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Multimodal memetic framework for low-resolution protein structure prediction

- Nazmul, Rumana, Chetty, Madhu, Chowdhury, Ashan

  • Authors: Nazmul, Rumana , Chetty, Madhu , Chowdhury, Ashan
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Swarm and Evolutionary Computation Vol. 52, no. (Feb 2020), p. 14
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  • Description: In this paper, we propose a systematic design of evolutionary optimization, namely Multimodal Memetic Framework (MMF), to effectively search the vast complex energy landscape. Our proposed memetic framework is implemented in hierarchical stages with the optimization of each stage performed in parallel in three different states: Exploratory, Exploitative and Central. Each state, with its own set of sub-populations, either explores or exploits by beneficial mixing of potential solutions to direct the search towards a global solution. Instead of implementing identical genetic operators, the proposed approach employs different selection and survival criteria in each state according to their designated task. The Exploratory state employs a knowledge-based initial population generation technique with appropriately tuned genetic operators to guide the search to the "nearest peak". The Exploitative state fine-tunes the individuals representing different regions by applying a building block based local search. Finally, by utilizing the imbibed knowledge from different peaks, the Central state carries out information-exchange among the highly fit solutions for exploring the undiscovered regions. The information exchange employs a novel non-random parental selection technique to distribute the reproduction opportunity intelligently among the individuals for making cross-over more effective. The method has been tested on a set of various benchmark protein sequences for 2D and 3D lattice models. The experimental results demonstrate the superiority of the proposed method over other state-of-the-art algorithms.

Pre-trained language models with limited data for intent classification

- Kasthuriarachchy, Buddhika, Chetty, Madhu, Karmakar, Gour, Walls, Darren

  • Authors: Kasthuriarachchy, Buddhika , Chetty, Madhu , Karmakar, Gour , Walls, Darren
  • Date: 2020
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2020 International Joint Conference on Neural Networks, IJCNN 2020
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  • Description: Intent analysis is capturing the attention of both the industry and academia due to its commercial and noncommercial significance. The rapid growth of unstructured data of micro-blogging platforms, such as Twitter and Facebook, are amongst the important sources for intent analysis. However, the social media data are often noisy and diverse, thus making the task very challenging. Further, the intent analysis frequently suffers from lack of sufficient data because the labeled datasets are often manually annotated. Recently, BERT (Bidirectional Encoder Representation from Transformers), a state-of-the-art language representation model, has attracted attention for accurate language modelling. In this paper, we investigate the application of BERT for its suitability for intent analysis. We study the fine-tuning of the BERT model through inductive transfer learning and investigate methods to overcome the challenges due to limited data availability by proposing a novel semantic data augmentation approach. This technique generates synthetic sentences while preserving the label-compatibility using the semantic meaning of the sentences, to improve the intent classification accuracy. Thus, based on the considerations for finetuning and data augmentation, a systematic and novel step-bystep methodology is presented for applying the linguistic model BERT for intent classification with limited data available. Our results show that the pre-trained language can be effectively used with noisy social media data to achieve state-of-the-art accuracy in intent analysis under low labeled-data regime. Moreover, our results also confirm that the proposed text augmentation technique is effective in eliminating noisy synthetic sentences, thereby achieving further performance improvements. © 2020 IEEE.

Assessing transformer oil quality using deep convolutional networks

- Alam, Mohammad, Karmakar, Gour, Islam, Syed, Kamruzzaman, Joarder, Chetty, Madhu, Lim, Suryani, Appuhamillage, Gayan, Chattopadhyay, Gopi, Wilcox, Steve, Verheyen, Vincent

