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Volumn 11, Issue 5, 2015, Pages

Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models

(54)  Karr, Jonathan R a,ad   Williams, Alex H b   Zucker, Jeremy D c   Raue, Andreas d,ae   Steiert, Bernhard d   Timmer, Jens d   Kreutz, Clemens d   Hu, Yucheng e   Baron, Michael f   Bryson, Kevin f   Barker, Brandon g,i   Bogart, Elijah h   Wang, Yiping g,i   Chandramohan, Dhruva g   Huang, Lei g   Zawack, Kelson g,i   Shestov, Alexander A j   Makadia, Hiren k   DeCicco, Danielle k   Yin, Alex l   more..


Author keywords

[No Author keywords available]

Indexed keywords

CELL ENGINEERING; CELLS; CYTOLOGY; REVERSE ENGINEERING;

EID: 84930603902     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1004096     Document Type: Article
Times cited : (33)

References (55)
  • 2
    • 80054069179 scopus 로고    scopus 로고
    • A comprehensive genome-scale reconstruction of Escherichia coli metabolism–2011
    • Orth JD, Conrad TM, Na J, Lerman JA, Nam H, et al. (2011) A comprehensive genome-scale reconstruction of Escherichia coli metabolism–2011. Mol Syst Biol 7: 535. doi: 10.1038/msb.2011.65 21988831
    • (2011) Mol Syst Biol , vol.7 , pp. 535
    • Orth, J.D.1    Conrad, T.M.2    Na, J.3    Lerman, J.A.4    Nam, H.5
  • 3
    • 0030797355 scopus 로고    scopus 로고
    • Robustness in simple biochemical networks
    • Barkai N, Leibler S, (1997) Robustness in simple biochemical networks. Nature 387: 913–917. 9202124
    • (1997) Nature , vol.387 , pp. 913-917
    • Barkai, N.1    Leibler, S.2
  • 4
    • 0031879114 scopus 로고    scopus 로고
    • Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells
    • Arkin A, Ross J, McAdams HH, (1998) Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells. Genetics 149: 1633–1648. 9691025
    • (1998) Genetics , vol.149 , pp. 1633-1648
    • Arkin, A.1    Ross, J.2    McAdams, H.H.3
  • 5
    • 84864258618 scopus 로고    scopus 로고
    • A whole-cell computational model predicts phenotype from genotype
    • Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, et al. (2012) A whole-cell computational model predicts phenotype from genotype. Cell 150: 389–401. doi: 10.1016/j.cell.2012.05.044 22817898
    • (2012) Cell , vol.150 , pp. 389-401
    • Karr, J.R.1    Sanghvi, J.C.2    Macklin, D.N.3    Gutschow, M.V.4    Jacobs, J.M.5
  • 6
    • 77749320898 scopus 로고    scopus 로고
    • What is flux balance analysis?
    • Orth JD, Thiele I, Palsson BO, (2010) What is flux balance analysis? Nat Biotechnol 28: 245–248. doi: 10.1038/nbt.1614 20212490
    • (2010) Nat Biotechnol , vol.28 , pp. 245-248
    • Orth, J.D.1    Thiele, I.2    Palsson, B.O.3
  • 8
    • 58149237830 scopus 로고    scopus 로고
    • Modelling cellular signalling systems
    • Rangamani P, Iyengar R, (2008) Modelling cellular signalling systems. Essays Biochem 45: 83–94. doi: 10.1042/BSE0450083 18793125
    • (2008) Essays Biochem , vol.45 , pp. 83-94
    • Rangamani, P.1    Iyengar, R.2
  • 9
    • 0033707946 scopus 로고    scopus 로고
    • Using Bayesian networks to analyze expression data
    • Friedman N, Linial M, Nachman I, Pe’er D, (2000) Using Bayesian networks to analyze expression data. J Comput Biol 7: 601–620. 11108481
    • (2000) J Comput Biol , vol.7 , pp. 601-620
    • Friedman, N.1    Linial, M.2    Nachman, I.3    Pe’er, D.4
  • 10
    • 84863509940 scopus 로고    scopus 로고
    • Utilizing RNA-Seq data for de novo coexpression network inference
    • Iancu OD, Kawane S, Bottomly D, Searles R, Hitzemann R, et al. (2012) Utilizing RNA-Seq data for de novo coexpression network inference. Bioinformatics 28: 1592–1597. doi: 10.1093/bioinformatics/bts245 22556371
