BACKGROUND
resolving the complex cellular signal transduction is a grand challenge in systems biology.
signal transduction involves cascade of protein-protein interaction and complex feedback loops  <cit>  across proteomic and genomic levels.
models of the dynamics of the combined regulatory networks provide in-depth analysis temporal characteristics of targeted biological process.
furthermore, in silico knockout experiments by these models could help biologists to prioritize target genes of interest and reduce time and cost of real experiments.
types of dynamic network models include kinetic models  <cit> , hidden markov models  <cit> , and logic-based models  <cit> .
kinetic models based on differential equations have been used to elaborate dynamics on numerous systems  <cit> .
however, they need detailed information about network structure, reaction mechanism and the respective kinetic parameters; which, unfortunately, are not easily obtainable.
hidden markov model  is a statistical model in acyclic pathway  <cit> .
the state is hidden, but the outcome dependent on the state is visible.
hence, hmms are usually used to model known results with unknown process mechanism.
boolean network is a qualitative logic-based model that was introduced in the 1960s  <cit> .
in the past few decades, scientists have frequently used boolean network to model gene regulatory networks , apoptosis, metabolic network, immune response and signaling pathways  <cit> .
since logic-based or qualitative knowledge of interaction is abundant, the network structure can be easily established.
moreover, only minimal information is required to describe the dynamics of boolean transfer function, they can be obtained using limited experimental data.
therefore boolean model is an effective and extendable way of modeling the dynamics of signal transduction.
the transcription factor nf-κb controls various inflammation mediators to orchestra interwoven cellular responses to inflammatory stimuli such as tnf, il- <dig> and tlr <dig> etc.
in this study, boolean model with time-delay was used to described the nf-κb signaling.
the objective is to integrate qualitative information on network interactions from published datasets and dynamic response data in literatures to reveal the regulatory mechanism of infection and inflammation.
RESULTS
model
boolean transfer functions were used for each edge.
each transfer function has two dynamic parameters.
the delayed activation θ denotes the duration that the input to a node must turned on before the reception node is turned on.
the sustained response r is the time that the output of a node can be sustained once it is turned on.
these parameters were obtained by fitting a training data set published in  <cit>  using ga.
even with a simplified network and limited number of dynamic parameters, convergence to a set of reasonable parameters was not easy.
in order to improve the modeling process, we trained model parameters in kernel pathway first with parameters of the rest of the edges set equal to  <dig>  using wild type data containing measurements of both ikk and nfkb measurements.
when kernel’s parameters were decided, the parameters of the remaining edges were determined by adding additional data involving a <dig> knockout, stimulus of various strengths and measurements of either ikk or nfkb only.
dynamics implications
the parameters obtained for the boolean model were shown in table  <dig>  the delay of transcription factor nfkb induced components, such as a <dig>  ikba, il <dig> and tnf are longer than other reactions in table  <dig>  it reveals transcription costs more time than phosphorylation.
tnf related pathways will response immediately in our model.
in contrast, lps costs longer delay time to start immune response.
the activation of ikk by lps through tab1/tab2/tak <dig> is much slower than activation through nik by tnf.
each component has a boolean transfer function that contains logic gates and two type of parameters: delay activation  and sustained response .
each time step corresponds to  <dig> second in real time
negative regulator
because nfkb regulates pro-inflammatory cytokines, such as il <dig>  tnf, and negative regulator a <dig>  <cit> , these effectors can generate positive and negative feedback control.
in our model, il <dig> and tnf were positive feedback.
excessive cytokine production is harmful to the host due to its effects on blood circulation system.
thus, “endotoxin tolerance” is a critical negative feedback mechanism to protect host from endotoxic shock  <cit> .
in our model, negative regulator a <dig> and ikba could suppress nfkb’s activity.
specifically, nfkb induced ikba  will supress nfkb activity in a self-regulatory cycle.
as shown in figure  <dig>  inflammation response will be prolonged by ikba deletion in tnf and lps stimulation.