  • Authors: Alam, Mohammad , Karmakar, Gour , Islam, Syed , Kamruzzaman, Joarder , Chetty, Madhu , Lim, Suryani , Appuhamillage, Gayan , Chattopadhyay, Gopi , Wilcox, Steve , Verheyen, Vincent
  • Date: 2019
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 29th Australasian Universities Power Engineering Conference, AUPEC 2019
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  • Description: Electrical power grids comprise a significantly large number of transformers that interconnect power generation, transmission and distribution. These transformers having different MVA ratings are critical assets that require proper maintenance to provide long and uninterrupted electrical service. The mineral oil, an essential component of any transformer, not only provides cooling but also acts as an insulating medium within the transformer. The quality and the key dissolved properties of insulating mineral oil for the transformer are critical with its proper and reliable operation. However, traditional chemical diagnostic methods are expensive and time-consuming. A transformer oil image analysis approach, based on the entropy value of oil, which is inexpensive, effective and quick. However, the inability of entropy to estimate the vital transformer oil properties such as equivalent age, Neutralization Number (NN), dissipation factor (tanδ) and power factor (PF); and many intuitively derived constants usage limit its estimation accuracy. To address this issue, in this paper, we introduce an innovative transformer oil analysis using two deep convolutional learning techniques such as Convolutional Neural Network (ConvNet) and Residual Neural Network (ResNet). These two deep neural networks are chosen for this project as they have superior performance in computer vision. After estimating the equivalent aging year of transformer oil from its image by our proposed method, NN, tanδ and PF are computed using that estimated age. Our deep learning based techniques can accurately predict the transformer oil equivalent age, leading to calculate NN, tanδ and PF more accurately. The root means square error of estimated equivalent age produced by entropy, ConvNet and ResNet based methods are 0.718, 0.122 and 0.065, respectively. ConvNet and ResNet based methods have reduced the error of the oil age estimation by 83% and 91%, respectively compared to that of the entropy method. Our proposed oil image analysis can calculate the equivalent age that is very close to the actual age for all images used in the experiment. © 2019 IEEE.
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Challenges and opportunities for blockchain technology adoption : a systematic review

- Chhina, Shipra, Chadhar, Mehmood, Vatanasakdakul, Savanid, Chetty, Madhu

  • Authors: Chhina, Shipra , Chadhar, Mehmood , Vatanasakdakul, Savanid , Chetty, Madhu
  • Date: 2019
  • Type: Text , Conference paper
  • Relation: 30th Australasian Conference on Information Systems (ACIS), 9-11 December, Perth (Australia)
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  • Description: Blockchain technology promises to significantly impact current business processes in industries from various sectors and reduce transactional cost. Firms, suppliers, government, financial institutions etc. are anticipating a business model transformation through blockchain by accomplishing a decentralized architecture of interorganizational dealings without intermediaries. In spite of its immense potential, however, there are key challenges of blockchain implementation which need to be studied for identifying the opportunities arising and for its successful implementations in future. In this paper, we aim to identify these challenges for blockchain adoption and classify them for clearer understanding. To pursue this effectively, this paper follows a hybrid model of systematic literature review. This paper also explicitly enumerates future research opportunities to lead industry and researchers in correct directions

Reverse engineering genetic networks using nonlinear saturation kinetics

- Youseph, Ahammed, Chetty, Madhu, Karmakar, Gour

  • Authors: Youseph, Ahammed , Chetty, Madhu , Karmakar, Gour
  • Date: 2019
  • Type: Text , Journal article
  • Relation: BioSystems Vol. 182, no. (2019), p. 30-41
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  • Description: A gene regulatory network (GRN) represents a set of genes along with their regulatory interactions. Cellular behavior is driven by genetic level interactions. Dynamics of such systems show nonlinear saturation kinetics which can be best modeled by Michaelis-Menten (MM) and Hill equations. Although MM equation is being widely used for modeling biochemical processes, it has been applied rarely for reverse engineering GRNs. In this paper, we develop a complete framework for a novel model for GRN inference using MM kinetics. A set of coupled equations is first proposed for modeling GRNs. In the coupled model, Michaelis-Menten constant associated with regulation by a gene is made invariant irrespective of the gene being regulated. The parameter estimation of the proposed model is carried out using an evolutionary optimization method, namely, trigonometric differential evolution (TDE). Subsequently, the model is further improved and the regulations of different genes by a given gene are made distinct by allowing varying values of Michaelis-Menten constants for each regulation. Apart from making the model more relevant biologically, the improvement results in a decoupled GRN model with fast estimation of model parameters. Further, to enhance exploitation of the search, we propose a local search algorithm based on hill climbing heuristics. A novel mutation operation is also proposed to avoid population stagnation and premature convergence. Real life benchmark data sets generated in vivo are used for validating the proposed model. Further, we also analyze realistic in silico datasets generated using GeneNetweaver. The comparison of the performance of proposed model with other existing methods shows the potential of the proposed model.

