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1.  Genomic Promoter Analysis Predicts Functional Transcription Factor Binding 
Advances in bioinformatics  2008;2008:3698301-3698309.
Background
The computational identification of functional transcription factor binding sites (TFBSs) remains a major challenge of computational biology.
Results
We have analyzed the conserved promoter sequences for the complete set of human RefSeq genes using our conserved transcription factor binding site (CONFAC) software. CONFAC identified 16296 human-mouse ortholog gene pairs, and of those pairs, 9107 genes contained conserved TFBS in the 3 kb proximal promoter and first intron. To attempt to predict in vivo occupancy of transcription factor binding sites, we developed a novel marginal effect isolator algorithm that builds upon Bayesian methods for multigroup TFBS filtering and predicted the in vivo occupancy of two transcription factors with an overall accuracy of 84%.
Conclusion
Our analyses show that integration of chromatin immunoprecipitation data with conserved TFBS analysis can be used to generate accurate predictions of functional TFBS. They also show that TFBS cooccurrence can be used to predict transcription factor binding to promoters in vivo.
doi:10.1155/2008/369830
PMCID: PMC2768302  PMID: 19865592
2.  Genomic Promoter Analysis Predicts Functional Transcription Factor Binding 
Advances in Bioinformatics  2008;2008:369830.
Background. The computational identification of functional transcription factor binding sites (TFBSs) remains a major challenge of computational biology. Results. We have analyzed the conserved promoter sequences for the complete set of human RefSeq genes using our conserved transcription factor binding site (CONFAC) software. CONFAC identified 16296 human-mouse ortholog gene pairs, and of those pairs, 9107 genes contained conserved TFBS in the 3 kb proximal promoter and first intron. To attempt to predict in vivo occupancy of transcription factor binding sites, we developed a novel marginal effect isolator algorithm that builds upon Bayesian methods for multigroup TFBS filtering and predicted the in vivo occupancy of two transcription factors with an overall accuracy of 84%. Conclusion. Our analyses show that integration of chromatin immunoprecipitation data with conserved TFBS analysis can be used to generate accurate predictions of functional TFBS. They also show that TFBS cooccurrence can be used to predict transcription factor binding to promoters in vivo.
doi:10.1155/2008/369830
PMCID: PMC2768302  PMID: 19865592
3.  A Brachytherapy Plan Evaluation Tool for Interstitial Applications 
Advances in Bioinformatics  2014;2014:376207.
Radiobiological metrics such as tumor control probability (TCP) and normal tissue complication probability (NTCP) help in assessing the quality of brachytherapy plans. Application of such metrics in clinics as well as research is still inadequate. This study presents the implementation of two indigenously designed plan evaluation modules: Brachy_TCP and Brachy_NTCP. Evaluation tools were constructed to compute TCP and NTCP from dose volume histograms (DVHs) of any interstitial brachytherapy treatment plan. The computation module was employed to estimate probabilities of tumor control and normal tissue complications in ten cervical cancer patients based on biologically effective equivalent uniform dose (BEEUD). The tumor control and normal tissue morbidity were assessed with clinical followup and were scored. The acute toxicity was graded using common terminology criteria for adverse events (CTCAE) version 4.0. Outcome score was found to be correlated with the TCP/NTCP estimates. Thus, the predictive ability of the estimates was quantified with the clinical outcomes. Biologically effective equivalent uniform dose-based formalism was found to be effective in predicting the complexities and disease control.
doi:10.1155/2014/376207
PMCID: PMC3934649  PMID: 24665263
4.  Prediction of B-Cell Epitopes in Listeriolysin O, a Cholesterol Dependent Cytolysin Secreted by Listeria monocytogenes 
Advances in Bioinformatics  2014;2014:871676.
Listeria monocytogenes is a gram-positive, foodborne bacterium responsible for disease in humans and animals. Listeriolysin O (LLO) is a required virulence factor for the pathogenic effects of L. monocytogenes. Bioinformatics revealed conserved putative epitopes of LLO that could be used to develop monoclonal antibodies against LLO. Continuous and discontinuous epitopes were located by using four different B-cell prediction algorithms. Three-dimensional molecular models were generated to more precisely characterize the predicted antigenicity of LLO. Domain 4 was predicted to contain five of eleven continuous epitopes. A large portion of domain 4 was also predicted to comprise discontinuous immunogenic epitopes. Domain 4 of LLO may serve as an immunogen for eliciting monoclonal antibodies that can be used to study the pathogenesis of L. monocytogenes as well as develop an inexpensive assay.
doi:10.1155/2014/871676
PMCID: PMC3909977  PMID: 24523732
5.  Comparing Imputation Procedures for Affymetrix Gene Expression Datasets Using MAQC Datasets 
Advances in Bioinformatics  2013;2013:790567.
