Amphun Chaiboonchoe | New York University Abu Dhabi (original) (raw)

Uploads

Papers by Amphun Chaiboonchoe

Research paper thumbnail of Neural networks and Fuzzy clustering methods for assessing the efficacy of microarray based intrinsic gene signatures in breast cancer classification and the character and relations of identified subtypes

Methods in molecular biology (Clifton, N.J.), 2015

In the classification of breast cancer subtypes using microarray data, hierarchical clustering is... more In the classification of breast cancer subtypes using microarray data, hierarchical clustering is commonly used. Although this form of clustering shows basic cluster patterns, more needs to be done to investigate the accuracy of clusters as well as to extract meaningful cluster characteristics and their relations to increase our confidence in their use in a clinical setting. In this study, an in-depth investigation of the efficacy of three reported gene subsets in distinguishing breast cancer subtypes was performed using four advanced computational intelligence methods-Self-Organizing Maps (SOM), Emergent Self-Organizing Maps (ESOM), Fuzzy Clustering by Local Approximation of Memberships (FLAME), and Fuzzy C-means (FCM)-each differing in the way they view data in terms of distance measures and fuzzy or crisp clustering. The gene subsets consisted of 71, 93, and 71 genes reported in the literature from three comprehensive experimental studies for distinguishing Luminal (A and B), Basal, Normal breast-like, and HER2 subtypes. Given the costly procedures involved in clinical studies, the proposed 93-gene set can be used for preliminary classification of breast cancer. Then, as a decision aid, SOM can be used to map the gene signature of a new patient to locate them with respect to all subtypes to get a comprehensive view of the classification. These can be followed by a deeper investigation in the light of the observations made in this study regarding overlapping subtypes. Results from the study could be used as the base for further refining the gene signatures from later experiments and from new experiments designed to separate overlapping clusters as well as to maximally separate all clusters.

Research paper thumbnail of Integrated analysis of gene network in childhood leukemia from microarray and pathway databases

BioMed research international, 2014

Glucocorticoids (GCs) have been used as therapeutic agents for children with acute lymphoblastic ... more Glucocorticoids (GCs) have been used as therapeutic agents for children with acute lymphoblastic leukaemia (ALL) for over 50 years. However, much remains to be understood about the molecular mechanism of GCs actions in ALL subtypes. In this study, we delineate differential responses of ALL subtypes, B-and T-ALL, to GCs treatment at systems level by identifying the differences among biological processes, molecular pathways, and interaction networks that emerge from the action of GCs through the use of a selected number of available bioinformatics methods and tools. We provide biological insight into GC-regulated genes, their related functions, and their networks specific to the ALL subtypes. We show that differentially expressed GC-regulated genes participate in distinct underlying biological processes affected by GCs in B-ALL and T-ALL with little to no overlap. These findings provide the opportunity towards identifying new therapeutic targets.

Research paper thumbnail of Saffron-Based Crocin Prevents Early Lesions of Liver Cancer: In Vivo, In Vitro and Network Analyses

Recent patents on anti-cancer drug discovery, 2015

The angiogenesis inhibitor, sorafenib, remains the only available therapy of hepatocellular carci... more The angiogenesis inhibitor, sorafenib, remains the only available therapy of hepatocellular carcinoma (HCC). Only recently patents of VEGF receptors-3 inhibitors are developed. Thus, a novel approach against HCC is essential for a better therapeutic outcome. The aims of this study were to examine the chemopreventive action of saffron's main biomolecule, crocin, against chemically-induced liver cancer in rats, and to explore the mechanisms by which crocin employs its anti-tumor effects. We investigated the anti-cancer effect of crocin on an experimental carcinogenesis model of liver cancer by studying the anti-oxidant, anti-inflammatory, anti-proliferation, pro-apoptotic activities of crocin in vivo. In addition, we provided a network analysis of differentially expressed genes in tissues of animals pre-treated with crocin in comparison to induced-HCC animals' tissues. To further support our results, in vitro analysis was carried out. We assessed the effects of crocin on HepG2...

