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Genetic Engineering

Q: What are the different types of Genetic Engineering techniques?

Genetic engineering encompasses a wide range of techniques, including gene insertion, gene deletion, gene modification, and genome editing. These methods allow researchers to precisely manipulate the genetic makeup of organisms, enabling the development of innovative therapies, enhanced crops, and a deeper understanding of biological systems.

Q: How can PubCompare.ai help with Genetic Engineering research?

PubCompare.ai allows researchers to screen protocol literature more efficiently and leverage AI to pinpoint critical insights. The platform's data-driven analysis can help identify the most effective protocols related to Genetic Engineering, enabling researchers to choose the best option for their specific goals and improve reproducibility and accuracy.

Genetic Engineering is the process of manipulating an organism's genetic material to alter its biological characteristics.
This powerful technology allows researchers to modify, insert, or remove specific genes, enabling the development of novel therapies, improved crops, and a deeper understanding of biological systems.
By leveraging the latest advancements in molecular biology and biotechnology, genetic engineering has become an essential tool in fields such as medicine, agriculture, and environmental science.
Expereince the transformative potential of this dynamic field and explore the cutting-edge techniques that are shaping the future of scientific discovery.

Most cited protocols related to «Genetic Engineering»

Reconstructing Developmental Trajectories from scRNA-seq

We check the scree plot to choose ten dimension as the intrinsic dimensions to reconstruct the developmental trajectory for the Paul dataset (cells used in Figure 1 of the original study^{9 (link)}). Five branch points and six terminal lineages (monocytes, neutrophils or eosinophil, basophils, dendritic cells, megakaryocytes, and erythrocytes) are revealed. We ordered the cells using genes Paul et al. used to cluster their data rather than the genes from dpFeature, for the sake of consistency with their clusetering analysis. Similarly, we reconstruct Olsson datasets in four dimensions. The major bifurcation between the granulocyte and monocyte branch (GMP) as well as the intricate branch between GMP and megakaryocyte/erythrocyte (Ery/Meg) are revealed. Top 1, 000 genes from dpFeature based on WT cells are used in both of the WT and full datasets. The distribution (related to confusion matrix) of percentages of cells in each cluster from the original papers over each segment (state in Monocle 2) of the principal graph are calculated and visualized in the heatmap.
We applied BEAM analysis to identify genes significantly bifurcating between Ery/Meg and GMP branch on the Olsson wildtype dataset. We then calculate the instant log ratios (ILRs) of gene expression between Ery/Meg and GMP branch and find genes have mean ILR larger than 0.5. The ILRs are defined as:

{ILR}_{t} = \log (\frac{Y_{1}^{t}}{Y_{2}^{t}})

{ILR}_{t}

is calculated as the log ratio of fitted value at interpolated pseudotime point

t

for the Ery/Meg lineage and that for the GMP lineage. Those genes are used to calculate the lineage score (simply calculated as average expression of those genes in each cell, same as stemness score below) for both of the Olsson and the Paul dataset which is used to color the cells in a tree plot transformed from the high dimensional principal graph (see Supplementary Notes). The same genes are used to create the multi-way heatmap for both of the Paul and Olsson dataset (see plot multiple_branches_heatmap function). Critical functional genes from this procedure are identified. Car1, Car2 (important erythroid functional genes for reversible hydration of carbon dioxide) as well as Elane, Prtn3 (important proteases hydrolyze proteins within specialized neutrophil lysosomes as well as proteins of the extracellular matrix) are randomly chosen as example for creating multi-lineage kinetic curves in both of the Olsson and Paul dataset (see plot_multiple_branches_pseudotime function).
In addition, pseudotime dependent genes for the Ery/Meg and GMP branch are identified in the Olsson wildtype dataset. All genes that always have lower expression from both lineages than the average in the progenitor cells are selected. Those genes are used to calculate the stemness score for both of the Olsson and the Paul dataset which is used to color the cells in the tree plot.

Qiu X., Mao Q., Tang Y., Wang L., Chawla R., Pliner H.A, & Trapnell C. (2017). Reversed graph embedding resolves complex single-cell trajectories. Nature methods, 14(10), 979-982.

