Long-term glucocorticoid treatment impairs the survival and bone formation of osteogenic cells, leading to bone mass loss. The Wnt inhibitor Dickkopf-1 (DKK1) acts as a potent bone-remodeling factor that mediates several types of skeletal disorders. Whereas excess glucocorticoid is known to disturb Wnt signaling in osteogenic cells, modulation of the skeletally deleterious effects of DKK1 to alleviate glucocorticoid induction of bone loss has not been tested. In this study, knockdown of DKK1 expression by end-capped phosphorothioate DKK1 antisense oligonucleotide (DKK1-AS) abrogated dexamethasone suppression of alkaline phosphatase activity and osteocalcin expression in MC3T3-E1 preosteoblasts. Exogenous DKK1-AS treatment alleviated dexamethasone suppression of mineral density, trabecular bone volume, osteoblast surface, and bone formation rate in bone tissue and ex vivo osteogenesis of primary bone-marrow mesenchymal cells. The DKK1-AS inhibited adipocyte volume in the marrow cavity of steroid-treated bone tissue. Immunohistochemical observation revealed that DKK1-AS abrogated dexamethasone-induced DKK1 expression and terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate-biotin nick end-labeling of osteoblasts adjacent to trabecular bone. Knocking down DKK1 abrogated dexamethasone-modulated expression of nuclear β-catenin and phosphorylated Ser473-Akt and survival of osteoblasts and adipocytic differentiation of mesenchymal progenitor cell cultures. Taken together, knocking down DKK1 alleviated the deleterious effect of glucocorticoid on bone microstructure. The DKK1-AS treatment appeared to protect bone tissue by modulating β-catenin and Akt-mediated survival as well as the osteogenic and adipogenic activities of glucocorticoid-stressed osteoprogenitor cells. Interference with the osteogenesis-inhibitory action of DKK1 has therapeutic potential for preventing glucocorticoid induction of osteopenia.

Motivation: Modern experimental biology is moving away from analyses of single elements to whole-organism measurements. Such measured time-course data contain a wealth of information about the structure and dynamic of the pathway or network. The dynamic modeling of the whole systems is formulated as a reverse problem that requires a well-suited mathematical model and a very efficient computational method to identify the model structure and parameters. Numerical integration for differential equations and finding global parameter values are still two major challenges in this field of the parameter estimation of nonlinear dynamic biological systems. Results: We compare three techniques of parameter estimation for nonlinear dynamic biological systems. In the proposed scheme, the modified collocation method is applied to convert the differential equations to the system of algebraic equations. The observed time-course data are then substituted into the algebraic system equations to decouple system interactions in order to obtain the approximate model profiles. Hybrid differential evolution (HDE) with population size of five is able to find a global solution. The method is not only suited for parameter estimation but also can be applied for structure identification. The solution obtained by HDE is then used as the starting point for a local search method to yield the refined estimates. Availability: The algorithm, implemented by Compaq Visual Fortran Professional Edition 6.6, and the supplements are available at http://www.che.ccu.edu.tw/~bioproc/index-english.html/. IMSL Math/Library is a commercial library included in Compaq Visual Fortran Professional Edition. Contact:chmfsw@ccu.edu.tw

Motivation: The inference of biochemical networks, such as gene regulatory networks, protein–protein interaction networks, and metabolic pathway networks, from time-course data is one of the main challenges in systems biology. The ultimate goal of inferred modeling is to obtain expressions that quantitatively understand every detail and principle of biological systems. To infer a realizable S-system structure, most articles have applied sums of magnitude of kinetic orders as a penalty term in the fitness evaluation. How to tune a penalty weight to yield a realizable model structure is the main issue for the inverse problem. No guideline has been published for tuning a suitable penalty weight to infer a suitable model structure of biochemical networks. Results: We introduce an interactive inference algorithm to infer a realizable S-system structure for biochemical networks. The inference problem is formulated as a multiobjective optimization problem to minimize simultaneously the concentration error, slope error and interaction measure in order to find a suitable S-system model structure and its corresponding model parameters. The multiobjective optimization problem is solved by the ε-constraint method to minimize the interaction measure subject to the expectation constraints for the concentration and slope error criteria. The theorems serve to guarantee the minimum solution for the ε-constrained problem to achieve the minimum interaction network for the inference problem. The approach could avoid assigning a penalty weight for sums of magnitude of kinetic orders. Contact:chmfsw@ccu.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online.

