Merge pull request #223 from MyersResearchGroup/pubs-2024

publications for 2024

content/publication/albin-phagebox-2024/cite.bib

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+@article{10183873,
+ author = {Albin, Dreycey and Buecherl, Lukas and Kochavi, Eitan and Niehaus, Elise and Novack, Sasha and Uragoda, Shenali and Myers, Chris J. and Alistar, Mirela},
+ doi = {10.1109/TBME.2023.3295418},
+ journal = {IEEE Transactions on Biomedical Engineering},
+ keywords = {Temperature sensors;Temperature measurement;Microfluidics;DNA;Software;Graphical user interfaces;Bacteriophages;Digital microfluidics;extension;open source;embedded software;bacteriophages},
+ number = {1},
+ pages = {217-226},
+ title = {PhageBox: An Open Source Digital Microfluidic Extension With Applications for Phage Discovery},
+ volume = {71},
+ year = {2024}
+}

content/publication/albin-phagebox-2024/index.md

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+---
+title: 'PhageBox: An Open Source Digital Microfluidic Extension With Applications
+  for Phage Discovery'
+authors:
+- Dreycey Albin
+- Lukas Buecherl
+- Eitan Kochavi
+- Elise Niehaus
+- Sasha Novack
+- Shenali Uragoda
+- Chris J. Myers
+- Mirela Alistar
+date: '2024-01-01'
+publishDate: '2024-12-12T20:57:09.262308Z'
+publication_types:
+- 2
+publication: '*IEEE Transactions on Biomedical Engineering*'
+doi: 10.1109/TBME.2023.3295418
+abstract: Objective Recent advancements demonstrate the significant role of digital microfluidics in automating laboratory work with DNA and on-site viral testing. However, since commercially available instruments are limited to droplet manipulation, our work addresses the need for accelerated integration of other components, such as temperature control, that can expand the application domain. Methods We developed PhageBox—an accessible device that can be used as a biochip extension. At hardware level, PhageBox integrates temperature and electromagnetic control modules. At software level, PhageBox is controlled by embedded software containing a unique model for bio-protocol programming, and a graphical user interface for visual device feedback and operation. Results To evaluate PhageBox's efficacy for biomedical applications, we performed functional testing. Similarly, we validated the temperature control using thermography, obtaining a range of ± 0.2 ∘C . The electromagnets produced a magnetic force of 15 milliTesla, demonstrating precise immobilization of magnetic beads. We show the potential of PhageBox for bacteriophage research through three initial protocols a universal framework for PCR, T7 bacteriophage restriction enzyme digestion, and concentrating ϕX174 RF genomic DNA. Conclusion Our work presents an open-source hardware and software extension for digital microfluidics devices. This extension integrates temperature and electromagnetic modules, demonstrating efficacy in biomedical applications and potential for bacteriophage research. Significance We developed PhageBox to be accessible the components are off-the-shelf at a low cost ( ≤ $ 200), and the hardware designs and software code are open-source. With the long aim of ensuring reproducibility and accelerating collaboration, we also provide a DIY-build document.
+tags:
+- Temperature sensors;Temperature measurement;Microfluidics;DNA;Software;Graphical
+  user interfaces;Bacteriophages;Digital microfluidics;extension;open source;embedded
+  software;bacteriophages
+---

content/publication/buecherl-synthetic-genetic-circuits-2024/cite.bib

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+@article{doi:10.1021/acssynbio.3c00708,
+ author = {Buecherl, Lukas and Myers, Chris J. and Fontanarrosa, Pedro},
+ doi = {10.1021/acssynbio.3c00708},
+ eprint = { 
+
+https://doi.org/10.1021/acssynbio.3c00708
+
+
+},
+ journal = {ACS Synthetic Biology},
+ note = {PMID: 39264040},
+ number = {9},
+ pages = {2742-2752},
+ title = {Evaluating the Contribution of Model Complexity in Predicting Robustness in Synthetic Genetic Circuits},
+ url = { 
+
+https://doi.org/10.1021/acssynbio.3c00708
+
+
+},
+ volume = {13},
+ year = {2024}
+}