Publication 2017

Basophils Carbon dioxide Dendritic Cells Endopeptidases Eosinophil Erythrocytes Extracellular Matrix Proteins Gene Expression Genes Genetic Engineering Granulocyte Kinetics lysosomal proteins Megakaryocytes Monocytes Neutrophil Stem Cells Trees

Reconstructing Developmental Trajectories from scRNA-seq

{ILR}_{t} = \log (\frac{Y_{1}^{t}}{Y_{2}^{t}})

{ILR}_{t}

is calculated as the log ratio of fitted value at interpolated pseudotime point

t

Qiu X., Mao Q., Tang Y., Wang L., Chawla R., Pliner H.A, & Trapnell C. (2017). Reversed graph embedding resolves complex single-cell trajectories. Nature methods, 14(10), 979-982.

Publication 2017

Fuzzy C-Means for Soft Clustering

Soft clustering has been implemented using the fuzzy c-means algorithm. [5 ] It is based on the iterative optimization of an
objective function to minimize the variation of objects within clusters. Poorly clustered objects have decreased influence on the resulting clusters making the clustering process less
sensitive to noise. Notably this is a valuable characteristic of fuzzy c-means method as microarray data tends to be inherently noisy. As a result, fuzzy c-means produces gradual membership
values µ_ij of a gene i between 0 and 1 indicating the degree of membership of this gene for cluster j. This strongly contrasts
hard clustering e.g. the commonly used k-means clustering that generates only membership values µ_ij of either 0 or 1. Thus, soft clustering can effectively reflect
the strength of a gene's association with a cluster. Obtaining gradual membership values allows the definition of cluster cores of tightly co-expressed genes. Moreover, as soft clustering displays
more noise robustness, the commonly used procedure of filtering genes to reduce noise in microarray data can be avoided and loss of the potentially important information can be prevented. [4 (link)]

Kumar L, & E. Futschik M. (2007). Mfuzz: A software package for soft clustering of microarray data. Bioinformation, 2(1), 5-7.

Publication 2007

Contrast Media Genes Genetic Engineering Microarray Analysis

Mendelian Randomization in Microbiome and Cardiometabolic Traits

The Mendelian Randomization procedure consists of two steps: i) identification of proper instrumental variables or genetic predictors, e.g. variants independently associated with the exposure factor, and ii) calculation of causal estimates. For each GWAS summary statistic, we first selected independent SNPs using the clumping procedure in PLINK v1.9 (see URLs) and setting a linkage disequilibrium threshold of r² < 0.1 in a 500-Kb window. Linkage disequilibrium was calculated using the LL-DEEP cohort when running the clumping procedure on the GWAS of microbiome features and short chain fatty acids, whereas for GWAS of anthropometric and glycemic traits we used the linkage disequilibrium estimates from the 1000 Genomes phase 3 European samples.
Furthermore, since the majority of the downloaded GWAS were based on the HapMap2 genetic map, for each independently associated variant, we identified the best HapMap2 proxy (r² > 0.8) or discarded that variant if no proxy was available.
Finally, we selected only variants that showed association at P < 1 × 10⁻⁵. We identified this as the optimal P-value threshold to use for selection of genetic predictors associated with microbiome features because this threshold led to a larger variance explained, on average, of the same microbiome features in the 500FG cohort (Supplementary Figure 1). For consistency, we used the same threshold and procedure for selecting genetic predictors from the downloaded GWAS on anthropometric and glycemic traits.
To calculate causal estimates, we used the inverse-variance weighted (IVW) method^{32 (link)} as a two-sample MR analysis of summary association statistics of the exposure and the outcome. Specifically, we estimated the causal effect in a fixed-effect meta-analysis framework, i.e. as a sum of single-SNP causal effects (derived as a ratio of the SNP-effect on the outcome by the SNP-effect on the exposure) weighted by the inverse of their variance (derived as a squared ratio of the SNP-standard deviation on the outcome on SNP-effect on the exposure). The P-value was calculated as P = 2* (1- Φ(Z)), where Φ(Z) is the standard normal cumulative distribution function and Z is ratio of the combined (using inverse variance weights) causal effect and its standard error. Of note, the causal estimate is equivalent to that obtained as a weighted linear regression of the outcome SNP-effects on the exposure SNP-effects with a fixed intercept of 0 and with the inverse of the variance of the effect sizes on the outcome as weights. For analyses, we set the effect allele of the genetic predictors to be the allele with the positive direction. We also calculated causal estimates using additional MR methods: MR-PRESSO^{30 (link)}, which removes pleiotropy by identifying and discarding influential outlier predictors from the IVW test and uses a t-test to calculate P-values; the weighted-median test^{31 (link)}, which uses a statistical estimator that is robust to the presence of pleiotropy in a subset (< 50%) of the predictors; and the MR-Egger^{32 (link)}, which adjusts for average horizontal pleiotropy and assumes that > 50% of the predictors have pleiotropy. Furthermore, we specifically evaluated presence of pleiotropy using MR-PRESSO Global test^{30 (link)} and the modified Rücker’s Q’ test³³.

