Studies of changes in concentration under different perturbation scenarios.

<p>In (A) time-series concentration values are calculated over a simulated time span of 5 days subject to a 90% decrease in individual enzyme velocities. A 90% knockdown of AdoMetDC enzyme concentration and a 90% prozyme knockdown were found to follow a similar pattern of dynamics, and only prozyme inhibition is shown. In (B) concentration values at the end of the simulated time span (5 days) are calculated subject to various degrees of knockdown (KD) for individual enzymes. In both figures, the percentage of concentration under perturbed () and normal () conditions is plotted. In all cases, the maximum velocity of each enzyme is a time-dependent variable subject to specific inhibition within 24 hours of simulation.</p

Orn dynamics over 2 days after ODC activity depression.

<p>During the simulation, the maximum velocity of ODC was modelled as a time-independent constant by multiplying the normal value by the percentage amount.</p

Time-series simulation of AdoMetDC inhibition on polyamine levels compared with observed values.

<p><i>Lines without symbols</i>, model predictions; <i>lines with symbols</i>, experimental observations from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053734#pone.0053734-Willert1" target="_blank">[16]</a> for (A) to (C) and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053734#pone.0053734-Bitonti1" target="_blank">[17]</a> for (D). In (A) to (C), during knockdown (KD) simulations, total AdoMetDC concentration ([]) was modelled as a time-dependent variable with equal to 0.0004 to represent the 70% activity down-regulation within 2 days of induction; during knockout (KO) simulations, the factor representing the percent of the complex AdoMetDC|prozyme taking up the total enzyme AdoMetDC is set to zero to represent full prozyme removal. In (D), MDL effects on Put and Spd dynamics were plotted. During the simulation, total enzyme concentration of AdoMetDC was modelled using a exponential decay function with set to 0.07 to mimic a 98% knockdown within 1 hour of induction as specified experimentally. Error bars are presented where appropriate data was available in the original papers.</p

A detailed graphical representation of total trypanothione metabolism.

<p>Edges represent chemical conversions between model components with arrows indicating reaction directionality. Metabolites and reactions constituting the polyamine biosynthetic pathway that are considered in this model are emphasised with bold type, with time-variant metabolites shown in green and constant metabolites shown in pink. Enzymes catalysing each active elementary step in the pathway are denoted with blue boxes. The remaining modules of the network shown in grey are not modelled but help gaining an overall picture of the metabolism. Abbreviations of polyamine metabolites: Met, methionine; AdoMet, S-adenosylmethionine; dAdoMet, decarboxylated AdoMet; MTA, methylthioadenosine; AdoHcy, S-adenosylhomocysteine; Orn, ornithine; Put, putrescine; Spd, spermidine; , total trypanothione; , exogenous methionine; exogenous ornithine. Abbreviations of intra-cellular polyamine enzymes: MetPt, Met uptake enzyme; MAT, AdoMet synthase; AHS, methyltransferase; AdoMetDC, AdoMet decarboxylase; MetRcy, Met recycling enzyme; OrnPt, Orn uptake enzyme; ODC, Orn decarboxylase; SpdS, Spd synthase; TSHSyn, synthesis catalyst; TSHCpt, consumption catalyst.</p

Time-series simulation of SpdS inhibition on polyamine levels compared with observed values.

<p><i>Lines without symbols</i>, model predictions; <i>lines with symbols</i>, experimental observations from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053734#pone.0053734-Xiao1" target="_blank">[14]</a>. The maximum velocity of SpdS was modelled as a time-dependent variable with equal to 0.0016. Error bars are presented where appropriate data was available in the original papers.</p

Basal condition of polyamine concentrations.

<p>Basal condition of polyamine concentrations.</p

Studies of combination chemotherapeutic regimens.

<p>Percentage of concentration under perturbed (, over a simulated time span of 5 days) and normal () conditions. In individual model simulations (A) and (B), a 10% enzyme knockdown (KD) of ODC and prozyme is applied in conjunction with down-regulation of other key pathway enzymes and the simulation results from individual and combined perturbations are compared. In (C) and (D), the inhibitory effects on were examined for combinations of medium to strong depression of prozyme and TSHSyn, respectively, with different levels of knockdowns of other enzymes. In all cases, the maximum velocity of each enzyme is a time-dependent variable subject to specific inhibition within 24 hours.</p

Time-series simulation of ODC inhibition on polyamine levels compared with observed values.

<p><i>Lines without symbols</i>, model predictions; <i>lines with symbols</i>, experimental observations from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053734#pone.0053734-Xiao1" target="_blank">[14]</a>. The maximum velocity of ODC was modelled as a time-dependent variable during the simulation with equal to 0.0016, where the ODC activity was decreased by 90% within 24 hours of RNAi induction. Error bars are presented where appropriate data was available in the original papers.</p

Differential equations for the time-dependent variables included in the model.

<p>Differential equations for the time-dependent variables included in the model.</p

New Analytical Tool for the Detection of Ractopamine Abuse in Goat Skeletal Muscle by Potential Gene Expression Biomarkers

In this study, quantification of mRNA gene expression was examined as biomarkers to detect ractopamaine abuse and ractopamaine residues in cashmere goats. It was focused on the identification of potential gene expression biomarkers and describing the coreletionship between gene expression and residue level by 58 animals for 49 days. The results showed that administration periods and residue levels significantly influenced mRNA expressions of the β₂-adrenergic receptor (β₂AR), the enzymes PRKACB, ADCY3, ATP1A3, ATP2A3, PTH, and MYLK, and the immune factors IL-1β and TNF-α. Statistical analysis like principal components analysis (PCA), hierarchical cluster analysis (HCA), and discriminant analysis (DA) showed that these genes can serve as potential biomarkers for ractopamine in skeletal muscle and that they are also suitable for describing different residue levels separately