Towards machine learning approach for digital-health intervention program

- Santhanagopalan, Meena, Chetty, Madhu, Foale, Cameron, Klein, Britt

  • Authors: Santhanagopalan, Meena , Chetty, Madhu , Foale, Cameron , Klein, Britt
  • Date: 2019
  • Type: Text , Journal article
  • Relation: Australian Journal of Intelligent Information Processing System Vol. 15, no. 4 (2019), p. 16-24
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  • Description: Digital-Health intervention (DHI) are used by health care providers to promote engagement within community. Effective assignment of participants into DHI programs helps increasing benefits from the most suitable intervention. A major challenge with the roll-out and implementation of DHI, is in assigning participants into different interventions. The use of biopsychosocial model [18] for this purpose is not wide spread, due to limited personalized interventions formed on evidence-based data-driven models. Machine learning has changed the way data extraction and interpretation works by involving automatic sets of generic methods that have replaced the traditional statistical techniques. In this paper, we propose to investigate relevance of machine learning for this purpose and is carried out by studying different non-linear classifiers and compare their prediction accuracy to evaluate their suitability. Further, as a novel contribution, real-life biopsychosocial features are used as input in this study. The results help in developing an appropriate predictive classication model to assign participants into the most suitable DHI. We analyze biopsychosocial data generated from a DHI program and study their feature characteristics using scatter plots. While scatter plots are unable to reveal the linear relationships in the data-set, the use of classifiers can successfully identify which features are suitable predictors of mental ill health.

Information technology and organizational learning interplay : A survey

- Malik, Saleem, Chetty, Madhu, Chadhar, Mehmood

  • Authors: Malik, Saleem , Chetty, Madhu , Chadhar, Mehmood
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: 29th Australasian Conference on Information Systems (ACIS 2018); Sydney, Australia; 3rd December 2018 p. 1-11
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  • Description: The objective of this paper is to provide a systematic review of the evolutionary trends in the research domain of information technology and organizational learning. Having surveyed various journals and key conferences between 2000 and 2018 on the topic, we observe that information technology (IT) has expanded from its general form to various contemporary information systems, e.g. knowledge organization systems, communication and collaborative systems and decision support systems. However, organization learning (OL) now essentially occurs through knowledge management activities, e.g. knowledge acquisition, storing, sharing and application of knowledge. The survey reported here not only validates the interplay of IT and OL but also reveals some important intervening factors between IT and OL, e.g. absorptive capacity, organization culture, user trust, acceptance and satisfaction that work as deterministic elements in the reciprocal relationship of IT and OL. We propose future research to explore interaction between big data analytical systems and organizational learning.

Large scale modeling of genetic networks using gene knockout data

- Youseph, Ahammed, Chetty, Madhu, Karmakar, Gour

  • Authors: Youseph, Ahammed , Chetty, Madhu , Karmakar, Gour
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: 2018 Australasian Computer Science Week Multiconference, ACSW 2018; Brisbane, Australia; 29th January-2nd February 2018; published in ACM International Conference Proceedings Series
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  • Description: Gene regulatory network (GRN) represents a set of genes and their regulatory interactions. The inference of the regulatory interactions between genes is usually carried out as an optimization problem using an appropriate mathematical model and the time-series gene expression data. Among the various models proposed for GRN inference, our recently proposed Michaelis-Menten kinetics based ODE model provides a good trade-off between the computational complexity and biological relevance. This model, like other known GRN models, also uses an evolutionary algorithm for parameter estimation. Since the search space for large networks is huge, leading to a low accuracy of inference, it is important to reduce the search region for improved performance of the optimization algorithm. In this paper, we propose a classification method using gene knockout data to eliminate a large infeasible region from the optimization search area. We also propose a method for partial inference of regulations when all the regulators of a given regulated gene are unregulated genes. The proposed method is evaluated by reconstructing in silico networks of large sizes. © 2018 ACM.

