Biomedical systems biology workflow orchestration and execution with PoSyMed

arXiv cs.AI / 4/25/2026

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Key Points

The paper highlights that scientific software growth has made bioinformatics work harder due to fragmented tool distribution, inconsistent documentation, complex dependencies, and unreproducible execution environments.
It introduces PoSyMed, an open and modular platform that integrates, composes, and executes bioinformatics tools using backend-centered architecture, formal tool descriptions, controlled containerized build/run, persistent workflow state, and a dialogue-based UI.
PoSyMed incorporates LLMs as bounded semantic assistants within a typed, validated, human-supervised execution environment, helping users find tools, propose workflow steps, and parameterize them without autonomous decision-making.
The authors present system architecture and evaluate PoSyMed across representative biological software scenarios, focusing on workflow support, interaction design, and extensibility.
PoSyMed is publicly available via https://apps.cosy.bio/posymed, aiming to improve reproducibility, traceability, and transparency in biomedical analyses within a single platform.

Abstract

The rapid growth of scientific software has created practical barriers for bioinformatics research. Although powerful statistical, artificial intelligence (AI)-based methods are now widely available, their effective use is often hindered by fragmented distribution, inconsistent documentation, complex dependencies, and difficult-to-reproduce execution environments. As a result, reusing published tools and workflow adaptation to own date remains technically demanding and time-intensive, even for experienced users. Here, we present PoSyMed, an open and modular platform for the controlled integration, composition, and execution of bioinformatics tools and workflows. PoSyMed combines a backend-centered platform architecture with formal tool descriptions, controlled container-based build and execution processes, persistent workflow state, and a dialogue-based user interface. Large language models (LLM) are integrated not as autonomous decision-makers, but as human-computer interface with bounded semantic assistants that help identify tools, propose workflow steps, and support parameterization within a typed, validated, and human-supervised execution environment. PoSyMed is designed to improve reproducibility, traceability, and transparency in practical biomedical analysis within one platform. We describe the system architecture and evaluate its behavior across representative biological software scenarios with respect to workflow support, interaction design, and platform extensibility. PoSyMed is publicly available at https://apps.cosy.bio/posymed.