Research paper thumbnail of The In Vitro Selection World

Methods, 2016

Through iterative cycles of selection, amplification, and mutagenesis, in vitro selection provide... more Through iterative cycles of selection, amplification, and mutagenesis, in vitro selection provides the ability to isolate molecules of desired properties and function from large pools (libraries) of random molecules with as many as 10(16) distinct species. This review, in recognition of a quarter of century of scientific discoveries made through in vitro selection, starts with a brief overview of the method and its history. It further covers recent developments in in vitro selection with a focus on tools that enhance the capabilities of in vitro selection and its expansion from being purely a nucleic acids selection to that of polypeptides and proteins. In addition, we cover how next generation sequencing and modern biological computational tools are being used to complement in vitro selection experiments. On the very least, sequencing and computational tools can translate the large volume of information associated with in vitro selection experiments to manageable, analyzable, and exploitable information. Finally, in vivo selection is briefly compared and contrasted to in vitro selection to highlight the unique capabilities of each method.

Research paper thumbnail of Integrated Analysis of Gene Network in Childhood Leukemia from Microarray and Pathway Databases

Biomed Research International, Apr 15, 2014

Glucocorticoids (GCs) have been used as therapeutic agents for children with acute lymphoblastic ... more Glucocorticoids (GCs) have been used as therapeutic agents for children with acute lymphoblastic leukaemia (ALL) for over 50 years. However, much remains to be understood about the molecular mechanism of GCs actions in ALL subtypes. In this study, we delineate differential responses of ALL subtypes, B-and T-ALL, to GCs treatment at systems level by identifying the differences among biological processes, molecular pathways, and interaction networks that emerge from the action of GCs through the use of a selected number of available bioinformatics methods and tools. We provide biological insight into GC-regulated genes, their related functions, and their networks specific to the ALL subtypes. We show that differentially expressed GC-regulated genes participate in distinct underlying biological processes affected by GCs in B-ALL and T-ALL with little to no overlap. These findings provide the opportunity towards identifying new therapeutic targets.

Research paper thumbnail of Integrated high throughput phenotype profiling for metabolic network refinement of C. reinhardtii

Research paper thumbnail of Modeling carbon assimilation in a species of Chloroidium isolated from the United Arab Emirates

Research paper thumbnail of Exploration of algal metabolism and evolution through network analysis and metabolic modeling

Research paper thumbnail of Systems Biology

This chapter introduces Systems Biology, its context, aims, concepts and strategies, then describ... more This chapter introduces Systems Biology, its context, aims, concepts and strategies, then describes approaches used in genomics, epigenomics, transcriptomics, proteomics, metabolomics and lipidomics, and how recent technological advances in these fields have moved the bottleneck from data production to data analysis. Methods for clustering, feature selection, prediction analysis, text mining and pathway analysis used to analyse and integrate the data produced are then presented.

Research paper thumbnail of Prospective Applications of Synthetic Biology for Algal Bioproduct Optimization

Biofuel and Biorefinery Technologies, 2015

ABSTRACT Synthetic Biology is an interdisciplinary approach combining biotechnology, evolutionary... more ABSTRACT Synthetic Biology is an interdisciplinary approach combining biotechnology, evolutionary biology, molecular biology, systems biology and biophysics. While the exact definition of Synthetic Biology might still be debatable, its focus on design and construction of biological devices that perform useful functions is clear and of great utility to engineering algae. This relies on the re-engineering of biological circuits and optimization of certain metabolic pathways to reprogram algae and introduce new functions in them via the use of genetic modules. Genetic editing tools are primary enabling techniques in Synthetic Biology and this chapter discusses common techniques that show promise for algal gene editing. The genetic editing tools discussed in this chapter include RNA interference (RNAi) and artificial microRNAs, RNA scaffolds, transcription activator-like effector nucleases (TALENs), RNA guided Cas9 endonucleases (CRISPR), and multiplex automated genome engineering (MAGE). DNA and whole genome synthesis is another enabling technology in Synthetic Biology and might present an alternative approach to drastically and readily modify algae. Clear and powerful examples of the potential of whole genome synthesis for algal engineering are presented. Also, the development of relevant computational tools, and genetic part registries has stimulated further advancements in the field and their utility in algal research and engineering is described. For now, the majority of synthetic biology efforts are focused on microbes as many pressing problems, such as sustainability in food and energy production rely on modification of microorganisms. Synthetic modifications of algal strains to enhance desired physiological properties could lead to improvements in their utility.