zinc finger protein a <dig>  also called tnfait <dig> , can also produce rip- or traf2- mediated signal to indirectly block the nfkb activity.
we have learned from the literatures that the negative regulator a <dig> blocked the nfkb activation while protecting the host cell from tnf-mediated apoptosis.
to mimic an a <dig> knockout assay done in the wet-bench experiments, we set the output of the a <dig> nod to  <dig> and keep it in off state during simulation as the deletion.
the resulting signaling pattern of wild type and a <dig> mutation in our model were shown in figure  <dig>  tnf induced ikk active from  <dig> min to  <dig> min in wild type and persist with a <dig> deletion.
nf-κb caused secondary activation when a <dig> is knocked out.
for lps-induced ikk  and nfkb , there is no difference between active patterns obtained with wild-type and a <dig> deletion.
this is because in a lps induced response, the secondary tnf response will be triggered after the transcription of nfkb.
hence a <dig> is apparently a key component in tnf-induced pathway but with no significant influence in lps-induced pathway.
clinical implication
CONCLUSIONS
in this work, a dynamic boolean model was generated by integrating and comprehensive qualitative knowledge about network structure and fitting a minimal amount of dynamic response data.
the model is capable of capturing feedback and crosstalk dynamics between diverse signaling pathways.
using this model, mechanisms of and factors affecting periodic pro-inflammatory and anti-inflammatory responses can be elucidated.
the proposed approach integrated intracellular and intercellular process.
hence it is possible for us to use this approach to develop system models for host defense against the shock from environmental or pathogen stimuli and predict the inflammatory response.
such a model will potentially be able to provide insight to a feedback treatment scheme for clinical therapy.

 resolving the complex cellular signal transduction is a grand challenge in systems biology. signal transduction involves cascade of protein-protein interaction and complex feedback loops across proteomic and genomic levels. models of the dynamics of the combined regulatory networks provide in-depth analysis temporal characteristics of targeted biological process. furthermore, in silico knockout experiments by these models could help biologists to prioritize target genes of interest and reduce time and cost of real experiments. types of dynamic network models include kinetic models, hidden markov models, and logic-based models. kinetic models based on differential equations have been used to elaborate dynamics on numerous systems. however, they need detailed information about network structure, reaction mechanism and the respective kinetic parameters; which, unfortunately, are not easily obtainable. hidden markov model is a statistical model in acyclic pathway. the state is hidden, but the outcome dependent on the state is visible. hence, hmms are usually used to model known results with unknown process mechanism. boolean network is a qualitative logic-based model that was introduced in the 1960s. in the past few decades, scientists have frequently used boolean network to model gene regulatory networks, apoptosis, metabolic network, immune response and signaling pathways. since logic-based or qualitative knowledge of interaction is abundant, the network structure can be easily established. moreover, only minimal information is required to describe the dynamics of boolean transfer function, they can be obtained using limited experimental data. therefore boolean model is an effective and extendable way of modeling the dynamics of signal transduction. the transcription factor nf-κb controls various inflammation mediators to orchestra interwoven cellular responses to inflammatory stimuli such as tnf, il- and tlr etc. in this study, boolean model with time-delay was used to described the nf-κb signaling. the objective is to integrate qualitative information on network interactions from published datasets and dynamic response data in literatures to reveal the regulatory mechanism of infection and inflammation.  model boolean transfer functions were used for each edge. each transfer function has two dynamic parameters. the delayed activation θ denotes the duration that the input to a node must turned on before the reception node is turned on. the sustained response r is the time that the output of a node can be sustained once it is turned on. these parameters were obtained by fitting a training data set published in using ga. even with a simplified network and limited number of dynamic parameters, convergence to a set of reasonable parameters was not easy. in order to improve the modeling process, we trained model parameters in kernel pathway first with parameters of the rest of the edges set equal to using wild type data containing measurements of both ikk and nfkb measurements. when kernel’s parameters were decided, the parameters of the remaining edges were determined by adding additional data involving a knockout, stimulus of various strengths and measurements of either ikk or nfkb only. dynamics