The gap gene network

Gap genes are involved in segment determination during the early development of the fruit fly Drosophila melanogaster as well as in other insects. This review attempts to synthesize the current knowledge of the gap gene network through a comprehensive survey of the experimental literature. I focus on genetic and molecular evidence, which provides us with an almost-complete picture of the regulatory interactions responsible for trunk gap gene expression. I discuss the regulatory mechanisms involved, and highlight the remaining ambiguities and gaps in the evidence. This is followed by a brief discussion of molecular regulatory mechanisms for transcriptional regulation, as well as precision and size-regulation provided by the system. Finally, I discuss evidence on the evolution of gap gene expression from species other than Drosophila. My survey concludes that studies of the gap gene system continue to reveal interesting and important new insights into the role of gene regulatory networks in development and evolution

Reverse engineering a gene network using an asynchronous parallel evolution strategy.

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.BACKGROUND: The use of reverse engineering methods to infer gene regulatory networks by fitting mathematical models to gene expression data is becoming increasingly popular and successful. However, increasing model complexity means that more powerful global optimisation techniques are required for model fitting. The parallel Lam Simulated Annealing (pLSA) algorithm has been used in such approaches, but recent research has shown that island Evolutionary Strategies can produce faster, more reliable results. However, no parallel island Evolutionary Strategy (piES) has yet been demonstrated to be effective for this task. RESULTS: Here, we present synchronous and asynchronous versions of the piES algorithm, and apply them to a real reverse engineering problem: inferring parameters in the gap gene network. We find that the asynchronous piES exhibits very little communication overhead, and shows significant speed-up for up to 50 nodes: the piES running on 50 nodes is nearly 10 times faster than the best serial algorithm. We compare the asynchronous piES to pLSA on the same test problem, measuring the time required to reach particular levels of residual error, and show that it shows much faster convergence than pLSA across all optimisation conditions tested. CONCLUSIONS: Our results demonstrate that the piES is consistently faster and more reliable than the pLSA algorithm on this problem, and scales better with increasing numbers of nodes. In addition, the piES is especially well suited to further improvements and adaptations: Firstly, the algorithm's fast initial descent speed and high reliability make it a good candidate for being used as part of a global/local search hybrid algorithm. Secondly, it has the potential to be used as part of a hierarchical evolutionary algorithm, which takes advantage of modern multi-core computing architectures

Drosophila blastoderm patterning

The Drosophila blastoderm embryo is a classic model for the study of the genetics of pattern formation. In recent years, quantitative empirical approaches have been employed extensively in the study of blastoderm pattern formation. This quantitative work has enabled the development of a number of data-driven computational models. More than in other systems, these models have been experimentally validated, and have informed new empirical work. They have led to insights into the establishment of morphogen gradients, the interpretation and transduction of positional information by downstream transcriptional networks, and the mechanisms by which spatial scaling and robustness of gene expression are achieved. Here we review the latest developments in the field

Drawing to Extend Waddington’s Epigenetic Landscape

We describe a collaboration between an artist, a mathematician, and a biologist, which examines the potential of drawing for understanding biological process. As a case study, it considers C. H. Waddington’s powerful visual representation of the “epigenetic landscape,” whose purpose it is to unify research in genetics, embryology, and evolutionary biology. We explore the strengths, but also the limitations of Waddington’s landscape and attempt to transcend the latter through a collaborative series of exploratory images. Through careful description of this drawing process, we touch on the epistemological consequences it had on all participants, artist and scientist alike

A blind spot for attractiveness discrimination

Discrimination remains a key challenge for social equity. There is widespread agreement that discrimination is unfair and should be punished. A prerequisite for this is that instances of discrimination are detected. Yet, some types of discrimination may be less apparent than others. Across seven studies (N = 3,486, five preregistered), we find that attractiveness discrimination often goes undetected compared to more prototypical types of discrimination (i.e., gender and race discrimination). This blind spot does not emerge because people perceive attractiveness discrimination to be unproblematic or desirable. Rather, our findings suggest that people’s ability to detect discrimination is bounded. People only focus on a few salient dimensions, such as gender and race, when scrutinizing decision outcomes (e.g., hiring or sentencing decisions) for bias. Consistent with this account, two interventions that increased the salience of attractiveness increased the detection of attractiveness discrimination, but also decreased the detection of gender and race discrimination

Parameter estimation and determinability analysis applied to Drosophila gap gene circuits.

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.BACKGROUND: Mathematical modeling of real-life processes often requires the estimation of unknown parameters. Once the parameters are found by means of optimization, it is important to assess the quality of the parameter estimates, especially if parameter values are used to draw biological conclusions from the model. RESULTS: In this paper we describe how the quality of parameter estimates can be analyzed. We apply our methodology to assess parameter determinability for gene circuit models of the gap gene network in early Drosophila embryos. CONCLUSION: Our analysis shows that none of the parameters of the considered model can be determined individually with reasonable accuracy due to correlations between parameters. Therefore, the model cannot be used as a tool to infer quantitative regulatory weights. On the other hand, our results show that it is still possible to draw reliable qualitative conclusions on the regulatory topology of the gene network. Moreover, it improves previous analyses of the same model by allowing us to identify those interactions for which qualitative conclusions are reliable, and those for which they are ambiguous

Vector Field Embryogeny

We present a novel approach toward evolving artificial embryogenies, which omits the graph representation of gene regulatory networks and directly shapes the dynamics of a system, i.e., its phase space. We show the feasibility of the approach by evolving cellular differentiation, a basic feature of both biological and artificial development. We demonstrate how a spatial hierarchy formulation can be integrated into the framework and investigate the evolution of a hierarchical system. Finally, we show how the framework allows the investigation of allometry, a biological phenomenon, and its role for evolution. We find that direct evolution of allometric change, i.e., the evolutionary adaptation of the speed of system states on transient trajectories in phase space, is advantageous for a cellular differentiation task

Renewable Energy Snapshots 2010

These Renewable Energy Snapshots are based on various data providers including grey data sources and tries to give an overview about the latest developments and trends in the different technologies. Due to the fact that unconsolidated data are used there is an uncertainty margin which should not be neglected. We have cross checked and validate the different data against each others, but do not take any responsibility about the use of these data.JRC.DDG.F.8-Renewable Energy (Ispra

Homology of process: developmental dynamics in comparative biology

Comparative biology builds up systematic knowledge of the diversity of life, across evolutionary lineages and levels of organization, starting with evidence from a sparse sample of model organisms. In developmental biology, a key obstacle to the growth of comparative approaches is that the concept of homology is not very well defined for levels of organization that are intermediate between individual genes and morphological characters. In this paper, we investigate what it means for ontogenetic processes to be homologous, focusing specifically on the examples of insect segmentation and vertebrate somitogenesis. These processes can be homologous without homology of the underlying genes or gene networks, since the latter can diverge over evolutionary time, while the dynamics of the process remain the same. Ontogenetic processes like these therefore constitute a dissociable level and distinctive unit of comparison requiring their own specific criteria of homology. In addition, such processes are typically complex and nonlinear, such that their rigorous description and comparison not only requires observation and experimentation, but also dynamical modeling. We propose six criteria of process homology, combining recognized indicators (sameness of parts, morphological outcome, and topological position) with novel ones derived from dynamical systems modeling (sameness of dynamical properties, dynamical complexity, and evidence for transitional forms). We show how these criteria apply to animal segmentation and other ontogenetic processes. We conclude by situating our proposed dynamical framework for homology of process in relation to similar research programs, such as process structuralism and developmental approaches to morphological homology