Computational modelling of the Δ4 and Δ5 adrenal steroidogenic pathways provides insight into hypocortisolism.

This study demonstrates the application of a mathematical steroidogenic model, constructed with individual in vitro enzyme characterisations, to simulate in vivo steroidogenesis in a diseased state. This modelling approach was applied to the South African Angora goat, that suffers from hypocortisolism caused by altered adrenal function. These animals are extremely vulnerable to cold stress, leading to substantial monetary loss in the mohair industry. The Angora goat has increased CYP17A1 17,20-lyase enzyme activity in comparison with hardy livestock species. Determining the effect of this altered adrenal function on adrenal steroidogenesis during a cold stress response is difficult. We developed a model describing adrenal steroidogenesis under control conditions, and under altered steroidogenic conditions where the animal suffers from hypocortisolism. The model is parameterised with experimental data from in vitro enzyme characterisations of a hardy control species. The increased 17,20-lyase activity of the Angora goat CYP17A1 enzyme was subsequently incorporated into the model and the response to physiological stress is simulated under both control and altered adrenal steroidogenic conditions.

Cytochrome b5 augments 3ß-hydroxysteroid dehydrogenase/Δ5-Δ4 isomerase activity.

During adrenal steroidogenesis the competition between 3ß-hydroxysteroid dehydrogenase/Δ(5)-Δ(4) isomerase (3ßHSD) and cytochrome P450 17α-hydroxylase/17,20 lyase (CYP17A1) for Δ(5) steroid intermediates greatly influences steroidogenic output. Cytochrome-b(5) (Cyt-b(5)), a small electron transfer hemoprotein, known to augment the lyase activity of CYP17A1, has been shown to alter the steroidogenic outcome of this competition. In this study, the influence of Cyt-b(5) on 3ßHSD activity was investigated. In COS-1 cells, Cyt-b(5) was shown to significantly increase the activity of both caprine and ovine 3ßHSD towards pregnenolone, 17-OH pregnenolone and dehydroepiandrosterone in a substrate and species specific manner. Furthermore, kinetic studies revealed Cyt-b(5) to have no influence on the K(m) values while significantly increasing the V(max) values of ovine 3ßHSD for all its respective substrates. In addition, the activity of ovine 3ßHSD in microsomal preparations was significantly influenced by the addition of either purified Cyt-b(5) or anti-Cyt-b(5) IgG. The results presented in this study indicate that Cyt-b(5) augments 3ßHSD activity and represents the first documentation of such augmentation in any species.

16α-hydroxyprogesterone: origin, biosynthesis and receptor interaction.

The metabolism of progesterone (PROG) by cytochrome P450 17α-hydroxylase/17,20-lyase (CYP17A1) results in the formation of both 17α-hydroxyprogesterone (17-OHPROG) and 16α-hydroxyprogesterone (16-OHPROG) in humans. Unlike 17-OHPROG, 16-OHPROG is not metabolised further in steroidogenic tissue. While this metabolite can be readily detected in serum and urine, its physiological role remains unclear. This paper reviews the production of 16-OHPROG by human CYP17A1 by providing insight into the catalysis of PROG by CYP17A1 and highlights the role of Ala105 in the 16α-hydroxylation reaction. As 16-OHPROG has been putatively linked to reproductive function, we investigated the interaction of this steroid metabolite with both isoforms of the human progesterone receptor (hPR). We show for the first time that 16-OHPROG can bind to both hPR-A and hPR-B and act as an agonist for both receptors.

Restriction point control of the mammalian cell cycle via the cyclin E/Cdk2:p27 complex.

Numerous top-down kinetic models have been constructed to describe the cell cycle. These models have typically been constructed, validated and analyzed using model species (molecular intermediates and proteins) and phenotypic observations, and therefore do not focus on the individual model processes (reaction steps). We have developed a method to: (a) quantify the importance of each of the reaction steps in a kinetic model for the positioning of a switch point [i.e. the restriction point (RP)]; (b) relate this control of reaction steps to their effects on molecular species, using sensitivity and co-control analysis; and thereby (c) go beyond a correlation towards a causal relationship between molecular species and effects. The method is generic and can be applied to responses of any type, but is most useful for the analysis of dynamic and emergent responses such as switch points in the cell cycle. The strength of the analysis is illustrated for an existing mammalian cell cycle model focusing on the RP [Novak B, Tyson J (2004) J Theor Biol230, 563-579]. The reactions in the model with the highest RP control were those involved in: (a) the interplay between retinoblastoma protein and E2F transcription factor; (b) those synthesizing the delayed response genes and cyclin D/Cdk4 in response to growth signals; (c) the E2F-dependent cyclin E/Cdk2 synthesis reaction; as well as (d) p27 formation reactions. Nine of the 23 intermediates were shown to have a good correlation between their concentration control and RP control. Sensitivity and co-control analysis indicated that the strongest control of the RP is mediated via the cyclin E/Cdk2:p27 complex concentration. Any perturbation of the RP could be related to a change in the concentration of this complex; apparent effects of other molecular species were indirect and always worked through cyclin E/Cdk2:p27, indicating a causal relationship between this complex and the positioning of the RP.

Systems biology towards life in silico: mathematics of the control of living cells.

Systems Biology is the science that aims to understand how biological function absent from macromolecules in isolation, arises when they are components of their system. Dedicated to the memory of Reinhart Heinrich, this paper discusses the origin and evolution of the new part of systems biology that relates to metabolic and signal-transduction pathways and extends mathematical biology so as to address postgenomic experimental reality. Various approaches to modeling the dynamics generated by metabolic and signal-transduction pathways are compared. The silicon cell approach aims to describe the intracellular network of interest precisely, by numerically integrating the precise rate equations that characterize the ways macromolecules' interact with each other. The non-equilibrium thermodynamic or 'lin-log' approach approximates the enzyme rate equations in terms of linear functions of the logarithms of the concentrations. Biochemical Systems Analysis approximates in terms of power laws. Importantly all these approaches link system behavior to molecular interaction properties. The latter two do this less precisely but enable analytical solutions. By limiting the questions asked, to optimal flux patterns, or to control of fluxes and concentrations around the (patho)physiological state, Flux Balance Analysis and Metabolic/Hierarchical Control Analysis again enable analytical solutions. Both the silicon cell approach and Metabolic/Hierarchical Control Analysis are able to highlight where and how system function derives from molecular interactions. The latter approach has also discovered a set of fundamental principles underlying the control of biological systems. The new law that relates concentration control to control by time is illustrated for an important signal transduction pathway, i.e. nuclear hormone receptor signaling such as relevant to bone formation. It is envisaged that there is much more Mathematical Biology to be discovered in the area between molecules and Life.

Data and model integration using JWS Online.

Systems Biology requires a tight integration of experimental data and detailed computer models to obtain a quantitative understanding of the system under study. To facilitate this integration a standardization of data and model representation and storage is important. We illustrate here such an integration using JWS Online, the modeling tool developed in our group. We follow the approach of the Silicon Cell project for the construction and validation of kinetic models and discuss some issues with respect to storage of experimental data and models. The majority of the published kinetic models for biological systems have been developed for metabolic networks and this will be our focus in this manuscript. It is not our aim to present here an all encompassing method for data and model integration, but rather to present our work on this topic to start a discussion in which the different initiatives, methods and tools can be compared.