The Simularium Viewer: an interactive online tool for sharing spatiotemporal biological models

Mesoscale explorer: Visual exploration of large-scale molecular models

The advent of cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET), coupled with computational modeling, has enabled the creation of integrative 3D models of viruses, bacteria, and cellular organelles. These models, composed of thousands of macromolecules and billions of atoms, have historically posed significant challenges for manipulation and visualization without specialized molecular graphics tools and hardware. With the recent advancements in GPU rendering power and web browser capabilities, it is now feasible to render interactively large molecular scenes directly on the web. In this work, we introduce Mesoscale Explorer, a web application built using the Mol* framework, dedicated to the visualization of large-scale molecular models ranging from viruses to cell organelles. Mesoscale Explorer provides unprecedented access and insight into the molecular fabric of life, enhancing perception, streamlining exploration, and simplifying visualization of diverse data types, showcasing the intricate details of these models with unparalleled clarity.

Mesoscale Explorer - Visual Exploration of Large-Scale Molecular Models

The advent of cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET), coupled with computational modeling, has enabled the creation of integrative 3D models of viruses, bacteria, and cellular organelles. These models, composed of thousands of macromolecules and billions of atoms, have historically posed significant challenges for manipulation and visualization without specialized molecular graphics tools and hardware. With the recent advancements in GPU rendering power and web browser capabilities, it is now feasible to render interactively large molecular scenes directly on the web. In this work, we introduce Mesoscale Explorer, a web application built using the Mol* framework, dedicated to the visualization of large-scale molecular models ranging from viruses to cell organelles. Mesoscale Explorer provides unprecedented access and insight into the molecular fabric of life, enhancing perception, streamlining exploration, and simplifying visualization of diverse data types, showcasing the intricate details of these models with unparalleled clarity.

PhysiCell Studio: a graphical tool to make agent-based modeling more accessible

Defining a multicellular model can be challenging. There may be hundreds of parameters that specify the attributes and behaviors of objects. In the best case, the model will be defined using some format specification - a markup language - that will provide easy model sharing (and a minimal step toward reproducibility). PhysiCell is an open-source, physics-based multicellular simulation framework with an active and growing user community. It uses XML to define a model and, traditionally, users needed to manually edit the XML to modify the model. PhysiCell Studio is a tool to make this task easier. It provides a GUI that allows editing the XML model definition, including the creation and deletion of fundamental objects: cell types and substrates in the microenvironment. It also lets users build their model by defining initial conditions and biological rules, run simulations, and view results interactively. PhysiCell Studio has evolved over multiple workshops and academic courses in recent years, which has led to many improvements. There is both a desktop and cloud version. Its design and development has benefited from an active undergraduate and graduate research program. Like PhysiCell, the Studio is open-source software and contributions from the community are encouraged.

3D animation as a tool for integrative modeling of dynamic molecular mechanisms

As the scientific community accumulates diverse data describing how molecular mechanisms occur, creating and sharing visual models that integrate the richness of this information has become increasingly important to help us explore, refine, and communicate our hypotheses. Three-dimensional (3D) animation is a powerful tool to capture dynamic hypotheses that are otherwise difficult or impossible to visualize using traditional 2D illustration techniques. This perspective discusses the current and future roles that 3D animation can play in the research sphere.

PhysiCell Studio: a graphical tool to make agent-based modeling more accessible

Defining a multicellular model can be challenging. There may be hundreds of parameters that specify the attributes and behaviors of objects. Hopefully the model will be defined using some format specification, e.g., a markup language, that will provide easy model sharing (and a minimal step toward reproducibility). PhysiCell is an open source, physics-based multicellular simulation framework with an active and growing user community. It uses XML to define a model and, traditionally, users needed to manually edit the XML to modify the model. PhysiCell Studio is a tool to make this task easier. It provides a graphical user interface that allows editing the XML model definition, including the creation and deletion of fundamental objects, e.g., cell types and substrates in the microenvironment. It also lets users build their model by defining initial conditions and biological rules, run simulations, and view results interactively. PhysiCell Studio has evolved over multiple workshops and academic courses in recent years which has led to many improvements. Its design and development has benefited from an active undergraduate and graduate research program. Like PhysiCell, the Studio is open source software and contributions from the community are encouraged.

