Gene Regulatory Network Analysis

Derniers papiers

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• Continuous surrogates versus threshold Boolean networks for modeling Arabidopsis ISR gene regulation

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• BRIDGE: Bottleneck-Aware Regulator-Set Inference and Diagnosis for Cooperative Gene Regulatory Recovery

  Maryam Rahimimovassagh, Clayton Thomas Barham, Ivan Garibay, Niloofar Yousefi · 22 juillet 2026

  Cooperative gene regulation often depends on groups of regulators acting jointly, but most gene regulatory network (GRN) inference methods output pairwise regulator-target rankings. We introduce Bottleneck-Aware Regulator-Set Inference and Diagnosis (BRIDGE), a framework for complete regulator-set r…

• Deep and Probabilistic Models for Gene Regulatory Network Inference

  Claudia Skok Gibbs · 20 juillet 2026

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• GenCircuit-RL: Reinforcement Learning from Hierarchical Verification for Genetic Circuit Design

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• CellScientist: Dual-Space Hierarchical Orchestration for Closed-Loop Refinement of Virtual Cell Models

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  Virtual Cell Modeling (VCM) requires models that not only predict perturbation responses, but also support targeted revision when predictions fail. Current LLM-assisted modeling workflows face a refinement-routing problem: prediction discrepancies are observed through executable implementations, but…

• Sequential Design of Genetic Circuits Under Uncertainty With Reinforcement Learning

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  The design of biological systems is hindered by uncertainty arising from both intrinsic stochasticity of biomolecular reactions and variability across laboratory or experimental conditions. In this work, we present a sequential framework to optimize genetic circuits under both forms of uncertainty. …

• BAss: Symbolic Reasoning in Abstract Dialectical Frameworks

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• FP-IRL: Fokker--Planck Inverse Reinforcement Learning -- A Physics-Constrained Approach to Markov Decision Processes

  Chengyang Huang, Siddhartha Srivastava, Kenneth K. Y. Ho, Kathy E. Luker, Gary D. Luker, Xun Huan, Krishna Garikipati · 1 mai 2026

  Inverse reinforcement learning (IRL) is a powerful paradigm for uncovering the incentive structure that drives agent behavior, by inferring an unknown reward function from observed trajectories within a Markov decision process (MDP). However, most existing IRL methods require access to the transitio…

• DPRM: A Plug-in Doob h transform-induced Token-Ordering Module for Diffusion Language Models

  Dake Bu, Wei Huang, Andi Han, Hau-San Wong, Qingfu Zhang, Taiji Suzuki, Atsushi Nitanda · 28 avril 2026

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• Rare Event Analysis via Stochastic Optimal Control

  Yuanqi Du, Jiajun He, Dinghuai Zhang, Eric Vanden-Eijnden, Carles Domingo-Enrich · 16 avril 2026

  Rare events such as conformational changes in biomolecules, phase transitions, and chemical reactions are central to the behavior of many physical systems, yet they are extremely difficult to study computationally because unbiased simulations seldom produce them. Transition Path Theory (TPT) provide…

• Learning Genetic Circuit Modules with Neural Networks: Full Version

  Jichi Wang, Eduardo D. Sontag, Domitilla Del Vecchio · 31 mars 2026

  In several applications, including in synthetic biology, one often has input/output data on a system composed of many modules, and although the modules' input/output functions and signals may be unknown, knowledge of the composition architecture can significantly reduce the amount of training data r…

• Exhaustive Circuit Mapping of a Single-Cell Foundation Model Reveals Massive Redundancy, Heavy-Tailed Hub Architecture, and Layer-Dependent Differentiation Control

  Ihor Kendiukhov · 13 mars 2026

  Mechanistic interpretability of biological foundation models has relied on selective feature sampling, pairwise interaction testing, and observational trajectory analysis. Each of these can introduce systematic bias. Here we present three experiments that address these limitations through exhaustive…

• Sign Identifiability of Causal Effects in Stationary Stochastic Dynamical Systems

  Gijs van Seeventer, Saber Salehkaleybar · 10 mars 2026

  We study identifiability in continuous-time linear stationary stochastic differential equations with known causal structure. Unlike existing approaches, we relax the assumption of a known diffusion matrix, thereby respecting the model's intrinsic scale invariance. Rather than recovering drift coeffi…

• Quantifying Ranking Instability Across Evaluation Protocol Axes in Gene Regulatory Network Benchmarking

  Ihor Kendiukhov · 5 mars 2026

  Benchmark rankings are routinely used to justify scientific claims about method quality in gene regulatory network (GRN) inference, yet the stability of these rankings under plausible evaluation protocol choices is rarely examined. We present a systematic diagnostic framework for measuring ranking i…

• Exact Discrete Stochastic Simulation with Deep-Learning-Scale Gradient Optimization

  Jose M. G. Vilar, Leonor Saiz · 24 février 2026

  Exact stochastic simulation of continuous-time Markov chains (CTMCs) is essential when discreteness and noise drive system behavior, but the hard categorical event selection in Gillespie-type algorithms blocks gradient-based learning. We eliminate this constraint by decoupling forward simulation fro…