Research paper thumbnail of Toward Applications of Genomics and Metabolic Modeling to Improve Algal Biomass Productivity

Biofuel and Biorefinery Technologies, 2015

ABSTRACT Genomic sequencing is the first step in a systems level study of an algal species, and s... more ABSTRACT Genomic sequencing is the first step in a systems level study of an algal species, and sequencing studies have grown steadily in recent years. Completed sequences can be tied to algal phenotypes at a systems level through constructing genome-scale metabolic network models. Those models allow the prediction of algal phenotypes and genetic or metabolic modifications, and are constructed by tying the genes to reactions using enzyme databases, then representing those reactions in a concise mathematical form by means of stoichiometric matrices. This is followed by experimental validation using gene deletion or proteomics and metabolomics studies that may result in adding reactions to the model and filling phenotypic gaps. In this chapter, we offer a summary of completed and ongoing algal genomic projects before proceeding to holistically describing the process of constructing genome-scale metabolic models. Relevant examples of algal metabolic models are presented and discussed. The analysis of an alga’s emergent properties from metabolic models is also demonstrated using flux balance analysis (FBA) and related constraint-based approaches to optimize a given metabolic phenotype, or sets of phenotypes such as algal biomass. We also summarize readily available optimization tools rooted in constraint-based modeling that allow for optimizing bioproduction and algal strains. Examples include tools used to develop knockout strategies, identify optimal bioproduction strains, analyze gene deletions, and explore functional relationships within sets in a metabolic model. All in all, this systems level approach can lead to a better understanding and prediction of algal metabolism leading to more robust and cheaper applications.

Research paper thumbnail of Faculty of 1000 evaluation for A systems biology approach identifies molecular networks defining skeletal muscle abnormalities in chronic obstructive pulmonary disease

F1000 - Post-publication peer review of the biomedical literature, 2000

Research paper thumbnail of Faculty of 1000 evaluation for Identification of aberrant pathways and network activities from high-throughput data

F1000 - Post-publication peer review of the biomedical literature, 2000

Research paper thumbnail of Faculty of 1000 evaluation for Differential network biology

F1000 - Post-publication peer review of the biomedical literature, 2000

Research paper thumbnail of Faculty of 1000 evaluation for Visualization of omics data for systems biology

F1000 - Post-publication peer review of the biomedical literature, 2000

Research paper thumbnail of Faculty of 1000 evaluation for Comparing statistical methods for constructing large scale gene networks

F1000 - Post-publication peer review of the biomedical literature, 2000

Research paper thumbnail of Integrated analysis of gene network in childhood leukemia from microarray and pathway databases

BioMed research international, 2014

Glucocorticoids (GCs) have been used as therapeutic agents for children with acute lymphoblastic ... more Glucocorticoids (GCs) have been used as therapeutic agents for children with acute lymphoblastic leukaemia (ALL) for over 50 years. However, much remains to be understood about the molecular mechanism of GCs actions in ALL subtypes. In this study, we delineate differential responses of ALL subtypes, B- and T-ALL, to GCs treatment at systems level by identifying the differences among biological processes, molecular pathways, and interaction networks that emerge from the action of GCs through the use of a selected number of available bioinformatics methods and tools. We provide biological insight into GC-regulated genes, their related functions, and their networks specific to the ALL subtypes. We show that differentially expressed GC-regulated genes participate in distinct underlying biological processes affected by GCs in B-ALL and T-ALL with little to no overlap. These findings provide the opportunity towards identifying new therapeutic targets.

Research paper thumbnail of Neural networks and Fuzzy clustering methods for assessing the efficacy of microarray based intrinsic gene signatures in breast cancer classification and the character and relations of identified subtypes

Methods in molecular biology (Clifton, N.J.), 2015

In the classification of breast cancer subtypes using microarray data, hierarchical clustering is... more In the classification of breast cancer subtypes using microarray data, hierarchical clustering is commonly used. Although this form of clustering shows basic cluster patterns, more needs to be done to investigate the accuracy of clusters as well as to extract meaningful cluster characteristics and their relations to increase our confidence in their use in a clinical setting. In this study, an in-depth investigation of the efficacy of three reported gene subsets in distinguishing breast cancer subtypes was performed using four advanced computational intelligence methods-Self-Organizing Maps (SOM), Emergent Self-Organizing Maps (ESOM), Fuzzy Clustering by Local Approximation of Memberships (FLAME), and Fuzzy C-means (FCM)-each differing in the way they view data in terms of distance measures and fuzzy or crisp clustering. The gene subsets consisted of 71, 93, and 71 genes reported in the literature from three comprehensive experimental studies for distinguishing Luminal (A and B), Bas...

