Biology subjects

Passban, P.

Publications and source records attributed to Passban, P..

5 recordsLinked to original sources

bioRxiv2026

From Sparse Visits to Continuous Molecular Trajectories with Flow Matching

Longitudinal transcriptomic studies often capture only a few molecular snapshots of each patient over time. This makes it challenging to understand how patients are progressing, compare them when progression rates differ, and capture molecular changes between observed visits. We introduce CohortFM, a flow-matching framework that reconstructs continuous patient-specific molecular trajectories from sparse longitudinal observations and organizes them through an integrated mixture model into a compact set of cohort-level progression patterns. CohortFM separates molecular progression from calendar time, places patients on a shared progression scale, uses observed intermediate states to guide the learned trajectories, and maps the resulting patterns back to real patient samples for biological interpretation. We evaluated CohortFM across COVID-19, sepsis, influenza, and tuberculosis. Despite different sampling patterns, the method generalizes well and consistently produces meaningful trajectories. These trajectories capture biological differences between patients that cannot be explained by the observed samples alone. Compared with state-of-the-art trajectory baselines, CohortFM produces more biologically distinct trajectories and a more complete representation of the patient population. Its finer progression scale also reveals biological programs and temporal dynamics that are not captured by visit-level analysis. We further evaluated interpolation and true forward prediction to test whether CohortFM can accurately recover unobserved states. CohortFM remains competitive with strong interpolation baselines and is among the strongest learned predictors in the harder forecasting setting. Ablation experiments further show that flow matching is the core learning mechanism and that our design choices improve continuous trajectory learning. Our results support CohortFM as a practical framework for turning sparse molecular snapshots into continuous, biologically interpretable trajectories. By going beyond visit labels, CohortFM makes it possible to study disease progression and predict future molecular states even when patients are sampled only a few times.

bioinformatics↗

bioRxiv2026

Talk2QSP: Deriving Executable Scenarios from Unstructured Literature via Human-in-the-Loop Agents

Quantitative Systems Pharmacology (QSP) models play an inherently interventional role in pharmaceutical research and development, functioning as executable causal systems for designing, evaluating, and replacing clinical trials. However, deploying QSP as an experimental planning engine remains constrained by the difficulty of translating unstructured literature descriptions of clinical or preclinical scenarios into reproducible, simulation-ready model interventions. Motivated by this issue, we propose an agent-based framework that operationalizes QSP models as intervention-ready experimental systems by automatically extracting and executing literature-derived scenarios. The framework combines semantic grounding of model entities with a large language model (LLM)-driven Scenario Extractor and a dual-agent Scenario Mapper. Rather than relying on opaque, single-shot reasoning, our pipeline converts free-text interventions into precise parameter configurations through discrete, verifiable work orders. Moreover, our dynamic Human-in-the-Loop (HITL) strategy empowers modelers to resolve biological ambiguities interactively. Across four diverse kinetic ordinary differential equation (ODE)/QSP models and seven Subject Matter Expert (SME)-curated literature scenarios, our model resolved all selected scenarios into correct executable parameter changes, including multi-dose interventions, unit conversions, no-op scenarios, and ambiguity-triggered HITL cases, demonstrating that structured collaboration between experts and agentic systems can resolve scenarios that standalone Systems Biology Markup Language (SBML) reasoning LLM calls or prior agentic efforts operating on processed SBML data handle unreliably.

systems biology↗

bioRxiv2025

Identification of Patient Trajectories in Timeseries Clinical Transcriptomics Data

In clinical trials, it is common for only a subset of patients to respond to a given therapy. Such variability may arise from genetic differences, environmental influences, or the presence of distinct disease endotypes. Longitudinal transcriptomic profiles collected in phase 2a/2b studies provide a unique opportunity not only to investigate drug-induced biological mechanisms in humans, but also to understand why certain individuals fail to respond and to uncover previously unrecognized disease endotypes--ultimately informing the development of targeted therapeutics. However, analyzing these datasets is challenged by substantial patient heterogeneity, variability in disease severity at each visit, and the coarse temporal resolution due to sparse sampling. To address these limitations, we introduce a classification-guided autoencoder framework that jointly optimizes gene-expression reconstruction and classification objective to learn disease-relevant sample embeddings. Sample embeddings from all patients are leveraged to establish a continuous representation of disease dynamics, which can be clustered to delineate discrete disease states. We then construct a patient-sample graph in the learned latent space and apply a multi-commodity-flow based algorithm to infer patient trajectories through these states, enabling the identification of different patient trajectories. We evaluated our approach on three interventional datasets--ulcerative colitis, psoriasis, and atopic dermatitis, each containing both responders and non-responders. The method recapitulates known pathway perturbations associated with anti-IL17 and anti-IL6 therapies, identifies intermediate states reflecting disease/treatment progression, and reveals biologically meaningful patient trajectories within the patient population. Code and data are available in a public GitHub repository - https://github.com/Sanofi-Public/EndotypeDetection-Timeseries

bioinformatics↗

bioRxiv2025

Finetuning Foundation Models for Temporal Clinical Transcriptomics Data

BackgroundTimeseries clinical transcriptomic datasets offer the opportunity to gain insights into the dynamics of disease mechanisms/treatment responses. However, their utility in uncovering temporal patterns is often limited by high noise levels and small sample sizes. Leveraging foundational gene embeddings and incorporating interaction information can help address these challenges, improve gene network analysis, and enable the detection of subtle changes that drive disease progression or drug response. ResultsWe finetuned gene embeddings from foundation models using healthy tissue gene expression data and used them in temporal GNNs to model gene expression of responder and non-responders to treatment in 3 disease datasets - ulcerative colitis, Crohns disease and psoriasis. Application of our method to these datasets confirmed known mechanisms associated with drug action, and also identified key differences between activated and repressed pathways for responders and non responders including B-Cell activation and mitochondria related activity in ulcerative colitis patients. ConclusionFinetuning gene embeddings from foundation models provide a richer context to model gene expression data compared to using them in their naive state. Even with smaller sample sizes, results from GNN-based temporal models outperform traditional methods by detecting known mechanisms of response and unraveling role of genes and mechanisms not known to be associated with response and non-response. Code AvailabilityCode and data are available in a public GitHub repository - https://github.com/Sanofi-Public/GNN-Timeseries

bioinformatics↗

bioRxiv2024

PyEvoCell: An LLM- Augmented Single Cell Trajectory Analysis Dashboard

MotivationSeveral methods have been developed for trajectory inference in single cell studies. However, identifying relevant lineages among several celltypes is a challenging task and requires deep understanding of various celltype transitions and progression patterns. Therefore, there is a need for methods that can aid researchers in the analysis and interpretation of such trajectories. ResultsWe developed PyEvoCell, a dashboard for trajectory interpretation and analysis that is augmented by large language model (LLM) capabilities. PyEvoCell applies the LLM to the outputs of trajectory inference methods such as Monocle3, to suggest biologically relevant lineages. Once a lineage is defined, users can conduct differential expression and functional analyses which are also interpreted by the LLM. Finally, any hypothesis or claim derived from the analysis can be validated using the veracity filter, a feature enabled by the LLM, to confirm or reject claims by providing relevant PubMed citations. Software Availability and ImplementationThe software is available at https://github.com/Sanofi-Public/PyEvoCell. It contains installation instructions, user manual, demo datasets, as well as license conditions (including limitation to non-commercial uses only). Supplementary informationSupplementary information is attached.

bioinformatics↗