publication . Preprint . Article . 2018

Fitting mathematical models of biochemical pathways to steady state perturbation response data without simulating perturbation experiments

Santra, Tapesh;
Open Access
  • Published: 01 Aug 2018
  • Publisher: Cold Spring Harbor Laboratory
Abstract
<jats:title>Abstract</jats:title><jats:p>A common experimental approach for studying signal transduction networks (STNs) is to measure the steady state concentrations of their components following perturbations to individual components. Such data is frequently used to reconstruct topological models of STNs, but, are rarely used for calibrating kinetic models of these networks. This is because, existing calibration algorithms operate by assigning different sets of values to the parameters of the kinetic models, and for each set of values simulating all perturbations performed in the biochemical experiments. This process is highly computation intensive and may be ...
Subjects
free text keywords: Article, Medicine, R, Science, Q, Multidisciplinary
18 references, page 1 of 2

M. Halasz, B. N. Kholodenko, W. Kolch, T. Santra, Integrating network reconstruction with mechanistic modeling to predict cancer therapies. Sci. Signal. 9, ra114-ra114 (2016). [OpenAIRE]

B. N. Kholodenko, A. Kiyatkin, F. J. Bruggeman, E. Sontag, Untangling the wires: A strategy to trace functional interactions in signaling and gene networks. PNAS 99, (2002). [OpenAIRE]

Durek, M. Merchant, R. Schäfer, C. Sers, N. Blüthgen, Network quantification of EGFR signaling unveils potential for targeted combination therapy. Molecular Systems Biology 9, 673-673 (2013).

Chaouiya, D. Thieffry, A. Poustka, S. Wiemann, T. Beissbarth, D. Arlt, in BMC Syst Biol. (2009).

S. D. M. Santos, P. J. Verveer, P. Bastiaens, Growth factor-induced MAPK network topology shapes Erk response determining PC-12 cell fate. Nat Cell Biol 9, (2007).

P. Bastiaens, M. R. Birtwistle, N. Blüthgen, F. J. Bruggeman, K.-H. Cho, C. Cosentino, A. De La Fuente, J. B. Hoek, A. Kiyatkin, S. Klamt, Silence on the relevant literature and errors in implementation. Nature biotechnology 33, 336-339 (2015). [OpenAIRE]

T. Santra, A bayesian framework that integrates heterogeneous data for inferring gene regulatory networks. Frontiers in bioengineering and biotechnology 2, (2014).

T. Santra, W. Kolch, B. N. Kholodenko, Integrating Bayesian variable selection with Modular Response Analysis to infer biochemical network topology. BMC Systems Biology 7, 1-19 (2013). [OpenAIRE]

B. B. Aldridge, J. M. Burke, D. A. Lauffenburger, P. K. Sorger, Physicochemical modelling of cell signalling pathways. Nat Cell Biol 8, 1195-1203 (2006).

C. J. Oates, F. Dondelinger, N. Bayani, J. Korkola, J. W. Gray, S. Mukherjee, Causal network inference using biochemical kinetics. Bioinformatics 30, i468-i474 (2014). [OpenAIRE]

A. Degasperi, D. Fey, B. N. Kholodenko, Performance of objective functions and optimisation procedures for parameter estimation in system biology models. npj Systems Biology and Applications 3, 20 (2017). [OpenAIRE]

M. Girolami, B. Calderhead, M. Girolami, B. Calderhead, Riemann manifold Langevin and Hamiltonian Monte Carlo methods. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES BSTATISTICAL METHODOLOGY 73, 123-214 (2011). [OpenAIRE]

A. Jensch, C. Thomaseth, N. E. Radde, Sampling-based Bayesian approaches reveal the importance of quasi-bistable behavior in cellular decision processes on the example of the MAPK signaling pathway in PC-12 cell lines. BMC Systems Biology 11, 11 (2017). [OpenAIRE]

Calderhead, N. Radde, A. Kramer, B. Calderhead, N. Radde, Hamiltonian Monte Carlo methods for efficient parameter estimation in steady state dynamical systems. BMC BIOINFORMATICS 15, 253-253 (2014). [OpenAIRE]

Journal of The Royal Society Interface 6, 187-202 (2009).