Introduction. The microarray datasets from the MicroArray Quality Control (MAQC) project have enabled the assessment of the precision, comparability of microarrays, and other various microarray analysis methods. However, to date no studies that we are aware of have reported the performance of missing value imputation schemes on the MAQC datasets. In this study, we use the MAQC Affymetrix datasets to evaluate several imputation procedures in Affymetrix microarrays. Results. We evaluated several cutting edge imputation procedures and compared them using different error measures. We randomly deleted 5% and 10% of the data and imputed the missing values using imputation tests. We performed 1000 simulations and averaged the results. The results for both 5% and 10% deletion are similar. Among the imputation methods, we observe the local least squares method with k = 4 is most accurate under the error measures considered. The k-nearest neighbor method with k = 1 has the highest error rate among imputation methods and error measures. Conclusions. We conclude for imputing missing values in Affymetrix microarray datasets, using the MAS 5.0 preprocessing scheme, the local least squares method with k = 4 has the best overall performance and k-nearest neighbor method with k = 1 has the worst overall performance. These results hold true for both 5% and 10% missing values.
doi:10.1155/2013/790567
PMCID: PMC3809938  PMID: 24223587
6.  A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks 
Advances in Bioinformatics  2013;2013:920325.
Visualization of large complex networks has become an indispensable part of systems biology, where organisms need to be considered as one complex system. The visualization of the corresponding network is challenging due to the size and density of edges. In many cases, the use of standard visualization algorithms can lead to high running times and poorly readable visualizations due to many edge crossings. We suggest an approach that analyzes the structure of the graph first and then generates a new graph which contains specific semantic symbols for regular substructures like dense clusters. We propose a multilevel gamma-clustering layout visualization algorithm (MLGA) which proceeds in three subsequent steps: (i) a multilevel γ-clustering is used to identify the structure of the underlying network, (ii) the network is transformed to a tree, and (iii) finally, the resulting tree which shows the network structure is drawn using a variation of a force-directed algorithm. The algorithm has a potential to visualize very large networks because it uses modern clustering heuristics which are optimized for large graphs. Moreover, most of the edges are removed from the visual representation which allows keeping the overview over complex graphs with dense subgraphs.
doi:10.1155/2013/920325
PMCID: PMC3707208  PMID: 23864855
8.  Reverse Engineering Sparse Gene Regulatory Networks Using Cubature Kalman Filter and Compressed Sensing 
Advances in Bioinformatics  2013;2013:205763.
This paper proposes a novel algorithm for inferring gene regulatory networks which makes use of cubature Kalman filter (CKF) and Kalman filter (KF) techniques in conjunction with compressed sensing methods. The gene network is described using a state-space model. A nonlinear model for the evolution of gene expression is considered, while the gene expression data is assumed to follow a linear Gaussian model. The hidden states are estimated using CKF. The system parameters are modeled as a Gauss-Markov process and are estimated using compressed sensing-based KF. These parameters provide insight into the regulatory relations among the genes. The Cramér-Rao lower bound of the parameter estimates is calculated for the system model and used as a benchmark to assess the estimation accuracy. The proposed algorithm is evaluated rigorously using synthetic data in different scenarios which include different number of genes and varying number of sample points. In addition, the algorithm is tested on the DREAM4 in silico data sets as well as the in vivo data sets from IRMA network. The proposed algorithm shows superior performance in terms of accuracy, robustness, and scalability.
doi:10.1155/2013/205763
PMCID: PMC3664478  PMID: 23737768
9.  [No title available] 
PMCID: PMC3638690  PMID: 23653640
10.  Gene Regulation, Modulation, and Their Applications in Gene Expression Data Analysis 
Advances in Bioinformatics  2013;2013:360678.