Research paper thumbnail of Computational Approaches for Microalgal Biofuel Optimization: A Review

BioMed Research International, 2014

The increased demand and consumption of fossil fuels have raised interest in finding renewable en... more The increased demand and consumption of fossil fuels have raised interest in finding renewable energy sources throughout the globe. Much focus has been placed on optimizing microorganisms and primarily microalgae, to efficiently produce compounds that can substitute for fossil fuels. However, the path to achieving economic feasibility is likely to require strain optimization through using available tools and technologies in the fields of systems and synthetic biology. Such approaches invoke a deep understanding of the metabolic networks of the organisms and their genomic and proteomic profiles. The advent of next generation sequencing and other high throughput methods has led to a major increase in availability of biological data. Integration of such disparate data can help define the emergent metabolic system properties, which is of crucial importance in addressing biofuel production optimization. Herein, we review major computational tools and approaches developed and used in order to potentially identify target genes, pathways, and reactions of particular interest to biofuel production in algae. As the use of these tools and approaches has not been fully implemented in algal biofuel research, the aim of this review is to highlight the potential utility of these resources toward their future implementation in algal research.

Research paper thumbnail of Functional Genomics, Proteomics, Metabolomics and Bioinformatics for Systems Biology

Systems Biology, 2013

ABSTRACT This chapter introduces Systems Biology, its context, aims, concepts and strategies, the... more ABSTRACT This chapter introduces Systems Biology, its context, aims, concepts and strategies, then describes approaches used in genomics, epigenomics, transcriptomics, proteomics, metabolomics and lipidomics, and how recent technological advances in these fields have moved the bottleneck from data production to data analysis. Methods for clustering, feature selection, prediction analysis, text mining and pathway analysis used to analyse and integrate the data produced are then presented.

Research paper thumbnail of Neural networks and Fuzzy clustering methods for assessing the efficacy of microarray based intrinsic gene signatures in breast cancer classification and the character and relations of identified subtypes

Methods in molecular biology (Clifton, N.J.), 2015

In the classification of breast cancer subtypes using microarray data, hierarchical clustering is... more In the classification of breast cancer subtypes using microarray data, hierarchical clustering is commonly used. Although this form of clustering shows basic cluster patterns, more needs to be done to investigate the accuracy of clusters as well as to extract meaningful cluster characteristics and their relations to increase our confidence in their use in a clinical setting. In this study, an in-depth investigation of the efficacy of three reported gene subsets in distinguishing breast cancer subtypes was performed using four advanced computational intelligence methods-Self-Organizing Maps (SOM), Emergent Self-Organizing Maps (ESOM), Fuzzy Clustering by Local Approximation of Memberships (FLAME), and Fuzzy C-means (FCM)-each differing in the way they view data in terms of distance measures and fuzzy or crisp clustering. The gene subsets consisted of 71, 93, and 71 genes reported in the literature from three comprehensive experimental studies for distinguishing Luminal (A and B), Basal, Normal breast-like, and HER2 subtypes. Given the costly procedures involved in clinical studies, the proposed 93-gene set can be used for preliminary classification of breast cancer. Then, as a decision aid, SOM can be used to map the gene signature of a new patient to locate them with respect to all subtypes to get a comprehensive view of the classification. These can be followed by a deeper investigation in the light of the observations made in this study regarding overlapping subtypes. Results from the study could be used as the base for further refining the gene signatures from later experiments and from new experiments designed to separate overlapping clusters as well as to maximally separate all clusters.

Research paper thumbnail of Integrated analysis of gene network in childhood leukemia from microarray and pathway databases

BioMed research international, 2014

Glucocorticoids (GCs) have been used as therapeutic agents for children with acute lymphoblastic ... more Glucocorticoids (GCs) have been used as therapeutic agents for children with acute lymphoblastic leukaemia (ALL) for over 50 years. However, much remains to be understood about the molecular mechanism of GCs actions in ALL subtypes. In this study, we delineate differential responses of ALL subtypes, B-and T-ALL, to GCs treatment at systems level by identifying the differences among biological processes, molecular pathways, and interaction networks that emerge from the action of GCs through the use of a selected number of available bioinformatics methods and tools. We provide biological insight into GC-regulated genes, their related functions, and their networks specific to the ALL subtypes. We show that differentially expressed GC-regulated genes participate in distinct underlying biological processes affected by GCs in B-ALL and T-ALL with little to no overlap. These findings provide the opportunity towards identifying new therapeutic targets.

