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 Methodological developments in systems biology
Submission Date: 2024-08-28

The field of systems biology has made significant progress in recent years, with advances in computational and experimental techniques enabling researchers to study biological systems in unprecedented detail. Furthermore, recent developments in the field have shown that systems biology-based approaches can provide an unprecedented trove of data for the early detection of disease transitions, the prediction of therapeutic responses and clinical outcomes, and the design of personalised treatments.

We invite submissions that focus on the latest developments in systems biology research methodology and its application in basic and translational research. Specifically, we encourage submissions on the following topics of interests but are not limited to:

    Novel computational methods for modelling and analysis of biological systems, in particular the development of computational frameworks that integrate deep learning with ODE or PDE models to provide efficient mechanisms for model fitting and prediction.
    Combining different computational methods and approaches to investigate mechanisms underlying emergent properties of biological systems, such as ODE with agent-based models or Boolean with genome-scale metabolic models.
    Classical mechanistic modelling, such as ODE, PDE, agent-based models, and Boolean, as applied to research in human disease, the microbiome, and plant biology.
    Network-based models, such as Petri-net and graph modelling, that integrate multi-omics data sets into computational models to study biological mechanisms, drug response, and personalised medicine.
    Single-cell modelling, which covers all areas of computational biology related to biological behaviour at the single-cell level, including stochastic dynamics, gene regulation, spatiotemporal dynamics, and a better understanding of cell self-organisation and cell response to stimuli.
    Multi-scale modelling addresses multiscale questions in biology through the integration of models and quantitative experiments, especially models that capture cellular dynamics and regulation, with an emphasis on the role played by the spatial organisation of its components.

Special Issue on Understanding Cancer Dynamics and Improving Treatment Strategies Using Mathematical and Computational Oncology
Submission Date: 2024-08-31

Mathematical and computational oncology are rapidly emerging as critical areas of research that aim to understand the intricate and complex nature of cancer. New quantitative methodologies and techniques are constantly being developed to better understand and predict how the disease evolves and adapts to treatment. Such models are used not only to check the validity of hypotheses that are postulated to explain experimental observations, but also improve experimental design by making testable predictions or revealing counter-intuitive physical principles. The advent of in silico predictions also plays an increasingly important role in complementing and directing experimental efforts that often occur in high-dimensional feature space. Emerging clinical trials have demonstrated the useful insight provided from these methods in cancer prognosis and treatment planning. Additionally, experimental collaborations have allowed for greater data collection and alternative ways to study the evolution of the disease. As these quantitative and experimental fields continue to evolve, it is essential to highlight recent advances and encourage new collaborations among both researchers and clinicians. In this Special Issue, we invite contributions from computational and mathematical oncology researchers developing new and innovative ways to better understand cancer dynamics, from intracellular to population levels, using continuum, stochastic or hybrid modeling techniques. We invite submissions from both early career and senior researchers, and particularly encourage research with high translational value.

This Collection supports and amplifies research related to SDG 3.

Special Issue on Virtual human development: merging experiments and theory to understand human development
Submission Date: 2024-11-30

All of us began as a single cell, the fertilized egg. This cell gave rise to a group of "stem cells" that can transform into any cell type in our body to create our complex tissues consisting of trillions of cells. The development of our tissues is controlled by a complex interplay of genetics, epigenetics, gene regulatory networks, protein configurations, environmental signaling, and metabolic states. Cells use these processes to determine whether to divide, differentiate, communicate, or persist, leading to the coordination of cells to produce functional tissues and organs. To understand how information flows across biological scales and time, we need a multidisciplinary approach that combines the generation of rich molecular and cellular data with computational tools and mathematical models. Advances in single-cell genomics, lineage tracing, genetic engineering, and stem cell-derived organoid models have made it possible to study development experimentally. In addition, advances in machine learning and artificial intelligence have made it possible to predict perturbations, and physical and agent-based modeling approaches allow us to model cell behavior at tissue scales.

Consolidating our knowledge and understanding the biological rules that govern embryonic development can help us communicate better across different fields of expertise and also reveal gaps in our collective knowledge. The embryo is a complex system where molecular networks shape cellular phenotype and, ultimately, the multicellular population. This provides an opportunity for testing hypotheses to understand how changes at one level can affect the other levels. We anticipate that this combination of theory-experiment cycles will accelerate our understanding of human development while providing valuable predictions for regenerative medicine approaches.

This collection will feature original research articles, perspectives and special reviews focused on the experimental and in silico models and techniques needed to understand human development. This includes but is not limited to organoid models, single cell ‘omics’ methods and analysis, imaging techniques, lineage tracing, genetic engineering, mathematical modeling, gene regulatory network inference, and AI/machine learning-mediated prediction of cell behaviour and function.

This Collection supports and amplifies research related to SDG 3.