Metabolic network prediction through pairwise rational kernels

Background

Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations.

Results

We create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy values have been improved, while maintaining lower construction and execution times.

Conclusions

The power of using kernels is that almost any sort of data can be represented using kernels. Therefore, completely disparate types of data can be combined to add power to kernel-based machine learning methods. When we compared our proposal using PRKs with other similar kernel, the execution times were decreased, with no compromise of accuracy. We also proved that by combining PRKs with other kernels that include evolutionary information, the accuracy can also also be improved. As our proposal can use any type of sequence data, genes do not need to be properly annotated, avoiding accumulation errors because of incorrect previous annotations.     

Global trade will accelerate plant invasions in emerging economies under climate change

Trade plays a key role in the spread of alien species and has arguably contributed to the recent enormous acceleration of biological invasions, thus homogenizing biotas worldwide. Combining data on 60‐year trends of bilateral trade, as well as on biodiversity and climate, we modeled the global spread of plant species among 147 countries. The model results were compared with a recently compiled unique global data set on numbers of naturalized alien vascular plant species representing the most comprehensive collection of naturalized plant distributions currently available. The model identifies major source regions, introduction routes, and hot spots of plant invasions that agree well with observed naturalized plant numbers. In contrast to common knowledge, we show that the ‘imperialist dogma,’ stating that Europe has been a net exporter of naturalized plants since colonial times, does not hold for the past 60 years, when more naturalized plants were being imported to than exported from Europe. Our results highlight that the current distribution of naturalized plants is best predicted by socioeconomic activities 20 years ago. We took advantage of the observed time lag and used trade developments until recent times to predict naturalized plant trajectories for the next two decades. This shows that particularly strong increases in naturalized plant numbers are expected in the next 20 years for emerging economies in megadiverse regions. The interaction with predicted future climate change will increase invasions in northern temperate countries and reduce them in tropical and (sub)tropical regions, yet not by enough to cancel out the trade‐related increase.     

RetroPred: A tool for prediction, classification and extraction of non-LTR retrotransposons (LINEs & SINEs) from the genome by integrating PALS, PILER, MEME and ANN

The problem of predicting non-long terminal repeats (LTR) like long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs) from the DNA sequence is still an open problem in bioinformatics. To elevate the quality of annotations of LINES and SINEs an automated tool "RetroPred" was developed. The pipeline allowed rapid and thorough annotation of non-LTR retrotransposons. The non-LTR retrotransposable elements were initially predicted by Pairwise Aligner for Long Sequences (PALS) and Parsimonious Inference of a Library of Elementary Repeats (PILER). Predicted non-LTR elements were automatically classified into LINEs and SINEs using ANN based on the position specific probability matrix (PSPM) generated by Multiple EM for Motif Elicitation (MEME). The ANN model revealed a superior model (accuracy = 78.79 +/- 6.86 %, Q(pred) = 74.734 +/- 17.08 %, sensitivity = 84.48 +/- 6.73 %, specificity = 77.13 +/- 13.39 %) using four-fold cross validation. As proof of principle, we have thoroughly annotated the location of LINEs and SINEs in rice and Arabidopsis genome using the tool and is proved to be very useful with good accuracy. Our tool is accessible at http://www.juit.ac.in/RepeatPred/home.html.     

Single-molecule imaging of β-actin mRNAs in the cytoplasm of a living cell

β-Actin mRNA labeled with an MS2-EGFP fusion protein was expressed in chicken embryo fibroblasts and its localization and movement were analyzed by single-molecule imaging. Most β-Actin mRNAs localized to the leading edge, while some others were observed in the perinuclear region. Singe-molecule tracking of individual mRNAs revealed that the majority of mRNAs were in unrestricted Brownian motion at the leading edge and in restricted Brownian motion in the perinuclear region. The macroscopic diffusion coefficient of mRNA (D_MACRO) at the leading edge was 0.3 μm²/s. On the other hand, D_MACRO in the perinuclear region was 0.02 μm²/s. The destruction of microfilaments with cytochalasin D, which is known to delocalize β-actin mRNAs, led to an increase in D_MACRO to 0.2 μm²/s in the perinuclear region. These results suggest that the microstructure, composed of microfilaments, serves as a barrier for the movement of β-actin mRNA.     

The yeast Golgi apparatus: Insights and mysteries

The Golgi apparatus is known to modify and sort newly synthesized secretory proteins. However, fundamental mysteries remain about the structure, operation, and dynamics of this organelle. Important insights have emerged from studying the Golgi in yeasts. For example, yeasts have provided direct evidence for Golgi cisternal maturation, a mechanism that is likely to be broadly conserved. Here, we highlight features of the yeast Golgi as well as challenges that lie ahead.     

What is plant behaviour?*

The nature of plant behaviour is discussed, and it is concluded that it is best described as what plants do. The possibility that plant behaviour is simply signal‐induced phenotypic plasticity is outlined, and some limitations of this assumption are considered. Natural environments present many challenges to growing plants, and the consequent signalling that plants perceive is becoming extremely complex. Plant behaviour is active, purposeful and intentional, and examples are discussed. Much plant behaviour, concerned with stress and herbivory, is also based on an assessment of the future likelihood of further damaging episodes and is therefore predictive. Plant behaviour involves the acquisition and processing of information. Informational terminology provides a suitable way of incorporating the concepts of learning, memory and intelligence into plant behaviour, capabilities that plants are rarely credited with. Finally, trade‐offs, cost–benefit assessments and decision making are common plant behavioural attributes. It is suggested that intelligent assessments that involve the whole plant are essential to optimize these adaptive capabilities.