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[show abstract]
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ABSTRACT: Analysis of different architectures of quorum sensing networks has been the center of attention in recent times. The approach employs mathematical models to uncover the factors behind the dynamics. Quorum sensing networks mostly display autoregulation such as Pseudomonas aeruginosa and Vibrio cholerae. However, Escherichia coli autoinducer-2 (AI-2) synthesis does not display autoinduction (i.e. autoregulation). This and other features have raised questions about the actual function of AI-2 inside the cell. In this paper we propose a model for lsr operon regulation which explains or at least is consistent with AI-2 uptake in E. coli. The model was employed to determine the main factors that control the concentration of the signal and the uptake activation. We investigated deterministic and stochastic variants of the network model and we found no states that could lead to the typical bistability in quorum sensing systems. However, stochastic simulations predict a transient bifurcation (positively regulated by AI-2 synthesis) that could provide some advantage in adapting to new environments. LsrR inactivation was found to play a crucial role in the uptake activation compared to AI-2 synthesis, lsr transcription and AI-2 excretion. Our hypothesis is that positive regulation of the level of expression is the main factor in understanding the function of the lsr operon. This is in contrast to the conventionally held belief that the main factor is the onset of activation.
Bio Systems 09/2009; 99(1):17-26. · 1.27 Impact Factor
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Glenn S Belinsky,
Ann L Parke,
Qihong Huang,
Kerry Blanchard,
Supriya Jayadev,
Raymond Stoll,
Marti Rothe, Luke E K Achenie,
Rishi R Gupta,
George Y Wu,
Daniel W Rosenberg
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ABSTRACT: Long-term administration of methotrexate (MTX) for management of chronic inflammatory diseases is associated with risk of liver damage. In this study, we examined the transcriptional profiles of livers from patients treated with MTX. The possibility that expression signatures correlate with grade of fibrosis or underlying rheumatic disease was evaluated. Twenty-seven patients taking MTX were accrued for this study. Ten non-MTX-exposed normal liver specimens were used as controls. Global mRNA expression was assayed using oligonucleotide arrays. A total of 205 genes were significantly altered in MTX-exposed livers. Six of these genes were validated by qPCR. Two genes, CLN8 and ANKH that map to chromosomal locations previously associated with rheumatoid arthritis, were found to be elevated in MTX-exposed samples. Subsequent pathway analysis indicates that MTX exposure is associated with the following key alterations: (1) upregulation of lipid biosynthetic genes, consistent with MTX-induced steatosis, (2) downregulation of proinflammatory chemokines, consistent with the anti-inflammatory effects of MTX, and (3) elevation of complement pathway gene expression. Complement 5, shown earlier to be correlated with liver fibrosis in mice, was found to be elevated (twofold) in MTX-exposed livers. In conclusion, we have found the expression of a number of genes associated with rheumatic disease and/or MTX exposure to be significantly different. Differences in complement expression provide the rationale for future correlative studies between MTX-induced liver fibrosis and C5 alleles in order to identify patients with increased risk for fibrosis.
Toxicological Sciences 07/2007; 97(2):582-94. · 4.65 Impact Factor
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[hide abstract]
ABSTRACT: Clustering algorithms divide a set of observations into groups so that members of the same group share common features. In most of the algorithms, tunable parameters are set arbitrarily or by trial and error, resulting in less than optimal clustering. This paper presents a global optimization strategy for the systematic and optimal selection of parameter values associated with a clustering method. In the process, a performance criterion for the optimization model is proposed and benchmarked against popular performance criteria from the literature (namely, the Silhouette coefficient, Dunn's index, and Davies-Bouldin index). The tuning strategy is illustrated using the support vector clustering (SVC) algorithm and simulated annealing. In order to reduce the computational burden, the paper also proposes an alternative to the adjacency matrix method (used for the assignment of cluster labels), namely the contour plotting approach. Datasets tested include the iris and the thyroid datasets from the UCI repository, as well as lymphoma and breast cancer data. The optimal tuning parameters are determined efficiently, while the contour plotting approach leads to significant reductions in computational effort (CPU time) especially for large datasets. The performance criteria comparisons indicate mixed results. Specifically, the Silhouette coefficient and the Davies-Bouldin index perform better, while the Dunn's index is worse on average than the proposed performance index.
