Treenut Saithong

Prediction of Photoperiodic Regulators from Quantitative Gene Circuit Models

Photoperiod sensors allow physiological adaptation to the changing seasons. The prevalent hypothesis is that day length perception is mediated through coupling of an endogenous rhythm with an external light signal. Sufficient molecular data are available to test this quantitatively in plants, though not yet in mammals. In Arabidopsis, the clock-regulated genes CONSTANS (CO) and FLAVIN, KELCH, F-BOX (FKF1) and their light-sensitive proteins are thought to form an external coincidence sensor. Here, we model the integration of light and timing information by CO, its target gene FLOWERING LOCUS T (FT), and the circadian clock. Among other predictions, our models show that FKF1 activates FT. We demonstrate experimentally that this effect is independent of the known activation of CO by FKF1, thus we locate a major, novel controller of photoperiodism. External coincidence is part of a complex photoperiod sensor: modeling makes this complexity explicit and may thus contribute to crop improvement.

The contributions of interlocking loops and extensive nonlinearity to the properties of circadian clock models

Background Sensitivity and robustness are essential properties of circadian clock systems, enabling them to respond to the environment but resist noisy variations. These properties should be recapitulated in computational models of the circadian clock. Highly nonlinear kinetics and multiple loops are often incorporated into models to match experimental time-series data, but these also impact on model properties for clock models. Methodology/Principal Findings Here, we study the consequences of complicated structure and nonlinearity using simple Goodwin-type oscillators and the complex Arabidopsis circadian clock models. Sensitivity analysis of the simple oscillators implies that an interlocked multi-loop structure reinforces sensitivity/robustness properties, enhancing the response to external and internal variations. Furthermore, we found that reducing the degree of nonlinearity could sometimes enhance the robustness of models, implying that ad hoc incorporation of nonlinearity could be detrimental to a models perceived credibility. Conclusion The correct multi-loop structure and degree of nonlinearity are therefore critical in contributing to the desired properties of a model as well as its capacity to match experimental data.

Starch biosynthesis in cassava: a genome-based pathway reconstruction and its exploitation in data integration

BackgroundCassava is a well-known starchy root crop utilized for food, feed and biofuel production. However, the comprehension underlying the process of starch production in cassava is not yet available.ResultsIn this work, we exploited the recently released genome information and utilized the post-genomic approaches to reconstruct the metabolic pathway of starch biosynthesis in cassava using multiple plant templates. The quality of pathway reconstruction was assured by the employed parsimonious reconstruction framework and the collective validation steps. Our reconstructed pathway is presented in the form of an informative map, which describes all important information of the pathway, and an interactive map, which facilitates the integration of omics data into the metabolic pathway. Additionally, to demonstrate the advantage of the reconstructed pathways beyond just the schematic presentation, the pathway could be used for incorporating the gene expression data obtained from various developmental stages of cassava roots. Our results exhibited the distinct activities of the starch biosynthesis pathway in different stages of root development at the transcriptional level whereby the activity of the pathway is higher toward the development of mature storage roots.ConclusionsTo expand its applications, the interactive map of the reconstructed starch biosynthesis pathway is available for download at the SBI group’s website (http://sbi.pdti.kmutt.ac.th/?page_id=33). This work is considered a big step in the quantitative modeling pipeline aiming to investigate the dynamic regulation of starch biosynthesis in cassava roots.

Genome-wide analysis reveals phytohormone action during cassava storage root initiation.

Development of storage roots is a process associated with a phase change from cell division and elongation to radial growth and accumulation of massive amounts of reserve substances such as starch. Knowledge of the regulation of cassava storage root formation has accumulated over time; however, gene regulation during the initiation and early stage of storage root development is still poorly understood. In this study, transcription profiling of fibrous, intermediate and storage roots at eight weeks old were investigated using a 60-mer-oligo microarray. Transcription and gene expression were found to be the key regulating processes during the transition stage from fibrous to intermediate roots, while homeostasis and signal transduction influenced regulation from intermediate roots to storage roots. Clustering analysis of significant genes and transcription factors (TF) indicated that a number of phytohormone-related TF were differentially expressed; therefore, phytohormone-related genes were assembled into a network of correlative nodes. We propose a model showing the relationship between KNOX1 and phytohormones during storage root initiation. Exogeneous treatment of phytohormones N6-benzylaminopurine and 1-Naphthaleneacetic acid were used to induce the storage root initiation stage and to investigate expression patterns of the genes involved in storage root initiation. The results support the hypothesis that phytohormones are acting in concert to regulate the onset of cassava storage root development. Moreover, MeAGL20 is a factor that might play an important role at the onset of storage root initiation when the root tip becomes swollen.

Consistent Robustness Analysis (CRA) Identifies Biologically Relevant Properties of Regulatory Network Models

Background A number of studies have previously demonstrated that “goodness of fit” is insufficient in reliably classifying the credibility of a biological model. Robustness and/or sensitivity analysis is commonly employed as a secondary method for evaluating the suitability of a particular model. The results of such analyses invariably depend on the particular parameter set tested, yet many parameter values for biological models are uncertain. Results Here, we propose a novel robustness analysis that aims to determine the “common robustness” of the model with multiple, biologically plausible parameter sets, rather than the local robustness for a particular parameter set. Our method is applied to two published models of the Arabidopsis circadian clock (the one-loop [1] and two-loop [2] models). The results reinforce current findings suggesting the greater reliability of the two-loop model and pinpoint the crucial role of TOC1 in the circadian network. Conclusions Consistent Robustness Analysis can indicate both the relative plausibility of different models and also the critical components and processes controlling each model.