    • (2012) Bioinformatics , vol.28 , pp. 1592-1597
    • Iancu, O.D.1    Kawane, S.2    Bottomly, D.3    Searles, R.4    Hitzemann, R.5
  • 11
    • 59649098900 scopus 로고    scopus 로고
    • Learning signaling network structures with sparsely distributed data
    • Sachs K, Itani S, Carlisle J, Nolan GP, Pe’er D, et al. (2009) Learning signaling network structures with sparsely distributed data. J Comput Biol 16: 201–212. doi: 10.1089/cmb.2008.07TT 19193145
    • (2009) J Comput Biol , vol.16 , pp. 201-212
    • Sachs, K.1    Itani, S.2    Carlisle, J.3    Nolan, G.P.4    Pe’er, D.5
  • 12
    • 84855274655 scopus 로고    scopus 로고
    • Systematic search for recipes to generate induced pluripotent stem cells
    • Chang R, Shoemaker R, Wang W, (2011) Systematic search for recipes to generate induced pluripotent stem cells. PLoS Comput Biol 7: e1002300. doi: 10.1371/journal.pcbi.1002300 22215993
    • (2011) PLoS Comput Biol , vol.7 , pp. e1002300
    • Chang, R.1    Shoemaker, R.2    Wang, W.3
  • 13
    • 57049089078 scopus 로고    scopus 로고
    • Parameter estimation and determinability analysis applied to Drosophila gap gene circuits
    • Ashyraliyev M, Jaeger J, Blom JG, (2008) Parameter estimation and determinability analysis applied to Drosophila gap gene circuits. BMC Syst Biol 2: 83. doi: 10.1186/1752-0509-2-83 18817540
    • (2008) BMC Syst Biol , vol.2 , pp. 83
    • Ashyraliyev, M.1    Jaeger, J.2    Blom, J.G.3
  • 14
    • 58149232578 scopus 로고    scopus 로고
    • Parameter estimation and optimal experimental design
    • Banga JR, Balsa-Canto E, (2008) Parameter estimation and optimal experimental design. Essays Biochem 45: 195–209. doi: 10.1042/BSE0450195 18793133
    • (2008) Essays Biochem , vol.45 , pp. 195-209
    • Banga, J.R.1    Balsa-Canto, E.2
  • 15
    • 84903716324 scopus 로고    scopus 로고
    • An evaluation of adaptive surrogate modeling based optimization with two benchmark problems
    • Wang C, Duan Q, Gong W, Ye A, Di Z, et al. (2014) An evaluation of adaptive surrogate modeling based optimization with two benchmark problems. Environ Model Softw 60: 167–179.
    • (2014) Environ Model Softw , vol.60 , pp. 167-179
    • Wang, C.1    Duan, Q.2    Gong, W.3    Ye, A.4    Di, Z.5
  • 16
    • 58549086381 scopus 로고    scopus 로고
    • Recent advances in surrogate-based optimization
    • Forrester AIJ, Keane AJ, (2009) Recent advances in surrogate-based optimization. Progr Aerosp Sci 45: 50–79.
    • (2009) Progr Aerosp Sci , vol.45 , pp. 50-79
    • Forrester, A.I.J.1    Keane, A.J.2
  • 17
    • 80051552992 scopus 로고    scopus 로고
    • Adaptive surrogate modeling for expedited estimation of nonlinear tissue properties through inverse finite element analysis
    • Halloran JP, Erdemir A, (2011) Adaptive surrogate modeling for expedited estimation of nonlinear tissue properties through inverse finite element analysis. Ann Biomed Eng 39: 2388–2397. doi: 10.1007/s10439-011-0317-2 21544674
    • (2011) Ann Biomed Eng , vol.39 , pp. 2388-2397
    • Halloran, J.P.1    Erdemir, A.2
  • 18
    • 0035577808 scopus 로고    scopus 로고
    • A taxonomy of global optimization methods based on response surfaces
    • Jones DR, (2001) A taxonomy of global optimization methods based on response surfaces. J Global Optim 21: 345–383.
    • (2001) J Global Optim , vol.21 , pp. 345-383
    • Jones, D.R.1
  • 19
    • 0037394089 scopus 로고    scopus 로고
    • Evolutionary optimization of computationally expensive problems via surrogate modeling
    • Ong YS, Nair PB, Keane AJ, (2003) Evolutionary optimization of computationally expensive problems via surrogate modeling. AIAA J 41: 687–696.
    • (2003) AIAA J , vol.41 , pp. 687-696