Common microarray and next-generation sequencing data analysis concentrate on tumor subtype classification, marker detection, and transcriptional regulation discovery during biological processes by exploring the correlated gene expression patterns and their shared functions. Genetic regulatory network (GRN) based approaches have been employed in many large studies in order to scrutinize for dysregulation and potential treatment controls. In addition to gene regulation and network construction, the concept of the network modulator that has significant systemic impact has been proposed, and detection algorithms have been developed in past years. Here we provide a unified mathematic description of these methods, followed with a brief survey of these modulator identification algorithms. As an early attempt to extend the concept to new RNA regulation mechanism, competitive endogenous RNA (ceRNA), into a modulator framework, we provide two applications to illustrate the network construction, modulation effect, and the preliminary finding from these networks. Those methods we surveyed and developed are used to dissect the regulated network under different modulators. Not limit to these, the concept of “modulation” can adapt to various biological mechanisms to discover the novel gene regulation mechanisms.
doi:10.1155/2013/360678
PMCID: PMC3610383  PMID: 23573084
11.  Correction of Spatial Bias in Oligonucleotide Array Data 
Advances in Bioinformatics  2013;2013:167915.
Background. Oligonucleotide microarrays allow for high-throughput gene expression profiling assays. The technology relies on the fundamental assumption that observed hybridization signal intensities (HSIs) for each intended target, on average, correlate with their target's true concentration in the sample. However, systematic, nonbiological variation from several sources undermines this hypothesis. Background hybridization signal has been previously identified as one such important source, one manifestation of which appears in the form of spatial autocorrelation. Results. We propose an algorithm, pyn, for the elimination of spatial autocorrelation in HSIs, exploiting the duality of desirable mutual information shared by probes in a common probe set and undesirable mutual information shared by spatially proximate probes. We show that this correction procedure reduces spatial autocorrelation in HSIs; increases HSI reproducibility across replicate arrays; increases differentially expressed gene detection power; and performs better than previously published methods. Conclusions. The proposed algorithm increases both precision and accuracy, while requiring virtually no changes to users' current analysis pipelines: the correction consists merely of a transformation of raw HSIs (e.g., CEL files for Affymetrix arrays). A free, open-source implementation is provided as an R package, compatible with standard Bioconductor tools. The approach may also be tailored to other platform types and other sources of bias.
doi:10.1155/2013/167915
PMCID: PMC3610395  PMID: 23573083
12.  Spectral Analysis on Time-Course Expression Data: Detecting Periodic Genes Using a Real-Valued Iterative Adaptive Approach 
Advances in Bioinformatics  2013;2013:171530.
Time-course expression profiles and methods for spectrum analysis have been applied for detecting transcriptional periodicities, which are valuable patterns to unravel genes associated with cell cycle and circadian rhythm regulation. However, most of the proposed methods suffer from restrictions and large false positives to a certain extent. Additionally, in some experiments, arbitrarily irregular sampling times as well as the presence of high noise and small sample sizes make accurate detection a challenging task. A novel scheme for detecting periodicities in time-course expression data is proposed, in which a real-valued iterative adaptive approach (RIAA), originally proposed for signal processing, is applied for periodogram estimation. The inferred spectrum is then analyzed using Fisher's hypothesis test. With a proper p-value threshold, periodic genes can be detected. A periodic signal, two nonperiodic signals, and four sampling strategies were considered in the simulations, including both bursts and drops. In addition, two yeast real datasets were applied for validation. The simulations and real data analysis reveal that RIAA can perform competitively with the existing algorithms. The advantage of RIAA is manifested when the expression data are highly irregularly sampled, and when the number of cycles covered by the sampling time points is very reduced.
doi:10.1155/2013/171530
PMCID: PMC3600260  PMID: 23533399
13.  Identification of Robust Pathway Markers for Cancer through Rank-Based Pathway Activity Inference 
Advances in Bioinformatics  2013;2013:618461.
One important problem in translational genomics is the identification of reliable and reproducible markers that can be used to discriminate between different classes of a complex disease, such as cancer. The typical small sample setting makes the prediction of such markers very challenging, and various approaches have been proposed to address this problem. For example, it has been shown that pathway markers, which aggregate the gene activities in the same pathway, tend to be more robust than gene markers. Furthermore, the use of gene expression ranking has been demonstrated to be robust to batch effects and that it can lead to more interpretable results. In this paper, we propose an enhanced pathway activity inference method that uses gene ranking to predict the pathway activity in a probabilistic manner. The main focus of this work is on identifying robust pathway markers that can ultimately lead to robust classifiers with reproducible performance across datasets. Simulation results based on multiple breast cancer datasets show that the proposed inference method identifies better pathway markers that can predict breast cancer metastasis with higher accuracy. Moreover, the identified pathway markers can lead to better classifiers with more consistent classification performance across independent datasets.
doi:10.1155/2013/618461
PMCID: PMC3600350  PMID: 23533400
14.  An Overview of the Statistical Methods Used for Inferring Gene Regulatory Networks and Protein-Protein Interaction Networks 
Advances in Bioinformatics  2013;2013:953814.
