Computational Systems Biochemistry Group

Lipid transport in blood plasma is mediated by lipoproteins whose concentrations are documented in lipoprotein profiles. They are used to clinically indicate disorders in lipid metabolism. Due to experimental limitations, lipoprotein profiles represent distinct lipoprotein density classes obtained by ultracentrifugation from blood plasma.

However, a density class comprises numerous lipoprotein complexes having similiar densities but differing in their protein and lipid content. Moreover, molecular processes involved in the synthesis, uptake and remodeling of lipoproteins act on individual lipoprotein particles. Alterations in the kinetics of these processes, e.g. due to inherited protein defects or varying diet, lead to temporal or permanent changes in the amount of individual lipoproteins, which is reflected in changes of the density profile. To analyze the interrelationship between the kinetics of individual biochemical processes and the dynamics of the whole lipoprotein population in a causative and quantitative manner, mathematical models are needed.

Previous modeling approaches ever published for the lipoprotein metabolism have considered lipoprotein density classes as dynamic variables of the system whose time-evolution is governed by forming and degrading processes. Traditionally, compartment models have been widely used to describe the dynamics of the lipoprotein metabolism. The transition rates between lipoprotein density classes (so-called compartments) are usually determined by radioactive or stable isotope tracer experiments (Barrett, J Lipid Res 2006).

Compartment Models

Compartmental models may provide a useful phenomenological description of the lipoprotein dynamics. However, compartment models have some serious limitations: By taking density classes as system variables

Heterogeneity of lipoproteins is neglected and
Transition rates between compartments lack in adequately describing the underlying physiological kinetic processes. More precisely, VLDL is not transformed into IDL by just one process rather as the result of several reactions. In addition, individual processes such as the transfer of cholesterol via the reverse cholesterol transport are not addressed in detail.
Modeling of changes in the level of lipoprotein classes reflects the current experimental state of the art, however, it provides only a faint insight into the dynamics taking place in the full space of individual lipoprotein complexes.
Depending on the available kinetic data, such compartment models have focused on specific parts of the lipoprotein metabolism, e.g. the metabolism of HDL based on kinetic measurements with labeled apoA-I and apoA-II or the metabolism of LDL and VLDL sub-fractions based on kinetic measurements with labeled apoB-100. Knoblauch and his colleagues have provided a more comprehensive model without using tracer experiments, however, again they formulated lipoproteins as distinct density classes (Knoblauch, J Mol Med, 2000).

Our Approach

We have developed a novel modeling approach for the lipoprotein metabolism enabling the calculation of lipoprotein profiles based on individual lipoprotein complexes without a priori density classification. In our modeling approach, the number of dynamic variables is basically given by the number of different lipoprotein complexes that can be formed from a given number of apolipoproteins and lipids (Hübner, PLoS Comput Biol, 2008).

Researchers

Katrin Hübner

References

Barrett PH, Chan DC, Watts GF. (2006) Thematic review series: Patient-Oriented Research. Design and analysis of lipoprotein tracer kinetics studies in humans. J Lipid Res., 47(8):1607-19. [PubMed]

Knoblauch H, Schuster H, Luft FC, Reich J. (2000) A pathway model of lipid metabolism to predict the effect of genetic variability on lipid levels. J Mol Med., 78(9):507-15. [PubMed]

Hübner K, Schwager T, Winkler K, Reich JG, Holzhütter HG. (2008) Computational lipidology: predicting lipoprotein density profiles in human blood plasma. PLoS Comput Biol., 23;4(5):e1000079. [PubMed]

		Modeling the Lipoprotein Metabolism: Computation of the Lipoprotein Distribution in Blood Plasma
Home Research VirtualLiver People Publications Software Positions Events Contact		Lipid transport in blood plasma is mediated by lipoproteins whose concentrations are documented in lipoprotein profiles. They are used to clinically indicate disorders in lipid metabolism. Due to experimental limitations, lipoprotein profiles represent distinct lipoprotein density classes obtained by ultracentrifugation from blood plasma. However, a density class comprises numerous lipoprotein complexes having similiar densities but differing in their protein and lipid content. Moreover, molecular processes involved in the synthesis, uptake and remodeling of lipoproteins act on individual lipoprotein particles. Alterations in the kinetics of these processes, e.g. due to inherited protein defects or varying diet, lead to temporal or permanent changes in the amount of individual lipoproteins, which is reflected in changes of the density profile. To analyze the interrelationship between the kinetics of individual biochemical processes and the dynamics of the whole lipoprotein population in a causative and quantitative manner, mathematical models are needed. Previous modeling approaches ever published for the lipoprotein metabolism have considered lipoprotein density classes as dynamic variables of the system whose time-evolution is governed by forming and degrading processes. Traditionally, compartment models have been widely used to describe the dynamics of the lipoprotein metabolism. The transition rates between lipoprotein density classes (so-called compartments) are usually determined by radioactive or stable isotope tracer experiments (Barrett, J Lipid Res 2006). Compartment Models Compartmental models may provide a useful phenomenological description of the lipoprotein dynamics. However, compartment models have some serious limitations: By taking density classes as system variables Heterogeneity of lipoproteins is neglected and Transition rates between compartments lack in adequately describing the underlying physiological kinetic processes. More precisely, VLDL is not transformed into IDL by just one process rather as the result of several reactions. In addition, individual processes such as the transfer of cholesterol via the reverse cholesterol transport are not addressed in detail. Modeling of changes in the level of lipoprotein classes reflects the current experimental state of the art, however, it provides only a faint insight into the dynamics taking place in the full space of individual lipoprotein complexes. Depending on the available kinetic data, such compartment models have focused on specific parts of the lipoprotein metabolism, e.g. the metabolism of HDL based on kinetic measurements with labeled apoA-I and apoA-II or the metabolism of LDL and VLDL sub-fractions based on kinetic measurements with labeled apoB-100. Knoblauch and his colleagues have provided a more comprehensive model without using tracer experiments, however, again they formulated lipoproteins as distinct density classes (Knoblauch, J Mol Med, 2000). Our Approach We have developed a novel modeling approach for the lipoprotein metabolism enabling the calculation of lipoprotein profiles based on individual lipoprotein complexes without a priori density classification. In our modeling approach, the number of dynamic variables is basically given by the number of different lipoprotein complexes that can be formed from a given number of apolipoproteins and lipids (Hübner, PLoS Comput Biol, 2008). Researchers Katrin Hübner References Barrett PH, Chan DC, Watts GF. (2006) Thematic review series: Patient-Oriented Research. Design and analysis of lipoprotein tracer kinetics studies in humans. J Lipid Res., 47(8):1607-19. [PubMed] Knoblauch H, Schuster H, Luft FC, Reich J. (2000) A pathway model of lipid metabolism to predict the effect of genetic variability on lipid levels. J Mol Med., 78(9):507-15. [PubMed] Hübner K, Schwager T, Winkler K, Reich JG, Holzhütter HG. (2008) Computational lipidology: predicting lipoprotein density profiles in human blood plasma. PLoS Comput Biol., 23;4(5):e1000079. [PubMed]