Structurally and functionally isolated domains in biological macromolecular evolution, both natural and artificial, are largely similar to "schemata", building blocks (BBs), in evolutionary computation (EC). The problem of preserving in subsequent evolutionary searches the already found domains / BBs is well known and quite relevant in biology as well as in EC. Both biology and EC are seeing parallel and independent development of several approaches to identifying and preserving previously identified domains / BBs. First, we notice the similarity of DNA shuffling methods in synthetic biology and multi-parent recombination algorithms in EC. Furthermore, approaches to computer identification of domains in proteins that are being developed in biology can be aligned with BB identification methods in EC. Finally, approaches to chimeric protein libraries optimization in biology can be compared to evolutionary search methods based on probabilistic models in EC. We propose to validate the prospects of mutual exchange of ideas and transfer of algorithms and approaches between evolutionary systems biology and EC in these three principal directions. A crucial aim of this transfer is the design of new advanced experimental techniques capable of solving more complex problems of in vitro evolution.

The segmentation gene network in Drosophila embryo solves the fundamental problem of embryonic patterning: how to establish a periodic pattern of gene expression, which determines both the positions and the identities of body segments. The gap gene network constitutes the first zygotic regulatory tier in this process. Here we have applied the systems-level approach to investigate the regulatory effect of gap gene Kruppel (Kr) on segmentation gene expression. We acquired a large dataset on the expression of gap genes in Kr null mutants and demonstrated that the expression levels of these genes are significantly reduced in the second half of cycle 14A. To explain this novel biological result we applied the gene circuit method which extracts regulatory information from spatial gene expression data. Previous attempts to use this formalism to correctly and quantitatively reproduce gap gene expression in mutants for a trunk gap gene failed, therefore here we constructed a revised model and showed that it correctly reproduces the expression patterns of gap genes in Kr null mutants. We found that the remarkable alteration of gap gene expression patterns in Kr mutants can be explained by the dynamic decrease of activating effect of Cad on a target gene and exclusion of Kr gene from the complex network of gap gene interactions, that makes it possible for other interactions, in particular, between hb and gt, to come into effect. The successful modeling of the quantitative aspects of gap gene expression in mutant for the trunk gap gene Kr is a significant achievement of this work. This result also clearly indicates that the oversimplified representation of transcriptional regulation in the previous models is one of the reasons for unsuccessful attempts of mutant simulations.

BACKGROUND: Accuracy of the data extracted from two-dimensional confocal images is limited due to experimental errors that arise in course of confocal scanning. The common way to reduce the noise in images is sequential scanning of the same specimen several times with the subsequent averaging of multiple frames. Attempts to increase the dynamical range of an image by setting too high values of microscope PMT parameters may cause clipping of single frames and introduce errors into the data extracted from the averaged images. For the estimation and correction of this kind of errors a method based on censoring technique (Myasnikova et al., 2009) is used. However, the method requires the availability of all the confocal scans along with the averaged image, which is normally not provided by the standard scanning procedure. RESULTS: To predict error size in the data extracted from the averaged image we developed a regression system. The system is trained on the learning sample composed of images obtained from three different microscopes at different combinations of PMT parameters, and for each image all the scans are saved. The system demonstrates high prediction accuracy and was applied for correction of errors in the data on segmentation gene expression in Drosophila blastoderm stored in the FlyEx database (http://urchin.spbcas.ru/flyex/, http://flyex.uchicago.edu/flyex/). The prediction method is realized as a software tool CorrectPattern freely available at http://urchin.spbcas.ru/asp/2011/emm/. CONCLUSIONS: We created a regression system and software to predict the magnitude of errors in the data obtained from a confocal image based on information about microscope parameters used for the image acquisition. An important advantage of the developed prediction system is the possibility to accurately correct the errors in data obtained from strongly clipped images, thereby allowing to obtain images of the higher dynamical range and thus to extract more detailed quantitative information from them.

MOTIVATION: Currently the confocal scanning microscopy of fluorescently tagged molecules is extensively employed to acquire quantitative data on gene expression at cellular resolution. Following this approach, we generated a large dataset on the expression of segmentation genes in the Drosophila blastoderm, that is widely used in systems biology studies. As data accuracy is of critical importance for the success of studies in this field, we took a shot to evaluate possible errors introduced in the data by acquisition and processing methods. This article deals with errors introduced by confocal microscope. RESULTS: In confocal imaging, the inevitable photon noise is commonly reduced by the averaging of multiple frames. The averaging may introduce errors into the data, if single frames are clipped by microscope hardware. A method based on censoring technique is used to estimate and correct this type of errors. Additional source of errors is the quantification of blurred images. To estimate and correct these errors, the Richardson-Lucy deconvolution method was modified to provide the higher accuracy of data read off from blurred images of the Drosophila blastoderm. We have found that the sizes of errors introduced by confocal imaging make up approximately 5-7% of the mean intensity values and do not disguise the dynamic behavior and characteristic features of gene expression patterns. We also defined a range of microscope parameters for the acquisition of sufficiently accurate data. AVAILABILITY: http://urchin.spbcas.ru/downloads/step/step.htm

Abstract Motivation: In this paper we address the problem of the determination of developmental age of an embryo from its segmentation gene expression patterns in Drosophila. Results: By applying support vector regression we have developed a fast method for automated staging of an embryo on the basis of its gene expression pattern. Support vector regression is a statistical method for creating regression functions of arbitrary type from a set of training data. The training set is composed of embryos for which the precise developmental age was determined by measuring the degree of membrane invagination. Testing the quality of regression on the training set showed good prediction accuracy. The optimal regression function was then used for the prediction of the gene expression based age of embryos in which the precise age has not been measured by membrane morphology. Moreover, we show that the same accuracy of prediction can be achieved when the dimensionality of the feature vector was reduced by applying factor analysis. The data reduction allowed us to avoid over-fitting and to increase the efficiency of the algorithm. Availability: This software may be obtained from the authors. Contact: [email protected] Keywords: gene expression patterns; development; embryo staging; support vector regression; segmentation genes; Drosophila. *To whom correspondence should be addressed.

Abstract Motivation: To construct an integrated map of Drosophila segmentation gene expression from partial data taken from individual embryos. Results: Spline and wavelet based registration techniques were developed to register Drosophila segmentation gene expression data. As ground control points for registration we used the locations of extrema on gene expression patterns, represented in 1D. The registration method was characterized by unprecedented high accuracy. A method for constructing the integrated pattern of gene expression at cellular resolution was designed. These patterns were constructed for 9 segmentation genes belonging to gap and pair-rule classes. Availability: Programs running on SPARC/SunOS 5.0 platform or Alpha/DU 4.0E systems may be obtained from the authors. Contact: [email protected] * To whom correspondence should be addressed.

A 1D wavelet-based registration technique was applied to Drosophila segmentation gene expression data. We apply the fast redundant dyadic wavelet transform with a continuous basis as a method for extracting the ground control points. This transform was chosen for its basic properties, such as noise reduction and good localization of characteristic features.