Mathematical Biosciences 03/2007; 205(2):252-70. · 1.54 Impact Factor
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Computers & Chemical Engineering. 01/2007; 31:950-961.
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Ivo Kalajzic,
Ada Staal,
Wen-Pin Yang,
Yuli Wu,
Susan E Johnson,
Jean H M Feyen,
Winfried Krueger,
Peter Maye,
Fang Yu,
Yifang Zhao,
Lynn Kuo,
Rishi R Gupta, Luke E K Achenie,
Hsin-Wei Wang,
Dong-Guk Shin,
David W Rowe
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ABSTRACT: The inherent heterogeneity of bone cells complicates the interpretation of microarray studies designed to identify genes highly associated with osteoblast differentiation. To overcome this problem, we have utilized Col1a1 promoter-green fluorescent protein transgenic mouse lines to isolate bone cells at distinct stages of osteoprogenitor maturation. Comparison of gene expression patterns from unsorted or isolated sorted bone cell populations at days 7 and 17 of calvarial cultures revealed an increased specificity regarding which genes are selectively expressed in a subset of bone cell types during differentiation. Furthermore, distinctly different patterns of gene expression associated with major signaling pathways (Igf1, Bmp, and Wnt) were observed at different levels of maturation. Some of our data differ from current models of osteoprogenitor cell differentiation and emphasize components of the pathways that were not revealed in studies based on a total cell population. Thus, applying methods to generate more homogeneous populations of cells at a defined level of cellular differentiation from a primary osteogenic culture is feasible and leads to a novel interpretation of the gene expression associated with increasing levels of osteoprogenitor maturation.
Journal of Biological Chemistry 08/2005; 280(26):24618-26. · 4.77 Impact Factor
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ABSTRACT: The DNA alkylating agent, azoxymethane (AOM), induces tumor formation in the distal colon of susceptible mice. Differential susceptibility to this colonotropic carcinogen has been well characterized in A/J (sensitive) and AKR/J (resistant) mice. However, the reasons underlying the differential response to AOM and the molecular mechanisms involved in colon tumor progression remain unclear. To address these issues, we used a cDNA microarray approach to determine time-related changes in gene expression patterns in A/J and AKR/J colons following carcinogen treatment. In the present study, mice were injected intraperitoneally with either AOM (10mg/kg body weight once a week for 6 weeks) or 0.9% NaCl solution (vehicle controls). Total RNA was isolated from the distal colons at 1, 4, and 24 weeks post-AOM exposure. RNA was reverse transcribed and cDNA samples labeled with Cy3 and Cy5 were hybridized to a glass chip containing 4608 mouse cDNA duplicate clones. The resulting mRNA expression levels were analyzed using GLEAMS 3.0, a Unix/Linux-based software program. Genes with more than twofold variations in expression levels were considered significant. Further clustering analysis was performed based on gene expression patterns at different time points using a novel adaptive centroid algorithm (ACA). Of the 4608 genes, 118 clustered into 11 significant groups that displayed similar and distinct expression patterns between the strains following carcinogen treatment. Nine clusters were selected for further analysis based on their divergence in response between A/J and AKR/J and their potential role in tumorigenesis. Overall, our data indicate time- and strain-specific genetic alterations during different stages of colon tumorigenesis following AOM treatment.
Cancer Letters 03/2003; 191(1):17-25. · 4.24 Impact Factor
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ABSTRACT: The search for new solvents is driven by the needs of new applications, new processing requirements, changing environmental regulations, and market demands. Many cleaning solvents used in the lithographic printing industry are on the environmental “hit list” and are to be phased out within the next few years. This paper discusses the systematic design of cleaning solvent blends for lithographic printing (commonly referred to as blanket washes). The design problem consists of a discrete problem involving selection of solvents from a set of pure-component solvents and a continuous problem of finding the blend composition. The simultaneous consideration of associated process constraints, property requirements, and environmental restrictions makes blanket wash design a rather difficult problem. To address this issue, we present a framework for designing cleaning solvent blends that meet thermophysical property requirements and environmental restrictions. The solvent design model is solved using interval analysis.
01/2003;
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Reliable Computing. 01/2003; 9:317-338.