Background: A mathematical model to understand, predict, control, or even design a real biological system is a central theme in systems biology. A dynamic biological system is always modeled as a nonlinear ordinary differential equation (ODE) system. How to simulate the dynamic behavior and dynamic parameter sensitivities of systems described by ODEs efficiently and accurately is a critical job. In many practical applications, e.g., the fed-batch fermentation systems, the system admissible input (corresponding to independent variables of the system) can be time-dependent. The main difficulty for investigating the dynamic log gains of these systems is the infinite dimension due to the time-dependent input. The classical dynamic sensitivity analysis does not take into account this case for the dynamic log gains. Results: We present an algorithm with an adaptive step size control that can be used for computing the solution and dynamic sensitivities of an autonomous ODE system simultaneously. Although our algorithm is one of the decouple direct methods in computing dynamic sensitivities of an ODE system, the step size determined by model equations can be used on the computations of the time profile and dynamic sensitivities with moderate accuracy even when sensitivity equations are more stiff than model equations. To show this algorithm can perform the dynamic sensitivity analysis on very stiff ODE systems with moderate accuracy, it is implemented and applied to two sets of chemical reactions: pyrolysis of ethane and oxidation of formaldehyde. The accuracy of this algorithm is demonstrated by comparing the dynamic parameter sensitivities obtained from this new algorithm and from the direct method with Rosenbrock stiff integrator based on the indirect method. The same dynamic sensitivity analysis was performed on an ethanol fed-batch fermentation system with a time-varying feed rate to evaluate the applicability of the algorithm to realistic models with time-dependent admissible input. Conclusion: By combining the accuracy we show with the efficiency of being a decouple direct method, our algorithm is an excellent method for computing dynamic parameter sensitivities in stiff problems. We extend the scope of classical dynamic sensitivity analysis to the investigation of dynamic log gains of models with time-dependent admissible input.

Cholestasis and hepatitis can cause continuous liver damage that may ultimately result in liver fibrosis. In a previous study, we demonstrated that microRNA-29a (miR-29a) protects against liver fibrosis. Toll-like receptor 2 (TLR2) and TLR4 are pattern recognition receptors of bacterial lipoprotein and lipopolysaccharide, both of which participate in activating hepatic stellate cells and liver fibrosis. The purpose of this study is to characterize the biological influence of miR-29a on TLR2 and TLR4 signaling in livers injured with bile duct ligation (BDL). We performed BDL on both miR-29a transgenic mice (miR-29aTg) and wild-type mice to induce cholestatic liver injury. Primary HSCs were transfected with a miR-29a mimic and inhibitor. In the wild-type mice, the BDL demonstrated significant α-smooth muscle actin fibrotic matrix formation and hepatic high mobility group box-1 expression. However, in the miR-29aTg mice, these factors were significantly reduced. Furthermore, miR-29a overexpression reduced the BDL exaggeration of TLR2, TLR4, MyD88, bromodomain-containing protein 4 (BRD4), phospho-p65 as well as proinflammatory cytokines, IL-1β, MCP-1, TGF-β, and TNF-α. In vitro, miR-29a mimic transfection reduced α-SMA, BRD4,TLR2, and TLR4 expressions in HSCs. This study provides new molecular insight into the ability of miR-29a to inhibit TLR2 and TLR4 signaling, which thus slows the progression of cholestatic liver deterioration.

Motivation: Mathematical modeling and optimization have been used for detecting enzyme targets in human metabolic disorders. Such optimal drug design methods are generally differentiated as two stages, identification and decision-making, to find optimal targets. We developed a unified method named fuzzy equal metabolic adjustment to formulate an optimal enzyme target design problem for drug discovery. The optimization framework combines the identification of enzyme targets and a decision-making strategy simultaneously. The objectives of this algorithm include evaluations of the therapeutic effect of target enzymes, the adverse effects of drugs and the minimum effective dose (MED). Results: An existing generalized mass action system model of human uric acid (UA) metabolism was used to formulate the fuzzy optimization method for detecting two types of enzymopathies: hyperuricemia caused by phosphoribosylpyrophosphate synthetase (PRPPS) overactivity and Lesch–Nyhan syndrome. The fuzzy objectives were set so that the concentrations of the metabolites were as close as possible to the healthy levels. The target design included a diet control of ribose-5-phospahate (R5P). The diet control of R5P served as an extra remedy to reduce phosphate uptake entering the purine metabolic pathway, so that we could obtain a more satisfactory treatment than obtained for those without a diet control. Moreover, enhancing UA excretion resulted in an effective treatment of hyperuricemia caused by PRPPS overactivity. This result correlates with using probenecid and benbromazone, which are uricosuric agents present in current clinical medications. By contrast, the Lesch–Nyhan syndrome required at least three enzyme targets to cure hyperuricemia. Contact:chmfsw@ccu.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online.