Sanna S., van Zuydam N.R., Mahajan A., Kurilshikov A., Vila A.V., Võsa U., Mujagic Z., Masclee A.A., Jonkers D.M., Oosting M., Joosten L.A., Netea M.G., Franke L., Zhernakova A., Fu J., Wijmenga C, & McCarthy M.I. (2019). Causal relationships between gut microbiome, short-chain fatty acids and metabolic diseases. Nature genetics, 51(4), 600-605.

Publication 2019

Alleles Chromosome Mapping Europeans Fatty Acids, Volatile Genetic Engineering Genetic Selection Genome Genome-Wide Association Study Microbiome Reproduction

Efficient Phage Purification and Endotoxin Removal

Notes:

The procedure for phage propagation is largely specific for each phage and bacterial host. Here we use propagation conditions for T4 phage and Escherichia coli B bacterial host. It is recommended to use appropriated growth and propagation conditions for your choice of phage and host.

Once a sufficiently high titer phage lysate is obtained please proceed to step 3.

It is recommended to only propagate and purify one phage at a time to prevent cross-contamination.

1| Phage plaque assay for determination of titer (Adams, 1959 )
2A| Phage isolation and propagation via plate lysate
2B| Phage propagation via liquid lysate
3| Phage cleanup (0.22 μm filtering and chloroform)
4| Phage concentration and wash via ultrafiltration
5| Endotoxin removal (Morrison & Leive, 1975 (link); Szermer-Olearnik & Boratyński, 2015 (link))
Notes:

This method is adapted from Szermer-Olearnik & Boratyński (2015) (link), which demonstrates the efficient removal of endotoxins from bacteriophage lysates using water immiscible solvents that are subsequently removed via dialysis. For detailed explanation of the methodology please see Morrison & Leive (1975) (link) and Szermer-Olearnik & Boratyński (2015) (link).

Our adapted method uses a speed vacuum to remove residual organic solvent from phage lysates, instead of the lengthy dialysis washes with similar efficiency.

This step is optional. If you do not require removal of bacterial endotoxins from your phage preparations please go to step 7.

6A| Dialysis removal of organic solvent (Szermer-Olearnik & Boratyński, 2015 (link))
Notes:

This method is adapted from Szermer-Olearnik & Boratyński (2015) (link) and describes the removal of residual organic solvents from phage lysates by dialysis.

Residual organic solvents disable downstream Pierce™ LAL Chromogenic Endotoxin Quantitation assays and must be removed in order to accurately quantify endotoxin concentrations.

Due to the ionic concentration of phage SM buffer used you may end up with greater than the starting volume.

6B| Speed vacuum removal of organic solvent
Notes:

This method is a faster alternative to the dialysis method for the removal of residual organic solvents from phage concentrates.

7| Phage bank storage

Bonilla N., Rojas M.I., Netto Flores Cruz G., Hung S.H., Rohwer F, & Barr J.J. (2016). Phage on tap–a quick and efficient protocol for the preparation of bacteriophage laboratory stocks. PeerJ, 4, e2261.

Publication 2016

azo rubin S Bacteria Bacteriophage Plaque Assay Bacteriophages Bacteriophage T4 Biological Assay Buffers Chloroform Dialysis Endotoxins Escherichia coli Genetic Engineering Ions isolation Solvents Vacuum Vacuum Extraction, Obstetrical

Most recents protocols related to «Genetic Engineering»

