Combined Model of Intrinsic and Extrinsic Variability for Computational Network Design with Application to Synthetic Biology
Open accessAbstract
Biological systems are inherently variable, with their dynamics influenced by intrinsic and extrinsic sources. These systems are often only partially characterized, with large uncertainties about specific sources of extrinsic variability and biochemical properties. Moreover, it is not yet well understood how different sources of variability combine and affect biological systems in concert. To successfully design biomedical therapies or synthetic circuits with robust performance, it is crucial to account for uncertainty and effects of variability. Here we introduce an efficient modeling and simulation framework to study systems that are simultaneously subject to multiple sources of variability, and apply it to make design decisions on small genetic networks that play a role of basic design elements of synthetic circuits. Specifically, the framework was used to explore the effect of transcriptional and post-transcriptional autoregulation on fluctuations in protein expression in simple genetic networks. We found that autoregulation could either suppress or increase the output variability, depending on specific noise sources and network parameters. We showed that transcriptional autoregulation was more successful than post-transcriptional in suppressing variability across a wide range of intrinsic and extrinsic magnitudes and sources. We derived the following design principles to guide the design of circuits that best suppress variability: (i) high protein cooperativity and low miRNA cooperativity, (ii) imperfect complementarity between miRNA and mRNA was preferred to perfect complementarity, and (iii) correlated expression of mRNA and miRNA--for example, on the same transcript--was best for suppression of protein variability. Results further showed that correlations in kinetic parameters between cells affected the ability to suppress variability, and that variability in transient states did not necessarily follow the same principles as variability in the steady state. Our model and findings provide a general framework to guide design principles in synthetic biology.
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Stochastic dynamics have a pronounced effect on the level of molecules especially when the level of the molecule is low.
“Such stochastic dynamics will have pronounced effect on the level of molecules especially when the level of molecule is low24.”
From Intra- and Inter-individual Variability of microRNA Levels in Human Cerebrospinal Fluid: Critical Implications for Biomarker Discovery (2017) · cites this paper as [24]
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Moorman, A. R. (2020) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 1 · see citing works
A systems engineering approach to model, tune and test synthetic gene circuits
Acosta, Y. F. B. (2018) · doi:10.4995/thesis/10251/112725 · Cited by 1 · see citing works
Reduction of population variability in protein expression: A control engineering approach
Boada, Y., Vignoni, A., & Picó, J. (2020) · Jornadas de Automática · doi:10.17979/spudc.9788497497749.0008
Computing intrinsic noise of the genetic regulation modeled by Hill functions
Ziraldo, R., & Ma, L. (2018) · Journal of Computational Systems Biology · doi:10.15744/2455-7625.3.101
Arce, S., & Andres, B. (2021)
A free-software educational tool for modeling gene expression in cell populations
Saavedra, B. A., & González‐Vargas, A. M. (2021) · IEEE International Congress of Biomedical Engineering and Bioengineering (CI-IB&BI) · doi:10.1109/ci-ibbi54220.2021.9626131
Cell-to-cell variability and its consequences for cellular communities
Keizer, E. M. (2022) · doi:10.18174/565337
Genomic Synthetic Biology: A Brief Introduction, Its Applications, and Ethical Laws
Rullah, N., Anis, S., Rajper, N., Gul, P., Ahmed, H. A., & Asif, M. (2025) · Pakistan Journal of Biochemistry and Biotechnology · doi:10.52700/pjbb.v6i2.358
- Authors
- Toni, T., & Tidor, B.
- Affiliations
- Massachusetts Institute of Technology (US)
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Corresponding author
- Toni, Tina
- Tidor, Bruce
- ORCID
- Tidor, Bruce
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-03-28
- Volume
- 9
- Issue
- 3
- Pages
- e1002960
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC3610654
- OpenAlex
- W1537859107
- Cited by
- 62
- References
- 61
- Citations by year
- 2024: 2 · 2022: 7 · 2021: 5 · 2020: 8 · 2019: 5 · 2018: 2 · 2017: 7 · 2016: 8 · 2015: 9 · 2014: 8 · 2013: 1
- FWCI
- 3.88
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Figshare (submittedVersion, cc-by)
- PLoS eBooks
- Funders
- Wellcome Trust (090433/Z/09/Z, 090433)
- National Institutes of Health (CA112967)
- National Cancer Institute (CA 112967, U54CA112967)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Keywords
- intrinsic variability, noise reduction, synthetic gene circuits, transcriptional autoregulation, microRNA, protein cooperativity
- MeSH terms
- Algorithms, Gene Expression Regulation, Models, Genetic, RNA, Messenger, Transcription, Genetic, Computational Biology, MicroRNAs, Synthetic Biology
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