Building better models of visual cortical receptive fields

Scientists usually study the receptive fields of visual cortical neurons by measuring responses to "optimal stimuli." In this issue of Neuron, Rust and colleagues have taken a promising alternative approach: build a receptive field model based on the cell responses to a stimulus subset and then use the model to predict responses to other stimuli.     

Building better beta cells

Type 1 diabetes represents an attractive target for human embryonic stem cell (hESC)-based cell replacement therapies. Recently, in Nature Biotechnology, Kroon et al. (2008) reported encouraging progress toward this goal with the development of functional islet-like structures in mice transplanted with hESC-derived pancreatic endoderm.     

Building better bioorthogonal reactions

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Making better transgenic models

Over the last decade transgenic mouse models have become a common experimental tool for unraveling gene function. During this time there has been a growing expectation that transgenes resemble the in vivo state as much as possible. To this end, a preference away from heterologous promoters has emerged, and transgene constructs often utilize the endogenous promoter and gene sequences in BAC, PAC and YAC form without the addition of selectable markers, or at least their subsequent removal. There has been a trend toward controlled integration by homologous recombination, either at a characterized chromosomal localization or in some cases within the allele of interest. Markers such as green fluorescent protein (GFP), beta-galactosidase (LacZ), and alkaline phosphatase (AP) continue to be useful to trace transgenic cells, or transgene expression. The development of technologies such as RNA interference (RNAi), are introducing new ways of using transgenic models. Future developments in RNAi technology may revolutionize tissue specific inactivation of gene function, without the requirement of generating conditionally targeted mice and tissue specific recombinase mice. Transgenic models are biological tools that aid discovery. Overall, the main consideration in the generation of transgenic models is that they are bona fide biological models that best impart the disease model or biological function of the gene that they represent. The main consideration is to make the best model for the biological question at heart and this review aims to simplify that task somewhat. Here we take a historical perspective on the development of transgenic models, with many of the important considerations to be made in design and development along the way.     

Building a better mouse test

As more mouse models are produced, researchers studying neuropsychiatric diseases will need better ways to evaluate them and more realistic assessment of the results.     

Building a better virus trap

The concept of ecological 'traps' is based in theory from ecology and conservation biology that has now found application to infectious diseases with a study from Paul Turner's group. This study is important because it offers a mathematical model of ecological traps, applies this model to viruses, and tests the model in a bacteria-phage system. Although there will be technical hurdles to overcome, this concept might lead to benefits for both health and industry.     

Designing better frog call recognition models

Advances in bioacoustic technology, such as the use of automatic recording devices, allow wildlife monitoring at large spatial scales. However, such technology can produce enormous amounts of audio data that must be processed and analyzed. One potential solution to this problem is the use of automated sound recognition tools, but we lack a general framework for developing and validating these tools. Recognizers are computer models of an animal sound assembled from “training data” (i.e., actual samples of vocalizations). The settings of variables used to create recognizers can impact performance, and the use of different settings can result in large differences in error rates that can be exploited for different monitoring objectives. We used Song Scope (Wildlife Acoustics Inc.) to build recognizers and vocalizations of the wood frog (Lithobates sylvaticus) to test how different settings and amounts of training data influence recognizer performance. Performance was evaluated using precision (the probability of a recognizer match being a true match) and sensitivity (the proportion of vocalizations detected) based on a receiver operating characteristic (ROC) curve‐determined score threshold. Evaluations were conducted using recordings not used to build the recognizer. Wood frog recognizer performance was sensitive to setting changes in four out of nine variables, and small improvements were achieved by using additional training data from different sites and from the same recording, but not from different recordings from the same site. Overall, the effect of changes to variable settings was much greater than the effect of increasing training data. Additionally, by testing the performance of the recognizer on vocalizations not used to build the recognizer, we discovered that Type I error rates appear idiosyncratic and do not recommend extrapolation from training to new data, whereas Type II errors showed more consistency and extrapolation can be justified. Optimizing variable settings on independent recordings led to a better match between recognizer performance and monitoring objectives. We provide general recommendations for application of this methodology with other species and make some suggestions for improvements.     

Building a better sphingosine kinase-1 inhibitor

Sphingosine 1-phosphate (S1P) is currently one of the most intensely studied lipid mediators. Interest in S1P has been propelled by the development of fingolimod, an S1P receptor agonist prodrug, which revealed both a theretofore unsuspected role of S1P in lymphocyte trafficking and that such modulation of the immune system achieves therapeutic benefit in multiple sclerosis patients. S1P is synthesized from sphingosine by two SphKs (sphingosine kinases) (SphK1 and SphK2). Manipulation of SphK levels using molecular biology and mouse genetic tools has implicated these enzymes, particularly SphK1, in a variety of pathological processes such as fibrosis, inflammation and cancer progression. The results of such studies have spurred interest in SphK1 as a drug target. In this issue of the Biochemical Journal, Schnute et al. describe a small molecule inhibitor of SphK1 that is both potent and selective. Such chemical tools are essential to learn whether targeting S1P signalling at the level of synthesis is a viable therapeutic strategy.     

Building clone-consistent ecosystem models

Many ecological studies employ general models that can feature an arbitrary number of populations. A critical requirement imposed on such models is clone consistency: If the individuals from two populations are indistinguishable, joining these populations into one shall not affect the outcome of the model. Otherwise a model produces different outcomes for the same scenario. Using functional analysis, we comprehensively characterize all clone-consistent models: We prove that they are necessarily composed from basic building blocks, namely linear combinations of parameters and abundances. These strong constraints enable a straightforward validation of model consistency. Although clone consistency can always be achieved with sufficient assumptions, we argue that it is important to explicitly name and consider the assumptions made: They may not be justified or limit the applicability of models and the generality of the results obtained with them. Moreover, our insights facilitate building new clone-consistent models, which we illustrate for a data-driven model of microbial communities. Finally, our insights point to new relevant forms of general models for theoretical ecology. Our framework thus provides a systematic way of comprehending ecological models, which can guide a wide range of studies.     

Building kinetic models for metabolic engineering

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Building better polymerases: Engineering the replication of expanded genetic alphabets

DNA polymerases are today used throughout scientific research, biotechnology, and medicine, in part for their ability to interact with unnatural forms of DNA created by synthetic biologists. Here especially, natural DNA polymerases often do not have the “performance specifications” needed for transformative technologies. This creates a need for science-guided rational (or semi-rational) engineering to identify variants that replicate unnatural base pairs (UBPs), unnatural backbones, tags, or other evolutionarily novel features of unnatural DNA. In this review, we provide a brief overview of the chemistry and properties of replicative DNA polymerases and their evolved variants, focusing on the Klenow fragment of Taq DNA polymerase (Klentaq). We describe comparative structural, enzymatic, and molecular dynamics studies of WT and Klentaq variants, complexed with natural or noncanonical substrates. Combining these methods provides insight into how specific amino acid substitutions distant from the active site in a Klentaq DNA polymerase variant (ZP Klentaq) contribute to its ability to replicate UBPs with improved efficiency compared with Klentaq. This approach can therefore serve to guide any future rational engineering of replicative DNA polymerases.