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 Next-Generation Mammalian Cell Bioprocessing: Systems Biology, Synthetic Biology, and Beyond
Día de Entrega: 2026-01-15

The use of animal cells, particularly mammalian cells such as CHO, HEK293, CAR-T/-NK, and stem cells, is highly prevalent in various therapeutic biomanufacturing applications, including biologics production and cell and gene therapies. Recently, the cultivation of various animal cells and tissues to produce cultivated meat has seen significant progress. Over the past two decades, systems biology tools have been incrementally applied to gain a holistic view of all these mammalian cell cultures, enabling a thorough characterization, design, and optimization of cellular processes. In parallel, synthetic biology advances offer innovative genetic circuit designs and genome editing techniques, which are being used to create efficient cellular factories and cell lines. These approaches allow for the synthesis, re-engineering, and design of biological pathways, regulators, and molecules, ultimately improving productivity and ensuring product consistency.

This Collection will highlight studies that employ multi-omics techniques such as genomic, epigenomic, transcriptomic, proteomic and metabolomic profiling or utilize first principles based mathematical modelling and AI/ML approaches to explore the genetic, regulatory, and metabolic networks in mammalian cells and their cultures. We are particularly interested in research that applies these methods to identify cellular engineering targets or optimize cell culture conditions for enhanced productivity, improved product quality, or increased cell proliferation. We invite manuscripts that focus on the application of systems biology and synthetic biology tools – either individually or in combination - in mammalian cell research. Both experimental and computational studies are welcome, though submissions do not necessarily need to incorporate both approaches.

Special Issue on Systems Biology methods for unraveling the complexity in developmental biology
Día de Entrega: 2026-01-16

Understanding embryo development requires comprehending how a single fertilized cell, the zygote, embarks on a remarkable transformation into a complex multicellular organism. Through precise cell divisions, programmed cell death, migration, and intricate gene regulation—along with concomitant changes in mechanical and metabolic states—the zygote develops into a diverse array of tissues, each with unique functions. These properties are not just traits;  they shape the organization of tissues into functional organs with specific mechanical and biochemical properties. While some of these processes are unique to certain species, others are remarkably conserved across diverse organisms, reflecting the intricate balance between plasticity and robustness that underpins the universal principles of development.

To grasp these principles, it is essential to understand how complex properties emerge from simple components, a challenge that necessitates the mathematical and computational approaches of systems biology. In this collection, we aim to unravel these underlying principles through manuscripts that employ systems biology and/or tissue mechanics models to elucidate and predict experimentally observed processes in developing biological systems, both in vivo (e.g., embryos) and in vitro (e.g., organoids). Through these contributions, we aim to shed light on the fundamental mechanisms that shape life, offering new insights and inspiring future research in this dynamic field.

Special Issue on Phenotypic Plasticity: A Systems Approach
Día de Entrega: 2026-01-30

organisms to exhibit different phenotypes whether in response to environmental perturbations, or stochastically, in the absence of any genetic mutations. It is now increasingly evident that, in addition to genetic mechanisms, phenotypic plasticity plays a key role both in organismal development, evolution, and in cancer. There is thus a need to recognize the genetic/non-genetic duality in biology and reconcile how two seemingly opposing mechanisms, namely Darwinian selection and Lamarckian induction, operate both in cancer and in evolution. This collection aims to accomplish this goal by synthesizing a coherent viewpoint, pointing to the gaps in our knowledge, and identifying key questions and new frontiers that need to be addressed to gain a deeper understanding of this phenomenon. Such a systems level perspective could potentially also aid in designing new therapeutics, and new treatment strategies to thwart cancer progression and metastasis, and preclude, or at least attenuate, true irreversible drug resistance that follows a reversible drug tolerant state. The recent appreciation of the role of phenotypic plasticity and non-genetic mechanisms in the emergence of drug tolerant persisters should attract investigators from across disciplines to contribute to this collection.

Special Issue on Discrete dynamical modeling of biological systems
Día de Entrega: 2026-02-19

biological behaviors emerge from interacting molecular, cellular, or ecological components remains a central challenge in systems biology. Discrete dynamical models — including Boolean networks, cellular automata, Petri nets, and agent-based approaches — provide powerful tools to investigate such systems, especially when detailed kinetic data are scarce or system states are inherently discrete. Discrete models have proven valuable in exploring gene regulatory networks, cell fate decisions, tissue patterning, and population-level dynamics.

This collection invites submissions that advance the theory, application, or integration of discrete modeling frameworks to study biological phenomena across scales—from intracellular regulation to multi-species ecosystems. We welcome methodological contributions that develop new algorithms or theoretical insights, as well as applied studies that leverage discrete models to interpret experimental data or generate testable predictions. Of particular interest are hybrid models combining discrete and continuous dynamics, data-informed model selection and analysis, and studies bridging discrete models with omics or imaging datasets. By gathering a diverse set of contributions, this collection aims to highlight the growing relevance and versatility of discrete dynamical modeling in uncovering the dynamic functioning and behaviors of living systems.

Special Issue on Evolutionary systems biology
Día de Entrega: 2026-02-28

Predicting evolutionary change is a grand challenge. Our current understanding of evolutionary dynamics allows qualitative commentary on phenotypic change over micro-evolutionary time scales and statistical predictions of trajectories. These predictions, however, have only limited extension to genotype. Moreover, how do lessons from laboratory experiments extend to ecology, and how do species interaction shape evolutionary trajectories.

In this Collection, we invite submissions (research and review articles) which aim to discuss and report work on systems evolutionary biology towards enhancing predictability of evolutionary trajectories from a variety of contexts, including but not limited to:

    protein evolution,
    evolution of regulatory topologies & motifs,
    gene-regulatory sequence co-evolution,
    mechanistic origins and statistical patterns of inter- and intragenic epistasis,
    developing frameworks to extend evolutionary predictions over longer timescales,
    empirical structures of fitness landscapes and their implications for adaptive walks.

Special Issue on Systems mechanobiology
Día de Entrega: 2026-03-28

as a powerful integrative framework to dissect how mechanical cues and molecular signals jointly shape cellular behaviour, organ development, and disease progression. From the remodelling of cell adhesion and cytoskeletal organisation in metastasising cancer cells, to the altered force balances in fibrotic tissues and the dynamic reorientation of organelles in cardiac disorders, mechanical perturbations are increasingly recognised as central drivers of physiological and pathophysiological processes. To obtain the deepest insights into the interplay of mechanical and molecular cues requires combining experimental tools—such as advanced imaging modalities and biophysical measurements—with computational and theoretical approaches. By systematically analysing mechanical and molecular signatures in parallel, researchers can untangle critical feedback loops, where mechanical stress activates specific intracellular pathways, which in turn remodel cell and tissue architecture, setting the stage for further mechanical alterations. Beyond disease contexts, mechanical cues also orchestrate developmental processes, guiding tissue morphogenesis and organ patterning. This thematic collection will showcase leading research at the intersection of mechanobiology and systems-level analysis, spanning diverse applications—from tumour microenvironment modelling and 3D tissue engineering to immuno-mechanical interactions in chronic inflammatory conditions. Similarly, the computational tools range from predictive mechanistic modelling to machine learning for spatial pattern recognition.

This Collection aims to present articles representing the breadth of systems-mechanobiology approaches. We also welcome contributions that shed light on how mechanobiological principles can guide novel diagnostic strategies, uncover therapeutic vulnerabilities, and shape our fundamental understanding of life’s dynamic interplay between forces and molecules.

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.