, and biophysics. In this review, we aim for a concise introduction to the intersection of these three fields. After briefly revisiting DNA nanotechnology, as well as the biological and mechanical properties of lipid bilayers and cellular membranes, we summarize strategies to mediate interactions between membranes and DNA nanostructures, with a focus on programmed delivery onto, into, and through lipid membranes. We also highlight emerging applications, including membrane sculpting, multicell self-assembly, spatial arrangement and organization of ligands and proteins, biomechanical sensing, synthetic DNA nanopores, biological imaging, and biomelecular sensing. Many critical but exciting challenges lie ahead, and we outline what strikes us as promising directions when translating DNA nanostructures for future in vitro and in vivo membrane applications. 

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