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

Evolution of biological cooperation: An algorithmic approach

This manuscript presents an algorithmic approach to cooperation in biological systems, drawing on fundamental ideas from statistical mechanics and probability theory. Fisher’s geometric model of adaptation suggests that the evolution of organisms well adapted to multiple constraints comes at a significant complexity cost. By utilizing combinatorial models of fitness, we demonstrate that the probability of adapting to all constraints decreases exponentially with the number of constraints, thereby generalizing Fisher’s result. Our main focus is understanding how cooperation can overcome this adaptivity barrier. Through these combinatorial models, we demonstrate that when an organism needs to adapt to a multitude of environmental variables, division of labor emerges as the only viable evolutionary strategy

Modeling of Gap Gene Expression in <i>Drosophila Kruppel</i> Mutants

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.</p

Reverse Engineering the Gap Gene Network of Drosophila melanogaster

A fundamental problem in functional genomics is to determine the structure and dynamics of genetic networks based on expression data. We describe a new strategy for solving this problem and apply it to recently published data on early Drosophila melanogaster development. Our method is orders of magnitude faster than current fitting methods and allows us to fit different types of rules for expressing regulatory relationships. Specifically, we use our approach to fit models using a smooth nonlinear formalism for modeling gene regulation (gene circuits) as well as models using logical rules based on activation and repression thresholds for transcription factors. Our technique also allows us to infer regulatory relationships de novo or to test network structures suggested by the literature. We fit a series of models to test several outstanding questions about gap gene regulation, including regulation of and by hunchback and the role of autoactivation. Based on our modeling results and validation against the experimental literature, we propose a revised network structure for the gap gene system. Interestingly, some relationships in standard textbook models of gap gene regulation appear to be unnecessary for or even inconsistent with the details of gap gene expression during wild-type development

Mechanisms of gap gene expression canalization in the Drosophila blastoderm

<p>Abstract</p> <p>Background</p> <p>Extensive variation in early gap gene expression in the <it>Drosophila </it>blastoderm is reduced over time because of gap gene cross regulation. This phenomenon is a manifestation of canalization, the ability of an organism to produce a consistent phenotype despite variations in genotype or environment. The canalization of gap gene expression can be understood as arising from the actions of attractors in the gap gene dynamical system.</p> <p>Results</p> <p>In order to better understand the processes of developmental robustness and canalization in the early <it>Drosophila </it>embryo, we investigated the dynamical effects of varying spatial profiles of Bicoid protein concentration on the formation of the expression border of the gap gene <it>hunchback</it>. At several positions on the anterior-posterior axis of the embryo, we analyzed attractors and their basins of attraction in a dynamical model describing expression of four gap genes with the Bicoid concentration profile accounted as a given input in the model equations. This model was tested against a family of Bicoid gradients obtained from individual embryos. These gradients were normalized by two independent methods, which are based on distinct biological hypotheses and provide different magnitudes for Bicoid spatial variability. We showed how the border formation is dictated by the biological initial conditions (the concentration gradient of maternal Hunchback protein) being attracted to specific attracting sets in a local vicinity of the border. Different types of these attracting sets (point attractors or one dimensional attracting manifolds) define several possible mechanisms of border formation. The <it>hunchback </it>border formation is associated with intersection of the spatial gradient of the maternal Hunchback protein and a boundary between the attraction basins of two different point attractors. We demonstrated how the positional variability for <it>hunchback </it>is related to the corresponding variability of the basin boundaries. The observed reduction in variability of the <it>hunchback </it>gene expression can be accounted for by specific geometrical properties of the basin boundaries.</p> <p>Conclusion</p> <p>We clarified the mechanisms of gap gene expression canalization in early <it>Drosophila </it>embryos. These mechanisms were specified in the case of <it>hunchback </it>in well defined terms of the dynamical system theory.</p