    • Ong, Y.S.1    Nair, P.B.2    Keane, A.J.3
  • 20
    • 84859099633 scopus 로고    scopus 로고
    • Numerical assessment of metamodelling strategies in computationally intensive optimization
    • Razavi S, Tolson BA, Burn DH, (2012) Numerical assessment of metamodelling strategies in computationally intensive optimization. Environ Model Softw 34: 67–86.
    • (2012) Environ Model Softw , vol.34 , pp. 67-86
    • Razavi, S.1    Tolson, B.A.2    Burn, D.H.3
  • 21
    • 0036672333 scopus 로고    scopus 로고
    • Surrogate modeling-based optimization for the integration of static and dynamic data into a reservoir description
    • Queipo NV, Pintos S, Rincón N, Contreras N, Colmenares J, (2002) Surrogate modeling-based optimization for the integration of static and dynamic data into a reservoir description. J Petrol Sci Eng 35: 167–181.
    • (2002) J Petrol Sci Eng , vol.35 , pp. 167-181
    • Queipo, N.V.1    Pintos, S.2    Rincón, N.3    Contreras, N.4    Colmenares, J.5
  • 22
    • 26444601262 scopus 로고    scopus 로고
    • Cooperative multi-agent learning: The state of the art
    • Panait L, Luke S, (2005) Cooperative multi-agent learning: The state of the art. Auton Agent Multi Agent Syst 11: 387–434.
    • (2005) Auton Agent Multi Agent Syst , vol.11 , pp. 387-434
    • Panait, L.1    Luke, S.2
  • 24
    • 14244262402 scopus 로고    scopus 로고
    • Distributed optimization for cooperative agents: application to formation flight
    • Raffard RL, Tomlin CJ, Boyd SP, (2004) Distributed optimization for cooperative agents: application to formation flight. Decis Contr 3: 2453–2459.
    • (2004) Decis Contr , vol.3 , pp. 2453-2459
    • Raffard, R.L.1    Tomlin, C.J.2    Boyd, S.P.3
  • 25
    • 3042699804 scopus 로고    scopus 로고
    • Rabbat M, Nowak R (2004) Distributed optimization in sensor networks. In: Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks. New York, NY, USA: ACM, IPSN ‘04, pp. 20–27. doi: 10.1145/984622.984626. URL http://doi.acm.org/10.1145/984622.984626.
  • 28
    • 77954383034 scopus 로고    scopus 로고
    • Nonlinear system identification employing automatic differentiation
    • Ramachandran R, Barton PI, (2010) Nonlinear system identification employing automatic differentiation. Chem Eng Sci 65: 4884–4893.
    • (2010) Chem Eng Sci , vol.65 , pp. 4884-4893
    • Ramachandran, R.1    Barton, P.I.2
  • 30
  • 31
    • 36249019789 scopus 로고    scopus 로고
    • Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference
    • Stolovitzky G, Monroe D, Califano A, (2007) Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference. Ann N Y Acad Sci 1115: 1–22. 17925349
    • (2007) Ann N Y Acad Sci , vol.1115 , pp. 1-22
    • Stolovitzky, G.1    Monroe, D.2    Califano, A.3
  • 32
    • 80052592949 scopus 로고    scopus 로고
    • Crowdsourcing network inference: the DREAM predictive signaling network challenge
    • Prill RJ, Saez-Rodriguez J, Alexopoulos LG, Sorger PK, Stolovitzky G, (2011) Crowdsourcing network inference: the DREAM predictive signaling network challenge. Sci Signal 4: mr7. doi: 10.1126/scisignal.2002212 21900204
    • (2011) Sci Signal , vol.4 , pp. mr7
    • Prill, R.J.1    Saez-Rodriguez, J.2    Alexopoulos, L.G.3    Sorger, P.K.4    Stolovitzky, G.5
  • 33
  • 34
    • 77949644952 scopus 로고    scopus 로고
    • Towards a rigorous assessment of systems biology models: the DREAM3 challenges
    • Prill RJ, Marbach D, Saez-Rodriguez J, Sorger PK, Alexopoulos LG, et al. (2010) Towards a rigorous assessment of systems biology models: the DREAM3 challenges. PLoS One 5: e9202. doi: 10.1371/journal.pone.0009202 20186320
    • (2010) PLoS One , vol.5 , pp. e9202
    • Prill, R.J.1    Marbach, D.2    Saez-Rodriguez, J.3    Sorger, P.K.4    Alexopoulos, L.G.5
