DBSolve Optimum: a software package for kinetic modeling which allows dynamic visualization of simulation results

Gizzatkulov, Nail; Goryanin, Igor; Metelkin, Eugeniy; Mogilevskaya, Ekaterina A.; Peskov, Kirill; Demin, Oleg

doi:10.1186/1752-0509-4-109

Cited by 41 publications

(26 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These parameters and their corresponding values are given in Additional file 1: Table S2. The other 31 parameters were evaluated based on the best fitting to the appropriate experimental data, using the Hook-Jeeves method as implemented in the DBSolve Optimum package [47,48]. As a criterion of goodness of fit, the sum of squares error function was used:

f ({italick}_{j,} K_{j}) = {true {true}_{\sum}^{i} (v_{i} - {\overset{v}{true}}_{i})}^{2}

…”

Section: Methodsmentioning

confidence: 99%

Modeling of celiac disease immune response and the therapeutic effect of potential drugs

Смирнов

Соколов

Cucurull-Sánchez

et al. 2013

BMC Systems Biology

Self Cite

View full text Add to dashboard Cite

BackgroundCeliac disease (CD) is an autoimmune disorder that occurs in genetically predisposed people and is caused by a reaction to the gluten protein found in wheat, which leads to intestinal villous atrophy. Currently there is no drug for treatment of CD. The only known treatment is lifelong gluten-free diet. The main aim of this work is to develop a mathematical model of the immune response in CD patients and to predict the efficacy of a transglutaminase-2 (TG-2) inhibitor as a potential drug for treatment of CD.ResultsA thorough analysis of the developed model provided the following results:1. TG-2 inhibitor treatment leads to insignificant decrease in antibody levels, and hence remains higher than in healthy individuals.2. TG-2 inhibitor treatment does not lead to any significant increase in villous area.3. The model predicts that the most effective treatment of CD would be the use of gluten peptide analogs that antagonize the binding of immunogenic gluten peptides to APC. The model predicts that the treatment of CD by such gluten peptide analogs can lead to a decrease in antibody levels to those of normal healthy people, and to a significant increase in villous area.ConclusionsThe developed mathematical model of immune response in CD allows prediction of the efficacy of TG-2 inhibitors and other possible drugs for the treatment of CD: their influence on the intestinal villous area and on the antibody levels. The model also allows to understand what processes in the immune response have the strongest influence on the efficacy of different drugs. This model could be applied in the pharmaceutical R&D arena for the design of drugs against autoimmune small intestine disorders and on the design of their corresponding clinical trials.

show abstract

f ({italick}_{j,} K_{j}) = {true {true}_{\sum}^{i} (v_{i} - {\overset{v}{true}}_{i})}^{2}

…”

Section: Methodsmentioning

confidence: 99%

Modeling of celiac disease immune response and the therapeutic effect of potential drugs

Смирнов

Соколов

Cucurull-Sánchez

et al. 2013

BMC Systems Biology

Self Cite

View full text Add to dashboard Cite

show abstract

“…Model construction and simulation was performed with the use of the DBSolve package [68]. Fitting the model to experimental data involved minimisation of least square deviation between experimental and theoretical curves, with the use of Hooke and Jeeves algorithm as implemented in DBsolve package.…”

Section: Methodsmentioning

confidence: 99%

A model of estrogen-related gene expression reveals non-linear effects in transcriptional response to tamoxifen