Transcriptomic Data Integration Inferring the Dominance of Starch Biosynthesis in Carbon Utilization of Developing Cassava Roots

Abstract Carbon metabolism, which is an important process underlying the plant development, has been extensively studied in model plant Arabidopsis, however the understanding in this process for cassava root crop is very little. To enhance our understanding into the process, we studied carbon partitioning during cassava root development at the transcriptional level via transciptomic data integration into the metabolic pathways. The transcriptome data of three different developmental stages of cassava roots—fibrous root (FR), developing storage root (DR), and mature storage root (MR) from Yang et al. [1] —was integrated into the key carbon metabolism pathways reconstructed following the protocol of Rongsirikul et al. [2] . According to the 43 differentially expressed genes (56 proteins IDs) mapped into the pathways, we found that the genes involved in starch biosynthesis are more up-regulated, in contrast to the expression of genes in the cell wall biosynthesis and respiration. The results may imply the significance of starch biosynthesis among the carbon utilization processes in the developing cassava roots. In other words, the carbon source from α-D-glucose-1-phosphate (G1P) might be mostly used for starch biosynthesis rather than cell wall biosynthesis and respiration pathways during cassava root development.

Analysis and Practical Guideline of Constraint-Based Boolean Method in Genetic Network Inference

Boolean-based method, despite of its simplicity, would be a more attractive approach for inferring a network from high-throughput expression data if its effectiveness has not been limited by high false positive prediction. In this study, we explored factors that could simply be adjusted to improve the accuracy of inferring networks. Our work focused on the analysis of the effects of discretisation methods, biological constraints, and stringency of Boolean function assignment on the performance of Boolean network, including accuracy, precision, specificity and sensitivity, using three sets of microarray time-series data. The study showed that biological constraints have pivotal influence on the network performance over the other factors. It can reduce the variation in network performance resulting from the arbitrary selection of discretisation methods and stringency settings. We also presented the master Boolean network as an approach to establish the unique solution for Boolean analysis. The information acquired from the analysis was summarised and deployed as a general guideline for an efficient use of Boolean-based method in the network inference. In the end, we provided an example of the use of such a guideline in the study of Arabidopsis circadian clock genetic network from which much interesting biological information can be inferred.

Reconstruction of Starch Biosynthesis Pathway in Cassava Using Comparative Genomic Approach

Cassava is one of the most attractive crops nowadays because it can produce and accumulate large amount of starch in its roots. Cassava starch is widely used as food, feed and raw materials for biochemical industries. Due to the increasing demand of cassava starch, the starch biosynthesis pathway is thus of interest for metabolic engineering, aiming at strain improvement. However, the uncertainties in the metabolic pathway of starch biosynthesis in cassava retard the rate of achievement. Availability of recently released cassava genome motivates us to reconstruct the starch biosynthesis pathway in cassava using comparative genomic approach. Here, nucleotide sequences of the template plants (i.e. Arabidopsis and potato) were compared with the sequence of cassava collected from three sources: Phytozome (genomic sequence), Cassava full-length cDNA and Cassava genome (ESTs) databases. The metabolic pathway of approximately 34 enzymes was constructed, including pathway from sucrose metabolism to amylose and amylopectin synthesis. The resulting pathway is a good initial point toward the complete pathway reconstruction.

Prediction of cassava protein interactome based on interolog method

Cassava is a starchy root crop whose role in food security becomes more significant nowadays. Together with the industrial uses for versatile purposes, demand for cassava starch is continuously growing. However, in-depth study to uncover the mystery of cellular regulation, especially the interaction between proteins, is lacking. To reduce the knowledge gap in protein-protein interaction (PPI), genome-scale PPI network of cassava was constructed using interolog-based method (MePPI-In, available at http://bml.sbi.kmutt.ac.th/ppi). The network was constructed from the information of seven template plants. The MePPI-In included 90,173 interactions from 7,209 proteins. At least, 39 percent of the total predictions were found with supports from gene/protein expression data, while further co-expression analysis yielded 16 highly promising PPIs. In addition, domain-domain interaction information was employed to increase reliability of the network and guide the search for more groups of promising PPIs. Moreover, the topology and functional content of MePPI-In was similar to the networks of Arabidopsis and rice. The potential contribution of MePPI-In for various applications, such as protein-complex formation and prediction of protein function, was discussed and exemplified. The insights provided by our MePPI-In would hopefully enable us to pursue precise trait improvement in cassava.

Genome-Based Screening for Drug Targets Identification: Application to Typhoid Fever

Salmonella enterica serovar Typhi CT18 (S. Typhi) is the causative agent of typhoid fever in human beings. Currently, most of the drugs used to treat this sickness have adverse side-effects. Moreover, drug-resistant strains are emerging as a serious threat for the disease. Therefore, the most effective drug targets are urgently demanded for the development of new faster-acting antibacterial agents. In this paper, a published method for drug targets identification in Mycobacterium tuberculosis metabolismby Kalapanulak was applied to typhoid fever. The whole genome of S. Typhi was investigated and 282 genes were proposed as new drug targets. Interestingly, 34 drug–affected and essential genes from the three current antibiotics are all found in our proposed drug targets.