Oxytocin Levels During Birth and Breastfeeding

The search strings were created by SB and AEB, together with librarians from the University of Queensland, Australia, and the University of Skövde, Sweden. Searches were performed in September 2017 in the following databases: PubMed, Scopus, CINAHL (Cumulative Index of Nursing and Allied Health Literature), and PsycInfo. Additional literature searches using the same search strings were completed in March 2020 and June 2022. The search terms comprise synonyms and database-specific terms for oxytocin AND levels AND blood/plasma AND labour/birth/breastfeeding/interventions/newborns. The full search strings are available in Additional file 1.
In total, 3847 articles were identified via database searches (PubMed n = 1598, Scopus n = 1769, CINAHL n = 247, and PsycInfo n = 233). The reference lists of all eligible publications were also hand searched and eight additional articles were found (total 3855). After the removal of 613 duplicates, the remaining 3242 articles were screened on title and abstract and 2914 were excluded. After the full-text screening of the remaining 328 articles, 35 articles were identified that met the inclusion criteria. These 35 publications are based on 31 clinical studies, as four publications reported findings from other included studies.
At each stage, articles were screened by at least two authors, working independently in pairs, based on the inclusion and exclusion criteria (Table 1). Initial title and abstract screening were performed using the Covidence© online platform by AEB, CM, GDB, KL, KUM, SB and ZP. In case inclusion was unclear, a third expert author (KUM) was involved. Subsequent screening, hand searches, full-text review, final inclusion, and data extraction were done by SB and KUM. The selection process is illustrated in Fig. 2, based on the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) protocol, including reasons for exclusion at full-text screening [37 (link)].Fig. 2

Selection process (Prisma)

Buckley S., Uvnäs-Moberg K., Pajalic Z., Luegmair K., Ekström-Bergström A., Dencker A., Massarotti C., Kotlowska A., Callaway L., Morano S., Olza I, & Magistretti C.M. (2023). Maternal and newborn plasma oxytocin levels in response to maternal synthetic oxytocin administration during labour, birth and postpartum – a systematic review with implications for the function of the oxytocinergic system. BMC Pregnancy and Childbirth, 23, 137.

Publication 2023

Genetic Engineering Infant, Newborn Oxytocin Plasma prisma

Comprehensive Transcript Annotation for Predictive Modeling

To engineer gene-centric features of protein binding events and gene annotation and sequence composition features as predictors in our machine learning models we obtained transcript annotations for protein-coding genes and non-coding RNAs from the GENCODE (41 (link)) database for the hg19 (GrCH37) genome build. We obtained 81 745 annotated protein-coding transcripts for 20 167 genes. Of these transcripts, 30 186 (18 889 genes) were supported by RefSeq (42 (link)) annotations and selected as high-confidence transcripts for the analysis. From the annotations, we obtained 5-prime, intronic, coding exonic and 3-prime genomic regions for each transcript which served to capture interpretable binding sites when integrating CHIP-seq and eCLIP-seq data sets (see CHIP-seq data integration & eCLIPseq data integration). HUGO gene nomenclatures (HGNC) (43 (link)) from GENCODE were used to further annotate the transcripts with their respective gene symbols.
A set of non-coding transcripts was obtained through appropriate filtering of the GENCODE transcript annotation set for transcripts that were annotated as one of miscRNA, miRNA, snoRNA, snRNA and lincRNA which represent miscellaneous, micro, small nucleolar, small nuclear and long intervening RNA biotypes, respectively. These non-coding transcripts were used to engineer features for the machine learning task as well as other downstream analyses, especially in the context of the 7SK non-coding RNA (see Identification of 7SK Interacting Proteins). Analogous to the protein-coding transcripts, the genomic regions (5-prime, intronic, exonic, and 3-prime) of non-coding transcripts were used to create binding site features based on CHIP-seq and eCLIP-seq data sets.

Akcan T.S., Vilov S, & Heinig M. (2023). Predictive model of transcriptional elongation control identifies trans regulatory factors from chromatin signatures. Nucleic Acids Research, 51(4), 1608-1624.

Publication 2023

Binding Sites Chromatin Immunoprecipitation Sequencing Exons Gene Annotation Gene Products, Protein Genes Genetic Engineering Genome Introns Long Intergenic Non-Protein Coding RNA MicroRNAs Protein Annotation Protein Domain Proteins RNA, Untranslated Small Nuclear RNA Small Nucleolar RNA

