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Optimizing DNA assembly based on statistical language modelling.

ABSTRACT: By successively assembling genetic parts such as BioBrick according to grammatical models, complex genetic constructs composed of dozens of functional blocks can be built. However, usually every category of genetic parts includes a few or many parts. With increasing quantity of genetic parts, the process of assembling more than a few sets of these parts can be expensive, time consuming and error prone. At the last step of assembling it is somewhat difficult to decide which part should be selected. Based on statistical language model, which is a probability distribution P(s) over strings S that attempts to reflect how frequently a string S occurs as a sentence, the most commonly used parts will be selected. Then, a dynamic programming algorithm was designed to figure out the solution of maximum probability. The algorithm optimizes the results of a genetic design based on a grammatical model and finds an optimal solution. In this way, redundant operations can be reduced and the time and cost required for conducting biological experiments can be minimized.

SUBMITTER: Fang G

PROVIDER: S-EPMC5727464 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Optimizing DNA assembly based on statistical language modelling.

Fang Gang G Zhang Shemin S Dong Yafei Y

Nucleic acids research 20171201 22

By successively assembling genetic parts such as BioBrick according to grammatical models, complex genetic constructs composed of dozens of functional blocks can be built. However, usually every category of genetic parts includes a few or many parts. With increasing quantity of genetic parts, the process of assembling more than a few sets of these parts can be expensive, time consuming and error prone. At the last step of assembling it is somewhat difficult to decide which part should be selecte ...[more]

PMID: 29036490

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Project description:Background/objectiveArtificial bone models (ABMs) are used in orthopaedics for research of biomechanics, development of implants and educational purposes. Most of the commercially available ABMs approximate the morphology of Europeans, but they may not depict the Asian anatomy. Therefore, our aim was to develop the first Asian ABM of the pelvis and compare it with the existing pelvic ABM (Synbone®; Caucasian male).MethodsOne hundred clinical computed tomography (CTs) of adult pelvises (male n = 50, female n = 50) of Malay, Chinese and Indian descent were acquired. CTs were segmented and defined landmarks were placed. Three 3D statistical pelvic model and mean models (overall, male, female) were generated. Anatomical variations were analysed using principal component analysis. To measure gender-related differences and differences to the existing ABM, distances between the anterior superior iliac spines (ASIS), the anterior inferior iliac spines (AIIS), the promontory and the symphysis (conjugate vera, CV) as well as the ischial spines (diameter transversa, DT) were quantified.ResultsPrincipal component analysis displayed large variability regarding the pelvic shape and size. Female and male statistical models were similar in ASIS (225 ± 20; 227 ± 13 mm; P = 0.4153) and AIIS (185 ± 11; 187 ± 10 mm; P = 0.3982) and differed in CV (116 ± 10; 105 ± 10 mm; P < 0.0001) and DT (105 ± 7; 88 ± 8 mm; P < 0.0001). Comparing the unisex mean model with the pre-existing ABM, the ASIS (226; 275 mm; P < 0.0001), the AIIS (186; 209 mm; P < 0.0001) and the CV (111; 105 mm; P < 0.0001) differed significantly. Both models were similar regarding DT (97; 95 mm; P = 0.6927). The analysis revealed notable gender- and size-dependent anatomical variations within the Asian population. Chinese, Malay and Indian descents did not differ notably. The overall Asian model was smaller than the existing ABM.The translation potential of this articleOwing to the large differences between the Asian ABM and the pre-existing ABM, as well as differences between genders, the use of an Asian- and gender-specific ABM is important to consider in research, biomechanics and implant development for this population.

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Optimizing DNA assembly based on statistical language modelling.

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Optimizing DNA assembly based on statistical language modelling.

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