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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.

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 , Conference Paper
  • 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.

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.

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.

Reconstruction of Large-scale gene regulatory network using s-system model

- Chowdhury, Ahsan, Chetty, Madhu

Stochastic S-system modeling of gene regulatory network

- Chowdhury, Ahsan, Chetty, Madhu, Evans, Rob

  • Authors: Chowdhury, Ahsan , Chetty, Madhu , Evans, Rob
  • Date: 2015
  • Type: Text , Journal article
  • Relation: Cognitive Neurodynamics Vol. 9, no. 5 (2015), p. 535-547
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  • Description: Microarray gene expression data can provide insights into biological processes at a system-wide level and is commonly used for reverse engineering gene regulatory networks (GRN). Due to the amalgamation of noise from different sources, microarray expression profiles become inherently noisy leading to significant impact on the GRN reconstruction process. Microarray replicates (both biological and technical), generated to increase the reliability of data obtained under noisy conditions, have limited influence in enhancing the accuracy of reconstruction. Therefore, instead of the conventional GRN modeling approaches which are deterministic, stochastic techniques are becoming increasingly necessary for inferring GRN from noisy microarray data. In this paper, we propose a new stochastic GRN model by investigating incorporation of various standard noise measurements in the deterministic S-system model. Experimental evaluations performed for varying sizes of synthetic network, representing different stochastic processes, demonstrate the effect of noise on the accuracy of genetic network modeling and the significance of stochastic modeling for GRN reconstruction. The proposed stochastic model is subsequently applied to infer the regulations among genes in two real life networks: (1) the well-studied IRMA network, a real-life in-vivo synthetic network constructed within the Saccharomycescerevisiae yeast, and (2) the SOS DNA repair network in Escherichiacoli. © 2015, Springer Science+Business Media Dordrecht.

Towards large scale genetic network modeling

- Khan, Rubaiya Rahtin, Chetty, Madhu

  • Authors: Khan, Rubaiya Rahtin , Chetty, Madhu
  • Date: 2015
  • Type: Text , Conference proceedings
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  • Description: Reverse Engineering Gene Regulatory Networks (GRNs) is an important and challenging problem of Systems Biology. For its superiority in both structure and parameter learning, the S-system model framework is often chosen for GRN reconstruction. The biggest challenge in reconstructing GRNs is the data having large number of genes and only a small number of samples. This "curse of dimensionality", along with the large number of model parameters to be learnt, makes it extremely difficult to reverse engineer even a small network. For a medium or large network, the complexity becomes enormous. In this paper, we propose a method for managing large scale GRN modeling. As first step, we propose an Affinity Propagation Based Clustering to identify appropriate clusters by grouping the genes based on their time expression profiles. In the second step, the largest cluster consisting of majority of the relevant genes is considered in full detail to act as the core of the network while the other remaining clusters, which are not so significant, are each represented by their single representative gene to obtain a reduced order GRN. In the third step, we optimize the entire network by initializing the model parameters of the genes of the largest cluster with the values obtained in the second step (which are near optimal) and proceed to optimize the entire network. The initial investigations are carried out using previously reported 20-gene synthetic network. The superiority of performance is evaluated not only using the standard metrics, namely, sensitivity, specificity, precision and F-score, but also by average mean error and by comparing the time responses with those of the actual network parameters. The results obtained are promising. © 2015 IEEE.

Sib-based survival selection technique for protein structure prediction in 3D-FCC Lattice Model

- Rumana, Nazmul, Chetty, Madhu

  • Authors: Rumana, Nazmul , Chetty, Madhu
  • Date: 2014
  • Type: Text , Conference paper
  • Relation: 21st International Conference on Neural Information Processing, ICONIP 2014; Kuching; Malaysia; 3rd - 6th November 2014; In Neural Information Processing (Lecture Notes in Computer Science ) Vol.8835 p.470-478 p. 470-478
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  • Description: Protein Structure Prediction (PSP) is a challenging optimization problem in computational biology. A large number of non-deterministic approaches such as Evolutionary Algorithms (EAs) have been have been effectively applied to a variety of fields though, in the rugged landscape of multimodal problem like PSP, it can perform unsatisfactorily, due to premature convergence. In EAs, selection plays a significant role to avoid getting trapped in local optima and also to guide the evolution towards an optimal solution. In this paper, we propose a new Sib-based survival selection strategy suitable for application in a genetic algorithm (GA) to deal with multimodal problems. The proposed strategy, inspired by the concept of crowding method, controls the flow of genetic material by pairing off the fittest offspring amongst all the sibs (offspring inheriting most of the genetic material from an ancestor) with its ancestor for survival. Furthermore, by selecting the survivors in a hybridized manner of deterministic and probabilistic selection, the method allows the exploitation of less fit solutions along with the fitter ones and thus facilitates escaping from local optima (minima in case of PSP). Experiments conducted on a set of widely used benchmark sequences for 3D-FCC HP lattice model, demonstrate the potential of the proposed method, both in terms of diversity and optimal energy in regard to various state-of-the-art selection methods.

