Description:
Because of the high dimensionality of the microarray data sets, feature selection (FS) has become an important challenge in molecular classification. Using the degree of differential prioritization (DDP) between relevance and antiredundancy, our proposed DDP-based FS technique is capable of achieving better accuracies than those previously reported, using a smaller predictor set. However, previously, we have neither devised nor used any method for determining the value of the DDP to be used for the data set of interest before the FS process. In this article, we propose a system for predicting the optimal value of the DDP, which costs less computationally than conventional tuning while maintaining the independence of the FS technique from the type of underlying classifier used
Description:
Mixotrophic cultivation of cyanobacteria in wastewaters with flue gas sparging has the potential to simultaneously sequester carbon content from gaseous and aqueous streams and convert to biomass and biofuels. Therefore, it was of interest to study the effect of mixotrophy and elevated CO2 on metabolism, morphology and rhythm of gene expression under diurnal cycles. We chose a diazotrophic unicellular cyanobacterium Cyanothece sp. ATCC 51142 as a model, which is a known hydrogen producer with robust circadian rhythm. Cyanothece 51142 grows faster with nitrate and/or an additional carbon source in the growth medium and at 3 % CO2. Intracellular glycogen contents undergo diurnal oscillations with greater accumulation under mixotrophy. While glycogen is exhausted by midnight under autotrophic conditions, significant amounts remain unutilized accompanied by a prolonged upregulation of nifH gene under mixotrophy. This possibly supports nitrogen fixation for longer periods thereby leading to better growth. To gain insights into the influence of mixotrophy and elevated CO2 on circadian rhythm, transcription of core clock genes kaiA, kaiB1 and kaiC1, the input pathway, cikA, output pathway, rpaA and representatives of key metabolic pathways was analyzed. Clock genes’ transcripts were lower under mixotrophy suggesting a dampening effect exerted by an external carbon source such as glycerol. Nevertheless, the genes of the clock and important metabolic pathways show diurnal oscillations in expression under mixotrophic and autotrophic growth at ambient and elevated CO2, respectively. Taken together, the results indicate segregation of light and dark associated reactions even under mixotrophy and provide important insights for further applications.
Description:
Carbon trading scheme is being established around the world as an instrument in reducing global GHG emission. Being an emerging market, there only a few simple simulation studies related to carbon trading that have been reported. In this paper, we propose a novel carbon trading simulator capable of modeling traits of human traders in carbon markets. The model is driven by the concept of Nash equilibrium within an agent based modeling paradigm. The model is capable of implementing crucial issues such as carbon emissions, Marginal Abatement Cost Curve (MAC), and complex human trading behaviour. Experiments carried out provide insights into interaction between traders’ behaviour and how the interaction affects profitability
Description:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, Vol.7064 (3)
Description:
A holistic understanding of genetic interactions, in the post-genomic era, is vital for analysing complex biological systems. In this paper, we present an information theory based novel gene regulatory network inference method using the dynamic Bayesian network (DBN) framework. The proposed approach, with strong theoretical underpinnings, employs mutual information based conditional independence tests to assess the regulatory relationships among genes. The method is flexible, computationally fast and allows a-priori incorporation of biological domain knowledge. We apply it to the analysis of synthetic data as well as Saccharomyces cerevisiae (yeast cell cycle) gene expression data. Performance measures applied to simulation studies show the superior performance of the proposed method
Description:
Protein structure prediction (PSP) remains one of the most challenging open problems in structural bioinformatics. Simplified models in terms of lattice structure and energy function have been proposed to ease the computational hardness of this combinatorial optimization problem. In this paper, we describe a clustered meme-based evolutionary approach for PSP using triangular lattice model. Under the framework of memetic algorithm, the proposed method extracts a pool of cultural information from different regions of the search space using data clustering technique. These highly observed local substructures, termed as meme, are then aggregated centrally for further refinements as second stage of evolution. The optimal utilization of 'explore-and-exploit' feature of evolutionary algorithms is ensured by the inherent parallel architecture of the algorithm and subsequent use of cultural information.
Description:
In this paper, we apply Bayesian networks (BN) to infer gene regulatory network (GRN) model from gene expression data. This inference process, consisting of structure search and conditional probability estimation, is challenging due to the size and quality of the data that is currently available. Our previous studies for GRN reconstruction involving evolutionary search algorithm obtained a most plausible graph structure referred as Independence-map (or simply I-map). However, the limitations of the data (large number of genes and less samples) can result in many plausible structures that equally satisfy the data set. In the present study, given the network structures, we estimate the conditional probability distribution of each variable (gene) from the data set to deduce a unique minimal I-map. This is achieved by using Markov Chain Monte Carlo (MCMC) method whereby the search space is iteratively reduced resulting in the required convergence within a reasonable computation time. We present empirical results on both, the synthetic and real-life data sets and also compare our approach with the plain MCMC sampling approach. The inferred minimal I-map on the real-life yeast data set is also presented.