Effects of ecstasy/polydrug use on memory for associative information

Rationale Associative learning underpins behaviours that are fundamental to the everyday functioning of the individual. Evidence pointing to learning deficits in recreational drug users merits further examination. Objectives A word pair learning task was administered to examine associative learning processes in ecstasy/polydrug users. Methods After assignment to either single or divided attention conditions, 44 ecstasy/polydrug users and 48 non-users were presented with 80 word pairs at encoding. Following this, four types of stimuli were presented at the recognition phase: the words as originally paired (old pairs), previously presented words in different pairings (conjunction pairs), old words paired with new words, and pairs of new words (not presented previously). The task was to identify which of the stimuli were intact old pairs. Results Ecstasy/ploydrug users produced significantly more false-positive responses overall compared to non-users. Increased long-term frequency of ecstasy use was positively associated with the propensity to produce false-positive responses. It was also associated with a more liberal signal detection theory decision criterion value. Measures of long term and recent cannabis use were also associated with these same word pair learning outcome measures. Conjunction word pairs, irrespective of drug use, generated the highest level of false-positive responses and significantly more false-positive responses were made in the divided attention condition compared to the single attention condition. Conclusions Overall, the results suggest that long-term ecstasy exposure may induce a deficit in associative learning and this may be in part a consequence of users adopting a more liberal decision criterion value

Modelo de rentabilidade das explorações leiteiras em S. Miguel : influência dos fatores de produção : da classificação morfológica e da produção leiteira dos bovinos