Significance of non-edge priors in gene regulatory network reconstruction

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

  • Authors: Nair, Ajay , Chetty, Madhu , Wangikar, Pramod
  • Date: 2014
  • Type: Text , Conference paper
  • Relation: 21st International Conference, ICONIP 2014 Kuching, Malaysia, November 3–6, 2014; published in Neural Information Processing, (Lecture Notes in Computer Science) Vol. 8834 p446-453
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  • Description: It is well known that incorporating prior knowledge improves gene regulatory network reconstruction from data. Two types of prior knowledge can be given for the gene regulatory network inference - known interactions (edge priors) and known absence of interactions (non-edge priors). However, previous studies have focused mainly on edge priors. This paper shows that the edge priors give only limited improvement. Moreover, non-edge priors are crucial for better overall performance and their effect dominates edge priors at larger data samples. The studies are carried out on two real networks and a computationally tractable synthetic network, using Bayesian network framework. Further, a method to obtain large numbers of non-edge priors for real gene regulatory networks is presented. © Springer International Publishing Switzerland 2014.

A knowledge-based initial population generation in memetic algorithm for protein structure prediction

- Nazmul, Rumana, Chetty, Madhu

  • Authors: Nazmul, Rumana , Chetty, Madhu
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: 20th International Conference, ICONIP 2013 p. 546-553
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  • Description: Predicting the minimum energy protein structure from its amino acid sequence, even under the rather simplified HP lattice model, continues to be an important and challenging problem in computational biology. In this paper, we propose a novel initial population generation strategy for evolutionary algorithm incorporating domain knowledge based on the concept of maximum hydrophobic core formation for Protein structure prediction (PSP) problem. The proposed technique helps the optimization process to commence with diverse seeds and thereby aids in converging to the global solution quickly. The experimental results, conducted on PSP problem using HP benchmark sequences for 2D square and 3D cubic lattice model, demonstrate that the proposed evolutionary algorithm with new core-based population initialization technique is very effective in improving the optimization process in terms of convergence as well as in achieving the optimal energy.

A model of the circadian clock in the cyanobacterium Cyanothece sp. ATCC 51142

- Nguyen, Vinh, Chetty, Madhu, Coppel, Ross, Gaudana, Sandeep, Wangikar, Pramod

  • Authors: Nguyen, Vinh , Chetty, Madhu , Coppel, Ross , Gaudana, Sandeep , Wangikar, Pramod
  • Date: 2013
  • Type: Text , Journal article
  • Relation: BMC Bioinformatics Vol. 14, no. (Supplement 2) (2013), p. s14-1-s14-9
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  • Description: Background The over consumption of fossil fuels has led to growing concerns over climate change and global warming. Increasing research activities have been carried out towards alternative viable biofuel sources. Of several different biofuel platforms, cyanobacteria possess great potential, for their ability to accumulate biomass tens of times faster than traditional oilseed crops. The cyanobacterium Cyanothece sp. ATCC 51142 has recently attracted lots of research interest as a model organism for such research. Cyanothece can perform efficiently both photosynthesis and nitrogen fixation within the same cell, and has been recently shown to produce biohydrogen--a byproduct of nitrogen fixation--at very high rates of several folds higher than previously described hydrogen-producing photosynthetic microbes. Since the key enzyme for nitrogen fixation is very sensitive to oxygen produced by photosynthesis, Cyanothece employs a sophisticated temporal separation scheme, where nitrogen fixation occurs at night and photosynthesis at day. At the core of this temporal separation scheme is a robust clocking mechanism, which so far has not been thoroughly studied. Understanding how this circadian clock interacts with and harmonizes global transcription of key cellular processes is one of the keys to realize the inherent potential of this organism. Results In this paper, we employ several state of the art bioinformatics techniques for studying the core circadian clock in Cyanothece sp. ATCC 51142, and its interactions with other key cellular processes. We employ comparative genomics techniques to map the circadian clock genes and genetic interactions from another cyanobacterial species, namely Synechococcus elongatus PCC 7942, of which the circadian clock has been much more thoroughly investigated. Using time series gene expression data for Cyanothece, we employ gene regulatory network reconstruction techniques to learn this network de novo, and compare the reconstructed network against the interactions currently reported in the literature. Next, we build a computational model of the interactions between the core clock and other cellular processes, and show how this model can predict the behaviour of the system under changing environmental conditions. The constructed models significantly advance our understanding of the Cyanothece circadian clock functional mechanisms.