MANGEM: A web app for multimodal analysis of neuronal gene expression, electrophysiology, and morphology

Single-cell techniques like Patch-seq have enabled the acquisition of multimodal data from individual neuronal cells, offering systematic insights into neuronal functions. However, these data can be heterogeneous and noisy. To address this, machine learning methods have been used to align cells from different modalities onto a low-dimensional latent space, revealing multimodal cell clusters. The use of those methods can be challenging without computational expertise or suitable computing infrastructure for computationally expensive methods. To address this, we developed a cloud-based web application, MANGEM (multimodal analysis of neuronal gene expression, electrophysiology, and morphology). MANGEM provides a step-by-step accessible and user-friendly interface to machine learning alignment methods of neuronal multimodal data. It can run asynchronously for large-scale data alignment, provide users with various downstream analyses of aligned cells, and visualize the analytic results. We demonstrated the usage of MANGEM by aligning multimodal data of neuronal cells in the mouse visual cortex.

Building the next generation of virtual cells to understand cellular biology

Cell science has made significant progress by focusing on understanding individual cellular processes through reductionist approaches. However, the sheer volume of knowledge collected presents challenges in integrating this information across different scales of space and time to comprehend cellular behaviors, as well as making the data and methods more accessible for the community to tackle complex biological questions. This perspective proposes the creation of next-generation virtual cells, which are dynamic 3D models that integrate information from diverse sources, including simulations, biophysical models, image-based models, and evidence-based knowledge graphs. These virtual cells would provide statistically accurate and holistic views of real cells, bridging the gap between theoretical concepts and experimental data, and facilitating productive new collaborations among researchers across related fields.

Depicting a cellular space occupied by condensates

Condensates have emerged as a new way to understand how cells are organized, and have been invoked to play crucial roles in essentially all cellular processes. In this view, the cell is occupied by numerous assemblies, each composed of member proteins and nucleic acids that preferentially interact with each other. However, available visual representations of condensates fail to communicate the growing body of knowledge about how condensates form and function. The resulting focus on only a subset of the potential implications of condensates can skew interpretations of results and hinder the generation of new hypotheses. Here we summarize the discussion from a workshop that brought together cell biologists, visualization and computation specialists, and other experts who specialize in thinking about space and ways to represent it. We place the recent advances in condensate research in a historical perspective that describes evolving views of the cell; highlight different attributes of condensates that are not well-served by current visual conventions; and survey potential approaches to overcome these challenges. An important theme of these discussions is that the new understanding on the roles of condensates exposes broader challenges in visual representations that apply to cell biological research more generally.

MANGEM: a web app for Multimodal Analysis of Neuronal Gene expression, Electrophysiology and Morphology

Single-cell techniques have enabled the acquisition of multi-modal data, particularly for neurons, to characterize cellular functions. Patch-seq, for example, combines patch-clamp recording, cell imaging, and single-cell RNA-seq to obtain electrophysiology, morphology, and gene expression data from a single neuron. While these multi-modal data offer potential insights into neuronal functions, they can be heterogeneous and noisy. To address this, machine-learning methods have been used to align cells from different modalities onto a low-dimensional latent space, revealing multi-modal cell clusters. However, the use of those methods can be challenging for biologists and neuroscientists without computational expertise and also requires suitable computing infrastructure for computationally expensive methods. To address these issues, we developed a cloud-based web application, MANGEM (Multimodal Analysis of Neuronal Gene expression, Electrophysiology, and Morphology) at https://ctc.waisman.wisc.edu/mangem. MANGEM provides a step-by-step accessible and user-friendly interface to machine-learning alignment methods of neuronal multi-modal data while enabling real-time visualization of characteristics of raw and aligned cells. It can be run asynchronously for large-scale data alignment, provides users with various downstream analyses of aligned cells and visualizes the analytic results such as identifying multi-modal cell clusters of cells and detecting correlated genes with electrophysiological and morphological features. We demonstrated the usage of MANGEM by aligning Patch-seq multimodal data of neuronal cells in the mouse visual cortex.

The dawn of interoperating spatial models in cell biology

Spatial simulations are becoming an increasingly ubiquitous component in the cycle of discovery, experimentation, and communication across the sciences. In cell biology, many researchers share a vision of developing multiscale models that recapitulate observable behaviors spanning from atoms to cells to tissues. For this dream to become a reality, however, simulation technologies must provide a means for integration and interoperability as they advance. Already, the field has developed numerous methods that span scales of length, time, and complexity to create an extensive body of effective simulation approaches, and although these approaches rarely interoperate, they collectively cover a large spectrum of knowledge that future models may handle in a more unified manner. Here, we discuss the importance of making the data, workflows, and outputs of spatial simulations shareable and interoperable; and how democratization could encourage diverse biologists to participate more easily in developing models to advance our understanding of biological systems.