The large influx of data from high-throughput genomic and proteomic technologies has encouraged the researchers to seek approaches for understanding the structure of gene regulatory networks and proteomic networks. This work reviews some of the most important statistical methods used for modeling of gene regulatory networks (GRNs) and protein-protein interaction (PPI) networks. The paper focuses on the recent advances in the statistical graphical modeling techniques, state-space representation models, and information theoretic methods that were proposed for inferring the topology of GRNs. It appears that the problem of inferring the structure of PPI networks is quite different from that of GRNs. Clustering and probabilistic graphical modeling techniques are of prime importance in the statistical inference of PPI networks, and some of the recent approaches using these techniques are also reviewed in this paper. Performance evaluation criteria for the approaches used for modeling GRNs and PPI networks are also discussed.
doi:10.1155/2013/953814
PMCID: PMC3594945  PMID: 23509452
15.  Using Protein Clusters from Whole Proteomes to Construct and Augment a Dendrogram 
Advances in Bioinformatics  2013;2013:191586.
In this paper we present a new ab initio approach for constructing an unrooted dendrogram using protein clusters, an approach that has the potential for estimating relationships among several thousands of species based on their putative proteomes. We employ an open-source software program called pClust that was developed for use in metagenomic studies. Sequence alignment is performed by pClust using the Smith-Waterman algorithm, which is known to give optimal alignment and, hence, greater accuracy than BLAST-based methods. Protein clusters generated by pClust are used to create protein profiles for each species in the dendrogram, these profiles forming a correlation filter library for use with a new taxon. To augment the dendrogram with a new taxon, a protein profile for the taxon is created using BLASTp, and this new taxon is placed into a position within the dendrogram corresponding to the highest correlation with profiles in the correlation filter library. This work was initiated because of our interest in plasmids, and each step is illustrated using proteomes from Gram-negative bacterial plasmids. Proteomes for 527 plasmids were used to generate the dendrogram, and to demonstrate the utility of the insertion algorithm twelve recently sequenced pAKD plasmids were used to augment the dendrogram.
doi:10.1155/2013/191586
PMCID: PMC3590580  PMID: 23509450
16.  Solving the 0/1 Knapsack Problem by a Biomolecular DNA Computer 
Advances in Bioinformatics  2013;2013:341419.
Solving some mathematical problems such as NP-complete problems by conventional silicon-based computers is problematic and takes so long time. DNA computing is an alternative method of computing which uses DNA molecules for computing purposes. DNA computers have massive degrees of parallel processing capability. The massive parallel processing characteristic of DNA computers is of particular interest in solving NP-complete and hard combinatorial problems. NP-complete problems such as knapsack problem and other hard combinatorial problems can be easily solved by DNA computers in a very short period of time comparing to conventional silicon-based computers. Sticker-based DNA computing is one of the methods of DNA computing. In this paper, the sticker based DNA computing was used for solving the 0/1 knapsack problem. At first, a biomolecular solution space was constructed by using appropriate DNA memory complexes. Then, by the application of a sticker-based parallel algorithm using biological operations, knapsack problem was resolved in polynomial time.
doi:10.1155/2013/341419
PMCID: PMC3588402  PMID: 23509451
17.  MRMPath and MRMutation, Facilitating Discovery of Mass Transitions for Proteotypic Peptides in Biological Pathways Using a Bioinformatics Approach 
Advances in Bioinformatics  2013;2013:527295.
Quantitative proteomics applications in mass spectrometry depend on the knowledge of the mass-to-charge ratio (m/z) values of proteotypic peptides for the proteins under study and their product ions. MRMPath and MRMutation, web-based bioinformatics software that are platform independent, facilitate the recovery of this information by biologists. MRMPath utilizes publicly available information related to biological pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. All the proteins involved in pathways of interest are recovered and processed in silico to extract information relevant to quantitative mass spectrometry analysis. Peptides may also be subjected to automated BLAST analysis to determine whether they are proteotypic. MRMutation catalogs and makes available, following processing, known (mutant) variants of proteins from the current UniProtKB database. All these results, available via the web from well-maintained, public databases, are written to an Excel spreadsheet, which the user can download and save. MRMPath and MRMutation can be freely accessed. As a system that seeks to allow two or more resources to interoperate, MRMPath represents an advance in bioinformatics tool development. As a practical matter, the MRMPath automated approach represents significant time savings to researchers.
doi:10.1155/2013/527295
PMCID: PMC3570921  PMID: 23424586
18.  Statistical Analysis of Terminal Extensions of Protein β-Strand Pairs 
Advances in Bioinformatics  2013;2013:909436.
