| BMC Systems Biology | |
| Toward modular biological models: defining analog modules based on referent physiological mechanisms | |
| C Anthony Hunt3  Glen EP Ropella2  Brenden K Petersen1  | |
| [1] UCSF/UCB Joint Graduate Group in Bioengineering, University of California, Berkeley, CA, USA;Tempus Dictum, Inc, Portland, OR, USA;Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA | |
| 关键词: Agent-based model; Mechanism; simulation; Modeling & Model reuse; Modularity; | |
| Others : 1159572 DOI : 10.1186/s12918-014-0095-1 |
|
| received in 2014-05-28, accepted in 2014-08-04, 发布年份 2014 | |
PDF
|
|
【 摘 要 】
Background
Currently, most biomedical models exist in isolation. It is often difficult to reuse or integrate models or their components, in part because they are not modular. Modular components allow the modeler to think more deeply about the role of the model and to more completely address a modeling project’s requirements. In particular, modularity facilitates component reuse and model integration for models with different use cases, including the ability to exchange modules during or between simulations. The heterogeneous nature of biology and vast range of wet-lab experimental platforms call for modular models designed to satisfy a variety of use cases. We argue that software analogs of biological mechanisms are reasonable candidates for modularization. Biomimetic software mechanisms comprised of physiomimetic mechanism modules offer benefits that are unique or especially important to multi-scale, biomedical modeling and simulation.
Results
We present a general, scientific method of modularizing mechanisms into reusable software components that we call physiomimetic mechanism modules (PMMs). PMMs utilize parametric containers that partition and expose state information into physiologically meaningful groupings. To demonstrate, we modularize four pharmacodynamic response mechanisms adapted from an in silico liver (ISL). We verified the modularization process by showing that drug clearance results from in silico experiments are identical before and after modularization. The modularized ISL achieves validation targets drawn from propranolol outflow profile data. In addition, an in silico hepatocyte culture (ISHC) is created. The ISHC uses the same PMMs and required no refactoring. The ISHC achieves validation targets drawn from propranolol intrinsic clearance data exhibiting considerable between-lab variability. The data used as validation targets for PMMs originate from both in vitro to in vivo experiments exhibiting large fold differences in time scale.
Conclusions
This report demonstrates the feasibility of PMMs and their usefulness across multiple model use cases. The pharmacodynamic response module developed here is robust to changes in model context and flexible in its ability to achieve validation targets in the face of considerable experimental uncertainty. Adopting the modularization methods presented here is expected to facilitate model reuse and integration, thereby accelerating the pace of biomedical research.
【 授权许可】
2014 Petersen et al.; licensee BioMed Central Ltd.
【 预 览 】
| Files | Size | Format | View |
|---|---|---|---|
| 20150409021250912.pdf | 2189KB | ||
| Figure 3. | 17KB | Image | |
| Figure 4. | 30KB | Image | |
| Figure 3. | 50KB | Image | |
| Figure 2. | 81KB | Image | |
| Figure 1. | 119KB | Image |
【 图 表 】
Figure 1.
Figure 2.
Figure 3.
Figure 4.
Figure 3.
【 参考文献 】
- [1]Hunt CA, Kennedy RC, Kim SH, Ropella GE: Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity. Wiley Interdiscip Rev Syst Biol Med 2013, 5:461-480.
- [2]Hartwell LH, Hopfield JJ, Leibler S, Murray AW: From molecular to modular cell biology. Nature 1999, 402:C47-C52.
- [3]Barba M, Dutoit R, Legrain C, Labedan B: Identifying reaction modules in metabolic pathways: bioinformatics deduction and experimental validation of a new putative route in purine catabolism. BMC Syst Biol 2013, 7(1):99. BioMed Central Full Text
- [4]McCullagh E, Farlow J, Fuller C, Girard J, Lipinski-Kruszka J, Lu D, Noriega T, Rollins G, Spitzer R, Todhunter M: Not all quiet on the noise front. Nat Chem Biol 2009, 5(10):699-704.
- [5]Reynolds JF, Acock B: Modularity and genericness in plant and ecosystem models. Ecol Modell 1997, 94(1):7-16.
- [6]Jones J, Keating B, Porter C: Approaches to modular model development. Agric Syst 2001, 70(2):421-433.
- [7]Iwata K, Onosato M, Teramoto K, Osaki S: A modeling and simulation architecture for virtual manufacturing systems. CIRP Ann Manuf Technol 1995, 44(1):399-402.
- [8]Zeigler BP, Praehofer H, Kim TG: Theory of modeling and simulation: integrating discrete event and continuous complex dynamic systems. Academic Press, San Diego, CA; 2000.
- [9]Zeigler BP: Hierarchical, modular discrete-event modeling in an object-oriented environment. Simulation 1987, 49(5):219-230.
