Hanen Ben Hassen

Causal inference in biomolecular pathways using a Bayesian network approach and an Implicit method

We introduce here the concept of Implicit networks which provide, like Bayesian networks, a graphical modelling framework that encodes the joint probability distribution for a set of random variables within a directed acyclic graph. We show that Implicit networks, when used in conjunction with appropriate statistical techniques, are very attractive for their ability to understand and analyze biological data. Particularly, we consider here the use of Implicit networks for causal inference in biomolecular pathways. In such pathways, an Implicit network encodes dependencies among variables (proteins, genes), can be trained to learn causal relationships (regulation, interaction) between them and then used to predict the biological response given the status of some key proteins or genes in the network. We show that Implicit networks offer efficient methodologies for learning from observations without prior knowledge and thus provide a good alternative to classical inference in Bayesian networks when priors are missing. We illustrate our approach by an application to simulated data for a simplified signal transduction pathway of the epidermal growth factor receptor (EGFR) protein.

OGDD (Olive Genetic Diversity Database): a microsatellite markers' genotypes database of worldwide olive trees for cultivar identification and virgin olive oil traceability

Olive (Olea europaea), whose importance is mainly due to nutritional and health features, is one of the most economically significant oil-producing trees in the Mediterranean region. Unfortunately, the increasing market demand towards virgin olive oil could often result in its adulteration with less expensive oils, which is a serious problem for the public and quality control evaluators of virgin olive oil. Therefore, to avoid frauds, olive cultivar identification and virgin olive oil authentication have become a major issue for the producers and consumers of quality control in the olive chain. Presently, genetic traceability using SSR is the cost effective and powerful marker technique that can be employed to resolve such problems. However, to identify an unknown monovarietal virgin olive oil cultivar, a reference system has become necessary. Thus, an Olive Genetic Diversity Database (OGDD) (http://www.bioinfo-cbs.org/ogdd/) is presented in this work. It is a genetic, morphologic and chemical database of worldwide olive tree and oil having a double function. In fact, besides being a reference system generated for the identification of unkown olive or virgin olive oil cultivars based on their microsatellite allele size(s), it provides users additional morphological and chemical information for each identified cultivar. Currently, OGDD is designed to enable users to easily retrieve and visualize biologically important information (SSR markers, and olive tree and oil characteristics of about 200 cultivars worldwide) using a set of efficient query interfaces and analysis tools. It can be accessed through a web service from any modern programming language using a simple hypertext transfer protocol call. The web site is implemented in java, JavaScript, PHP, HTML and Apache with all major browsers supported. Database URL: http://www.bioinfo-cbs.org/ogdd/

SNP marker analysis for validating the authenticity of Tunisian olive oil.

Olive (Olea europaea L.), which is an important oilproducing crop, is one of the oldest agricultural plant in the Mediterranean basin. The oil obtained is known for its nutritional and healthy benefits compared to other vegetable oils, and can be consumed in its crude form (Roche et al. 2000; Elloumi et al. 2012). Moreover, the olive oil sector plays an important role in the culture and socio-economy of many Mediterranean countries, including Tunisia. Traditionally, genetic variation analyses relying on morphological and chemical markers are insufficient to study the relationship and traceability between cultivars due to the environmental effect on the possibly large phenotype and the chemical composition, thus making it expensive (Busconi et al. 2003; Ben-Ayed et al. 2009, 2013). Recently, several molecular marker types, such as random amplified polymorphic DNA (RAPDs) (Busconi et al. 2003), amplified fragment length polymorphisms (AFLPs) (Pafundo et al. 2005; Grati Kamoun et al. 2006), simple sequence repeats (SSR) (Testolin and Lain 2005; Rekik et al. 2008; Ben-Ayed et al. 2009, 2012, 2014) and single nucleotide polymorphism (SNP) (Reale et al. 2006; Consolandi et al. 2008; RekikHakim et al. 2010) have been developed. These can be used as both detection of DNA polymorphisms and for effective distinction between different cultivars, thus solving traceability without any environmental influence. Despite the potential advantages of using SNPs for the authentication of major crop species as coffee (Spaniolas et al. 2006), to the best of our knowledge, the identification of SNP markers has not yet been documented in olive oil. Compared with other genetic markers, SNPs are beneficial from a technological viewpoint change in a single nucleotide allows the distinction of very similar cultivars. Moreover, these molecular markers requiring short DNA amplicons for genotyping and are genetically stable; their high density

Inference in Signal Transduction Pathways Using EM Algorithm and an Implicit Algorithm: Incomplete Data Case

We summarize here the Implicit statistical inference approach as an alternative to Bayesian networks and we give an effective iterative algorithm analogous to the Expectation Maximization algorithm to infer signal transduction network when the set of data is incomplete. We proved the convergence of our algorithm that we called Implicit algorithm and we apply it to simulated data for a simplified signal transduction pathway of the EGFR protein.

