TY - GEN
T1 - Modeling response of biological signal pathways using a hybrid boolean framework
AU - Chang, Young Hwan
AU - Tomlin, Claire
PY - 2012
Y1 - 2012
N2 - Mathematical models in systems biology are often constructed by either Ordinary Differential Equation (ODE) modeling or logical (Boolean) modeling. We develop a Hybrid Boolean Model (ODE+Boolean) for biological signal pathways with postulated epigenomic feedback. The basic idea in this model is to combine continuous dynamical systems (an ODE model for already well-known parts of the network) with a discrete transition system (Boolean, for postulated but largely unknown components). We use the existing or well-known ODE model to trigger signal pathways represented by a Boolean model. This framework is easier to validate than a complete ODE model for large and complex signal pathways, for example to find unknown pathways to match the response to experimental data. The advantage of using a Boolean model for the unknown parts of the network is that relatively few parameters are needed. Thus, the framework avoids over-fitting, covers a broad range of pathways and easily represents various experimental conditions. The overall goal of the hybrid model is to predict the behavior of biological signal pathways, thus helping to understand unknown parts of the pathway between experimental results and qualitative/ quantitative results. Extensions are discussed, and numerical examples in biological systems and one engineering example are provided.
AB - Mathematical models in systems biology are often constructed by either Ordinary Differential Equation (ODE) modeling or logical (Boolean) modeling. We develop a Hybrid Boolean Model (ODE+Boolean) for biological signal pathways with postulated epigenomic feedback. The basic idea in this model is to combine continuous dynamical systems (an ODE model for already well-known parts of the network) with a discrete transition system (Boolean, for postulated but largely unknown components). We use the existing or well-known ODE model to trigger signal pathways represented by a Boolean model. This framework is easier to validate than a complete ODE model for large and complex signal pathways, for example to find unknown pathways to match the response to experimental data. The advantage of using a Boolean model for the unknown parts of the network is that relatively few parameters are needed. Thus, the framework avoids over-fitting, covers a broad range of pathways and easily represents various experimental conditions. The overall goal of the hybrid model is to predict the behavior of biological signal pathways, thus helping to understand unknown parts of the pathway between experimental results and qualitative/ quantitative results. Extensions are discussed, and numerical examples in biological systems and one engineering example are provided.
UR - http://www.scopus.com/inward/record.url?scp=84885936955&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84885936955&partnerID=8YFLogxK
U2 - 10.1115/DSCC2012-MOVIC2012-8729
DO - 10.1115/DSCC2012-MOVIC2012-8729
M3 - Conference contribution
AN - SCOPUS:84885936955
SN - 9780791845295
T3 - ASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference, DSCC 2012-MOVIC 2012
SP - 335
EP - 344
BT - ASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference, DSCC 2012-MOVIC 2012
T2 - ASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference, DSCC 2012-MOVIC 2012
Y2 - 17 October 2012 through 19 October 2012
ER -