- [10]Pidd M: Reusing simulation components: simulation software and model reuse: a polemic. In Proceedings of the 34thConference on Winter Simulation: Exploring new Frontiers. San Diego, CA: Winter Simulation Conference; 2002.
- [11]Kasputis S, Ng HC: Model composability: formulating a research thrust: composable simulations. In Proceedings of the 32ndConference on Winter Simulation. Orlando, FL: Society for Computer Simulation International; 2000.
- [12]Robinson S, Nance RE, Paul RJ, Pidd M, Taylor SJE: Simulation model reuse: definitions, benefits and obstacles. Simul Modell Pract Theory 2004, 12(7):479-494.
- [13]Darden L: Thinking again about biological mechanisms. Philos Sci 2008, 75(5):958-969.
- [14]Xu C, Li CY-T, Kong A-NT: Induction of phase I, II and III drug metabolism/transport by xenobiotics. Arch Pharm Res 2005, 28(3):249-268.
- [15]Guengerich FP: Common and uncommon cytochrome P450 reactions related to metabolism and chemical toxicity. Chem Res Toxicol 2001, 14(6):611-650.
- [16]Blinov ML, Ruebenacker O, Moraru II: Complexity and modularity of intracellular networks: a systematic approach for modelling and simulation. IET Syst Biol 2008, 2(5):363-368.
- [17]Mallavarapu A, Thomson M, Ullian B, Gunawardena J: Programming with models: modularity and abstraction provide powerful capabilities for systems biology. J R Soc Interface 2009, 6(32):257-270.
- [18]Mirschel S, Steinmetz K, Rempel M, Ginkel M: ProMot: modular modeling for systems biology. Bioinformatics 2009, 25(5):687-689.
- [19]Chandran D, Bergmann FT, Sauro HM: Computer-aided design of biological circuits using TinkerCell. Bioeng Bugs 2010, 1(4):274-281.
- [20]Snoep JL, Bruggeman F, Olivier BG, Westerhoff HV: Towards building the silicon cell: a modular approach. Biosystems 2006, 83(2):207-216.
- [21]An G, Mi Q, Dutta-Moscato J, Vodovotz Y: Agent-based models in translational systems biology. Wiley Interdiscip Rev Syst Biol Med 2009, 1(2):159-171.
- [22]Palsson S, Hickling TP, Bradshaw-Pierce EL, Zager M, Jooss K, Peter J, Spilker ME, Palsson BO, Vicini P: The development of a fully-integrated immune response model (FIRM) simulator of the immune response through integration of multiple subset models. BMC Syst Biol 2013, 7(1):95. BioMed Central Full Text
- [23]Liu G, Qutub AA, Vempati P, Gabhann FM, Popel AS: Module-based multiscale simulation of angiogenesis in skeletal muscle. Theor Biol Med Model 2011, 8(6):1-26.
- [24]Sheikh-Bahaei S, Hunt CA: Enabling clearance predictions to emerge from in silico actions of quasi-autonomous hepatocyte components. Drug Metab Dispos 2011, 39(10):1910-1920.
- [25]Douvin I: Abduction. [http://plato.stanford.edu/archives/spr2011/entries/abduction/] webciteIn The Stanford Encyclopedia of Philosophy Edited by Zalta EN. ᅟ. URL = http://plato.stanford.edu/archives/spr2011/entries/abduction/
- [26][http://plato.stanford.edu/archives/fall2013/entries/reasoning-analogy] webcite Bartha P: Analogy and Analogical Reasoning. In The Stanford Encyclopedia of Philosophy, Fall 2013 Edition. Edited by Zalta EN. URL = .
- [27]Hunt CA, Ropella GE, Lam TN, Tang J, Kim SH, Engelberg JA, Sheikh-Bahaei S: At the biological modeling and simulation frontier. Pharmacol Res 2009, 26(11):2369-2400.
- [28]Goresky CA: Kinetic interpretation of hepatic multiple-indicator dilution studies. Am J Physiol Gastrointest Liver Physiol 1983, 245(1):G1-G12.
- [29]Varin F, Huet P-M: Hepatic microcirculation in the perfused cirrhotic rat liver. J Clin Invest 1985, 76(5):1904.
- [30]Hung DY, Chang P, Weiss M, Roberts MS: Structure-hepatic disposition relationships for cationic drugs in isolated perfused rat livers: transmembrane exchange and cytoplasmic binding process. J Pharmacol Exp Ther 2001, 297(2):780-789.
- [31]Goresky CA: A linear method for determining liver sinusoidal and extravascular volumes. Am J Physiol 1963, 204(4):626-640.
- [32]Chou C, Evans AM, Fornasini G, Rowland M: Relationship between lipophilicity and hepatic dispersion and distribution for a homologous series of barbiturates in the isolated perfused in situ rat liver. Drug Metab Dispos 1993, 21(5):933-938.
