Of mice and models: improved animal models for biomedical research

2002 ◽  
Vol 11 (3) ◽  
pp. 115-132 ◽  
Author(s):  
Ernesto Bockamp ◽  
Marko Maringer ◽  
Christian Spangenberg ◽  
Stephan Fees ◽  
Stuart Fraser ◽  
...  
Keyword(s):  
Animal Models ◽  
Mouse Models ◽  
Biomedical Research ◽  
Applied Research ◽  
Mouse Genome ◽  
Gene Knockout ◽  
Genetic Disorders ◽  
Murine Models ◽  
Good State ◽  
Basic And Applied Research

The ability to engineer the mouse genome has profoundly transformed biomedical research. During the last decade, conventional transgenic and gene knockout technologies have become invaluable experimental tools for modeling genetic disorders, assigning functions to genes, evaluating drugs and toxins, and by and large helping to answer fundamental questions in basic and applied research. In addition, the growing demand for more sophisticated murine models has also become increasingly evident. Good state-of-principle knowledge about the enormous potential of second-generation conditional mouse technology will be beneficial for any researcher interested in using these experimental tools. In this review we will focus on practice, pivotal principles, and progress in the rapidly expanding area of conditional mouse technology. The review will also present an internet compilation of available tetracycline-inducible mouse models as tools for biomedical research ( http://www.zmg.uni-mainz.de/tetmouse/ ).

scholarly journals Consideration of Gut Microbiome in Murine Models of Diseases

2021 ◽  
Vol 9 (5) ◽  
pp. 1062
Author(s):  
Chunye Zhang ◽  
Craig L. Franklin ◽  
Aaron C. Ericsson
Keyword(s):  
Animal Models ◽  
Mouse Models ◽  
Biomedical Research ◽  
Gut Microbiome ◽  
Close Association ◽  
Disease Model ◽  
Potential Contributor ◽  
Potential Factors ◽  
Models Of Disease ◽  
The Impact

The gut microbiome (GM), a complex community of bacteria, viruses, protozoa, and fungi located in the gut of humans and animals, plays significant roles in host health and disease. Animal models are widely used to investigate human diseases in biomedical research and the GM within animal models can change due to the impact of many factors, such as the vendor, husbandry, and environment. Notably, variations in GM can contribute to differences in disease model phenotypes, which can result in poor reproducibility in biomedical research. Variation in the gut microbiome can also impact the translatability of animal models. For example, standard lab mice have different pathogen exposure experiences when compared to wild or pet store mice. As humans have antigen experiences that are more similar to the latter, the use of lab mice with more simplified microbiomes may not yield optimally translatable data. Additionally, the literature describes many methods to manipulate the GM and differences between these methods can also result in differing interpretations of outcomes measures. In this review, we focus on the GM as a potential contributor to the poor reproducibility and translatability of mouse models of disease. First, we summarize the important role of GM in host disease and health through different gut–organ axes and the close association between GM and disease susceptibility through colonization resistance, immune response, and metabolic pathways. Then, we focus on the variation in the microbiome in mouse models of disease and address how this variation can potentially impact disease phenotypes and subsequently influence research reproducibility and translatability. We also discuss the variations between genetic substrains as potential factors that cause poor reproducibility via their effects on the microbiome. In addition, we discuss the utility of complex microbiomes in prospective studies and how manipulation of the GM through differing transfer methods can impact model phenotypes. Lastly, we emphasize the need to explore appropriate methods of GM characterization and manipulation.

scholarly journals Methods Used and Application of the Mouse Grimace Scale in Biomedical Research 10 Years On: A Scoping Review

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 673
Author(s):  
Alexandra L. Whittaker ◽  
Yifan Liu ◽  
Timothy H. Barker
Keyword(s):  
Animal Models ◽  
Mouse Models ◽  
Biomedical Research ◽  
Scoping Review ◽  
Facial Features ◽  
Inclusion Criteria ◽  
Academic Literature ◽  
Research Fields ◽  
Neuroscience Research ◽  
Wide Range

The Mouse Grimace Scale (MGS) was developed 10 years ago as a method for assessing pain through the characterisation of changes in five facial features or action units. The strength of the technique is that it is proposed to be a measure of spontaneous or non-evoked pain. The time is opportune to map all of the research into the MGS, with a particular focus on the methods used and the technique’s utility across a range of mouse models. A comprehensive scoping review of the academic literature was performed. A total of 48 articles met our inclusion criteria and were included in this review. The MGS has been employed mainly in the evaluation of acute pain, particularly in the pain and neuroscience research fields. There has, however, been use of the technique in a wide range of fields, and based on limited study it does appear to have utility for pain assessment across a spectrum of animal models. Use of the method allows the detection of pain of a longer duration, up to a month post initial insult. There has been less use of the technique using real-time methods and this is an area in need of further research.

scholarly journals Genetically Altered Mouse Models: the Good, the Bad, and the Ugly