Identifying Gene Regulatory Networks

Based on the trained NN model, we build a gene regulatory network by identifying for each predicted gene at the output of the NN, the genes at the input that contribute to the predicted value. Let F denote the function implemented by our NN model, and (y₁, …, y_N) = F(x₁, …, x_N, x_ct, x_pid) the computation of the N output genes from the N input genes plus the ‘ct’ and ‘pid’ variables, we would like to identify for each data point a matrix of ‘relevance scores’ of size N × N containing the contribution of each input gene i to each output gene l.
The problem of computing these scores for some function F evaluated at some data point x is known as attribution. Many approaches have been proposed for attribution, e.g. (11 (link),24 ,25 ). Here, we use the Layer-wise Relevance Propagation (LRP) (11 (link)) approach for its robustness and advantageous computational properties as it lets us extract for each output y_l the collection of scores in the order of a single forward/backward pass. The LRP procedure starts at the output of the NN with a particular predicted gene value y_l and redistributes this score to the input of the NN in an iterative layer-wise manner. Let j and k be indices for neurons at two adjacent layers, and let a_j and a_k denote their respective activations. Activations at these two layers are related via the neuron equation: where _jk, b_k are the neuron parameters learned from the data, where ρ is a ReLU or linear activation function, and where ∑_{0, j} sums over all input neurons j plus a bias (represented as a constant activation a₀ = 1 and weight _0k = b_k). Denote by R_k the relevance score that has been attributed on neuron k by propagation of y_l from the top-layer back to the layer of neuron k. To propagate relevance scores one layer below (i.e. onto the layer of neuron j), we use the propagation rule: where ( · )⁺ and ( · )⁻ are shortcut notations for max (0, ·) and min (0, ·). This rule is known as ‘generalized LRP-γ’, and used in (19 (link),26 ). The parameter γ is a hyperparameter that needs to be selected to maximize explanation quality. When reaching the input layer, we get 2 · N explanation scores representing gene contributions (each gene being represented as a pair of two values at the input of the NN), plus a few more scores associated to ‘ct’ and ‘pid’ features. We reach the desired N explanation scores by ignoring the ‘ct’ and ‘pid’ scores, and then reducing (i.e. summing) the remaining 2 · N scores into a N-dimensional vector representing the contribution of each gene.
The LRP procedure is repeated K =100 times for random sets of predicting genes and the ‘raw’ LRP score (which we denote by LRP_r) is then defined as the average over these random sets of genes. LRP_r scores are then computed for all predicted genes which leaves us with a N × N matrix of LRP scores representing gene-to-gene interactions. Subsequently, self-loops are excluded and the absolute undirected LRP_au scores for every pair of genes A and B are calculated as .

Keyl P., Bischoff P., Dernbach G., Bockmayr M., Fritz R., Horst D., Blüthgen N., Montavon G., Müller K.R, & Klauschen F. (2023). Single-cell gene regulatory network prediction by explainable AI. Nucleic Acids Research, 51(4), e20.

Publication 2023

Cloning Vectors Gene Regulatory Networks Genes Genetic Engineering Ipex Syndrome Neurons

Establishing a Cerebral Palsy Registry

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2023

Auditory Perception Birth Weight Cardio-Facio-Cutaneous Syndrome Cerebral Palsy Cesarean Section Child Childbirth Epilepsy Genetic Engineering Gestational Age Hemiplegia, Spastic Little's Disease Obstetric Delivery Operative Surgical Procedures Orthotic Devices Parent Pharmaceutical Preparations Rehabilitation Respiratory Therapy Self-Help Devices Spastic Quadriplegia Speech Therapy Therapies, Occupational Therapy, Physical Vagina Walkers Woman

Single-cell RNA-seq analysis of human atherosclerotic plaques

scRNA-seq datasets of human atherosclerotic plaques were downloaded from GEO database and re-analysed using Cellranger (v6.1.2) [54 (link)]. The raw gene-expression matrix was transformed into a Seurat object using the Seurat package (v4.0.5) of R (v4.1.1) [55 ]. In order to exclude low-quality cells, cells with greater than 25,000 unique molecular identifiers (UMIs) were removed and only expressing between 200 and 4500 genes and genes expressed in at least 3 cells were used for further analysis. The doublet cells were further identified and removed from the remaining cells by R package DoubletFinder (v2.0.3) [56 ]. After the above quality control, the integration workflow recommended by Seurat 4 (v4.0.5) is followed [57 ]. We identified the “anchors” in the different batches to construct a reference. First, we used the “SplitObject” function to divide the combined object into a list. Before finding the anchors, log-normalisation was performed, and 2000 highly variable genes (HGVs) were identified using the “vst” method. Next, we used the “FindIntegrationAnchors” function with default parameters to identify the anchors. The “IntegrateData” function, which returns a Seurat object with a batch-corrected expression matrix for all cells, was used to integrate the batches using the anchors. All of these cells were subsequently dimensionally reduced based on HGVs and top 20 principal components estimated by an Elbow plot. The data clustering was done using the graph-based clustering approach implemented in the Seurat package’s “FindNeighbor” function with the top 20 principal components and “FindClusters” function with the “resolution” parameter set to 0.5. The “RunTSNE” function was used for the visualisation plot with the two-dimensional t-distributed stochastic neighbour embedding (t-SNE) model, setting “dims” to 1:30. And known cell lineages were assigned to major cell clusters projected in the t-SNE model using well-known marker genes. Cell clusters were then manually assigned to the major cell types in accordance with these established markers. Any cluster that had multiple markers for two different cell types was manually eliminated as a doublet. The “FindAllMarkers” function with default parameters was used to list the markers of all cell populations. For subclustering of the major cell populations (CD45⁺ cells, myeloid cells and T/NK cells), the same procedure of finding HGVs, removing batch effects, dimensionality reduction, and clustering were repeated. Any cluster with an extraordinarily high number of detected genes or UMI count was manually discarded as a doublet.