  • 35
    • 84893710009 scopus 로고    scopus 로고
    • Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach
    • Meyer P, Cokelaer T, Chandran D, Kim KH, Loh PR, et al. (2014) Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach. BMC Syst Biol 8: 13. doi: 10.1186/1752-0509-8-13 24507381
    • (2014) BMC Syst Biol , vol.8 , pp. 13
    • Meyer, P.1    Cokelaer, T.2    Chandran, D.3    Kim, K.H.4    Loh, P.R.5
  • 36
    • 0142000477 scopus 로고    scopus 로고
    • Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces
    • Storn R, Price K, (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11: 341–359.
    • (1997) J Global Optim , vol.11 , pp. 341-359
    • Storn, R.1    Price, K.2
  • 37
    • 79955818164 scopus 로고    scopus 로고
    • Metaheuristic optimization: algorithm analysis and open problems
    • Yang XS, (2011) Metaheuristic optimization: algorithm analysis and open problems. Lect Notes Comput Sc 6630: 21–32.
    • (2011) Lect Notes Comput Sc , vol.6630 , pp. 21-32
    • Yang, X.S.1
  • 38
    • 84930605261 scopus 로고    scopus 로고
    • Chakraborty UK, (2008) Advances in differential evolution. Berlin: Springer-Verlag. 340 p.
    • (2008)
    • Chakraborty, U.K.1
  • 39
    • 84879455542 scopus 로고    scopus 로고
    • Optimization and model reduction in the high dimensional parameter space of a budding yeast cell cycle model
    • Oguz C, Laomettachit T, Chen KC, Watson LT, Baumann WT, et al. (2013) Optimization and model reduction in the high dimensional parameter space of a budding yeast cell cycle model. BMC Syst Biol 7: 53. doi: 10.1186/1752-0509-7-53 23809412
    • (2013) BMC Syst Biol , vol.7 , pp. 53
    • Oguz, C.1    Laomettachit, T.2    Chen, K.C.3    Watson, L.T.4    Baumann, W.T.5
  • 40
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • Breiman L, (2001) Random forests. Machine Learning 45: 5–32.
    • (2001) Machine Learning , vol.45 , pp. 5-32
    • Breiman, L.1
  • 41
    • 77955491294 scopus 로고    scopus 로고
    • Predicting protein structures with a multiplayer online game
    • Cooper S, Khatib F, Treuille A, Barbero J, Lee J, et al. (2010) Predicting protein structures with a multiplayer online game. Nature 466: 756–760. doi: 10.1038/nature09304 20686574
    • (2010) Nature , vol.466 , pp. 756-760
    • Cooper, S.1    Khatib, F.2    Treuille, A.3    Barbero, J.4    Lee, J.5
  • 42
    • 84893831061 scopus 로고    scopus 로고
    • RNA design rules from a massive open laboratory
    • Lee J, Kladwang W, Lee M, Cantu D, Azizyan M, et al. (2014) RNA design rules from a massive open laboratory. Proc Natl Acad Sci U S A 111: 2122–2127. doi: 10.1073/pnas.1313039111 24469816
    • (2014) Proc Natl Acad Sci U S A , vol.111 , pp. 2122-2127
    • Lee, J.1    Kladwang, W.2    Lee, M.3    Cantu, D.4    Azizyan, M.5
  • 43
    • 84891874938 scopus 로고    scopus 로고
    • Crowdsourcing natural products discovery to access uncharted dimensions of fungal metabolite diversity
    • Du L, Robles AJ, King JB, Powell DR, Miller AN, et al. (2014) Crowdsourcing natural products discovery to access uncharted dimensions of fungal metabolite diversity. Angew Chem Int Ed Engl 53: 804–809. doi: 10.1002/anie.201306549 24285637
    • (2014) Angew Chem Int Ed Engl , vol.53 , pp. 804-809
    • Du, L.1    Robles, A.J.2    King, J.B.3    Powell, D.R.4    Miller, A.N.5
  • 44
    • 0019034031 scopus 로고
    • Parameter and structural identifiability concepts and ambiguities: a critical review and analysis
    • Cobelli C D, Jr (1980) Parameter and structural identifiability concepts and ambiguities: a critical review and analysis. Am J Physiol 239: R7–24. 7396041