implications the parameters obtained for the boolean model were shown in table the delay of transcription factor nfkb induced components, such as a ikba, il and tnf are longer than other reactions in table it reveals transcription costs more time than phosphorylation. tnf related pathways will response immediately in our model. in contrast, lps costs longer delay time to start immune response. the activation of ikk by lps through tab1/tab2/tak is much slower than activation through nik by tnf. each component has a boolean transfer function that contains logic gates and two type of parameters: delay activation and sustained response. each time step corresponds to second in real time negative regulator because nfkb regulates pro-inflammatory cytokines, such as il tnf, and negative regulator a, these effectors can generate positive and negative feedback control. in our model, il and tnf were positive feedback. excessive cytokine production is harmful to the host due to its effects on blood circulation system. thus, “endotoxin tolerance” is a critical negative feedback mechanism to protect host from endotoxic shock. in our model, negative regulator a and ikba could suppress nfkb’s activity. specifically, nfkb induced ikba will supress nfkb activity in a self-regulatory cycle. as shown in figure inflammation response will be prolonged by ikba deletion in tnf and lps stimulation. zinc finger protein a also called tnfait, can also produce rip- or traf2- mediated signal to indirectly block the nfkb activity. we have learned from the literatures that the negative regulator a blocked the nfkb activation while protecting the host cell from tnf-mediated apoptosis. to mimic an a knockout assay done in the wet-bench experiments, we set the output of the a nod to and keep it in off state during simulation as the deletion. the resulting signaling pattern of wild type and a mutation in our model were shown in figure tnf induced ikk active from min to min in wild type and persist with a deletion. nf-κb caused secondary activation when a is knocked out. for lps-induced ikk and nfkb, there is no difference between active patterns obtained with wild-type and a deletion. this is because in a lps induced response, the secondary tnf response will be triggered after the transcription of nfkb. hence a is apparently a key component in tnf-induced pathway but with no significant influence in lps-induced pathway. clinical implication  in this work, a dynamic boolean model was generated by integrating and comprehensive qualitative knowledge about network structure and fitting a minimal amount of dynamic response data. the model is capable of capturing feedback and crosstalk dynamics between diverse signaling pathways. using this model, mechanisms of and factors affecting periodic pro-inflammatory and anti-inflammatory responses can be elucidated. the proposed approach integrated intracellular and intercellular process. hence it is possible for us to use this approach to develop system models for host defense against the shock from environmental or pathogen stimuli and predict the inflammatory response. such a model will potentially be able to provide insight to a feedback treatment scheme for clinical therapy. 
in present study, we employed a genetic algorithm-based boolean model to represent nf-κb signaling pathway. thus, a systematic approach to integrate experimental data and qualitative knowledge to identify the physiological consequences of environmental stimuli is needed. signal transduction is the major mechanism through which cells transmit external stimuli to evoke intracellular biochemical responses. we designed an effective algorithm to elucidate the process of immune response using comprehensive knowledge about network structure and limited experimental data on dynamic responses. understanding relationship between external stimuli and corresponding cellular responses, as well as the subsequent effects on downstream genes, is a major challenge in systems biology.
 signal transduction is the major mechanism through which cells transmit external stimuli to evoke intracellular biochemical responses. understanding relationship between external stimuli and corresponding cellular responses, as well as the subsequent effects on downstream genes, is a major challenge in systems biology. thus, a systematic approach to integrate experimental data and qualitative knowledge to identify the physiological consequences of environmental stimuli is needed.  in present study, we employed a genetic algorithm-based boolean model to represent nf-κb signaling pathway. we were able to capture feedback and crosstalk characteristics to enhance our understanding on the acute and chronic inflammatory response. key network components affecting the response dynamics were identified.  we designed an effective algorithm to elucidate the process of immune response using comprehensive knowledge about network structure and limited experimental data on dynamic responses. this approach can potentially be implemented for large-scale analysis on cellular processes and organism behaviors. 11– january the ninth asia pacific bioinformatics conference inchon, korea 