Marianne Menglin Liu, Daniel Garcia, Fjona Parllaku, Vikas Upadhyay, Fahad Shah, Dan Roth. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Computational pathway design and retro-biosynthetic approaches can facilitate the development of innovative biochemical production routes, biodegradation strategies, and the funneling of multiple precursors into a single bioproduct. However, effective pathway design necessitates a comprehensive understanding of biochemistries, enzyme activities, and thermodynamic feasibility. Herein, we introduce novoStoic2.0, an integrated platform that combines tools for estimating overall stoichiometry, designing de novo synthesis pathways, assessing thermodynamic feasibility, and selecting enzymes. novoStoic2.0 offers a unified web-based interface as a part of the AlphaSynthesis platform (http://novostoic.platform.moleculemaker.org/) tailored for the synthesis of thermodynamically viable pathways as well as the selection of enzymes for re-engineering required for novel reaction steps. We exemplify the utility of the platform to identify novel pathways for hydroxytyrosol synthesis, which are shorter than the known pathways and require reduced cofactor usage. In summary, novoStoic2.0 aims to streamline the process of pathway design contributing to the development of sustainable biotechnological solutions.

Two-dimensional pin array displays enable access to tactile graphics that are important for the education of students with visual impairments. Due to their prohibitive cost and limited access, there is limited research within HCI and the rules to design graphics on these low-resolution tactile displays are unclear. In this paper, eight tactile readers with visual impairments qualitatively evaluate the implementation of Pixel Art to create tactile graphical primitives on a pin array display. Every pin of the pin array is assumed to be a pixel on a pixel grid. Our findings suggest that Pixel Art tactile graphics on a pin array are clear and comprehensible to tactile readers, positively confirming its use to design basic tactile shapes and line segments. The guidelines provide a consistent framework to create tactile media which implies that they can be used to downsize basic shapes for refreshable pin-array displays.

Estimating the location of a mobile device using Bluetooth signal strength observations is an active research area due to its promising application in location-based services. Field deployment of localization systems is facing many challenges. This includes optimal placement of sensors to improve the localization coverage and modeling the uncertainty in wireless signals given the complexity of signal propagation in the presence of heterogeneous factors like operating environment and obstacles. In this paper, we first present a heuristics-based technique for the placement of beacons assuming map data is available. Our beacon placement algorithm results in a significant improvement in configuration time and coverage while using a smaller number of beacons. Further, we propose a Gaussian-process-based novel hybrid kernel function to generate a robust likelihood model for signal strength estimation. Our experiments over 83 randomly sampled data points demonstrate a superior ranging and localization performance of our approach.

Group contribution (GC) methods are conventionally used in thermodynamics analysis of metabolic pathways to estimate the standard Gibbs energy change (ΔrG'o) of enzymatic reactions from limited experimental measurements. However, these methods are limited by their dependence on manually curated groups and inability to capture stereochemical information, leading to low reaction coverage. Herein, we introduce an automated molecular fingerprint-based thermodynamic analysis tool called dGPredictor that enables the consideration of stereochemistry within metabolite structures and thus increases reaction coverage. dGPredictor has comparable prediction accuracy compared to existing GC methods and can capture Gibbs energy changes for isomerase and transferase reactions, which exhibit no overall group changes. We also demonstrate dGPredictor's ability to predict the Gibbs energy change for novel reactions and seamless integration within de novo metabolic pathway design tools such as novoStoic for safeguarding against the inclusion of reaction steps with infeasible directionalities. To facilitate easy access to dGPredictor, we developed a graphical user interface to predict the standard Gibbs energy change for reactions at various pH and ionic strengths. The tool allows customized user input of known metabolites as KEGG IDs and novel metabolites as InChI strings (https://github.com/maranasgroup/dGPredictor).