Aims & Scope

npj Systems Biology and Applications considers all aspects of research covering computational and mathematical approaches to analysis and modeling of complex biological systems.

The journal covers a broad range of topics including but not limited to:

Computational modeling of biological systems
Application of systems biology approaches to disease modeling, pharmacology, drug discovery, biotechnology, and industry
Network biology and interactome analysis
Multi-omics data integration
Synthetic biology and metabolic engineering
Single-cell systems biology
Systems immunology and host-pathogen interactions
Systems neuroscience and brain function
Environmental systems biology
Evolutionary systems biology

The journal offers more choice to Nature Portfolio authors who are seeking a fully open-access and more inclusive platform for publishing their work. The journal is led by systems biology experts who collaborate to cultivate high-quality research. As part of the npj Series, this journal focuses on fostering global partnerships with the research community and other Springer Nature journals.

Special Issue on Fostering Cross-Disciplinary Modeling in Biology and Medicine
Día de Entrega: 2026-05-15

high-throughput technologies, and artificial intelligence have transformed the landscape of biomedical research. This thematic collection highlights cutting-edge developments in data-driven modeling approaches that integrate experimental data, computational techniques, and machine learning to gain mechanistic insights into complex biological systems and diseases.

We invite contributions that explore the intersection of data science and biomedicine, including but not limited to the development of predictive models for disease progression, personalized medicine, integrative multi-omics analysis, and digital twins for patient-specific diagnosis and treatment. Submissions addressing the use of data-driven methods to complement mechanistic models, uncover causal relationships, or improve trust and interpretability of AI in clinical settings are especially welcome.

This collection aims to bring together researchers from diverse backgrounds—including mathematics, computer science, engineering, and biology—to showcase how data-driven modeling can advance our understanding of biomedical processes and improve patient outcomes. We encourage contributions from both early-career and established researchers, and we are committed to fostering diversity across disciplines, geography, and career stages.

Special Issue on Systems approaches for virtual clinical trials and digital twins
Día de Entrega: 2026-07-31

Virtual clinical trials and digital twins are emerging approaches to understanding mechanisms of drug responses and drug treatment heterogeneity. With the goal of providing the right drug at the right dose to the right patient, virtual clinical trials and digital twins help to identify disease and treatment biomarkers, individualize therapeutic strategies, and optimize clinical trial design, ultimately reducing the time and cost of drug development. The methodological frameworks of both virtual clinical trials and digital twins have important implications beyond drug development and therapeutic optimization, helping to establish pathophysiological processes and discern how key mechanisms drive biological heterogeneity. This thematic collection will highlight cutting-edge research into virtual clinical trials and digital twins. This includes the development of new methodological approaches and applications to a variety of diseases and drug contexts. In particular, we welcome submissions merging experimentation and/or clinical trial data with predictive mechanistic mathematical models.

Special Issue on Systems immunology: multi-omics approaches, dynamical modeling and novel agentic AI approaches
Día de Entrega: 2026-09-12

Over the past decade, experimental and computational approaches for deep molecular and cellular profiling have become readily available and widely used for profiling the immune system both in human and in model organisms. The broad focus of this systems immunology collection is on computational and experimenal approaches that can be used to generate and interrogate combinations of these datasets in a principled fashion to uncover phenotypes and mechanisms underlying immunological states and disorders, and predictive dynamical models that can connect such high-throughput data to phenotypic/cell-state aspects. We are especially interested in manuscripts leveraging recent advances in AI/agentic AI approaches to study immune regulation and dysregulation. This will be a key focus of the collection. Manuscripts do not necessarily need to include both aspects; studies describing either novel computational approaches for the analyses of multi-omic datasets or creative experimental techniques for generating one or more of these datasets are welcome. However, manuscripts simply describing computational methods without demonstrating their applications on real-world datasets will not be considered a good fit for this collection.