Reverse Engineering: Mechanisms, Structures, Systems & Materials
Kategoria: Technologie 5. Cena: od:. Robert W. Messler ; Messler. An advanced yet accessible treatment of the welding process and its underlying science. Despite the critically important role welding plays in nearly every type of human endeavor, most books on this process either focus on basic technical issues and leave the science out, or vice versa.
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Messler, Jr. Promising to become Despite the critically important role welding plays in ne This, in turn, will decrease the barrier for applying reverse-engineering methodology to other developmental systems, many of which are similar in kind and complexity to the gap gene network. The quality of a gene circuit model depends directly on the quality of the data it was fit to.
What matters most in this regard is the timing and position of expression domain boundaries with respect to each other. The relative level of expression in each domain is less crucial. For instance, early gap gene circuit models did not capture the formation of the abdominal kni domain correctly see Figure 2 in . This was due to the incorrect relative position of this domain in the data resulting in a large gap between it and the posterior hb domain see Figure 1 , ibid. This defect is no longer present in more recent models based on data with the abdominal kni domain positioned accurately while still only measuring relative levels of protein concentration  ,  — .
As an example we show quantification of kni expression. Whole-embryo DIC images as shown in C are subjected to a sequence of image segmentation steps: 1. Only the largest blob is kept, and Gaussian smoothing is applied to generate a binary mask covering the embryo F. The midline of the embryo is identified by using the skeleton of the whole-embryo mask, which is then approximated by a spline curve for smoothing and pruning of superfluous skeleton branches K. A raw expression profile is extracted from this band M , which shows high and irregular non-specific background.
To eliminate this background, profiles are manually annotated and expression boundaries are approximated by cubic spline curves M. We calculate the median position of extracted boundary positions for each expression profile per time stage and normalise the data N: boundaries from individual embryos in black, median boundary in red. Median boundaries from multiple time classes are integrated to create an expression profile along the A—P axis though developmental time O: contour plot with interpolated data between time classes. See main text for details. In this study, we present a simplified reverse-engineering protocol and apply it to a new, quantitative data set of gap gene mRNA expression in Drosophila.
We demonstrate how mRNA expression data derived from a colorimetric enzymatic protocol for in situ hybridisation can be used to infer the regulatory structure and dynamics of the gap gene network. We compare our results with those obtained in previous studies based on protein expression data, and show that they predict equivalent regulatory mechanisms that are consistent with experimental evidence. In addition, we show that our simplified data set can be reduced even further while still yielding correct predictions.
In this way, we define a set of minimal requirements for the successful inference of gap gene regulatory network structure and dynamics. These minimal requirements suggest that the adapted gene circuit method can be applied to a variety of developmental systems with a reasonable amount of effort. Such wider application of reverse-engineering methods will enable us to carry out systematic and comparative analyses of developmental gene regulatory networks. Wild-type blastoderm-stage Drosophila embryos were collected after 4 hrs of egg laying and stained with a colorimetric in situ hybridisation protocol adapted from  and .
Embryos were incubated in PBT containing 0. Hybridisation was carried out overnight: 0. Staining was carried out in the dark by the addition of AP buffer containing 0.
Staining was stopped with 3x washes in PBT. Blocking, antibody incubation and washing steps were carried out as described above. Nuclei were counter-stained by a min incubation in PBT containing 0. All washes were done on a nutator, except for those in proteinase K.
An overview of image acquisition and processing steps is shown in Figure 2. Images A—C were acquired using a 10x objective, image D using a 40x objective. Images A and B are focused on the surface, images C and D on the sagittal plane of the embryo. All images were taken at 8-bit accuracy, thus setting the range per RGB channel to [0,]. Only laterally oriented embryos were selected for processing.
Gene expression patterns were extracted from embryo images as follows. Binary masks covering the whole embryo are calculated using a sequence of image segmentation steps on the DIC image Figure 2C. Sobel edge detection is carried out, 5. This results in a number of contiguous binary blobs in the mask image. All blobs touching the image border are removed, and only the largest blob is retained.
Finally, a smooth whole-embryo mask is created by applying a Gaussian filter to the remaining blob. This mask and all raw images A—C were rotated and cropped as described in  such that the embryo's major, or antero-posterior A—P axis is horizontal.
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If necessary, embryo images were flipped manually to a canonical orientation such that anterior is to the left, and dorsal is up Figure 2G—J. To extract gene expression profiles from an embryo, a smooth cubic spline was generated with five equidistant knots through the main branch of the skeleton of the embryo mask Figure 2K ;  , . Inspecting 1D-graphs of the resulting profiles Figure 2M , boundaries for gene expression domains were extracted manually for each embryo: each boundary was labelled with a unique identification number see Supplementary Material , and two points x 0 , y 0 and x 2 , y 2 were determined that indicate the beginning and end of the boundary, where staining levels approach background and maximum levels respectively.
- Reverse Engineering: Mechanisms, Structures, Systems & Materials by Robert Messler
A middle, third control point x 1 , y 1 was automatically calculated from the other two points by taking the average for x, and locating the corresponding expression level y. Points x 0,2 and y 0,2 were used as anchor points for cubic splines with fixed zero-derivatives at their end knots. Finally, splines were normalised such that the expression level at the starting point was 0, and the expression level at the end point was 1 Figure 2N. Integrated time-series of gene expression were prepared as follows. Embryos were staged into separate cleavage cycles defined as the period between mitotic divisions n-1 and n ; e.
C14A was further subdivided into eight equally spaced time classes T1—8 based on membrane morphology from high-resolution DIC images Figure 2D ; time classification for late embryos as described in . Expression domain boundaries were grouped by gene, stage and boundary identification number see above. Average boundary positions were determined by calculating separately the median start and end points for each group, which were then used for fitting a median-boundary spline as described for individual boundaries above.
Finally, we combined different domain boundaries for each gene at each time class into an integrated, normalised expression profile along the A—P axis Figure 2O. Spatial registration of domains was performed by checking the integrated expression profiles against double stained embryos i.
The following post-processing steps had to be applied to our data to make them suitable for model fitting and comparison see Results , and Figure S1 of the online Supporting Information : 1 We collected our normalised, integrated mRNA expression data into 50 C13 or C14A bins to reflect the approximate number of nuclei along the A—P axis  ,  , .
For Drosophila expression data, this is a reasonable assumption, but not an essential requirement. Omitting this post-processing step resulted in qualitatively equivalent results data not shown. Normalised boundaries were scaled to 0.
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This makes the scale of both mRNA and protein data match as closely as possible, and therefore facilitates comparison to models obtained with Drosophila protein data  — . The posterior Kr domain, which arises in late C14A, was removed from the data used for model fitting to avoid modelling artefacts. This domain is known to be under regulatory control of the terminal gap genes with additional inputs from the Forkhead Fkh transcription factor not included in this study , and it does not participate in segment determination .
Fitting models using a weighted least squares WLS protocol see below requires a weight for each data point indicating its associated variation.