The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Do cells use passwords in cell-state transitions? Is cell signaling sometimes encrypted?
Open accessPublished version of a preprint
Abstract
Organisms must maintain proper regulation including defense and healing. Life-threatening problems may be caused by pathogens or by a multicellular organism's own cells through cancer or autoimmune disorders. Life evolved solutions to these problems that can be conceptualized through the lens of information security, which is a well-developed field in computer science. Here I argue that taking an information security view of cells is not merely semantics, but useful to explain features of signaling, regulation, and defense. An information security perspective also offers a conduit for cross-fertilization of advanced ideas from computer science and the potential for biology to inform computer science. First, I consider whether cells use passwords, i.e., initiation sequences that are required for subsequent signals to have effects, by analyzing the concept of pioneer transcription factors in chromatin regulation and cellular reprogramming. Second, I consider whether cells may encrypt signal transduction cascades. Encryption could benefit cells by making it more difficult for pathogens or oncogenes to hijack cell networks. By using numerous molecules, cells may gain a security advantage in particular against viruses, whose genome sizes are typically under selection pressure. I provide a simple conceptual argument for how cells may perform encryption through posttranslational modifications, complex formation, and chromatin accessibility. I invoke information theory to provide a criterion of an entropy spike to assess whether a signaling cascade has encryption-like features. I discuss how the frequently invoked concept of context dependency may oversimplify more advanced features of cell signaling networks, such as encryption. Therefore, by considering that biochemical networks may be even more complex than commonly realized we may be better able to understand defenses against pathogens and pathologies.
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Research works printed but not linked: 1
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Metadata
- Authors
- Root, A.
- Affiliations
- Memorial Sloan Kettering Cancer Center (US)
- Corresponding author
- Root, Alex
- ORCID
- Root, Alex
- Venue
- Theory in Biosciences
- Published
- 2019-06-07
- Volume
- 139
- Issue
- 1
- Pages
- 87-93
- ISSN
- 1431-7613 (ISSN-L)
- 1611-7530
- OpenAlex
- W2955923099
- Cited by
- 0
- References
- 61
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
- Locations
- Theory in Biosciences (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Division of Cancer Epidemiology and Genetics, National Cancer Institute (P30 CA008748)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Systems and Dynamics
- Keywords
- signal transduction, encryption, cell state transitions, pioneer transcription factors, chromatin regulation, cellular reprogramming, signaling pathways, post-translational modification, chromatin accessibility, information theory, entropy (information theory), context sensitivity, antiviral defense
- MeSH terms
- Algorithms, Animals, Autoimmune Diseases, Biological Phenomena, Chromatin, Environment, Biological Evolution, Humans, Immune System, Models, Biological, Neoplasms, Neurons, Semantics, Signal Transduction, Genome, Entropy, Computational Biology
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- Root, A. (2020). Do cells use passwords in cell-state transitions? Is cell signaling sometimes encrypted? Theory in Biosciences. https://doi.org/10.1007/s12064-019-00295-1
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.