et al. 2012

View full text Add to dashboard Cite

BackgroundEstrogen receptors alpha (ER) are implicated in many types of female cancers, and are the common target for anti-cancer therapy using selective estrogen receptor modulators (SERMs, such as tamoxifen). However, cell-type specific and patient-to-patient variability in response to SERMs (from suppression to stimulation of cancer growth), as well as frequent emergence of drug resistance, represents a serious problem. The molecular processes behind mixed effects of SERMs remain poorly understood, and this strongly motivates application of systems approaches. In this work, we aimed to establish a mathematical model of ER-dependent gene expression to explore potential mechanisms underlying the variable actions of SERMs.ResultsWe developed an equilibrium model of ER binding with 17β-estradiol, tamoxifen and DNA, and linked it to a simple ODE model of ER-induced gene expression. The model was parameterised on the broad range of literature available experimental data, and provided a plausible mechanistic explanation for the dual agonism/antagonism action of tamoxifen in the reference cell line used for model calibration. To extend our conclusions to other cell types we ran global sensitivity analysis and explored model behaviour in the wide range of biologically plausible parameter values, including those found in cancer cells. Our findings suggest that transcriptional response to tamoxifen is controlled in a complex non-linear way by several key parameters, including ER expression level, hormone concentration, amount of ER-responsive genes and the capacity of ER-tamoxifen complexes to stimulate transcription (e.g. by recruiting co-regulators of transcription). The model revealed non-monotonic dependence of ER-induced transcriptional response on the expression level of ER, that was confirmed experimentally in four variants of the MCF-7 breast cancer cell line.ConclusionsWe established a minimal mechanistic model of ER-dependent gene expression, that predicts complex non-linear effects in transcriptional response to tamoxifen in the broad range of biologically plausible parameter values. Our findings suggest that the outcome of a SERM’s action is defined by several key components of cellular micro-environment, that may contribute to cell-type-specific effects of SERMs and justify the need for the development of combinatorial biomarkers for more accurate prediction of the efficacy of SERMs in specific cell types.

show abstract

“…The values for other parameters were chosen on the basis of the best coincidence between modeling results and corresponding experimental data. To select the values of the parameters we used the algorithm of fitting based on the Hook-Jeeves method [35] implemented in the DBSolve Optimum package [36]. As a criterion of fitness, the following function was used:…”

Section: Methodsmentioning

confidence: 99%

Regulation of leukotriene and 5oxoETE synthesis and the effect of 5-lipoxygenase inhibitors: a mathematical modeling approach

Karelina

Zhudenkov

Demin

et al. 2012

BMC Systems Biology

Self Cite

View full text Add to dashboard Cite

Background5-lipoxygenase (5-LO) is a key enzyme in the synthesis of leukotrienes and 5-Oxo-6E,8Z,11Z,14Z-eicosatetraenoic acid (oxoETE). These inflammatory signaling molecules play a role in the pathology of asthma and so 5-LO inhibition is a promising target for asthma therapy. The 5-LO redox inhibitor zileuton (Zyflo IR/CR®) is currently marketed for the treatment of asthma in adults and children, but widespread use of zileuton is limited by its efficacy/safety profile, potentially related to its redox characteristics. Thus, a quantitative, mechanistic description of its functioning may be useful for development of improved anti-inflammatory targeting this mechanism.ResultsA mathematical model describing the operation of 5-LO, phospholipase A2, glutathione peroxidase and 5-hydroxyeicosanoid dehydrogenase was developed. The catalytic cycles of the enzymes were reconstructed and kinetic parameters estimated on the basis of available experimental data. The final model describes each stage of cys-leukotriene biosynthesis and the reactions involved in oxoETE production. Regulation of these processes by substrates (phospholipid concentration) and intracellular redox state (concentrations of reduced glutathione, glutathione (GSH), and lipid peroxide) were taken into account. The model enabled us to reveal differences between redox and non-redox 5-LO inhibitors under conditions of oxidative stress. Despite both redox and non-redox inhibitors suppressing leukotriene A4 (LTA4) synthesis, redox inhibitors are predicted to increase oxoETE production, thus compromising efficacy. This phenomena can be explained in terms of the pseudo-peroxidase activity of 5-LO and the ability of lipid peroxides to transform 5-LO into its active form even in the presence of redox inhibitors.ConclusionsThe mathematical model developed described quantitatively different mechanisms of 5-LO inhibition and simulations revealed differences between the potential therapeutic outcomes for these mechanisms.

show abstract

DBSolve Optimum: a software package for kinetic modeling which allows dynamic visualization of simulation results

Cited by 41 publications

References 23 publications

Modeling of celiac disease immune response and the therapeutic effect of potential drugs

Modeling of celiac disease immune response and the therapeutic effect of potential drugs

A model of estrogen-related gene expression reveals non-linear effects in transcriptional response to tamoxifen

Regulation of leukotriene and 5oxoETE synthesis and the effect of 5-lipoxygenase inhibitors: a mathematical modeling approach

Contact Info

Product

Resources

About