Parameter optimization by using differential elimination: a general approach for introducing constraints into objective functions

Nakatsui, Masahiko; Horimoto, Katsuhisa; Okamoto, Masahiro; Tokumoto, Yuki; Miyake, Jun

doi:10.1186/1752-0509-4-s2-s9

Cited by 6 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(Nakatsui et al, 2010), in order to serve for the study of specific biological problems regarding plant central metabolism in general, just as that of Brassica napus more specifically. The adaptation to a particular metabolic engineering problem is depending on the nature and extent of constraints applied, in order to limit the flux space to the parts of metabolism that must be analyzed.…”

Section: Discussionmentioning

confidence: 99%

An in silico compartmentalized metabolic model of Brassica napus enables the systemic study of regulatory aspects of plant central metabolism

Pilalis

Chatziioannou

Thomasset

et al. 2011

Biotech & Bioengineering

View full text Add to dashboard Cite

Biochemical network reconstructions represent valuable tools for the computational metabolic modeling of organisms that present a great biotechnological interest. An in silico multi-compartmental model of the central metabolism of the plant Brassica napus (Rapeseed) was constructed, aiming to investigate the metabolic properties of the Brassicaceae family. This family comprises many plants with major importance for the energy and nutrition sector, including the model plant Arabidopsis thaliana. The model utilized as objective function to be subsequently optimized, the biomass production of rapeseed developing embryos, which are characterized by a very high, oil content, up to 60% of biomass weight. In order to study global network properties of seed metabolism, various methods were employed, like Flux Balance Analysis, Principal Component Analysis of the flux space and reaction deletion studies, which simulate the effect of gene knock-out experiments. The model successfully simulated seed growth during the stage of oil accumulation and provided insight, regarding certain aspects of network plasticity, with the emphasis given in lipid biosynthesis regulation.

show abstract

Section: Discussionmentioning

confidence: 99%

An in silico compartmentalized metabolic model of Brassica napus enables the systemic study of regulatory aspects of plant central metabolism

Pilalis

Chatziioannou

Thomasset

et al. 2011

Biotech & Bioengineering

View full text Add to dashboard Cite

show abstract

“…Initial conditions of the model were obtained from previous studies [ 14 15 ], as summarized in Table 3 . To estimate the model parameters in Table 4 , we performed iterative simulations using a genetic algorithm (GA) method in MATLAB with differential elimination of the Rosenfeld–Gröbner algorithm [ 16 ]. We then selected an optimal model parameter set by fitting the model simulation results to the experimental data for 50 µM cisplatin ( Table 5 ).…”

Section: Methodsmentioning

confidence: 99%

Minimal systems analysis of mitochondria-dependent apoptosis induced by cisplatin

Hong¹,

Hara²,

Kim

et al. 2016

Korean J Physiol Pharmacol

View full text Add to dashboard Cite

Recently, it was reported that the role of mitochondria-reactive oxygen species (ROS) generating pathway in cisplatin-induced apoptosis is remarkable. Since a variety of molecules are involved in the pathway, a comprehensive approach to delineate the biological interactions of the molecules is required. However, quantitative modeling of the mitochondria-ROS generating pathway based on experiment and systemic analysis using the model have not been attempted so far. Thus, we conducted experiments to measure the concentration changes of critical molecules associated with mitochondrial apoptosis in both human mesothelioma H2052 and their ρ0 cells lacking mitochondrial DNA (mtDNA). Based on the experiments, a novel mathematical model that can represent the essential dynamics of the mitochondrial apoptotic pathway induced by cisplatin was developed. The kinetic parameter values of the mathematical model were estimated from the experimental data. Then, we have investigated the dynamical properties of this model and predicted the apoptosis levels for various concentrations of cisplatin beyond the range of experiments. From parametric perturbation analysis, we further found that apoptosis will reach its saturation level beyond a certain critical cisplatin concentration.

show abstract

“…While trajectory matching is known to be statistically efficient [24] (the parameter estimates achieve a lower bound on the asymptotic variance) it can be computationally intractable for large networks. This often remains true even after one takes advantage of techniques such as differential elimination [25] for reducing the dimensionality of the system. Consequently, most ODE methods for network reconstruction employ a gradient-matching estimation scheme.…”

Section: Introductionmentioning

confidence: 99%

Network Reconstruction Using Nonparametric Additive ODE Models

Henderson

Michailidis

2014

PLoS ONE

View full text Add to dashboard Cite

Network representations of biological systems are widespread and reconstructing unknown networks from data is a focal problem for computational biologists. For example, the series of biochemical reactions in a metabolic pathway can be represented as a network, with nodes corresponding to metabolites and edges linking reactants to products. In a different context, regulatory relationships among genes are commonly represented as directed networks with edges pointing from influential genes to their targets. Reconstructing such networks from data is a challenging problem receiving much attention in the literature. There is a particular need for approaches tailored to time-series data and not reliant on direct intervention experiments, as the former are often more readily available. In this paper, we introduce an approach to reconstructing directed networks based on dynamic systems models. Our approach generalizes commonly used ODE models based on linear or nonlinear dynamics by extending the functional class for the functions involved from parametric to nonparametric models. Concomitantly we limit the complexity by imposing an additive structure on the estimated slope functions. Thus the submodel associated with each node is a sum of univariate functions. These univariate component functions form the basis for a novel coupling metric that we define in order to quantify the strength of proposed relationships and hence rank potential edges. We show the utility of the method by reconstructing networks using simulated data from computational models for the glycolytic pathway of Lactocaccus Lactis and a gene network regulating the pluripotency of mouse embryonic stem cells. For purposes of comparison, we also assess reconstruction performance using gene networks from the DREAM challenges. We compare our method to those that similarly rely on dynamic systems models and use the results to attempt to disentangle the distinct roles of linearity, sparsity, and derivative estimation.

show abstract

Parameter optimization by using differential elimination: a general approach for introducing constraints into objective functions

Cited by 6 publications

References 17 publications

An in silico compartmentalized metabolic model of Brassica napus enables the systemic study of regulatory aspects of plant central metabolism

An in silico compartmentalized metabolic model of Brassica napus enables the systemic study of regulatory aspects of plant central metabolism

Minimal systems analysis of mitochondria-dependent apoptosis induced by cisplatin

Network Reconstruction Using Nonparametric Additive ODE Models

Contact Info

Product

Resources

About