TY - JOUR
T1 - Hybrid modeling of cell signaling and transcriptional reprogramming and its application in C. elegans development
AU - Fertig, Elana J.
AU - Danilova, Ludmila V.
AU - Favorov, Alexander V.
AU - Ochs, Michael F.
PY - 2011
Y1 - 2011
N2 - Modeling of signal driven transcriptional reprogramming is critical for understanding of organism development, human disease, and cell biology. Many current modeling techniques discount key features of the biological sub-systems when modeling multiscale, organism-level processes. We present a mechanistic hybrid model, GESSA, which integrates a novel pooled probabilistic Boolean network model of cell signaling and a stochastic simulation of transcription and translation responding to a diffusion model of extracellular signals. We apply the model to simulate the well studied cell fate decision process of the vulval precursor cells (VPCs) in C. elegans, using experimentally derived rate constants wherever possible and shared parameters to avoid overfitting. We demonstrate that GESSA recovers (1) the effects of varying scaffold protein concentration on signal strength, (2) amplification of signals in expression, (3) the relative external ligand concentration in a known geometry, and (4) feedback in biochemical networks. We demonstrate that setting model parameters based on wild-type and LIN-12 loss-of-function mutants in C. elegans leads to correct prediction of a wide variety of mutants including partial penetrance of phenotypes. Moreover, the model is relatively insensitive to parameters, retaining the wild-type phenotype for a wide range of cell signaling rate parameters.
AB - Modeling of signal driven transcriptional reprogramming is critical for understanding of organism development, human disease, and cell biology. Many current modeling techniques discount key features of the biological sub-systems when modeling multiscale, organism-level processes. We present a mechanistic hybrid model, GESSA, which integrates a novel pooled probabilistic Boolean network model of cell signaling and a stochastic simulation of transcription and translation responding to a diffusion model of extracellular signals. We apply the model to simulate the well studied cell fate decision process of the vulval precursor cells (VPCs) in C. elegans, using experimentally derived rate constants wherever possible and shared parameters to avoid overfitting. We demonstrate that GESSA recovers (1) the effects of varying scaffold protein concentration on signal strength, (2) amplification of signals in expression, (3) the relative external ligand concentration in a known geometry, and (4) feedback in biochemical networks. We demonstrate that setting model parameters based on wild-type and LIN-12 loss-of-function mutants in C. elegans leads to correct prediction of a wide variety of mutants including partial penetrance of phenotypes. Moreover, the model is relatively insensitive to parameters, retaining the wild-type phenotype for a wide range of cell signaling rate parameters.
KW - Cell signaling
KW - Computational molecular biology
KW - Development
KW - Probabilistic models
KW - Stochastic processes
KW - Transcriptional reprogramming
UR - http://www.scopus.com/inward/record.url?scp=84855367309&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84855367309&partnerID=8YFLogxK
U2 - 10.3389/fgene.2011.00077
DO - 10.3389/fgene.2011.00077
M3 - Article
C2 - 22303372
AN - SCOPUS:84855367309
SN - 1664-8021
VL - 2
JO - Frontiers in Genetics
JF - Frontiers in Genetics
IS - NOV
M1 - Article 77
ER -