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ABSTRACT: This paper addresses the efficient solution of computer aided molecular design (CAMD) problems, which have been posed as mixed-integer nonlinear programming models. The models of interest are those in which the number of linear constraints far exceeds the number of nonlinear constraints, and with most variables participating in the nonconvex terms. As a result global optimization methods are needed. A branch-and-bound algorithm (BB) is proposed that is specifically tailored to solving such problems. In a conventional BB algorithm, branching is performed on all the search variables that appear in the nonlinear terms. This translates to a large number of node traversals. To overcome this problem, we have proposed a new strategy for branching on a set of linear branchingfunctions, which depend linearly on the search variables. This leads to a significant reduction in the dimensionality of the search space. The construction of linear underestimators for a class of functions is also presented. The CAMD problem that is considered is the design of optimal solvents to be used as cleaning agents in lithographic printing.
Computers & Chemistry 12/2002; 26(6):645-60.
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[show abstract]
[hide abstract]
ABSTRACT: The search for new solvents is driven by the needs of new applications and processing requirements, replacement of solvents whose continued use poses a threat to environmental health and safety, a response to changing environmental regulations, and a response to market demands. Many cleaning solvents (commonly referred to as blanket washes) used in the lithographic printing industry are on the environmental hit list and are to be phased out within the next few years. This paper discusses the design of novel single component solvents for this application. The simultaneous consideration of recovery targets (and associated process constraints), physical property requirements, and environmental restrictions renders identification/selection of blanket washes a complicated design problem. To address this, we present a framework for the design of novel blanket wash solvents. The framework aims at designing solvents that meet property requirements and the environmental restrictions while minim...
12/2000;
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ABSTRACT: Strict assignment of genes to one class, dimensionality reduction, a priori specification of the number of classes, the need for a training set, nonunique solution, and complex learning mechanisms are some of the inadequacies of current clustering algorithms. Existing algorithms cluster genes on the basis of high positive correlations between their expression patterns. However, genes with strong negative correlations can also have similar functions and are most likely to have a role in the same pathways. To address some of these issues, we propose the adaptive centroid algorithm (ACA), which employs an analysis of variance (ANOVA)-based performance criterion. The ACA also uses Euclidian distances, the center-of-mass principle for heterogeneously distributed mass elements, and the given data set to give unique solutions. The proposed approach involves three stages. In the first stage a two-way ANOVA of the gene expression matrix is performed. The two factors in the ANOVA are gene expression and experimental condition. The residual mean squared error (MSE) from the ANOVA is used as a performance criterion in the ACA. Finally, correlated clusters are found based on the Pearson correlation coefficients. To validate the proposed approach, a two-way ANOVA is again performed on the discovered clusters. The results from this last step indicate that MSEs of the clusters are significantly lower compared to that of the fibroblast-serum gene expression matrix. The ACA is employed in this study for single- as well as multi-cluster gene assignments.
Biotechnology Progress 19(4):1142-8. · 2.34 Impact Factor
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ABSTRACT: Advances in microarray technology have resulted in an exponential rise in gene expression data. Partially as a result of this, full genome sequences have been reported for many organisms. In addition several methods have been developed (a) for assigning functionality to previously unknown genes and (b) for measuring the output (i.e. gene expression) of the gene regulatory network. The knowledge of the gene regulatory network further gives insights about gene pathways. This information leads to many potential applications in medicine and molecular biology, examples of which are identification of metabolic pathways, complex genetic diseases, drug discovery and toxicology analysis. Also, gene regulatory networks allow comparison of expression patterns of many uncharacterized genes; this comparison provides clues to gene function. A variety of models (such as neural networks, Boolean networks, and Bayesian) have been proposed in recent times. Although each of these models have individual strengths, none of them addresses important issues such as time delay, or make use of available biological information. In the work presented here we demonstrate that networks can efficiently model natural biological processes, specifically gene regulatory systems. Through the modeling approach, we have inferred gene regulatory networks using a time course data set for (a) lambda bacteriophage infection, (b) osteoblast study, and (c) rat central nervous system (CNS) development. The results compare favorably with experimental results from the literature.
Computers & Chemical Engineering.
