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Journal Information

By making connections through the application of computational methods among disparate areas of biology, PLOS Computational Biology provides substantial new insight into living systems at all scales, from the nano to the macro, and across multiple disciplines, from molecular science, neuroscience and physiology to ecology and population biology.

Scope

PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods including applications of artificial intelligence and machine learning. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery.

Research articles model all aspects of biological systems and demonstrate novel scientific advances, through the introduction of novel methods, software, or tools, or through the application of computational methods to provide profound new biological insights. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines.

PLOS Computational Biology publishes three types of research articles: Research, Methods, and Software. Articles specifically designated as Methods or Software papers should describe outstanding new methods or software of exceptional importance that can provide new biological insights. The method or software must have the potential for being widely adopted by a broad community of users. Enhancements to existing published methods or software will only be considered if those enhancements bring exceptional new capabilities.

Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies and/or application to real-world data. Inclusion of experimental validation is not required for publication, but should be referenced where possible.

For all submissions, authors must clearly provide details, data, code, and software to ensure readers' ability to reproduce the models, methods, and results. The journal has data availability and code availability policies for all research articles.

Contents 

Research articles

  • Outstanding primary Research articles on all aspects of computational biology applied to different and integrated biological scales, from molecules and cells to patient populations and ecosystems.
  • Software articles describing an open-source tool of broad utility that represents a significant advance in providing new biological insights.
  • Methods articles describing outstanding methods with demonstrated potential to provide new biological insights across the field.

Front Matter articles

  • Invited and submitted reviews, Ten Simple Rules, and perspectives on topics of broad interest to the readership.
  • Historical reviews and high-quality tutorials (including multimedia presentations) teaching important concepts in the field of computational biology.
Read more about the article types we publish.

Journal Sections

Kindly note these section descriptions are intended to provide an overview of the topics covered and are not an exhaustive list. For manuscripts employing artificial intelligence or machine learning approaches, please consider which biological application is most relevant to your work when selecting a section.

Biological Macromolecules
Analysis and method development for predicting macromolecular structure, interactions, dynamics, and behavior in health or disease. Areas of emphasis include, but are not limited to, macromolecular structure, machine learning providing novel biological insights, dynamics, assembly, folding and interactions.

Cell Biology & Physiology
Modeling and analysis of the molecules, cells, and organ systems that inform physiology and disease, often integrating across scales. Areas of emphasis include, but are not limited to: molecular regulation of cellular behavior; multicellular behavior; systems pharmacology; developmental biology; and multi-system regulation.

Epidemiology & Public Health
Process-based approaches to understanding and modeling the distribution and determinants of disease in human populations. Areas of emphasis include, but are not limited to: infectious disease modeling; causal inference; simulation-based approaches to understanding disease progression and the impact of interventions.

Evolution, Ecology, & Behavior
Development and application of mathematical models and computational analysis to understand ecological and evolutionary processes across various scales. This section welcomes studies focused on ecosystem patterns and the biosphere, as well as broader investigations in community ecology, population dynamics, species coexistence, and population viability analysis. Areas of emphasis include but are not limited to: emergence of collective behavior in populations; evolutionary game theory; ecological networks; niche modeling; and mechanisms of species interactions and community assembly.

Genomics, Epigenomics, & Proteomics
Methods for, and applications of, analysis of -omic data, unlocking the flow of information from gene to protein and roadmaps for development, disease, and treatment. Areas of emphasis include, but are not limited to: novel algorithms; benchmarking methods; identifying biomarkers; and multi-omic integration.

Immunology & Microbes
Development and application of mathematical models, computer simulations and computational analysis to quantitatively understand the interplay between pathogens and the immune system. Areas of emphasis include, but are not limited to: microbial ecology and systems biology; viral dynamics; pharmacokinetic/pharmacodynamic models; computational immunology; molecular epidemiology.

Neuroscience
Studies that use mathematical models and simulations to study how neural systems process information and to understand brain function. Areas of emphasis include, but are not limited to: cognition, learning and memory, behavior, sensory systems, biomechanics and biokinetics, and neural network studies that provide biological insights.

Systems Biology
Integrative modeling and analysis of complex and often multimodal experimental data in order to understand complex biological systems. Areas of emphasis include, but are not limited to: organ systems and biomechanics, novel multimodal methods applied to biological system data; integrative studies of interacting components; network biology and modeling complex feedback and regulatory systems; systems pharmacology; and computational image analysis.

Criteria for Publication

To be considered for publication in PLOS Computational Biology, any given manuscript must satisfy the following criteria:

  • Originality

  • Innovation

  • High importance to researchers in the field

  • Significant biological and/or methodological insight

  • Rigorous methodology

  • Substantial evidence for its conclusions
Consideration of complementary research

Scientists commonly refer to research as “scooped” when independent groups working on the same topic reach similar conclusions and one group publishes the results first. Although originality is one criterion for studies published in PLOS Computational Biology, “scooped” manuscripts that confirm, replicate, extend, or are complementary to a recently published, significant advance are still eligible for consideration in PLOS Computational Biology. The complementary manuscript must present equally or more rigorous findings than the published study and any submission must also meet the criteria for publication listed above. Authors of the complementary work have six months after the first article’s publication date to submit their manuscript to PLOS Computational Biology. Studies must be performed comprehensively, and preliminary placeholder studies will not be considered.

Editorial Oversight

PLOS Computational Biology is run by an international Editorial Board, headed by the Editors-in-Chief, Feilim Mac Gabhann (John Hopkins Universitym Baltimore, MD, USA) and Jason Papin (University of Virginia, Charlottesville, VA, USA).

Submit Your Manuscript

For more information about submitting to PLOS Computational Biology, read our checklist for getting started and our guidelines for preparing a submission.

Publication Fees

PLOS employs several business models to support equitable Open Access. A full list of our publication fees, funding initiatives and fee assistance information is available here.

Open Access

PLOS applies the Creative Commons Attribution (CC BY) license to works we publish. Under this license, authors agree to make articles legally available for reuse, without permission or fees, for virtually any purpose. Anyone may copy, distribute, or reuse these articles, as long as the author and original source are properly cited. Learn more.

Journal Impact and Article Metrics

PLOS does not consider Impact Factor to be a reliable or useful metric to assess the performance of individual articles. PLOS supports DORA – the San Francisco Declaration on Research Assessment – and does not promote our journal Impact Factors. We will provide the metric to individuals when specifically requested.

Read more about our pledge with DORA.

PLOS promotes the use of Article-Level Metrics (ALMs), which enable scientists and the general public to engage more dynamically with published research. ALMs reflect the changing impact of research over time, incorporate academic as well as social impacts of research, and assess the impact of research before the accrual of academic citations. Read more about ALMs.

Indexing and Archiving

All PLOS journals are widely indexed by major services such as Crossref, Dimensions, DOAJ, Google Scholar, PubMed, PubMed Central, Scopus, and Web of Science.

PLOS Computational Biology is also indexed by the following services to ensure research content is accessible and discoverable as widely as possible: Biological Abstracts, BIOSYS Previews, CABI CAB Abstracts, CABI Global Health, CAPES, CAS, CNKI, Embase, Journal Guide, and MEDLINE.

PLOS

PLOS is a nonprofit, Open Access publisher empowering researchers to accelerate progress in science and medicine by leading a transformation in research communication.

Contact

Visit the Contact page for details about whom to contact with different queries.