Mathematical models of lipoprotein metabolism and kinetics: current status and future perspective

References

• Hübner K, Sahle S, Kummer U. Applications and trends in systems biology in biochemistry. FEBS J. 278(16), 2767–2857 (2012). ▪ Reviews approximately 400 articles in which laboratory experiments and mathematical models have been combined synergistically to study complex biological systems during the last 11 years.
   Google Scholar
• de Graaf AA, van Schalkwijk DB. Computational models for analyzing lipoprotein profiles. Clin. Lipidol. 6(1), 25–33 (2011).
   Google Scholar
• Parhofer KG, Barrett PH. Thematic review series: patient-oriented research. What we have learned about VLDL and LDL metabolism from human kinetics studies. J. Lipid Res. 47(8), 1620–1630 (2006).
   Google Scholar
• Barrett PH, Chan DC, Watts GF. Thematic review series: patient-oriented research. Design and analysis of lipoprotein tracer kinetics studies in humans. J. Lipid Res. 47(8), 1607–1619 (2006).
   Google Scholar
• Rashid S, Patterson BW, Lewis GF. Thematic review series: patient-oriented research. What have we learned about HDL metabolism from kinetics studies in humans? J. Lipid Res. 47(8), 1631–1642 (2006).
   Google Scholar
• Larach DB, deGoma EM, Rader DJ. Targeting high density lipoproteins in the prevention of cardiovascular disease? Curr. Cardiol. Rep. 14, 684–691 (2012).
   Google Scholar
• Schwartz GG, Olsson AG, Ballantyne CM et al. Effects of dalcetrapib in patients with a recent acute coronary syndrome. N. Engl. J. Med. 367(22), 2089–2099 (2012).
   Google Scholar
• Shen BW, Scanu AM, Kézdy FJ. Structure of human serum lipoproteins inferred from compositional analysis. Proc. Natl Acad. Sci. USA 74(3), 837–841 (1977).
   Google Scholar
• Durrant JD, McCammon JA. Molecular dynamics simulations and drug discovery. BMC Biol. 9, 71 (2011).
   Google Scholar
• Vuorela T, Catte A, Niemelä PS et al. Role of lipids in spheroidal high density lipoproteins. PLoS Comput. Biol. 6(10), e1000964 (2010).
   Google Scholar
• Wu Z, Gogonea V, Lee X et al. The low resolution structure of apoA1 in spherical high density lipoprotein revealed by small angle neutron scattering. J. Biol. Chem. 286(14), 12495–12508 (2011).
   Google Scholar
• Silva RA, Huang R, Morris J et al. Structure of apolipoprotein A-I in spherical high density lipoproteins of different sizes. Proc. Natl Acad. Sci. USA 105(34), 12176–12181 (2008).
   Google Scholar
• Huang R, Silva RA, Jerome WG et al. Apolipoprotein A-I structural organization in high-density lipoproteins isolated from human plasma. Nat. Struct. Mol. Biol. 18(4), 416–422 (2011).
   Google Scholar
• Yetukuri L, Huopaniemi I, Koivuniemi A et al. High density lipoprotein structural changes and drug response in lipidomic profiles following the long-term fenofibrate therapy in the FIELD substudy. PLoS ONE 6(8), e23589 (2011).
   Google Scholar
• Mazer NA, Giulianini F, Paynter NP, Jordan P, Mora S. A comparison of the theoretical relationship between HDL size and the ratio of HDL cholesterol to apolipoprotein A-I with experimental results from the women’s health study. Clin. Chem. 59(6), 949–958 (2013). ▪▪ Details a model relating the size of HDL particles to the number of apoAI molecules on it. This relationship could be used in models of HDL metabolism to derive the rate of regeneration of pre‑b HDL particles.
   Google Scholar
• Kontush A, Chapman MJ. Lipidomics as a tool for the study of lipoprotein metabolism. Curr. Atheroscler. Rep. 12(3), 194–201 (2010).
   Google Scholar
• Meikle PJ, Christopher MJ. Lipidomics is providing new insight into the metabolic syndrome and its sequelae. Curr. Opin. Lipidol. 22(3), 210–215 (2011).
   Google Scholar
• Kiebish MA, Bell R, Yang K et al. Dynamic simulation of cardiolipin remodeling: greasing the wheels for an interpretative approach to lipidomics. J. Lipid Res. 51(8), 2153–2170 (2010).
   Google Scholar
• Sysi-Aho M, Vehtari A, Velagapudi VR et al. Exploring the lipoprotein composition using Bayesian regression on serum lipidomic profiles. Bioinformatics 23(13), 519–528 (2007).
   Google Scholar
• Puri R, Duong M, Uno K, Kataoka Y, Nicholls SJ. The emerging role of plasma lipidomics in cardiovascular drug discovery. Expert Opin. Drug Discov. 7(1), 63–72 (2012).
   Google Scholar
• Bergheanu SC, Reijmers T, Zwinderman AH et al. Lipidomic approach to evaluate rosuvastatin and atorvastatin at various dosages: investigating differential effects among statins. Curr. Med. Res. Opin. 24(9), 2477–2487 (2008).
   Google Scholar
• van Bochove K, van Schalkwijk DB, Parnell LD et al. Clustering by plasma lipoprotein profile reveals two distinct subgroups with positive lipid response to fenofibrate therapy. PLoS ONE 7(6), e38072 (2012). ▪ Subgroup analysis of clinical data using a clustering method and a model‑based approach for revealing mechanistic differences between subgroups of patients.