2003 ◽  
Vol 14 (3) ◽  
pp. 154-174 ◽  
Author(s):  
Tamizchelvi Thyagarajan ◽  
Satish Totey ◽  
Mary Jo S. Danton ◽  
Ashok B. Kulkarni
Keyword(s):  
Gene Targeting ◽  
Mouse Models ◽  
Molecular Mechanisms ◽  
Gene Knockout ◽  
Genetically Engineered ◽  
Murine Models ◽  
Functional Roles ◽  
Targeted Gene Disruption ◽  
Gene Knockout Mouse

Targeted gene disruption in mice is a powerful tool for generating murine models for human development and disease. While the human genome program has helped to generate numerous candidate genes, few genes have been characterized for their precise in vivo functions. Gene targeting has had an enormous impact on our ability to delineate the functional roles of these genes. Many gene knockout mouse models faithfully mimic the phenotypes of the human diseases. Because some models display an unexpected or no phenotype, controversy has arisen about the value of gene-targeting strategies. We argue in favor of gene-targeting strategies, provided they are used with caution, particularly in interpreting phenotypes in craniofacial and oral biology, where many genes have pleiotropic roles. The potential pitfalls are outweighed by the unique opportunities for developing and testing different therapeutic strategies before they are introduced into the clinic. In the future, we believe that genetically engineered animal models will be indispensable for gaining important insights into the molecular mechanisms underlying development, as well as disease pathogenesis, diagnosis, prevention, and treatment.

scholarly journals An Introduction to Machine Learning Approaches for Biomedical Research

2021 ◽  
Vol 8 ◽  
Author(s):  
Juan Jovel ◽  
Russell Greiner
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Biomedical Research ◽  
Applied Research ◽  
State Of The Art ◽  
Learning Approaches ◽  
Therapy Outcomes ◽  
Computational Approaches ◽  
Specific Goal ◽  
Basic And Applied Research

Machine learning (ML) approaches are a collection of algorithms that attempt to extract patterns from data and to associate such patterns with discrete classes of samples in the data—e.g., given a series of features describing persons, a ML model predicts whether a person is diseased or healthy, or given features of animals, it predicts weather an animal is treated or control, or whether molecules have the potential to interact or not, etc. ML approaches can also find such patterns in an agnostic manner, i.e., without having information about the classes. Respectively, those methods are referred to as supervised and unsupervised ML. A third type of ML is reinforcement learning, which attempts to find a sequence of actions that contribute to achieving a specific goal. All of these methods are becoming increasingly popular in biomedical research in quite diverse areas including drug design, stratification of patients, medical images analysis, molecular interactions, prediction of therapy outcomes and many more. We describe several supervised and unsupervised ML techniques, and illustrate a series of prototypical examples using state-of-the-art computational approaches. Given the complexity of reinforcement learning, it is not discussed in detail here, instead, interested readers are referred to excellent reviews on that topic. We focus on concepts rather than procedures, as our goal is to attract the attention of researchers in biomedicine toward the plethora of powerful ML methods and their potential to leverage basic and applied research programs.

Murine Models of Diabetic Nephropathy

2012 ◽  
Vol 120 (04) ◽  
pp. 191-193 ◽  
Author(s):  
V. Peters ◽  
C. Schmitt
Keyword(s):  
Diabetic Nephropathy ◽  
Animal Models ◽  
Mouse Models ◽  
Critical Analysis ◽  
Disease Process ◽  
Diabetic Animal ◽  
Renal Diseases ◽  
Murine Models ◽  
Renal Changes ◽  
End Stage

AbstractDiabetic nephropathy is the leading cause of end stage renal diseases worldwide. Even though several diabetic animal models exist, not a single one develops renal changes sufficiently reflecting those seen in humans. This review provides an overview on mouse models presenting with various features of diabetic nephropathy. The critical analysis and comparison of existing mouse models substantially enhances our understanding of the disease process and should provide a guide for choosing the most suitable mouse model for the investigation of diabetic nephropathy.

scholarly journals Use of Customizable Nucleases for Gene Editing and Other Novel Applications

Genes ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 976
Author(s):  
Pradeep Reddy ◽  
Felipe Vilella ◽  
Juan Carlos Izpisua Belmonte ◽  
Carlos Simón
Keyword(s):  
Animal Models ◽  
Genome Editing ◽  
Biomedical Research ◽  
Laboratory Animal ◽  
Gene Editing ◽  
Genetic Disorders ◽  
Basic Research ◽  
The Past ◽  
Novel Applications ◽  
In Cells

The development of novel genome editing tools has unlocked new opportunities that were not previously possible in basic and biomedical research. During the last two decades, several new genome editing methods have been developed that can be customized to modify specific regions of the genome. However, in the past couple of years, many newer and more exciting genome editing techniques have been developed that are more efficient, precise, and easier to use. These genome editing tools have helped to improve our understanding of genetic disorders by modeling them in cells and animal models, in addition to correcting the disease-causing mutations. Among the genome editing tools, the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) system has proven to be the most popular one due to its versatility and has been successfully used in a wide variety of laboratory animal models and plants. In this review, we summarize the customizable nucleases currently used for genome editing and their uses beyond the modification of genome. We also discuss the potential future applications of gene editing tools for both basic research and clinical purposes.

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