Description:
Proteins are sequences of amino acids bound into a linear chain that adopt a specific folded three-dimensional (3D) shape. This specific folded shape enables proteins to perform specific tasks. The protein structure prediction (PSP) by ab initio or de novo approach is promising amongst various available computational methods and can help to unravel the important relationship between sequence and its corresponding structure. This article presents the ab initio protein structure prediction as a conformational search problem in low resolution model using genetic algorithm. As a review, the essence of twin removal, intelligence in coding, the development and application of domain specific heuristics garnered from the properties of the resulting model and the protein core formation concept discussed are all highly relevant in attempting to secure the best solution.
Description:
Proteins are sequences of amino acids bound into a linear chain that adopt a specific folded three-dimensional (3D) shape. This specific folded shape enables proteins to perform specific tasks. The protein structure prediction (PSP) by ab initio or de novo approach is promising amongst various available computational methods and can help to unravel the important relationship between sequence and its corresponding structure. This article presents the ab initio protein structure prediction as a conformational search problem in low resolution model using genetic algorithm. As a review, the essence of twin removal, intelligence in coding, the development and application of domain specific heuristics garnered from the properties of the resulting model and the protein core formation concept discussed are all highly relevant in attempting to secure the best solution.
Description:
Although living organisms can have some genetic interactions occurring instantaneously while others with time-delay, current modeling techniques for genetic network reconstruction make simplifications and assume that the interactions can be either of these but not both. In this paper, we propose a gene regulatory network reconstruction algorithm that can model concurrent occurrence of both, instantaneous as well as time-delayed interactions, thus providing a better representation of the original biological processes. First we introduce a novel framework using the Bayesian network (BN) formalism that can model both types of interactions. A gene regulatory network reconstruction algorithm using this proposed framework is then developed that employs an evolutionary search strategy and a decomposable scoring metric based on information theoretic quantities. Investigations of our approach are performed using both, the synthetic data as well as Saccharomyces cerevisiae gene expression data. Comparisons with recent reconstruction methods show the superiority of the proposed method.
Description:
Selection is an important and critical aspect in evolutionary computation. This paper presents a novel parental selection technique that includes the advantages of both the deterministic and the stochastic selection techniques and helps to reduce the loss of diversity by distributing the reproduction opportunity among all the members of the population. Moreover, the proposed selection strategy promotes the concept of non-random mating by clustering the population into groups according to the fitness values and then by persuading the mating between individuals from different groups based on performance determined dynamically over the evolution. Computational results using widely used benchmark functions show significant improvements in the convergence characteristics of the proposed selection method over two well-known selection techniques.
Description:
Due to various complexities, as well as noise and high dimensionality, reconstructing a gene regulatory network (GRN) from a high-throughput microarray data becomes computationally intensive.In our earlier work on causal model approach for GRN reconstruction, we had shown the superiority of Markov blanket (MB) algorithm compared to the algorithm using the existing Y and V causal models. In this paper, we show the MB algorithm can be enhanced further by application of the proposed constraint logic minimization (CLM) technique. We describe a framework for minimizing the constraint logic involved (condition independent tests) by exploiting the Markov blanket learning methods developed for a Bayesian network (BN). The constraint relationships are represented in the form of logic using K-map and with the aid of CLM increase the algorithm efficiency and the accuracy. We show improved results by investigations on both the synthetic as well as the real life yeast cell cycle data sets.
Description:
Background In any gene regulatory network (GRN), the complex interactions occurring amongst transcription factors and target genes can be either instantaneous or time-delayed. However, many existing modeling approaches currently applied for inferring GRNs are unable to represent both these interactions simultaneously. As a result, all these approaches cannot detect important interactions of the other type. S-System model, a differential equation based approach which has been increasingly applied for modeling GRNs, also suffers from this limitation. In fact, all S-System based existing modeling approaches have been designed to capture only instantaneous interactions, and are unable to infer time-delayed interactions. Results In this paper, we propose a novel Time-Delayed S-System (TDSS) model which uses a set of delay differential equations to represent the system dynamics. The ability to incorporate time-delay parameters in the proposed S-System model enables simultaneous modeling of both instantaneous and time-delayed interactions. Furthermore, the delay parameters are not limited to just positive integer values (corresponding to time stamps in the data), but can also take fractional values. Moreover, we also propose a new criterion for model evaluation exploiting the sparse and scale-free nature of GRNs to effectively narrow down the search space, which not only reduces the computation time significantly but also improves model accuracy. The evaluation criterion systematically adapts the max-min in-degrees and also systematically balances the effect of network accuracy and complexity during optimization. Conclusion The four well-known performance measures applied to the experimental studies on synthetic networks with various time-delayed regulations clearly demonstrate that the proposed method can capture both instantaneous and delayed interactions correctly with high precision. The experiments carried out on two well-known real-life networks, namely IRMA and SOS DNA repair network in Escherichia coli show a significant improvement compared with other state-of-the-art approaches for GRN modeling.