Modeling neurocognitive reaction time with gamma distribution

- Santhanagopalan, Meena, Chetty, Madhu, Foale, Cameron, Aryal, Sunil, Klein, Britt

  • Authors: Santhanagopalan, Meena , Chetty, Madhu , Foale, Cameron , Aryal, Sunil , Klein, Britt
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: ACSW'18 . Proceedings of the Australasian Computer Science Week Multiconference; Brisbane, QLD; January 2018; Article 28 p. 1-10
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  • Description: As a broader effort to build a holistic biopsychosocial health metric, reaction time data obtained from participants undertaking neurocognitive tests have been examined using Exploratory Data Analysis (EDA) for assessing its distribution. Many of the known existing methods assume, that the reaction time data follows a Gaussian distribution and thus commonly use statistical measures such as Analysis of Variance (ANOVA) for analysis. However, it is not mandatory for the reaction time data, to necessarily follow Gaussian distribution and in many instances, it can be better modeled by other representations such as Gamma distribution. Unlike Gaussian distribution which is defined using mean and variance, the Gamma distribution is defined using shape and scale parameters which also considers higher order moments of data such as skewness and kurtosis. Generalized Linear Models (GLM), based on the family exponential distributions such as Gamma distribution, which have been used to model reaction time in other domains, have not been fully explored for modeling reaction time data in psychology domain. While limited use of Gamma distribution have been reported [5, 17, 21], for analyzing response times, their application has been somewhat ad-hoc rather than systematic. For this proposed research, we use a real life biopsychosocial dataset, generated from the 'digital health' intervention programs conducted by the Faculty of Health, Federation University, Australia. The two digital intervention programs were the 'Mindfulness' program and 'Physical Activity' program. The neurocognitive tests were carried out as part of the 'Mindfulness' program. In this paper, we investigate the participants' reaction time distributions in neurocognitive tests such as the Psychology Experiment Building Language (PEBL) Go/No-Go test [19], which is a subset of the larger biopsychosocial data set. PEBL is an open source software system for designing and running psychological experiments. Analysis of participants' reaction time in the PEBL Go/No-Go test, shows that the reaction time data are more compatible with a Gamma distribution and clearly demonstrate that these can be better modeled by Gamma distribution.

PCA based population generation for genetic network optimization

- Youseph, Ahammed, Chetty, Madhu, Karmakar, Gour

  • Authors: Youseph, Ahammed , Chetty, Madhu , Karmakar, Gour
  • Date: 2018
  • Type: Text , Journal article
  • Relation: Cognitive Neurodynamics Vol. 12, no. 4 (2018), p. 417-429
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  • Description: A gene regulatory network (GRN) represents a set of genes and its regulatory interactions. The inference of the regulatory interactions between genes is usually carried out using an appropriate mathematical model and the available gene expression profile. Among the various models proposed for GRN inference, our recently proposed Michaelis–Menten based ODE model provides a good trade-off between the computational complexity and biological relevance. This model, like other known GRN models, also uses an evolutionary algorithm for parameter estimation. Considering various issues associated with such population based stochastic optimization approaches (e.g. diversity, premature convergence due to local optima, accuracy, etc.), it becomes important to seed the initial population with good individuals which are closer to the optimal solution. In this paper, we exploit the inherent strength of principal component analysis (PCA) in a novel manner to initialize the population for GRN optimization. The benefit of the proposed method is validated by reconstructing in silico and in vivo networks of various sizes. For the same level of accuracy, the approach with PCA based initialization shows improved convergence speed.

Relevance of frequency of heart-rate peaks as indicator of ‘Biological’ Stress level

- Santhanagopalan, Meena, Chetty, Madhu, Foale, Cameron, Aryal, Sunil, Klein, Britt