- [33]Griffin SJ, Houston JB: Prediction of in vitro intrinsic clearance from hepatocytes: comparison of suspensions and monolayer cultures. Drug Metab Dispos 2005, 33(1):115-120.
- [34]Naritomi Y, Terashita S, Kagayama A, Sugiyama Y: Utility of hepatocytes in predicting drug metabolism: comparison of hepatic intrinsic clearance in rats and humans in vivo and in vitro. Drug Metab Dispos 2003, 31(5):580-588.
- [35]Brian Houston J: Utility of in vitro drug metabolism data in predicting in vivo metabolic clearance. Biochem Pharmacol 1994, 47(9):1469-1479.
- [36]Rane A, Wilkinson G, Shand D: Prediction of hepatic extraction ratio from in vitro measurement of intrinsic clearance. J Pharmacol Exp Ther 1977, 200(2):420-424.
- [37]Park S, Kim SH, Ropella GE, Roberts MS, Hunt CA: Tracing multiscale mechanisms of drug disposition in normal and diseased livers. J Pharmacol Exp Ther 2010, 334(1):124-136.
- [38]Zhu BT: On the general mechanism of selective induction of cytochrome P450 enzymes by chemicals: some theoretical considerations. Expert Opin Drug Metab Toxicol 2010, 6(4):483-494.
- [39]Winther RG: The map analogy.Trans Inst Brit Geogr 2014, in press.
- [40]Yilmaz L, Ören TI: Discrete-event multimodels and their agent-supported activation and update. Proceedings of the Agent-Directed Simulation Symposium of the Spring Simulation Multiconference (SMC’05) 2005.
- [41]Bierman G, Hicks M, Sewell P, Stoyle G: Formalizing dynamic software updating. Proceedings of the Second International Workshop on Unanticipated Software Evolution (USE) 2003.
- [42]Ropella GE, Kennedy RC, Hunt CA: Falsifying an enzyme induction mechanism within a validated, multiscale liver model. Int J Agent Technol Syst 2012, 4(3):1-14.
- [43]Jungermann K, Kietzmann T: Oxygen: modulator of metabolic zonation and disease of the liver. Hepatology 2000, 31(2):255-260.
- [44]Beier K, Völkl A, Metzger C, Mayer D, Bannasch P, Fahimi H: Hepatic zonation of the induction of cytochrome P450 IVA, peroxisomal lipid beta-oxidation enzymes and peroxisome proliferation in rats treated with dehydroepiandrosterone (DHEA). Evidence of distinct zonal and sex-specific differences. Carcinogenesis 1997, 18(8):1491-1498.
- [45]Allen JW, Bhatia SN: Formation of steady-state oxygen gradients in vitro: Application to liver zonation. Biotechnol Bioeng 2003, 82(3):253-262.
- [46]Luke S, Cioffi-Revilla C, Panait L, Sullivan K: Mason: A new multi-agent simulation toolkit. Proceedings of the 2004 SwarmFest Workshop 2004.
- [47]Kim SH, Park S, Ropella GE, Hunt CA: Agent-based simulation of drug disposition in cirrhotic liver. In Proceedings of the 2010 Spring Simulation Multiconference. Society for Computer Simulation International, Orlando, FL; 2010.
- [48]Lam TN, Hunt CA: Discovering plausible mechanistic details of hepatic drug interactions. Drug Metab Dispos 2009, 37(1):237-246.
- [49]Yan L, Ropella GE, Park S, Roberts MS, Hunt CA: Modeling and simulation of hepatic drug disposition using a physiologically based, multi-agent in silico liver. Pharmacol Res 2008, 25(5):1023-1036.
- [50]Yan L, Sheihk-Bahaei S, Park S, Ropella GE, Hunt CA: Predictions of hepatic disposition properties using a mechanistically realistic, physiologically based model. Drug Metab Dispos 2008, 36(4):759-768.
- [51]Mizuno N, Niwa T, Yotsumoto Y, Sugiyama Y: Impact of drug transporter studies on drug discovery and development. Pharmacol Rev 2003, 55(3):425-461.
- [52]Lavé T, Dupin S, Schmitt C, Chou R, Jaeck D, Coassolo P: Integration of in vitro data into allometric scaling to predict hepatic metabolic clearance in man: application to 10 extensively metabolized drugs. J Pharmacol Sci 1997, 86(5):584-590.
- [53]Lau YY, Sapidou E, Cui X, White RE, Cheng K-C: Development of a novel in vitro model to predict hepatic clearance using fresh, cryopreserved, and sandwich-cultured hepatocytes. Drug Metab Dispos 2002, 30(12):1446-1454.
- [54]R: A language and environment for statistical computing R Foundation for Statistical Computing 2012.
- [55]Hunt CA, Ropella GE, ning Lam T, Gewitz AD: Relational grounding facilitates development of scientifically useful multiscale models. Theor Biol Med Model 2011, 8(35):1-22.
PDF