Description:
Background In any gene regulatory network (GRN), the complex interactions occurring amongst transcription factors and target genes can be either instantaneous or time-delayed. However, many existing modeling approaches currently applied for inferring GRNs are unable to represent both these interactions simultaneously. As a result, all these approaches cannot detect important interactions of the other type. S-System model, a differential equation based approach which has been increasingly applied for modeling GRNs, also suffers from this limitation. In fact, all S-System based existing modeling approaches have been designed to capture only instantaneous interactions, and are unable to infer time-delayed interactions. Results In this paper, we propose a novel Time-Delayed S-System (TDSS) model which uses a set of delay differential equations to represent the system dynamics. The ability to incorporate time-delay parameters in the proposed S-System model enables simultaneous modeling of both instantaneous and time-delayed interactions. Furthermore, the delay parameters are not limited to just positive integer values (corresponding to time stamps in the data), but can also take fractional values. Moreover, we also propose a new criterion for model evaluation exploiting the sparse and scale-free nature of GRNs to effectively narrow down the search space, which not only reduces the computation time significantly but also improves model accuracy. The evaluation criterion systematically adapts the max-min in-degrees and also systematically balances the effect of network accuracy and complexity during optimization. Conclusion The four well-known performance measures applied to the experimental studies on synthetic networks with various time-delayed regulations clearly demonstrate that the proposed method can capture both instantaneous and delayed interactions correctly with high precision. The experiments carried out on two well-known real-life networks, namely IRMA and SOS DNA repair network in Escherichia coli show a significant improvement compared with other state-of-the-art approaches for GRN modeling.
Description:
Understanding gene interactions is a fundamental question in systems biology. Currently, modeling of gene regulations assumes that genes interact either instantaneously or with time delay. In this paper, we introduce a framework based on the Bayesian Network (BN) formalism that can represent both instantaneous and time-delayed interactions between genes simultaneously. Also, a novel scoring metric having firm mathematical underpinnings is then proposed that, unlike other recent methods, can score both interactions concurrently and takes into account the biological fact that multiple regulators may regulate a gene jointly, rather than in an isolated pair-wise manner. Further, a gene regulatory network inference method employing evolutionary search that makes use of the framework and the scoring metric is also presented. Experiments carried out using synthetic data as well as the well known Saccharomyces cerevisiae gene expression data show the effectiveness of our approach.
Description:
In this paper, we propose novel algorithmic models based on fusion of independent and correlated gene features for multiclass microarray gene expression classification. It is possible for genes to get co-expressed via different pathways. Moreover, a gene may or may not be co-active for all samples. In this paper, we approach this problem with a optimal feature selection technique using analysis based on statistical techniques to model the complex interactions between genes. The two different types of correlation modelling techniques based on the cross modal factor analysis (CFA) and canonical correlation analysis (CCA) were examined. The subsequent fusion of CCA/CFA features with principal component analysis (PCA) features at feature-level, and at score-level result in significant enhancement in classification accuracy for different data sets corresponding to multiclass microarray gene expression data.
Description:
This study investigates the influence of mixotrophy on physiology and metabolism by analysis of global gene expression in unicellular diazotrophic cyanobacterium Cyanothece sp. ATCC 51142 ( henceforth Cyanothece 51142). It was found that Cyanothece 51142 continues to oscillate between photosynthesis and respiration in continuous light under mixotrophy with cycle time of similar to 13 h. Mixotrophy is marked by an extended respiratory phase compared with photoautotrophy. It can be argued that glycerol provides supplementary energy for nitrogen fixation, which is derived primarily from the glycogen reserves during photoautotrophy. The genes of NDH complex, cytochrome c oxidase and ATP synthase are significantly overexpressed in mixotrophy during the day compared to autotrophy with synchronous expression of the bidirectional hydrogenase genes possibly to maintain redox balance. However, nitrogenase complex remains exclusive to nighttime metabolism concomitantly with uptake hydrogenase. This study throws light on interrelations between metabolic pathways with implications in design of hydrogen producer strains. (C) 2015 Elsevier Ltd. All rights reserved.
Description:
Understanding gene interactions is a fundamental question in uncovering the underlying biological relations that enable successful functioning of living organisms. The modeling of gene regulations is usually done using DNA microarray data. However, presence of noise and the scarcity of microarray data affect the reconstruction of gene regulatory networks. In this paper, we propose a novel co-learning based fusion algorithm using the dynamic Bayesian netowrk (DBN) formalism for reconstruction of gene regulatory networks which incorporates knowledge obtained from protein-protein interaction networks to improve network accuracy. The proposed approach is efficient and naturally amenable to parallel computation. We apply the algorithm on the well-known Saccharomyces cerevisiae gene expression data that shows the effectiveness of our approach.