  • Authors: Santhanagopalan, Meena , Chetty, Madhu , Foale, Cameron , Aryal, Sunil , Klein, Britt
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: ICONIP 2018 International on Neural Information Processing; Siem Reap, Cambodia; 13th-16th December, 2018 p. 598-609
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  • Description: The biopsychosocial (BPS) model proposes that health is best understood as a combination of bio-physiological, psychological and social determinants, and thus advocates for a far more comprehensive investigation of the relationships between ‘mind-body’ health. For this holistic analysis, we need a suitable measure to indicate participants’ ‘biological’ stress. With the advent of wearable sensor devices, health monitoring is becoming easier. In this study, we focus on bio-physiological indicators of stress, from wearable devices using the heart-rate data. The analysis of such heart-rate data presents a set of practical challenges. We review various measures currently in use for stress measurement and their relevance and significance with the wearables’ heart-rate data. In this paper, we propose to use the novel ‘peak heart-rate count’ metric to quantify level of ‘biological’ stress. Real life biometric data obtained from digital health intervention program was considered for the study. Our study indicates the significance of using frequency of ‘peak heart-rate count’ as a ‘biological’ stress measure.

Exploiting temporal genetic correlations for enhancing regulatory network optimization

- Youseph, Ahammed, Chetty, Madhu, Karmakar, Gour

  • Authors: Youseph, Ahammed , Chetty, Madhu , Karmakar, Gour
  • Date: 2016
  • Type: Text , Conference proceedings
  • Relation: 23rd International Conference on Neural Information Processing, ICONIP 2016; Kyoto, Japan; 16th-21st October 2016; published in Neural Information Processing (Lecture Notes in Computer Science series) Vol. 9947 LNCS, p. 479-487
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  • Description: Inferring gene regulatory networks (GRN) from microarray gene expression data is a highly challenging problem in computational and systems biology. To make GRN reconstruction process more accurate and faster, in this paper, we develop a technique to identify the gene having maximum in-degree in the network using the temporal correlation of gene expression profiles. The in-degree of the identified gene is estimated applying evolutionary optimization algorithm on a decoupled S-system GRN model. The value of in-degree thus obtained is set as the maximum in-degree for inference of the regulations in other genes. The simulations are carried out on in silico networks of small and medium sizes. The results show that both the prediction accuracy in terms of well known performance metrics and the computational time of the optimization process have been improved when compared with the traditional S-system model based inference. © Springer International Publishing AG 2016.
  • Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Coupling of cellular processes and their coordinated oscillations under continuous light in Cyanothece sp. ATCC 51142, a diazotrophic unicellular cyanobacterium

- Krishnakumar, Sujatha, Gaudana, Sandeep, Vinh, Nguyen, Viswanathan, Ganesh, Chetty, Madhu, Wangikar, Pramod

  • Authors: Krishnakumar, Sujatha , Gaudana, Sandeep , Vinh, Nguyen , Viswanathan, Ganesh , Chetty, Madhu , Wangikar, Pramod
  • Date: 2015
  • Type: Text , Journal article
  • Relation: PLoS ONE Vol. 10, no. 5 (2015), p. 1-23
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  • Description: Unicellular diazotrophic cyanobacteria such as Cyanothece sp. ATCC 51142 (henceforth Cyanothece), temporally separate the oxygen sensitive nitrogen fixation from oxygen evolving photosynthesis not only under diurnal cycles (LD) but also in continuous light (LL). However, recent reports demonstrate that the oscillations in LL occur with a shorter cycle time of ∼11 h. We find that indeed, majority of the genes oscillate in LL with this cycle time. Genes that are upregulated at a particular time of day under diurnal cycle also get upregulated at an equivalent metabolic phase under LL suggesting tight coupling of various cellular events with each other and with the cell's metabolic status. A number of metabolic processes get upregulated in a coordinated fashion during the respiratory phase under LL including glycogen degradation, glycolysis, oxidative pentose phosphate pathway, and tricarboxylic acid cycle. These precede nitrogen fixation apparently to ensure sufficient energy and anoxic environment needed for the nitrogenase enzyme. Photosynthetic phase sees upregulation of photosystem II, carbonate transport, carbon concentrating mechanism, RuBisCO, glycogen synthesis and light harvesting antenna pigment biosynthesis. In Synechococcus elongates PCC 7942, a non-nitrogen fixing cyanobacteria, expression of a relatively smaller fraction of genes oscillates under LL condition with the major periodicity being 24 h. In contrast, the entire cellular machinery of Cyanothece orchestrates coordinated oscillation in anticipation of the ensuing metabolic phase in both LD and LL. These results may have important implications in understanding the timing of various cellular events and in engineering cyanobacteria for biofuel production. © 2015 Krishnakumar et al.

