Physically-Inspired Gaussian Process Models for Post-Transcriptional Regulation in Drosophila.

The regulatory process of Drosophila is thoroughly studied for understanding a great variety of biological principles. While pattern-forming gene networks are analyzed in the transcription step, post-transcriptional events (e.g., translation, protein processing) play an important role in establishing protein expression patterns and levels. Since the post-transcriptional regulation of Drosophila depends on spatiotemporal interactions between mRNAs and gap proteins, proper physically-inspired stochastic models are required to study the link between both quantities. Previous research attempts have shown that using Gaussian processes (GPs) and differential equations lead to promising predictions when analyzing regulatory networks. Here, we aim at further investigating two types of physically-inspired GP models based on a reaction-diffusion equation where the main difference lies in where the prior is placed. While one of them has been studied previously using protein data only, the other is novel and yields a simple approach requiring only the differentiation of kernel functions. In contrast to other stochastic frameworks, discretizing the spatial space is not required here. Both GP models are tested under different conditions depending on the availability of gap gene mRNA expression data. Finally, their performances are assessed on a high-resolution dataset describing the blastoderm stage of the early embryo of Drosophila melanogaster.

Switched Latent Force Models for Reverse-Engineering Transcriptional Regulation in Gene Expression Data.

To survive environmental conditions, cells transcribe their response activities into encoded mRNA sequences in order to produce certain amounts of protein concentrations. The external conditions are mapped into the cell through the activation of special proteins called transcription factors (TFs). Due to the difficult task to measure experimentally TF behaviors, and the challenges to capture their quick-time dynamics, different types of models based on differential equations have been proposed. However, those approaches usually incur in costly procedures, and they present problems to describe sudden changes in TF regulators. In this paper, we present a switched dynamical latent force model for reverse-engineering transcriptional regulation in gene expression data which allows the exact inference over latent TF activities driving some observed gene expressions through a linear differential equation. To deal with discontinuities in the dynamics, we introduce an approach that switches between different TF activities and different dynamical systems. This creates a versatile representation of transcription networks that can capture discrete changes and non-linearities. We evaluate our model on both simulated data and real data (e.g., microaerobic shift in E. coli, yeast respiration), concluding that our framework allows for the fitting of the expression data while being able to infer continuous-time TF profiles.

Initial diet composition affects weight gain and total length of Pacu (Piaractus brachypomus) larvae / La dieta usada en la primera alimentación afecta la ganancia de peso y longitud total de larvas de cachama blanca (Piaractus brachypomus) / Efeitos dos alimentos utilizados na primeira alimentação sobre ganho de peso e comprimento total em larvas de pirapitinga (Piaractus brachypomus)

Weigh gain, length, and survival of Pacu (Piaractus brachypomus) larvae were analyzed after they were fed one of four diets. At 36 hours post-hatching larvae were fed for the first time and during three consecutive days: Artemia salina nauplii (T1), wild plankton filtered to 200 microns (T2) , concentrated powder with 48% crude protein (T3) or no diet (fasting, T4). Animals were kept at a density of 70 larvae per liter. Statistically significant differences were observed with respect to the final weight for T1, T2, T3, and T4 (2.2 ± 0.3a, 1.7 ± 0.3b, 1.5 ± 0.3bc, 1.5 ± 0.2c mg, respectively) , final length (6.37 ± 0.29a, 0.20b ± 6.16, 5.95 ± 0.27c, 0.26c ± 5.87 mm, respectively), weight gain (0.77 ± 0.39a, 0.31b ± 0.34, 0.16 ± 0.37bc, 0.08 ± 0.21 c mg, respectively) and length gain (0.39 ± 0.17A, 0.18 ± 0.15b, 0.16c ± -0023, -0102 ± 0.15c mm, respectively). The T1 diet had the best results for optimal growth, followed by T2. The survival rate was not statistically different among treatments. These data suggest that the initial diet composition can affect subsequent growth characteristics of Pacu larvae.

Se evaluó la ganancia de peso, la longitud y la supervivencia de larvas de cachama blanca (Piaractus brachypomus) alimentadas con varias dietas. A las 36 horas post-eclosión las larvas se alimentaron por primera vez y durante tres días consecutivos con nauplios de Artemia salina (T1), plancton silvestre filtrado a 200 μm (T2), concentrado pulverizado con 48% de proteína bruta (T3), o ayuno (T4). Los animales se mantuvieron a una densidad de 70 larvas por litro. Se observaron diferencias estadísticas significativas (p<0.05) con respecto al peso final para los tratamientos T1, T2, T3, y T4 (2.2 ± 0.3a; 1.7 ± 0.3b; 1.5 ± 0.3bc; 1.5 ± 0.2c mg, respectivamente), longitud final (6.37 ± 0.29a; 6.16 ± 0.20b; 5.95 ± 0.27c; 5.87 ± 0.26c mm, respectivamente), ganancia de peso (0.77 ± 0.39a; 0.34 ± 0.31b; 0.16 ± 0.37bc; 0.08 ± 0.21c mg, respectivamente) y ganancia de longitud (0.39 ± 0.17a; 0.18 ± 0.15b; -0.023 ± 0.16c; -0.102 ± 0.15c mm, respectivamente). El T1 presentó los mejores resultados, seguido por T2. El porcentaje de sobrevivencia no tuvo diferencia estadística significativa entre tratamientos.

Foram utilizadas larvas de pirapitinga (Piaractus brachypomus) foram mantidas em densidade de 70 larvas/L. Por três dias consecutivos, as larvas eram alimentadas com náuplios de Artemia salina (T1), plâncton selvagem filtrado (200 μm) (T2), ração em pó com 48% de PB (T3) e o tratamento controle era mantido em jejum (T4). Os resultados mostraram diferença significativa (p <0.05) no peso final (2.2 ± 0.3 a; 1.7 ± 0.3 b; 1.5 ± 0.3 bc; 1.5 ± 0. 2c mg.), no comprimento final (6.37 ± 0.29 a; 6.16 ± 0.20 b; 5.95 ± 0.27 c; c 5.87 ± 0.26 mm.), no ganho de peso (0.77 ± Um 0.39 a; 0.34 ± 0.31 b; 0.16 ± 0.37 bc; c 0.08 ± 0.21c mg.) e no ganho de comprimento (0.39 ± 0.17 a; 0.18 ± 0.15 b; -0.023 ± 0.16 c; -0.102 ± 0.15c mm). Os tratamentos T1 e T2 mostraram os melhores resultados em relação aos parâmetros anteriormente citados. Contudo, nenhum tratamento mostrou diferença significativa na porcentagem de sobrevivência.