Research paper thumbnail of Saffron-Based Crocin Prevents Early Lesions of Liver Cancer: In Vivo, In Vitro and Network Analyses

Recent patents on anti-cancer drug discovery, 2015

The angiogenesis inhibitor, sorafenib, remains the only available therapy of hepatocellular carci... more The angiogenesis inhibitor, sorafenib, remains the only available therapy of hepatocellular carcinoma (HCC). Only recently patents of VEGF receptors-3 inhibitors are developed. Thus, a novel approach against HCC is essential for a better therapeutic outcome. The aims of this study were to examine the chemopreventive action of saffron's main biomolecule, crocin, against chemically-induced liver cancer in rats, and to explore the mechanisms by which crocin employs its anti-tumor effects. We investigated the anti-cancer effect of crocin on an experimental carcinogenesis model of liver cancer by studying the anti-oxidant, anti-inflammatory, anti-proliferation, pro-apoptotic activities of crocin in vivo. In addition, we provided a network analysis of differentially expressed genes in tissues of animals pre-treated with crocin in comparison to induced-HCC animals' tissues. To further support our results, in vitro analysis was carried out. We assessed the effects of crocin on HepG2...

Research paper thumbnail of The In Vitro Selection World

Methods, 2016

Through iterative cycles of selection, amplification, and mutagenesis, in vitro selection provide... more Through iterative cycles of selection, amplification, and mutagenesis, in vitro selection provides the ability to isolate molecules of desired properties and function from large pools (libraries) of random molecules with as many as 10(16) distinct species. This review, in recognition of a quarter of century of scientific discoveries made through in vitro selection, starts with a brief overview of the method and its history. It further covers recent developments in in vitro selection with a focus on tools that enhance the capabilities of in vitro selection and its expansion from being purely a nucleic acids selection to that of polypeptides and proteins. In addition, we cover how next generation sequencing and modern biological computational tools are being used to complement in vitro selection experiments. On the very least, sequencing and computational tools can translate the large volume of information associated with in vitro selection experiments to manageable, analyzable, and exploitable information. Finally, in vivo selection is briefly compared and contrasted to in vitro selection to highlight the unique capabilities of each method.

Research paper thumbnail of Integrated Analysis of Gene Network in Childhood Leukemia from Microarray and Pathway Databases

Biomed Research International, Apr 15, 2014

Glucocorticoids (GCs) have been used as therapeutic agents for children with acute lymphoblastic ... more Glucocorticoids (GCs) have been used as therapeutic agents for children with acute lymphoblastic leukaemia (ALL) for over 50 years. However, much remains to be understood about the molecular mechanism of GCs actions in ALL subtypes. In this study, we delineate differential responses of ALL subtypes, B-and T-ALL, to GCs treatment at systems level by identifying the differences among biological processes, molecular pathways, and interaction networks that emerge from the action of GCs through the use of a selected number of available bioinformatics methods and tools. We provide biological insight into GC-regulated genes, their related functions, and their networks specific to the ALL subtypes. We show that differentially expressed GC-regulated genes participate in distinct underlying biological processes affected by GCs in B-ALL and T-ALL with little to no overlap. These findings provide the opportunity towards identifying new therapeutic targets.

Research paper thumbnail of Integrated high throughput phenotype profiling for metabolic network refinement of C. reinhardtii

Research paper thumbnail of Modeling carbon assimilation in a species of Chloroidium isolated from the United Arab Emirates

Research paper thumbnail of Exploration of algal metabolism and evolution through network analysis and metabolic modeling

Research paper thumbnail of Systems Biology

This chapter introduces Systems Biology, its context, aims, concepts and strategies, then describ... more This chapter introduces Systems Biology, its context, aims, concepts and strategies, then describes approaches used in genomics, epigenomics, transcriptomics, proteomics, metabolomics and lipidomics, and how recent technological advances in these fields have moved the bottleneck from data production to data analysis. Methods for clustering, feature selection, prediction analysis, text mining and pathway analysis used to analyse and integrate the data produced are then presented.