Decoupled modeling of gene regulatory networks using Michaelis-Menten kinetics

- Youseph, Ahammed, Chetty, Madhu, Karmakar, Gour

  • Authors: Youseph, Ahammed , Chetty, Madhu , Karmakar, Gour
  • Date: 2015
  • Type: Text , Conference proceedings
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  • Description: A set of genes and their regulatory interactions are represented in a gene regulatory network (GRN). Since GRNs play a major role in maintaining the cellular activities, inferring these networks is significant for understanding biological processes. Among the models available for GRN reconstruction, our recently developed nonlinear model [1] using Michaelis-Menten kinetics is considered to be more biologically relevant. However, the model remains coupled in the current form making the process computationally expensive, especially for large GRNs. In this paper, we enhance the existing model leading to a decoupled form which not only speeds up the computation, but also makes the model more realistic by representing the strength of each regulatory arc by a distinct Michaelis-Menten constant. The parameter estimation is carried out using differential evolution algorithm. The model is validated by inferring two synthetic networks. Results show that while the accuracy of reconstruction is similar to the coupled model, they are achieved at a faster speed. © Springer International Publishing Switzerland 2015.

Evaluating influence of microRNA in reconstructing gene regulatory networks

- Chowdhury, Ahsan, Chetty, Madhu, Nguyen, Vinh

  • Authors: Chowdhury, Ahsan , Chetty, Madhu , Nguyen, Vinh
  • Date: 2015
  • Type: Text , Journal article
  • Relation: Cognitive neurodynamics Vol. 8, no. 3 (2015), p. 251-9
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  • Description: Gene regulatory network (GRN) consists of interactions between transcription factors (TFs) and target genes (TGs). Recently, it has been observed that micro RNAs (miRNAs) play a significant part in genetic interactions. However, current microarray technologies do not capture miRNA expression levels. To overcome this, we propose a new technique to reverse engineer GRN from the available partial microarray data which contains expression levels of TFs and TGs only. Using S-System model, the approach is adapted to cope with the unavailability of information about the expression levels of miRNAs. The versatile Differential Evolutionary algorithm is used for optimization and parameter estimation. Experimental studies on four in silico networks, and a real network of Saccharomyces cerevisiae called IRMA network, show significant improvement compared to traditional S-System approach.

Frequency decomposition based gene clustering

- Rahman, Md Abdur, Chetty, Madhu, Bulach, Dieter, Wangikar, Pramod

  • Authors: Rahman, Md Abdur , Chetty, Madhu , Bulach, Dieter , Wangikar, Pramod
  • Date: 2015
  • Type: Text , Conference paper
  • Relation: 22nd International Conference on Neural Information Processing, ICONIP 2015; Istanbul, Turkey; 9th-12th November 2015 Vol. 9490, p. 170-181
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  • Description: Gene expressions have been commonly applied to understand the inherent underlying mechanism of known biological processes. Although the microarray gene expressions usually appear aperiodic, with proper signal processing techniques, its periodic components can be easily obtained. Thus, if expressions of interconnected (regulatory and regulated) genes are decomposed, at least one common frequency component will appear in these genes. Exploiting this novel concept, we propose a frequency decomposition approach for gene clustering to better understand the gene interconnection topology. This method, based on Hilbert Huang Transform (HHT) enables us to segregate every periodic component of the gene expressions. Next, a multilevel clustering is performed based on these frequency components. Unlike existing clustering algorithms, the proposed method assimilates a meaningful knowledge of the gene interactions topology. The information related to underlying gene interactions is vital and can prove useful in many existing evolutionary optimisation algorithms for genetic network reconstruction. We validate the entire approach by its application to a 15-gene synthetic network. © Springer International Publishing Switzerland 2015.