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[show abstract]
[hide abstract]
ABSTRACT: The search for new solvents is driven by the needs of new applications and processing requirements, replacement of solvents whose continued use poses a threat to environmental health and safety, a response to changing environmental regulations, and a response to market demands. Many cleaning solvents (commonly referred to as blanket washes) used in the lithographic printing industry are on the environmental hit list and are to be phased out within the next few years. This paper discusses the design of novel single component solvents for this application. The simultaneous consideration of recovery targets (and associated process constraints), physical property requirements, and environmental restrictions renders identification/selection of blanket washes a complicated design problem. To address this, we present a framework for the design of novel blanket wash solvents. The framework aims at designing solvents that meet property requirements and the environmental restrictions while minimizing the operating cost associated with safe recovery and recycle operation. Molecular structural attributes are modeled by binary variables and the process variables are continuous variables. This framework is formulated as a mixed-integer non-linear programming problem (MINLP) and solved by an appropriate solver. To demonstrate the viability of the framework and the solution approach, blanket wash solvents are designed.
Advances in Environmental Research.
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[show abstract]
[hide abstract]
ABSTRACT: Analysis of different architectures of quorum sensing networks has been the center of attention in recent times. The approach employs mathematical models to uncover the factors behind the dynamics. Quorum sensing networks mostly display autoregulation such as Pseudomonas aeruginosa and Vibrio cholerae. However, Escherichia coli autoinducer-2 (AI-2) synthesis does not display autoinduction (i.e. autoregulation). This and other features have raised questions about the actual function of AI-2 inside the cell. In this paper we propose a model for lsr operon regulation which explains or at least is consistent with AI-2 uptake in E. coli. The model was employed to determine the main factors that control the concentration of the signal and the uptake activation. We investigated deterministic and stochastic variants of the network model and we found no states that could lead to the typical bistability in quorum sensing systems. However, stochastic simulations predict a transient bifurcation (positively regulated by AI-2 synthesis) that could provide some advantage in adapting to new environments. LsrR inactivation was found to play a crucial role in the uptake activation compared to AI-2 synthesis, lsr transcription and AI-2 excretion. Our hypothesis is that positive regulation of the level of expression is the main factor in understanding the function of the lsr operon. This is in contrast to the conventionally held belief that the main factor is the onset of activation.
Biosystems.
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[show abstract]
[hide abstract]
ABSTRACT: Clustering algorithms divide a set of observations into groups so that members of the same group share common features. In most of the algorithms, tunable parameters are set arbitrarily or by trial and error, resulting in less than optimal clustering. This paper presents a global optimization strategy for the systematic and optimal selection of parameter values associated with a clustering method. In the process, a performance criterion for the optimization model is proposed and benchmarked against popular performance criteria from the literature (namely, the Silhouette coefficient, Dunn’s index, and Davies–Bouldin index). The tuning strategy is illustrated using the support vector clustering (SVC) algorithm and simulated annealing. In order to reduce the computational burden, the paper also proposes an alternative to the adjacency matrix method (used for the assignment of cluster labels), namely the contour plotting approach. Datasets tested include the iris and the thyroid datasets from the UCI repository, as well as lymphoma and breast cancer data. The optimal tuning parameters are determined efficiently, while the contour plotting approach leads to significant reductions in computational effort (CPU time) especially for large datasets. The performance criteria comparisons indicate mixed results. Specifically, the Silhouette coefficient and the Davies–Bouldin index perform better, while the Dunn’s index is worse on average than the proposed performance index.
Mathematical Biosciences.
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[show abstract]
[hide abstract]
ABSTRACT: This paper addresses the efficient solution of computer aided molecular design (CAMD) problems, which have been posed as mixed-integer nonlinear programming models. The models of interest are those in which the number of linear constraints far exceeds the number of nonlinear constraints, and with most variables participating in the nonconvex terms. As a result global optimization methods are needed. A branch-and-bound algorithm (BB) is proposed that is specifically tailored to solving such problems. In a conventional BB algorithm, branching is performed on all the search variables that appear in the nonlinear terms. This translates to a large number of node traversals. To overcome this problem, we have proposed a new strategy for branching on a set of linear branching functions, which depend linearly on the search variables. This leads to a significant reduction in the dimensionality of the search space. The construction of linear underestimators for a class of functions is also presented. The CAMD problem that is considered is the design of optimal solvents to be used as cleaning agents in lithographic printing.
Computers & Chemistry.