The long-range interactions, required to the accurate predictions of tertiary structures of β-sheet-containing proteins, are still difficult to simulate. To remedy this problem and to facilitate β-sheet structure predictions, many efforts have been made by computational methods. However, known efforts on β-sheets mainly focus on interresidue contacts or amino acid partners. In this study, to go one step further, we studied β-sheets on the strand level, in which a statistical analysis was made on the terminal extensions of paired β-strands. In most cases, the two paired β-strands have different lengths, and terminal extensions exist. The terminal extensions are the extended part of the paired strands besides the common paired part. However, we found that the best pairing required a terminal alignment, and β-strands tend to pair to make bigger common parts. As a result, 96.97%  of β-strand pairs have a ratio of 25% of the paired common part to the whole length. Also 94.26% and 95.98%  of β-strand pairs have a ratio of 40% of the paired common part to the length of the two β-strands, respectively. Interstrand register predictions by searching interacting β-strands from several alternative offsets should comply with this rule to reduce the computational searching space to improve the performances of algorithms.
doi:10.1155/2013/909436
PMCID: PMC3569888  PMID: 23424587
20.  In Silico Docking of HNF-1a Receptor Ligands 
Advances in Bioinformatics  2012;2012:705435.
Background. HNF-1a is a transcription factor that regulates glucose metabolism by expression in various tissues. Aim. To dock potential ligands of HNF-1a using docking software in silico. Methods. We performed in silico studies using HNF-1a protein 2GYP·pdb and the following softwares: ISIS/Draw 2.5SP4, ARGUSLAB 4.0.1, and HEX5.1. Observations. The docking distances (in angstrom units: 1 angstrom unit (Å) = 0.1 nanometer or 1 × 10−10 metres) with ligands in decreasing order are as follows: resveratrol (3.8 Å), aspirin (4.5 Å), stearic acid (4.9 Å), retinol (6.0 Å), nitrazepam (6.8 Å), ibuprofen (7.9 Å), azulfidine (9.0 Å), simvastatin (9.0 Å), elaidic acid (10.1 Å), and oleic acid (11.6 Å). Conclusion. HNF-1a domain interacted most closely with resveratrol and aspirin
doi:10.1155/2012/705435
PMCID: PMC3535823  PMID: 23316227
21.  Do Peers See More in a Paper Than Its Authors? 
Advances in Bioinformatics  2012;2012:750214.
Recent years have shown a gradual shift in the content of biomedical publications that is freely accessible, from titles and abstracts to full text. This has enabled new forms of automatic text analysis and has given rise to some interesting questions: How informative is the abstract compared to the full-text? What important information in the full-text is not present in the abstract? What should a good summary contain that is not already in the abstract? Do authors and peers see an article differently? We answer these questions by comparing the information content of the abstract to that in citances—sentences containing citations to that article. We contrast the important points of an article as judged by its authors versus as seen by peers. Focusing on the area of molecular interactions, we perform manual and automatic analysis, and we find that the set of all citances to a target article not only covers most information (entities, functions, experimental methods, and other biological concepts) found in its abstract, but also contains 20% more concepts. We further present a detailed summary of the differences across information types, and we examine the effects other citations and time have on the content of citances.
doi:10.1155/2012/750214
PMCID: PMC3514807  PMID: 23227044
22.  Wavelet Packet Entropy for Heart Murmurs Classification 
Advances in Bioinformatics  2012;2012:327269.