Purification and characterization of an amylase from Opuntiaficus‐indica seeds

BACKGROUND In Tunisia, prickly pear fruit grow spontaneously; it is consumed as fresh fruit, juice or jam. When the fruit is used for juice production, the seeds are discarded and go to waste. Our study aimed to extract biomolecules from seeds by producing value-added products from the fruits. RESULTS An amylase from Opuntia ficus-indica seeds was extracted and purified to homogeneity. An increase in specific activity of 113-fold was observed. The apparent molecular mass of the enzyme is 64 kDa. The optimum pH and temperature for enzyme activity were pH 5 and 60 °C, respectively. Under these conditions, the specific activity is 245.5 U mg(-1) . The enzyme was activated by Co(2+) and Mg(2+) (relative activity 117% and 113% respectively) at lower ion concentrations. It was strongly inhibited by Mn(2+) and Fe(2+) . Cu(2+) inhibited totally the activity of this enzyme, but Ca(2+) has an inhibitory effect which increases with ion concentration. CONCLUSION The extracted enzyme belongs to the exo type of amylases and is classified as a β-cyclodextrin glycosyltransferase since it generates mainly β-cyclodextrin from starch. It exhibits high thermal stability and a broad range of pH stability, making it a promising prospect for industrial and food applications.

ANALYSIS OF BREAST CANCER PROFILES USING BAYESIAN NETWORK MODELING

Breast cancer is the leading cause of cancer-related death for women in Tunisia and the prognosis of its metastasis remains a major problem for oncologists despite advances in treatment. In this work we use Bayesian networks to develop a decision support system that is based on the modeling of relationships between key signaling proteins and clinical and pathological characteristics of breast tumors and patients. Motivated by the lack of prior information on the parameters of the problem, we use the Implicit inference for the structure and parameter learning. A dataset of 84 Tunisian breast cancer patients was used and new prognosis factors were identified. The system predicts a metastasis risk for different patients by computing a score that is the joint probability of the Bayesian network using parameters estimated on the learning database. Based on the results of the developed system we identified that overexpression of ErbB2, ErbB3, bcl2 as well as of oestrogen and progesterone receptors associated wi...

Experimental design and Bayesian networks for enhancement of delta-endotoxin production by Bacillus thuringiensis

Bacillus thuringiensis (Bt) is a Gram-positive bacterium. The entomopathogenic activity of Bt is related to the existence of the crystal consisting of protoxins, also called delta-endotoxins. In order to optimize and explain the production of delta-endotoxins of Bacillus thuringiensis kurstaki, we studied seven medium components: soybean meal, starch, KH₂PO₄, K₂HPO₄, FeSO₄, MnSO₄, and MgSO₄and their relationships with the concentration of delta-endotoxins using an experimental design (Plackett-Burman design) and Bayesian networks modelling. The effects of the ingredients of the culture medium on delta-endotoxins production were estimated. The developed model showed that different medium components are important for the Bacillus thuringiensis fermentation. The most important factors influenced the production of delta-endotoxins are FeSO₄, K2HPO₄, starch and soybean meal. Indeed, it was found that soybean meal, K₂HPO₄, KH₂PO₄and starch also showed positive effect on the delta-endotoxins production. However, FeSO4 and MnSO4 expressed opposite effect. The developed model, based on Bayesian techniques, can automatically learn emerging models in data to serve in the prediction of delta-endotoxins concentrations. The constructed model in the present study implies that experimental design (Plackett-Burman design) joined with Bayesian networks method could be used for identification of effect variables on delta-endotoxins variation.

Modeling of soil penetration resistance using multiple linear regression (MLR)

In agricultural areas, the use of machinery leads to improved yields. Nevertheless, its inadequate implementation and excessive utilization can seriously affect the soil efficiency. In fact, latter can be generated by increasing the penetration resistance and subsequently, it results in the compaction phenomenon. This problem becomes considerable with the increasing report wheel/soil. The aim of this work was to evaluate the efficiency through the prediction of soil penetration resistance (Rp) using a statistical model based on moisture content, density, tractor weight, number of passes, and the wheel inflation pressure. Experimental works (211 measurements) were analyzed and the penetration resistance was modeled using multiple linear regressions (MLR). Besides, the developed model elucidates the variables affecting the accentuation of soil Rp and allows the investigation of equations for novel sampled soils. Our results showed that the parameters related to soil and tractors were significant to explain Rp. The adopted model in the MLR analysis emphasizes that the mechanical parameters of ground measurements are statistically significant in estimating and evaluating Rp. The statistical calculation of the R2 expresses 83% of the variance in Rp generated by the various parameters related to soil and tractor. In view of the importance of estimating the penetration resistance (Rp), the regression equation shows that the weight of the tractor and the number of passages contributed the most to the proposed model for the soil.