Gene regulatory network inference using Michaelis-Menten kinetics

- Youseph, Ahammed, Chetty, Madhu, Karmakar, Gour

  • Authors: Youseph, Ahammed , Chetty, Madhu , Karmakar, Gour
  • Date: 2015
  • Type: Text , Conference proceedings
  • Relation: 2015 IEEE Congress on Evolutionary Computation (Cec); Sendai, Japan; 25th-28th May 2015 p. 2392-2397
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  • Description: A gene regulatory network (GRN) represents a collection of genes, connected via regulatory interactions. Reverse engineering GRNs is a challenging problem in systems biology. Various models have been proposed for modeling GRNs. However, many of these models lack the capability to explain the molecular mechanisms underlying the biological process. Michaelis-Menten kinetics can be used to model the biomolecular mechanisms and is a widely used non-linear approach to represent biochemical systems. However, the model in its current form is not suitable for reverse engineering biological systems. In this paper, based on Michaelis-Menten kinetics, we develop a new model to reverse engineer GRNs. The parameter estimation is formulated as an optimization problem which is solved by adapting trigonometric differential evolution (TDE), a variant of differential evolution (DE). The model is applied for reconstructing both in silico and in vivo networks. The results are promising and as the model is fully biologically relevant, it provides a new perspective for accurate GRN inference.

Improving gene regulatory network inference using network topology information

- Nair, Ajay, Chetty, Madhu, Wangikar, Pramod

  • Authors: Nair, Ajay , Chetty, Madhu , Wangikar, Pramod
  • Date: 2015
  • Type: Text , Journal article
  • Relation: Molecular BioSystems Vol. 11, no. 9 (2015), p. 2449-2463
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  • Description: Inferring the gene regulatory network (GRN) structure from data is an important problem in computational biology. However, it is a computationally complex problem and approximate methods such as heuristic search techniques, restriction of the maximum-number-of-parents (maxP) for a gene, or an optimal search under special conditions are required. The limitations of a heuristic search are well known but literature on the detailed analysis of the widely used maxP technique is lacking. The optimal search methods require large computational time. We report the theoretical analysis and experimental results of the strengths and limitations of the maxP technique. Further, using an optimal search method, we combine the strengths of the maxP technique and the known GRN topology to propose two novel algorithms. These algorithms are implemented in a Bayesian network framework and tested on biological, realistic, and in silico networks of different sizes and topologies. They overcome the limitations of the maxP technique and show superior computational speed when compared to the current optimal search algorithms.

Influence of mixotrophic growth on rhythmic oscillations in expression of metabolic pathways in diazotrophic cyanobacterium Cyanothece sp ATCC 51142

- Krishnakumar, Sujatha, Gaudana, Sandeep, Digmurti, Madhuri, Viswanathan, Ganesh, Chetty, Madhu, Wangikar, Pramod

  • Authors: Krishnakumar, Sujatha , Gaudana, Sandeep , Digmurti, Madhuri , Viswanathan, Ganesh , Chetty, Madhu , Wangikar, Pramod
  • Date: 2015
  • Type: Text , Journal article
  • Relation: Bioresource Technology Vol. 188, no. (2015), p. 145-152
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  • 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.

Network decomposition based large-scale reverse engineering of gene regulatory network

- Chowdhury, Ahsan, Chetty, Madhu

  • Authors: Chowdhury, Ahsan , Chetty, Madhu
  • Date: 2015
  • Type: Text , Journal article
  • Relation: Neurocomputing Vol. 160, no. (2015), p. 213-227
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  • Description: A Gene Regulatory Network (GRN) is the functional circuitry of a living organism that exhibits the regulatory relationships among genes of a cellular system at the gene level. In real-life biological networks, the number of genes present are very large exhibiting both, the instantaneous and time-delayed regulations. While our recent technique [1] addresses the modeling of time-delays occurring in genetic interactions, the issue of large-scale GRN modeling still remains. In this paper, we propose a novel methodology for large-scale modeling of GRNs by decomposing the GRN into two independent sub-networks utilizing its biological traits. Using the time-delayed S-system model [1], these two sub-networks are learnt separately and then combined to get the entire GRN. To speed up the inference mechanism, a cardinality-based fitness function, especially developed for inferring large-scale GRNs is proposed to allow incorporation of knowledge of maximum in-degree. A novel local-search method is also proposed to further facilitate the incorporation of biological knowledge by gene clustering and gene ranking. Experimental studies demonstrate that the proposed approach is successful in learning large genetic networks, currently not achievable with existing S-system based modeling approaches.