Introducing legacy program scripting to molecular biology toolkit (MBT)

Successful navigating of the ever-changing landscape of molecular visualization programs requires a common thread that can offer users an oasis from the maelstrom of new languages, proprietary applications, and miniscule software life-cycles. Crossapplication scripting remains the benchmark, allowing scientists and researchers to speak a common tongue. The introduction of a new, powerful visualization language, Molecular Biology Toolkit (MBT), has tempted many users to abandon previous methodologies and adopt a new mode of research. MBT, however, is not without drawbacks. Its lack of scripting capabilities creates unmanageable complexity for unsophisticated end-users, namely those without the ability to program. MBT, thus, lacks the basic handholds for its widespread acceptance in the molecular visualization community. As a toolkit package without its own mode of execution, its design challenges users to develop their own customized features and applications. However, able to contribute as a text based virtual molecular collection or a fully rendered 3D molecular representation, MBT has the tools researchers want in a new visualization program. Using JavaCC to parse legacy commands and in turn executing MBT methods all from a single, simple command, I have reintroduced scripting to the modern molecular visualization landscape. Combining these two programs, this project takes steps to encourage the exciting molecular manipulations capable in MBT while bridging to a friendly, user-centric scripting patterns required by end-users not entrenched in software development

VOICE: Visual Oracle for Interaction, Conversation, and Explanation

We present VOICE, a novel approach for connecting large language models' (LLM) conversational capabilities with interactive exploratory visualization. VOICE introduces several innovative technical contributions that drive our conversational visualization framework. Our foundation is a pack-of-bots that can perform specific tasks, such as assigning tasks, extracting instructions, and generating coherent content. We employ fine-tuning and prompt engineering techniques to tailor bots' performance to their specific roles and accurately respond to user queries, and a new prompt-based iterative scene-tree generation establishes a coupling with a structural model. Our text-to-visualization method generates a flythrough sequence matching the content explanation. Finally, 3D natural language interaction provides capabilities to navigate and manipulate the 3D models in real-time. The VOICE framework can receive arbitrary voice commands from the user and responds verbally, tightly coupled with corresponding visual representation with low latency and high accuracy. We demonstrate the effectiveness and high generalizability potential of our approach by applying it to two distinct domains: analyzing three 3D molecular models with multi-scale and multi-instance attributes, and showcasing its effectiveness on a cartographic map visualization. A free copy of this paper and all supplemental materials are available at https://osf.io/g7fbr/

MBEToolbox: a Matlab toolbox for sequence data analysis in molecular biology and evolution

BACKGROUND: MATLAB is a high-performance language for technical computing, integrating computation, visualization, and programming in an easy-to-use environment. It has been widely used in many areas, such as mathematics and computation, algorithm development, data acquisition, modeling, simulation, and scientific and engineering graphics. However, few functions are freely available in MATLAB to perform the sequence data analyses specifically required for molecular biology and evolution. RESULTS: We have developed a MATLAB toolbox, called MBEToolbox, aimed at filling this gap by offering efficient implementations of the most needed functions in molecular biology and evolution. It can be used to manipulate aligned sequences, calculate evolutionary distances, estimate synonymous and nonsynonymous substitution rates, and infer phylogenetic trees. Moreover, it provides an extensible, functional framework for users with more specialized requirements to explore and analyze aligned nucleotide or protein sequences from an evolutionary perspective. The full functions in the toolbox are accessible through the command-line for seasoned MATLAB users. A graphical user interface, that may be especially useful for non-specialist end users, is also provided. CONCLUSION: MBEToolbox is a useful tool that can aid in the exploration, interpretation and visualization of data in molecular biology and evolution. The software is publicly available at and

NaviCell: a web-based environment for navigation, curation and maintenance of large molecular interaction maps

Molecular biology knowledge can be systematically represented in a computer-readable form as a comprehensive map of molecular interactions. There exist a number of maps of molecular interactions containing detailed description of various cell mechanisms. It is difficult to explore these large maps, to comment their content and to maintain them. Though there exist several tools addressing these problems individually, the scientific community still lacks an environment that combines these three capabilities together. NaviCell is a web-based environment for exploiting large maps of molecular interactions, created in CellDesigner, allowing their easy exploration, curation and maintenance. NaviCell combines three features: (1) efficient map browsing based on Google Maps engine; (2) semantic zooming for viewing different levels of details or of abstraction of the map and (3) integrated web-based blog for collecting the community feedback. NaviCell can be easily used by experts in the field of molecular biology for studying molecular entities of their interest in the context of signaling pathways and cross-talks between pathways within a global signaling network. NaviCell allows both exploration of detailed molecular mechanisms represented on the map and a more abstract view of the map up to a top-level modular representation. NaviCell facilitates curation, maintenance and updating the comprehensive maps of molecular interactions in an interactive fashion due to an imbedded blogging system. NaviCell provides an easy way to explore large-scale maps of molecular interactions, thanks to the Google Maps and WordPress interfaces, already familiar to many users. Semantic zooming used for navigating geographical maps is adopted for molecular maps in NaviCell, making any level of visualization meaningful to the user. In addition, NaviCell provides a framework for community-based map curation.Comment: 20 pages, 5 figures, submitte

Dynamic Influence Networks for Rule-based Models

We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle.Comment: Accepted to TVCG, in pres

Materials Informatics Transformer: A Language Model for Interpretable Materials Properties Prediction

Recently, the remarkable capabilities of large language models (LLMs) have been illustrated across a variety of research domains such as natural language processing, computer vision, and molecular modeling. We extend this paradigm by utilizing LLMs for material property prediction by introducing our model Materials Informatics Transformer (MatInFormer). Specifically, we introduce a novel approach that involves learning the grammar of crystallography through the tokenization of pertinent space group information. We further illustrate the adaptability of MatInFormer by incorporating task-specific data pertaining to Metal-Organic Frameworks (MOFs). Through attention visualization, we uncover the key features that the model prioritizes during property prediction. The effectiveness of our proposed model is empirically validated across 14 distinct datasets, hereby underscoring its potential for high throughput screening through accurate material property prediction