Dissertação de Mestrado em Engenharia Zootécnica.Tendo presente que a situação económica das explorações leiteiras dos Açores está a atravessar grandes dificuldades económicas e financeiras, e com o aproximar do fim anunciado das quotas leiteiras e consequente liberalização total na produção de leite (PL) na Europa, a pecuária açoriana necessita de se preparar para esta nova mudança de estratégia europeia sob o risco de falência da atividade. Dessa forma, pretende-se conhecer a situação técnico-económica e a eficiência técnica das explorações, avaliar o efeito que a produção de leite aos 305 dias (PL305) e a classificação morfológica (CM) terão na rentabilidade líquida (RL), para depois criar um modelo que possa explicar a rentabilidade das explorações. Foram realizadas a análise não paramétrica de eficiência, a análise de variância, a análise cluster e a regressão linear dos registos da PL305, gordura aos 305 dias (PG305), proteína aos 305 dias (PP305) e a CM de 91 explorações que realizaram contraste leiteiro (CL) e foram inscritas em regime de contabilidade organizada no ano de 2010. Constatou-se que as explorações tinham auferindo um RL anual de 1.002,93€/ha, representando um lucro médio de 19,80%. Contudo, cada exploração recebe de subsídios por hectare (Subs/ha) 1.090,06€/ha, o que representa 22,43% do total das receitas e uma clara dependência das explorações aos subsídios (Subs), caso contrário, o lucro médio passaria a -4,20%. Com a certeza do fim anunciado das quotas leiteiras e o corte do Subs aos Produtos Lácteos, prevê-se uma quebra média dos rendimentos em 10,36%. Do lado das despesas, os custos alimentares (CAlim) são os que representam a maior percentagem da despesa com 25,32%. Após a análise não paramétrica de eficiência dos registos verificou-se, que apenas 7,69% das explorações estudadas são eficientes, baixando para os 5,49% quando se retira os Subs/ha atribuídos como forma de rendimento. Na análise de variância observou-se, que não existe qualquer efeito significativo da PL 305 e a Pontuação Final (PF) das explorações na RL das mesmas. Em contrapartida, existe uma forte correlação e um efeito significativo entre a PL305 e a PF (r=0,748, p<0,001) comprovando, que as explorações com as melhores PF são ao mesmo tempo as melhores produtoras de leite. O mesmo se passa na PG305 e na PP305. Na análise de cluster constatou-se, que as explorações mais rentáveis têm um PL305 de 9.188,56 kg, venda de leite (VL) de 5.146,29€, CAlim de 771,65€, Cabeças Normais/ha (CNha) de 3,07e PF de 82,3 pontos. A confirmar estes dados, o modelo criado pela regressão linear múltipla com R2=0,736 estabelece, que de todas as variáveis independentes somente a VL (p<0,001), Calim (p<0,001), Custos com Salários (CSalár, p<0,001), Custos com as Rendas (CRend, p<0,01) e a CNha (p<0,05) são as variáveis com efeito significativo na RL das explorações leiteiras. Assim, os dados revelam que as explorações necessitam de melhorar a qualidade morfológica das vacas leiteiras, de forma a aumentar a PL, proteína e gordura, levando a um aumento da VL, atendendo ao limite máximo dos CAlim e do CNha, de forma a melhorar a eficiência técnica e serem rentáveis sem Subs.ABSTRACT: As we know the dairy farms in Azores are crossing some economical and financial difficulties and with the announced order of milk quotas and complete liberalization of the markets, the farmers in Azores need to be well prepared to face this change in Europe. Because if they don't, they may crash. That is why, it is very important to know the real economical and technical conditions of the farms, as well as the technical efficiency of the farms, and evaluate the effect that the milk production has at 305 days (PL305) and the morphological classification (CM) will have in the net profitability (RL). After that it is important to create a model that can explain the profitability of farms. In order to do that, several things have been made such as: non-parametric analysis of efficiency, analysis of variance, analysis of the cluster, the linear regression of the records of PL305, fat at 305 days (PG305), protein at 305 days (PP305) and the CM of 91 farms that have made their milk recording (CL) and have made their proper accounting in 2010. We found that the farms have earned around 1.002,93€ per hectare, which represented an average profit of 19,80%. However, each farm receives 1.090,06€ per hectare of EU subsidies (Subs/ha), representing 22,43% of the profits and shows a clear dependence on EU subsidies (Subs), because without them, the average profit would be -4.20%. With the end of the milk quotas and the cut in subs for Dairy Products we anticipate an average break in profits around 10.36%.Speaking about the expense, the food costs (CAlim) represent the major percentage of the expense: 25,32%. After the non-parametric analysis of efficiency, we found that only 7,69% of the analyses farms are efficient, decreasing for 5,49%, if we take out the subs/ha. In the analysis of variance we noticed that there isn't any significant effect of the PL305 and the final score (PF) in the RL of the farms. On the other hand, there is a strong relation between the PL 305 and the PF (r=0,748, p<0,001) proving that the farms with the best PF are at the same time the best milk producers. The same happens at PG305 and at PP305. In the cluster analysis we observed that the most profitable farms have a 9.188,56kg of PL305, milk sales (VL) of 5.146,29€, CAlim 771,65€, normal heads /ha (CNha) of 3,07 and PF of 82,3 points. Confirming this facts, the model created by the multiple linear regression with R2=0,736 sets that between all the independent variables only the VL (p<0,001), CAlim (p<0,001), wage costs (CSalár, p<0,001), costs with rents (CRend, p<0,01) and the CNha (p<0,05) are the variables with significant effect in the RL of the farms. Therefore, data shows that the farms need to improve the morphological quality of cows, in order to increase the PL, proteins and fat, taking to an increase of VL, so that they could improve the technical efficiency and be rentable without subs

Studying the functional conservation of cis-regulatory modules and their transcriptional output