    • (1980) Am J Physiol , vol.239 , pp. R7-24
    • Cobelli, C.D.1
  • 45
    • 81555207959 scopus 로고    scopus 로고
    • Structural identifiability of systems biology models: a critical comparison of methods
    • Chis OT BCE, Banga JR, (2011) Structural identifiability of systems biology models: a critical comparison of methods. PLoS One 6: e27755. doi: 10.1371/journal.pone.0027755 22132135
    • (2011) PLoS One , vol.6 , pp. e27755
    • Chis, O.T.B.C.E.1    Banga, J.R.2
  • 46
    • 35748931160 scopus 로고    scopus 로고
    • Data-based identifiability analysis of non-linear dynamical models
    • Hengl S, Kreutz C, Timmer J, Maiwald T, (2007) Data-based identifiability analysis of non-linear dynamical models. Bioinformatics 23: 2612–2618. 17660526
    • (2007) Bioinformatics , vol.23 , pp. 2612-2618
    • Hengl, S.1    Kreutz, C.2    Timmer, J.3    Maiwald, T.4
  • 47
    • 42249092698 scopus 로고    scopus 로고
    • Using complicated, wide dynamic range driving to develop models of single neurons in single recording sessions
    • Hobbs KH, Hooper SL, (2008) Using complicated, wide dynamic range driving to develop models of single neurons in single recording sessions. J Neurophysiol 99: 1871–1883. doi: 10.1152/jn.00032.2008 18256169
    • (2008) J Neurophysiol , vol.99 , pp. 1871-1883
    • Hobbs, K.H.1    Hooper, S.L.2
  • 48
    • 0035923604 scopus 로고    scopus 로고
    • Degeneracy and complexity in biological systems
    • Edelman GM, Gally JA, (2001) Degeneracy and complexity in biological systems. Proc Natl Acad Sci U S A 98: 13763–13768. 11698650
    • (2001) Proc Natl Acad Sci U S A , vol.98 , pp. 13763-13768
    • Edelman, G.M.1    Gally, J.A.2
  • 51
    • 13744259954 scopus 로고    scopus 로고
    • Similar network activity from disparate circuit parameters
    • Prinz AA, Bucher D, Marder E, (2004) Similar network activity from disparate circuit parameters. Nat Neurosci 7: 1345–1352. 15558066
    • (2004) Nat Neurosci , vol.7 , pp. 1345-1352
    • Prinz, A.A.1    Bucher, D.2    Marder, E.3
  • 52
    • 33746752672 scopus 로고    scopus 로고
    • Efficient estimation of detailed single-neuron models
    • Huys QJ, Ahrens MB, Paninski L, (2006) Efficient estimation of detailed single-neuron models. J Neurophysiol 96: 872–890. 16624998
    • (2006) J Neurophysiol , vol.96 , pp. 872-890
    • Huys, Q.J.1    Ahrens, M.B.2    Paninski, L.3
  • 53
    • 68749118345 scopus 로고    scopus 로고
    • Defining network topologies that can achieve biochemical adaptation
    • Ma W, Trusina A, El-Samad H, Lim WA, Tang C, (2009) Defining network topologies that can achieve biochemical adaptation. Cell 138: 760–773. doi: 10.1016/j.cell.2009.06.013 19703401
    • (2009) Cell , vol.138 , pp. 760-773
    • Ma, W.1    Trusina, A.2    El-Samad, H.3    Lim, W.A.4    Tang, C.5
  • 54
    • 65649117293 scopus 로고    scopus 로고
    • How multiple conductances determine electrophysiological properties in a multicompartment model
    • Taylor AL, Goaillard JM, Marder E, (2009) How multiple conductances determine electrophysiological properties in a multicompartment model. J Neurosci 29: 5573–5586. doi: 10.1523/JNEUROSCI.4438-08.2009 19403824
    • (2009) J Neurosci , vol.29 , pp. 5573-5586
    • Taylor, A.L.1    Goaillard, J.M.2    Marder, E.3
  • 55
    • 79251541199 scopus 로고    scopus 로고
    • Multiple models to capture the variability in biological neurons and networks
    • Marder E, Taylor AL, (2011) Multiple models to capture the variability in biological neurons and networks. Nat Neurosci 14: 133–138. doi: 10.1038/nn.2735 21270780
    • (2011) Nat Neurosci , vol.14 , pp. 133-138
    • Marder, E.1    Taylor, A.L.2


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