Research paper thumbnail of Prospective Applications of Synthetic Biology for Algal Bioproduct Optimization

Biofuel and Biorefinery Technologies, 2015

ABSTRACT Synthetic Biology is an interdisciplinary approach combining biotechnology, evolutionary... more ABSTRACT Synthetic Biology is an interdisciplinary approach combining biotechnology, evolutionary biology, molecular biology, systems biology and biophysics. While the exact definition of Synthetic Biology might still be debatable, its focus on design and construction of biological devices that perform useful functions is clear and of great utility to engineering algae. This relies on the re-engineering of biological circuits and optimization of certain metabolic pathways to reprogram algae and introduce new functions in them via the use of genetic modules. Genetic editing tools are primary enabling techniques in Synthetic Biology and this chapter discusses common techniques that show promise for algal gene editing. The genetic editing tools discussed in this chapter include RNA interference (RNAi) and artificial microRNAs, RNA scaffolds, transcription activator-like effector nucleases (TALENs), RNA guided Cas9 endonucleases (CRISPR), and multiplex automated genome engineering (MAGE). DNA and whole genome synthesis is another enabling technology in Synthetic Biology and might present an alternative approach to drastically and readily modify algae. Clear and powerful examples of the potential of whole genome synthesis for algal engineering are presented. Also, the development of relevant computational tools, and genetic part registries has stimulated further advancements in the field and their utility in algal research and engineering is described. For now, the majority of synthetic biology efforts are focused on microbes as many pressing problems, such as sustainability in food and energy production rely on modification of microorganisms. Synthetic modifications of algal strains to enhance desired physiological properties could lead to improvements in their utility.

Research paper thumbnail of Toward Applications of Genomics and Metabolic Modeling to Improve Algal Biomass Productivity

Biofuel and Biorefinery Technologies, 2015

ABSTRACT Genomic sequencing is the first step in a systems level study of an algal species, and s... more ABSTRACT Genomic sequencing is the first step in a systems level study of an algal species, and sequencing studies have grown steadily in recent years. Completed sequences can be tied to algal phenotypes at a systems level through constructing genome-scale metabolic network models. Those models allow the prediction of algal phenotypes and genetic or metabolic modifications, and are constructed by tying the genes to reactions using enzyme databases, then representing those reactions in a concise mathematical form by means of stoichiometric matrices. This is followed by experimental validation using gene deletion or proteomics and metabolomics studies that may result in adding reactions to the model and filling phenotypic gaps. In this chapter, we offer a summary of completed and ongoing algal genomic projects before proceeding to holistically describing the process of constructing genome-scale metabolic models. Relevant examples of algal metabolic models are presented and discussed. The analysis of an alga’s emergent properties from metabolic models is also demonstrated using flux balance analysis (FBA) and related constraint-based approaches to optimize a given metabolic phenotype, or sets of phenotypes such as algal biomass. We also summarize readily available optimization tools rooted in constraint-based modeling that allow for optimizing bioproduction and algal strains. Examples include tools used to develop knockout strategies, identify optimal bioproduction strains, analyze gene deletions, and explore functional relationships within sets in a metabolic model. All in all, this systems level approach can lead to a better understanding and prediction of algal metabolism leading to more robust and cheaper applications.

Research paper thumbnail of Faculty of 1000 evaluation for A systems biology approach identifies molecular networks defining skeletal muscle abnormalities in chronic obstructive pulmonary disease

F1000 - Post-publication peer review of the biomedical literature, 2000

Research paper thumbnail of Faculty of 1000 evaluation for Identification of aberrant pathways and network activities from high-throughput data

F1000 - Post-publication peer review of the biomedical literature, 2000

Research paper thumbnail of Faculty of 1000 evaluation for Differential network biology

F1000 - Post-publication peer review of the biomedical literature, 2000

Research paper thumbnail of Faculty of 1000 evaluation for Visualization of omics data for systems biology

F1000 - Post-publication peer review of the biomedical literature, 2000

Research paper thumbnail of Faculty of 1000 evaluation for Comparing statistical methods for constructing large scale gene networks

F1000 - Post-publication peer review of the biomedical literature, 2000

Research paper thumbnail of Integrated analysis of gene network in childhood leukemia from microarray and pathway databases

BioMed research international, 2014

Glucocorticoids (GCs) have been used as therapeutic agents for children with acute lymphoblastic ... more Glucocorticoids (GCs) have been used as therapeutic agents for children with acute lymphoblastic leukaemia (ALL) for over 50 years. However, much remains to be understood about the molecular mechanism of GCs actions in ALL subtypes. In this study, we delineate differential responses of ALL subtypes, B- and T-ALL, to GCs treatment at systems level by identifying the differences among biological processes, molecular pathways, and interaction networks that emerge from the action of GCs through the use of a selected number of available bioinformatics methods and tools. We provide biological insight into GC-regulated genes, their related functions, and their networks specific to the ALL subtypes. We show that differentially expressed GC-regulated genes participate in distinct underlying biological processes affected by GCs in B-ALL and T-ALL with little to no overlap. These findings provide the opportunity towards identifying new therapeutic targets.