18 references, page 1 of 2
Abstract
<jats:title>Abstract</jats:title><jats:p>A common experimental approach for studying signal transduction networks (STNs) is to measure the steady state concentrations of their components following perturbations to individual components. Such data is frequently used to reconstruct topological models of STNs, but, are rarely used for calibrating kinetic models of these networks. This is because, existing calibration algorithms operate by assigning different sets of values to the parameters of the kinetic models, and for each set of values simulating all perturbations performed in the biochemical experiments. This process is highly computation intensive and may be ...
Subjects
free text keywords: Article, Medicine, R, Science, Q, Multidisciplinary
18 references, page 1 of 2

M. Halasz, B. N. Kholodenko, W. Kolch, T. Santra, Integrating network reconstruction with mechanistic modeling to predict cancer therapies. Sci. Signal. 9, ra114-ra114 (2016). [OpenAIRE]

B. N. Kholodenko, A. Kiyatkin, F. J. Bruggeman, E. Sontag, Untangling the wires: A strategy to trace functional interactions in signaling and gene networks. PNAS 99, (2002). [OpenAIRE]

Durek, M. Merchant, R. Schäfer, C. Sers, N. Blüthgen, Network quantification of EGFR signaling unveils potential for targeted combination therapy. Molecular Systems Biology 9, 673-673 (2013).

Chaouiya, D. Thieffry, A. Poustka, S. Wiemann, T. Beissbarth, D. Arlt, in BMC Syst Biol. (2009).

S. D. M. Santos, P. J. Verveer, P. Bastiaens, Growth factor-induced MAPK network topology shapes Erk response determining PC-12 cell fate. Nat Cell Biol 9, (2007).

P. Bastiaens, M. R. Birtwistle, N. Blüthgen, F. J. Bruggeman, K.-H. Cho, C. Cosentino, A. De La Fuente, J. B. Hoek, A. Kiyatkin, S. Klamt, Silence on the relevant literature and errors in implementation. Nature biotechnology 33, 336-339 (2015). [OpenAIRE]

T. Santra, A bayesian framework that integrates heterogeneous data for inferring gene regulatory networks. Frontiers in bioengineering and biotechnology 2, (2014).

T. Santra, W. Kolch, B. N. Kholodenko, Integrating Bayesian variable selection with Modular Response Analysis to infer biochemical network topology. BMC Systems Biology 7, 1-19 (2013). [OpenAIRE]

B. B. Aldridge, J. M. Burke, D. A. Lauffenburger, P. K. Sorger, Physicochemical modelling of cell signalling pathways. Nat Cell Biol 8, 1195-1203 (2006).

C. J. Oates, F. Dondelinger, N. Bayani, J. Korkola, J. W. Gray, S. Mukherjee, Causal network inference using biochemical kinetics. Bioinformatics 30, i468-i474 (2014). [OpenAIRE]

A. Degasperi, D. Fey, B. N. Kholodenko, Performance of objective functions and optimisation procedures for parameter estimation in system biology models. npj Systems Biology and Applications 3, 20 (2017). [OpenAIRE]

M. Girolami, B. Calderhead, M. Girolami, B. Calderhead, Riemann manifold Langevin and Hamiltonian Monte Carlo methods. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES BSTATISTICAL METHODOLOGY 73, 123-214 (2011). [OpenAIRE]

A. Jensch, C. Thomaseth, N. E. Radde, Sampling-based Bayesian approaches reveal the importance of quasi-bistable behavior in cellular decision processes on the example of the MAPK signaling pathway in PC-12 cell lines. BMC Systems Biology 11, 11 (2017). [OpenAIRE]

Calderhead, N. Radde, A. Kramer, B. Calderhead, N. Radde, Hamiltonian Monte Carlo methods for efficient parameter estimation in steady state dynamical systems. BMC BIOINFORMATICS 15, 253-253 (2014). [OpenAIRE]

Journal of The Royal Society Interface 6, 187-202 (2009).

18 references, page 1 of 2
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