Xiong J., Li Z., Tang H., Duan Y., Ban X., Xu K., Guo Y, & Tu Y. (2023). Bulk and single-cell characterisation of the immune heterogeneity of atherosclerosis identifies novel targets for immunotherapy. BMC Biology, 21, 46.

Publication 2023

3,3'-diindolylmethane Cells Elbow Gene Clusters Gene Expression Genes Genetic Engineering Homo sapiens Myeloid Cells Plaque, Atherosclerotic Population Group Single-Cell RNA-Seq T-Lymphocyte

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Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.

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What are the different types of Genetic Engineering techniques?

How can PubCompare.ai help with Genetic Engineering research?

What are some common applications of Genetic Engineering?

Genetic Engineering has a wide range of applications, including the development of personalized medicine, the creation of disease-resistant crops, the production of biofuels, and the conservation of endangered species. This powerful technology has transformed fields such as medicine, agriculture, and environmental science, unlocking new possibilities for scientific discovery and innovation.

How does Genetic Engineering differ from traditional breeding methods?

Unlike traditional breeding techniques, which rely on the natural recombination of genes, Genetic Engineering allows for the direct manipulation of an organism's genetic material. This precision enables researchers to introduce specific traits, remove undesirable characteristics, and create novel organisms with enhanced capabilities. The ability to precisely target and modify genes is a key advantage of Genetic Engineering over conventional breeding methods.

What are some of the ethical considerations surrounding Genetic Engineering?

The use of Genetic Engineering raises important ethical questions, such as the potential for misuse, the impact on human health and the environment, and the equitable access to these technologies. As this field continues to evolve, ongoing dialogue and oversight are crucial to ensure the responsible development and application of Genetic Engineering for the betterment of society.

More about "Genetic Engineering"

Genetic engineering, also known as genetic modification or recombinant DNA technology, is a powerful tool that allows scientists to manipulate the genetic material of organisms.
This dynamic field encompasses a wide range of techniques, such as gene insertion, deletion, and modification, enabling the development of novel therapies, improved crops, and a deeper understanding of biological systems.
Leveraging advancements in molecular biology and biotechnology, genetic engineering has become an essential tool in fields like medicine, agriculture, and environmental science.
Researchers can now modify specific genes, insert desired traits, or remove undesirable characteristics, opening up endless possibilities for innovation and discovery.
From utilizing FBS (Fetal Bovine Serum) and DMEM (Dulbecco's Modified Eagle Medium) as cell culture media, to employing Lipofectamine 2000 for efficient transfection, genetic engineering relies on a suite of specialized techniques and materials.
Giga-prep kits, for instance, facilitate the large-scale purification of plasmid DNA, while Phusion High-Fidelity DNA Polymerase ensures accurate DNA amplification.
Software tools, such as Origin, provide powerful data analysis capabilities, enabling researchers to visualize and interpret their findings.
The Amersham ECL Direct Labeling and Detection System, on the other hand, allows for sensitive protein detection and quantification, while TRIzol reagent is widely used for efficient RNA extraction.
Furthermore, the integration of computational tools, like MATLAB, enhances the ability to model and simulate genetic systems, aiding in the design and optimization of genetic engineering strategies.
The use of Coelenterazine, a bioluminescent compound, enables real-time monitoring of gene expression and cellular activities.
As the field of genetic engineering continues to evolve, the transformative potential of this dynamic discipline is truly remarkable, shaping the future of scientific discovery and paving the way for groundbreaking advancements across various domains.