Paper List
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Exactly Solvable Population Model with Square-Root Growth Noise and Cell-Size Regulation
This paper addresses the fundamental gap in understanding how microscopic growth fluctuations, specifically those with size-dependent (square-root) no...
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Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
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Realistic Transition Paths for Large Biomolecular Systems: A Langevin Bridge Approach
This paper addresses the core challenge of generating physically realistic and computationally efficient transition paths between distinct protein con...
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MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python
This work addresses the computational bottleneck in simulating prebiotic RNA reactor dynamics by developing a Python package that tracks sequence moti...
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The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
This paper addresses the critical gap in cystic fibrosis exacerbation management by providing a formal causal framework that integrates expert knowled...
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A Theoretical Framework for the Formation of Large Animal Groups: Topological Coordination, Subgroup Merging, and Velocity Inheritance
This paper addresses the core problem of how large, coordinated animal groups form in nature, challenging the classical view of gradual aggregation by...
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ANNE Apnea Paper
This paper addresses the core challenge of achieving accurate, event-level sleep apnea detection and characterization using a non-intrusive, multimoda...
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DeeDeeExperiment: Building an infrastructure for integrating and managing omics data analysis results in R/Bioconductor
This paper addresses the critical bottleneck of managing and organizing the growing volume of differential expression and functional enrichment analys...
Hierarchical Molecular Language Models (HMLMs)
Department of Chemical Engineering, University of Arkansas, Fayetteville, AR 72701, USA
The 30-Second View
IN SHORT: This paper addresses the core challenge of accurately modeling context-dependent signaling, pathway cross-talk, and temporal dynamics across multiple biological scales in cellular signaling networks.
Innovation (TL;DR)
- Methodology Introduces cellular signaling as a molecular language with unique grammar and semantics, establishing a theoretical foundation for molecular artificial intelligence (MAI).
- Methodology Develops HMLMs as a novel computational architecture adapting transformer architecture to model signaling networks as information-processing systems across molecular, pathway, and cellular scales.
- Methodology Implements graph-structured attention mechanisms and hierarchical scale-bridging operators (aggregation, decomposition, translation) to accommodate signaling network topology and multi-scale organization.
Key conclusions
- HMLM achieved MSE of 0.058 for temporal signaling predictions, representing 30% improvement over GNNs (0.083) and 52% improvement over ODE models (0.121).
- Under sparse temporal sampling with only 4 timepoints, HMLM maintained superior performance with MSE = 0.041, demonstrating robustness to limited temporal data.
- Attention mechanisms identified biologically plausible pathway interactions including mechanotransduction-MAPK coupling and TGFβ to ERK signaling, validating the model's ability to capture meaningful biological relationships.
Abstract: Cellular signaling networks represent complex information processing systems that have been modeled via traditional mathematical or statistical approaches. However, these methods often struggle to capture context-dependent signaling, pathway cross-talk, and temporal dynamics across multiple biological scales. Here, we introduce hierarchical molecular language models (HMLMs), a novel architecture that proposes a molecular network-specifiac large language model (LLM) to use in intracellular communication as a specialized molecular language, which includes molecules as tokens, protein interactions, post-translational modifications, and regulatory events modeled as semantic relationships within an adapted transformer architecture. HMLMs employ graph-structured attention mechanisms to accommodate signaling network topology while integrating information across the molecular, pathway, and cellular scales through hierarchical attention patterns. We demonstrate HMLM superiority using a cardiac fibroblast signaling network comprising over 100 molecular species across functional modules connected by regulatory edges. HMLM achieved a mean squared error (MSE) of 0.058 for temporal signaling predictions, representing 30% improvement over graph neural networks (GNNs: 0.083) and 52% improvement over ordinary differential equation models (ODEs: 0.121), with particular advantages under sparse temporal sampling conditions where HMLM maintained MSE = 0.041 with only 4 timepoints. The attention-based computational analysis identified key inter-pathway cross-talk patterns through learned attention mechanisms, including mechanotransduction-MAPK coupling and TGFβ to ERK signaling, demonstrating the model's capability to capture biologically plausible pathway interactions from network topology and temporal dynamics and convergent regulatory mechanisms controlling fibrosis markers in simulated cardiac fibroblast networks. The HMLMs offer a foundation for AI-driven biology and medicine with predictable scaling characteristics suitable for interactive applications. By bridging molecular mechanisms with cellular phenotypes through AI-driven molecular language representation, HMLMs provide a powerful paradigm for systems biology that advances precision medicine applications and therapeutic discovery in the era of AI.