<p>Abstract</p> <p>Background</p> <p><it>Cis</it>-regulatory modules (CRMs) are distinct, genomic regions surrounding the target gene that can independently activate the promoter to drive transcription. The activation of a CRM is controlled by the binding of a certain combination of transcription factors (TFs). It would be of great benefit if the transcriptional output mediated by a specific CRM could be predicted. Of equal benefit would be identifying <it>in silico </it>a specific CRM as the driver of the expression in a specific tissue or situation. We extend a recently developed biochemical modeling approach to manage both prediction tasks. Given a set of TFs, their protein concentrations, and the positions and binding strengths of each of the TFs in a putative CRM, the model predicts the transcriptional output of the gene. Our approach predicts the location of the regulating CRM by using predicted TF binding sites in regions near the gene as input to the model and searching for the region that yields a predicted transcription rate most closely matching the known rate.</p> <p>Results</p> <p>Here we show the ability of the model on the example of one of the CRMs regulating the <it>eve </it>gene, MSE2. A model trained on the MSE2 in <it>D. melanogaster </it>was applied to the surrounding sequence of the <it>eve </it>gene in seven other <it>Drosophila </it>species. The model successfully predicts the correct MSE2 location and output in six out of eight <it>Drosophila </it>species we examine.</p> <p>Conclusion</p> <p>The model is able to generalize from <it>D. melanogaster </it>to other <it>Drosophila </it>species and accurately predicts the location and transcriptional output of MSE2 in those species. However, we also show that the current model is not specific enough to function as a genome-wide CRM scanner, because it incorrectly predicts other genomic regions to be MSE2s.</p

Canalization of Gene Expression and Domain Shifts in the Drosophila Blastoderm by Dynamical Attractors

The variation in the expression patterns of the gap genes in the blastoderm of the fruit fly Drosophila melanogaster reduces over time as a result of cross regulation between these genes, a fact that we have demonstrated in an accompanying article in PLoS Biology (see Manu et al., doi:10.1371/journal.pbio.1000049). This biologically essential process is an example of the phenomenon known as canalization. It has been suggested that the developmental trajectory of a wild-type organism is inherently stable, and that canalization is a manifestation of this property. Although the role of gap genes in the canalization process was established by correctly predicting the response of the system to particular perturbations, the stability of the developmental trajectory remains to be investigated. For many years, it has been speculated that stability against perturbations during development can be described by dynamical systems having attracting sets that drive reductions of volume in phase space. In this paper, we show that both the reduction in variability of gap gene expression as well as shifts in the position of posterior gap gene domains are the result of the actions of attractors in the gap gene dynamical system. Two biologically distinct dynamical regions exist in the early embryo, separated by a bifurcation at 53% egg length. In the anterior region, reduction in variation occurs because of stability induced by point attractors, while in the posterior, the stability of the developmental trajectory arises from a one-dimensional attracting manifold. This manifold also controls a previously characterized anterior shift of posterior region gap domains. Our analysis shows that the complex phenomena of canalization and pattern formation in the Drosophila blastoderm can be understood in terms of the qualitative features of the dynamical system. The result confirms the idea that attractors are important for developmental stability and shows a richer variety of dynamical attractors in developmental systems than has been previously recognized

Noise-Driven Phenotypic Heterogeneity with Finite Correlation Time in Clonal Populations

There has been increasing awareness in the wider biological community of the role of clonal phenotypic heterogeneity in playing key roles in phenomena such as cellular bet-hedging and decision making, as in the case of the phage-λ lysis/lysogeny and B. Subtilis competence/vegetative pathways. Here, we report on the effect of stochasticity in growth rate, cellular memory/intermittency, and its relation to phenotypic heterogeneity. We first present a linear stochastic differential model with finite auto-correlation time, where a randomly fluctuating growth rate with a negative average is shown to result in exponential growth for sufficiently large fluctuations in growth rate. We then present a non-linear stochastic self-regulation model where the loss of coherent self-regulation and an increase in noise can induce a shift from bounded to unbounded growth. An important consequence of these models is that while the average change in phenotype may not differ for various parameter sets, the variance of the resulting distributions may considerably change. This demonstrates the necessity of understanding the influence of variance and heterogeneity within seemingly identical clonal populations, while providing a mechanism for varying functional consequences of such heterogeneity. Our results highlight the importance of a paradigm shift from a deterministic to a probabilistic view of clonality in understanding selection as an optimization problem on noise-driven processes, resulting in a wide range of biological implications, from robustness to environmental stress to the development of drug resistance