Heart murmurs are the first signs of cardiac valve disorders. Several studies have been conducted in recent years to automatically differentiate normal heart sounds, from heart sounds with murmurs using various types of audio features. Entropy was successfully used as a feature to distinguish different heart sounds. In this paper, new entropy was introduced to analyze heart sounds and the feasibility of using this entropy in classification of five types of heart sounds and murmurs was shown. The entropy was previously introduced to analyze mammograms. Four common murmurs were considered including aortic regurgitation, mitral regurgitation, aortic stenosis, and mitral stenosis. Wavelet packet transform was employed for heart sound analysis, and the entropy was calculated for deriving feature vectors. Five types of classification were performed to evaluate the discriminatory power of the generated features. The best results were achieved by BayesNet with 96.94% accuracy. The promising results substantiate the effectiveness of the proposed wavelet packet entropy for heart sounds classification.
doi:10.1155/2012/327269
PMCID: PMC3512213  PMID: 23227043
23.  On the Meaning of Affinity Limits in B-Cell Epitope Prediction for Antipeptide Antibody-Mediated Immunity 
Advances in Bioinformatics  2012;2012:346765.
B-cell epitope prediction aims to aid the design of peptide-based immunogens (e.g., vaccines) for eliciting antipeptide antibodies that protect against disease, but such antibodies fail to confer protection and even promote disease if they bind with low affinity. Hence, the Immune Epitope Database (IEDB) was searched to obtain published thermodynamic and kinetic data on binding interactions of antipeptide antibodies. The data suggest that the affinity of the antibodies for their immunizing peptides appears to be limited in a manner consistent with previously proposed kinetic constraints on affinity maturation in vivo and that cross-reaction of the antibodies with proteins tends to occur with lower affinity than the corresponding reaction of the antibodies with their immunizing peptides. These observations better inform B-cell epitope prediction to avoid overestimating the affinity for both active and passive immunization; whereas active immunization is subject to limitations of affinity maturation in vivo and of the capacity to accumulate endogenous antibodies, passive immunization may transcend such limitations, possibly with the aid of artificial affinity-selection processes and of protein engineering. Additionally, protein disorder warrants further investigation as a possible supplementary criterion for B-cell epitope prediction, where such disorder obviates thermodynamically unfavorable protein structural adjustments in cross-reactions between antipeptide antibodies and proteins.
doi:10.1155/2012/346765
PMCID: PMC3505629  PMID: 23209458
24.  Application of an Integrative Computational Framework in Trancriptomic Data of Atherosclerotic Mice Suggests Numerous Molecular Players 
Advances in Bioinformatics  2012;2012:453513.
Atherosclerosis is a multifactorial disease involving a lot of genes and proteins recruited throughout its manifestation. The present study aims to exploit bioinformatic tools in order to analyze microarray data of atherosclerotic aortic lesions of ApoE knockout mice, a model widely used in atherosclerosis research. In particular, a dynamic analysis was performed among young and aged animals, resulting in a list of 852 significantly altered genes. Pathway analysis indicated alterations in critical cellular processes related to cell communication and signal transduction, immune response, lipid transport, and metabolism. Cluster analysis partitioned the significantly differentiated genes in three major clusters of similar expression profile. Promoter analysis applied to functional related groups of the same cluster revealed shared putative cis-elements potentially contributing to a common regulatory mechanism. Finally, by reverse engineering the functional relevance of differentially expressed genes with specific cellular pathways, putative genes acting as hubs, were identified, linking functionally disparate cellular processes in the context of traditional molecular description.
doi:10.1155/2012/453513
PMCID: PMC3502768  PMID: 23193398
25.  Intervention in Biological Phenomena via Feedback Linearization 
Advances in Bioinformatics  2012;2012:534810.
The problems of modeling and intervention of biological phenomena have captured the interest of many researchers in the past few decades. The aim of the therapeutic intervention strategies is to move an undesirable state of a diseased network towards a more desirable one. Such an objective can be achieved by the application of drugs to act on some genes/metabolites that experience the undesirable behavior. For the purpose of design and analysis of intervention strategies, mathematical models that can capture the complex dynamics of the biological systems are needed. S-systems, which offer a good compromise between accuracy and mathematical flexibility, are a promising framework for modeling the dynamical behavior of biological phenomena. Due to the complex nonlinear dynamics of the biological phenomena represented by S-systems, nonlinear intervention schemes are needed to cope with the complexity of the nonlinear S-system models. Here, we present an intervention technique based on feedback linearization for biological phenomena modeled by S-systems. This technique is based on perfect knowledge of the S-system model. The proposed intervention technique is applied to the glycolytic-glycogenolytic pathway, and simulation results presented demonstrate the effectiveness of the proposed technique.
doi:10.1155/2012/534810
PMCID: PMC3502753  PMID: 23209459

Results 1-25 (100)