Research paper thumbnail of Neural networks and Fuzzy clustering methods for assessing the efficacy of microarray based intrinsic gene signatures in breast cancer classification and the character and relations of identified subtypes

Methods in molecular biology (Clifton, N.J.), 2015

In the classification of breast cancer subtypes using microarray data, hierarchical clustering is... more In the classification of breast cancer subtypes using microarray data, hierarchical clustering is commonly used. Although this form of clustering shows basic cluster patterns, more needs to be done to investigate the accuracy of clusters as well as to extract meaningful cluster characteristics and their relations to increase our confidence in their use in a clinical setting. In this study, an in-depth investigation of the efficacy of three reported gene subsets in distinguishing breast cancer subtypes was performed using four advanced computational intelligence methods-Self-Organizing Maps (SOM), Emergent Self-Organizing Maps (ESOM), Fuzzy Clustering by Local Approximation of Memberships (FLAME), and Fuzzy C-means (FCM)-each differing in the way they view data in terms of distance measures and fuzzy or crisp clustering. The gene subsets consisted of 71, 93, and 71 genes reported in the literature from three comprehensive experimental studies for distinguishing Luminal (A and B), Bas...

Research paper thumbnail of Computational Approaches for Microalgal Biofuel Optimization: A Review

BioMed Research International, 2014

The increased demand and consumption of fossil fuels have raised interest in finding renewable en... more The increased demand and consumption of fossil fuels have raised interest in finding renewable energy sources throughout the globe. Much focus has been placed on optimizing microorganisms and primarily microalgae, to efficiently produce compounds that can substitute for fossil fuels. However, the path to achieving economic feasibility is likely to require strain optimization through using available tools and technologies in the fields of systems and synthetic biology. Such approaches invoke a deep understanding of the metabolic networks of the organisms and their genomic and proteomic profiles. The advent of next generation sequencing and other high throughput methods has led to a major increase in availability of biological data. Integration of such disparate data can help define the emergent metabolic system properties, which is of crucial importance in addressing biofuel production optimization. Herein, we review major computational tools and approaches developed and used in order to potentially identify target genes, pathways, and reactions of particular interest to biofuel production in algae. As the use of these tools and approaches has not been fully implemented in algal biofuel research, the aim of this review is to highlight the potential utility of these resources toward their future implementation in algal research.

Research paper thumbnail of Functional Genomics, Proteomics, Metabolomics and Bioinformatics for Systems Biology

Systems Biology, 2013

ABSTRACT This chapter introduces Systems Biology, its context, aims, concepts and strategies, the... more ABSTRACT This chapter introduces Systems Biology, its context, aims, concepts and strategies, then describes approaches used in genomics, epigenomics, transcriptomics, proteomics, metabolomics and lipidomics, and how recent technological advances in these fields have moved the bottleneck from data production to data analysis. Methods for clustering, feature selection, prediction analysis, text mining and pathway analysis used to analyse and integrate the data produced are then presented.

Research paper thumbnail of Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation

Molecular BioSystems, Jun 14, 2016

Metabolic networks are reconstructed to provide computational platforms to guide metabolic engine... more Metabolic networks are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses{,} such as interrogation of phylogenetic relationships within the network{,} can provide further guidance on modification of metabolic circuitries. Chlamydomonas reinhardtii{,} a biofuel relevant green alga that has retained key genes with plant{,} animal{,} and protist affinities{,} serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational. Here{,} using detailed topological and functional analyses{,} coupled with trancriptomics studies on a metabolic network that we have reconstructed for C. reinhardtii{,} we show that network connectivity has a significant concordance with the co-conservation of genes in the C. reinhardtii while a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast{,} genes with predicted synthetic interactions{,} or genes involved in coupled reactions{,} show significant enrichment for both shorter and longer phylogenetic distances. Based on our results{,} we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically{,} while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii{'}s network in dealing with varied environmental challenges that the species may face. The defined evolutionary constraints within the network{,} which identify important pairings of genes in metabolism{,} may offer guidance in synthetic biology approaches to optimize production of desirable metabolites.