content/publication/buecherl-synthetic-genetic-circuits-2024/index.md

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+---
+title: Evaluating the Contribution of Model Complexity in Predicting Robustness in
+  Synthetic Genetic Circuits
+authors:
+- Lukas Buecherl
+- Chris J. Myers
+- Pedro Fontanarrosa
+date: '2024-01-01'
+publishDate: '2024-12-12T20:41:38.164564Z'
+publication_types:
+- 2
+publication: '*ACS Synthetic Biology*'
+doi: 10.1021/acssynbio.3c00708
+abstract: The design–build–test–learn workflow is pivotal in synthetic biology as it seeks to broaden access to diverse levels of expertise and enhance circuit complexity through recent advancements in automation. The design of complex circuits depends on developing precise models and parameter values for predicting the circuit performance and noise resilience. However, obtaining characterized parameters under diverse experimental conditions is a significant challenge, often requiring substantial time, funding, and expertise. This work compares five computational models of three different genetic circuit implementations of the same logic function to evaluate their relative predictive capabilities. The primary focus is on determining whether simpler models can yield conclusions similar to those of more complex ones and whether certain models offer greater analytical benefits. These models explore the influence of noise, parametrization, and model complexity on predictions of synthetic circuit performance through simulation. The findings suggest that when developing a new circuit without characterized parts or an existing design, any model can effectively predict the optimal implementation by facilitating qualitative comparison of designs’ failure probabilities (e.g., higher or lower). However, when characterized parts are available and accurate quantitative differences in failure probabilities are desired, employing a more precise model with characterized parts becomes necessary, albeit requiring additional effort.
+links:
+- name: URL
+  url: 'https://doi.org/10.1021/acssynbio.3c00708'
+---

content/publication/golebiewski-standards-2024/cite.bib

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+@article{GolebiewskiBaderGleesonGorochowskiKeatingKönigMyersNickersonSommerWaltemathSchreiber+2024,
+ author = {Martin Golebiewski and Gary Bader and Padraig Gleeson and Thomas E. Gorochowski and Sarah M. Keating and Matthias König and Chris J. Myers and David P. Nickerson and Björn Sommer and Dagmar Waltemath and Falk Schreiber},
+ doi = {doi:10.1515/jib-2024-0015},
+ journal = {Journal of Integrative Bioinformatics},
+ lastchecked = {2024-12-11},
+ number = {1},
+ pages = {20240015},
+ title = {Specifications of standards in systems and synthetic biology: status, developments, and tools in 2024},
+ url = {https://doi.org/10.1515/jib-2024-0015},
+ volume = {21},
+ year = {2024}
+}

content/publication/golebiewski-standards-2024/index.md

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+---
+title: 'Specifications of standards in systems and synthetic biology: status, developments,
+  and tools in 2024'
+authors:
+- Martin Golebiewski
+- Gary Bader
+- Padraig Gleeson
+- Thomas E. Gorochowski
+- Sarah M. Keating
+- Matthias König
+- Chris J. Myers
+- David P. Nickerson
+- Björn Sommer
+- Dagmar Waltemath
+- Falk Schreiber
+date: '2024-01-01'
+publishDate: '2024-12-12T20:47:27.715608Z'
+publication_types:
+- 2
+publication: '*Journal of Integrative Bioinformatics*'
+doi: doi:10.1515/jib-2024-0015
+links:
+- name: URL
+  url: https://doi.org/10.1515/jib-2024-0015
+---

content/publication/mante-seqimprove-2024/cite.bib

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+@article{doi:10.1021/acssynbio.4c00392,
+ author = {Mante, Jeanet and Sents, Zach and Britt, Duncan and Mo, William and Liao, Chunxiao and Greer, Ryan and Myers, Chris J.},
+ doi = {10.1021/acssynbio.4c00392},
+ eprint = { 
+
+https://doi.org/10.1021/acssynbio.4c00392
+
+
+},
+ journal = {ACS Synthetic Biology},
+ note = {PMID: 39230953},
+ number = {9},
+ pages = {3051-3055},
+ title = {SeqImprove: Machine-Learning-Assisted Curation of Genetic Circuit Sequence Information},
+ url = { 
+
+https://doi.org/10.1021/acssynbio.4c00392
+
+
+},
+ volume = {13},
+ year = {2024}
+}

content/publication/mante-seqimprove-2024/index.md

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+---
+title: 'SeqImprove: Machine-Learning-Assisted Curation of Genetic Circuit Sequence
+  Information'
+authors:
+- Jeanet Mante
+- Zach Sents
+- Duncan Britt
+- William Mo
+- Chunxiao Liao
+- Ryan Greer
+- Chris J. Myers
+date: '2024-01-01'
+publishDate: '2024-12-12T20:26:13.541987Z'
+publication_types:
+- 2
+publication: '*ACS Synthetic Biology*'
+doi: 10.1021/acssynbio.4c00392
+abstract: The progress and utility of synthetic biology is currently hindered by the lengthy process of studying literature and replicating poorly documented work. Reconstruction of crucial design information through post hoc curation is highly noisy and error-prone. To combat this, author participation during the curation process is crucial. To encourage author participation without overburdening them, an ML-assisted curation tool called SeqImprove has been developed. Using named entity recognition, called entity normalization, and sequence matching, SeqImprove creates machine-accessible sequence data and metadata annotations, which authors can then review and edit before submitting a final sequence file. SeqImprove makes it easier for authors to submit sequence data that is FAIR (findable, accessible, interoperable, and reusable).
+
+links:
+- name: URL
+  url: 'https://doi.org/10.1021/acssynbio.4c00392'
+---