   Google Scholar
• Chétiveaux M, Ouguerram K, Zair Y et al. New model for kinetic studies of HDL metabolism in humans. Eur. J. Clin. Invest. 34(4), 262–267 (2004). ▪ Offers insight into the transfer rates of apoAI between the a‑HDL and pre‑b pools.
   Google Scholar
• Shah A, Rader DJ, Millar JS. The effect of PPAR-a agonism on apolipoprotein metabolism in humans. Atherosclerosis 1, 35–40 (2010).
   Google Scholar
• Schwartz CC, Berman M, Vlahcevic ZR, Swell L. Multicompartmental analysis of cholesterol metabolism in man. Quantitative kinetic evaluation of precursor sources and turnover of high density lipoprotein cholesterol esters. J. Clin. Invest. 70(4), 863–876 (1982).
   Google Scholar
• Schwartz CC, VandenBroek JM, Cooper PS. Lipoprotein cholesteryl ester production, transfer, and output in vivo in humans. J. Lipid Res. 45(9), 1594–1607 (2004).
   Google Scholar
• Ouguerram K, Krempf M, Maugeais C, Maugère P, Darmaun D, Magot T. A new labeling approach using stable isotopes to study in vivo plasma cholesterol metabolism in humans. Metabolism 51(1), 5–11 (2002). ▪ Offers insight into cholesterol ester fluxes into plasma and its exchange between lipoprotein classes.
   Google Scholar
• Turner S, Voogt J, Davidson M et al. Measurement of reverse cholesterol transport pathways in humans: in vivo rates of free cholesterol efflux, esterification, and excretion. J. Am. Heart Assoc. 4, e001826 (2012).
   Google Scholar
• Funahashi A, Tanimura N, Morohashi M, Kitano H. CellDesigner: a process diagram editor for gene-regulatory and biochemical networks. BIOSILICO 1, 159–162 (2003).
   Google Scholar
• Mendes P, Hoops S, Sahle S, Gauges R, Dada J, Kummer U. Computational modeling of biochemical networks using COPASI. Methods Mol. Biol. 500, 17–59 (2009). ▪▪ Powerful user‑friendly and freely available software package for modeling, simulation and analysis of biochemical systems.
   Google Scholar
• Barrett PH, Bell BM, Cobelli C et al. SAAM II: simulation, analysis, and modeling software for tracer and pharmacokinetic studies. Metabolism 47, 484–492 (1998).
   Google Scholar
• Potter LK, Sprecher DL, Walker MC, Tobin FL. Mechanism of inhibition defines CETP activity: a mathematical model for CETP in vitro. J. Lipid Res. 50(11), 2222–2234 (2009). ▪ Reveals that CETP mediates lipid transfer via a shuttle mechanism.
   Google Scholar
• Knoblauch H, Schuster H, Luft FC, Reich JG. A pathway model of lipid metabolism to predict the effect of genetic variability on lipid levels. J. Mol. Med. 78, 507–515 (2000).
   Google Scholar
• Hübner K, Schwager T, Winkler K, Reich JG, Holzhütter HG. Computational lipidology: predicting lipoprotein density profiles in human blood plasma. PLoS Comput. Biol. 4, e1000079 (2008). ▪▪ Offers the analysis of lipoprotein profiles in high resolution for the identification of potential new clinical markers for cardiovascular disease risk and of potential mechanisms underlying dyslipidemia.
   Google Scholar
• van Schalkwijk DB, van Ommen B, Freidig AP, van der Greef J, de Graaf AA. Diagnostic markers based on a computational model of lipoprotein metabolism. J. Clin. Bioinforma. 1(1), 29 (2011). ▪ Demonstrates how a mathematical model can be used to derive potential new candidate risk markers for cardiovascular disease.
   Google Scholar
• McAuley MT, Wilkinson DJ, Jones JJ, Kirkwood TB. A whole-body mathematical model of cholesterol metabolism and its ageassociated dysregulation. BMC Syst. Biol. 6, 130 (2012).
   Google Scholar
• van de Pas NC, Woutersen RA, van Ommen B, Rietjens IM, de Graaf AA. A physiologically based in silico kinetic model predicting plasma cholesterol concentrations in humans. J. Lipid Res. 53(12), 2734–2746 (2012).
   Google Scholar
• Lu J, Nanjee MN, Brinton EA, Hübner K, Mazer NA. ABCA1 up-regulation but not CETP inhibition is predicted to increase the reverse cholesterol transport (RCT) input rate: a simulation study of HDL-C raising targets using a novel in-silico model of lipoprotein metabolism and kinetics. Arterioscler. Thromb. Vasc. Biol. Abstract 443 (2013).
   Google Scholar
• Lu J, Hübner K, Brinton EA, Nanjee MN, Mazer NA. Association between reverse cholesterol transport rate and lipoprotein biomarkers using a novel in silico model of lipoprotein metabolism and kinetics. Eur. Atheroscler. Soc. Congr. Abstract A-547-0010-01108 (2013).
   Google Scholar
• McIntosh JEA, McIntosh RP. Mathematical Modeling and Computers in Endocrinology. Springer Verlag, Berlin, Germany (1980).
   Google Scholar