refs.bib

@article{green_toehold_2014,
	title = {Toehold {Switches}: {De}-{Novo}-{Designed} {Regulators} of {Gene} {Expression}},
	volume = {159},
	issn = {00928674},
	shorttitle = {Toehold {Switches}},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0092867414012896},
	doi = {10.1016/j.cell.2014.10.002},
	language = {en},
	number = {4},
	urldate = {2021-07-23},
	journal = {Cell},
	author = {Green, Alexander A. and Silver, Pamela A. and Collins, James J. and Yin, Peng},
	month = nov,
	year = {2014},
	pages = {925--939},
	file = {Full Text:/Users/sandrachua/Zotero/storage/Y769S8SU/Green et al. - 2014 - Toehold Switches De-Novo-Designed Regulators of G.pdf:application/pdf},
}

@article{fornace_unified_2020,
	title = {A {Unified} {Dynamic} {Programming} {Framework} for the {Analysis} of {Interacting} {Nucleic} {Acid} {Strands}: {Enhanced} {Models}, {Scalability}, and {Speed}},
	volume = {9},
	issn = {2161-5063, 2161-5063},
	shorttitle = {A {Unified} {Dynamic} {Programming} {Framework} for the {Analysis} of {Interacting} {Nucleic} {Acid} {Strands}},
	url = {https://pubs.acs.org/doi/10.1021/acssynbio.9b00523},
	doi = {10.1021/acssynbio.9b00523},
	language = {en},
	number = {10},
	urldate = {2021-07-28},
	journal = {ACS Synthetic Biology},
	author = {Fornace, Mark E. and Porubsky, Nicholas J. and Pierce, Niles A.},
	month = oct,
	year = {2020},
	pages = {2665--2678},
}

@article{mckerns_building_2012,
	title = {Building a {Framework} for {Predictive} {Science}},
	url = {http://arxiv.org/abs/1202.1056},
	abstract = {Key questions that scientists and engineers typically want to address can be formulated in terms of predictive science. Questions such as: "How well does my computational model represent reality?", "What are the most important parameters in the problem?", and "What is the best next experiment to perform?" are fundamental in solving scientific problems. Mystic is a framework for massively-parallel optimization and rigorous sensitivity analysis that enables these motivating questions to be addressed quantitatively as global optimization problems. Often realistic physics, engineering, and materials models may have hundreds of input parameters, hundreds of constraints, and may require execution times of seconds or longer. In more extreme cases, realistic models may be multi-scale, and require the use of high-performance computing clusters for their evaluation. Predictive calculations, formulated as a global optimization over a potential surface in design parameter space, may require an already prohibitively large simulation to be performed hundreds, if not thousands, of times. The need to prepare, schedule, and monitor thousands of model evaluations, and dynamically explore and analyze results, is a challenging problem that requires a software infrastructure capable of distributing and managing computations on large-scale heterogeneous resources. In this paper, we present the design behind an optimization framework, and also a framework for heterogeneous computing, that when utilized together, can make computationally intractable sensitivity and optimization problems much more tractable.},
	urldate = {2021-07-28},
	journal = {arXiv:1202.1056 [cs]},
	author = {McKerns, Michael M. and Strand, Leif and Sullivan, Tim and Fang, Alta and Aivazis, Michael A. G.},
	month = feb,
	year = {2012},
	note = {arXiv: 1202.1056},
	keywords = {Computer Science - Discrete Mathematics, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Mathematical Software},
	file = {arXiv Fulltext PDF:/Users/sandrachua/Zotero/storage/79UPQASH/McKerns et al. - 2012 - Building a Framework for Predictive Science.pdf:application/pdf;arXiv.org Snapshot:/Users/sandrachua/Zotero/storage/24T36A58/1202.html:text/html},
}

@article{kozomara_mirbase_2019,
	title = {{miRBase}: from {microRNA} sequences to function},
	volume = {47},
	issn = {0305-1048},
	shorttitle = {{miRBase}},
	url = {https://doi.org/10.1093/nar/gky1141},
	doi = {10.1093/nar/gky1141},
	abstract = {miRBase catalogs, names and distributes microRNA gene sequences. The latest release of miRBase (v22) contains microRNA sequences from 271 organisms: 38 589 hairpin precursors and 48 860 mature microRNAs. We describe improvements to the database and website to provide more information about the quality of microRNA gene annotations, and the cellular functions of their products. We have collected 1493 small RNA deep sequencing datasets and mapped a total of 5.5 billion reads to microRNA sequences. The read mapping patterns provide strong support for the validity of between 20\% and 65\% of microRNA annotations in different well-studied animal genomes, and evidence for the removal of \>200 sequences from the database. To improve the availability of microRNA functional information, we are disseminating Gene Ontology terms annotated against miRBase sequences. We have also used a text-mining approach to search for microRNA gene names in the full-text of open access articles. Over 500 000 sentences from 18 542 papers contain microRNA names. We score these sentences for functional information and link them with 12 519 microRNA entries. The sentences themselves, and word clouds built from them, provide effective summaries of the functional information about specific microRNAs. miRBase is publicly and freely available at http://mirbase.org/.},
	number = {D1},
	urldate = {2021-08-19},
	journal = {Nucleic Acids Research},
	author = {Kozomara, Ana and Birgaoanu, Maria and Griffiths-Jones, Sam},
	month = jan,
	year = {2019},
	pages = {D155--D162},
	file = {Snapshot:/Users/sandrachua/Zotero/storage/EZR7LVAN/5179337.html:text/html;Full Text PDF:/Users/sandrachua/Zotero/storage/XRGDD82A/Kozomara et al. - 2019 - miRBase from microRNA sequences to function.pdf:application/pdf},
}