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Application of circuit simulation method for differential modeling of TIM-2 iron uptake and metabolism in mouse kidney cells.

Xie Z, Harrison SH, Torti SV, Torti FM, Han J - Front Physiol (2013)

Bottom Line: At the end of endocytosis, about 28% HFt remained intact and the rest was degraded.Iron released from degraded HFt was in the labile iron pool (LIP) and stimulated the generation of endogenous HFt for new storage.Both experimental data and the model showed that TIM-2 was not involved in the process of iron export.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, North Carolina Agricultural and Technical State University Greensboro, NC, USA.

ABSTRACT
Circuit simulation is a powerful methodology to generate differential mathematical models. Due to its highly accurate modeling capability, circuit simulation can be used to investigate interactions between the parts and processes of a cellular system. Circuit simulation has become a core technology for the field of electrical engineering, but its application in biology has not yet been fully realized. As a case study for evaluating the more advanced features of a circuit simulation tool called Advanced Design System (ADS), we collected and modeled laboratory data for iron metabolism in mouse kidney cells for a H ferritin (HFt) receptor, T cell immunoglobulin and mucin domain-2 (TIM-2). The internal controlling parameters of TIM-2 associated iron metabolism were extracted and the ratios of iron movement among cellular compartments were quantified by ADS. The differential model processed by circuit simulation demonstrated a capability to identify variables and predict outcomes that could not be readily measured by in vitro experiments. For example, an initial rate of uptake of iron-loaded HFt (Fe-HFt) was 2.17 pmol per million cells. TIM-2 binding probability with Fe-HFt was 16.6%. An average of 8.5 min was required for the complex of TIM-2 and Fe-HFt to form an endosome. The endosome containing HFt lasted roughly 2 h. At the end of endocytosis, about 28% HFt remained intact and the rest was degraded. Iron released from degraded HFt was in the labile iron pool (LIP) and stimulated the generation of endogenous HFt for new storage. Both experimental data and the model showed that TIM-2 was not involved in the process of iron export. The extracted internal controlling parameters successfully captured the complexity of TIM-2 pathway and the use of circuit simulation-based modeling across a wider range of cellular systems is the next step for validating the significance and utility of this method.

No MeSH data available.


Related in: MedlinePlus

View of ADS simulation bench for simulation of TIM-2 pathway model. (A) Simulation setup defines the time duration and resolution; (B) Variable setup defines parameters and their range for optimization or tuning; (C) Initial condition and environment (external source) as applied to the cell subcircuit through wire connections; (D) Optimization setup of optimization methods and iteration time; (E) Goals for optimization.
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Figure 4: View of ADS simulation bench for simulation of TIM-2 pathway model. (A) Simulation setup defines the time duration and resolution; (B) Variable setup defines parameters and their range for optimization or tuning; (C) Initial condition and environment (external source) as applied to the cell subcircuit through wire connections; (D) Optimization setup of optimization methods and iteration time; (E) Goals for optimization.

Mentions: A simulation “bench” was constructed with Agilent ADS as shown in Figure 4 (Agilent ADS manual, http://www.agilent.com, Santa Clara, CA, USA). With proper conversion, mathematical equations can be mapped to equivalent circuits and solved through transient simulation for dynamic process or direct current (DC) simulation for stable states. Conversion consists of three parts: mathematical equation conversion, initial condition conversion, and output parameter conversion. Controlling parameters were extracted from in vitro experiments of iron uptake, storage and export as were performed in TCMK-1 vector and TIM-2 containing cells. Although the experiments were performed independently, the underlying mechanisms for iron metabolism would be the same since the same cell line was used. Therefore, the model with the same internal controlling parameters should be able to describe the underlying mechanisms for these three experiments. The goal of optimization for the overall model is to find a set of internal controlling parameters that will minimize error which is modeled by the sum of squares due to normalized error (SSNE) as shown in Equation 11. N is the total number of data points collected from three in vitro experiments. For each point j, yj represents the mean, σj represents the standard deviation, and mj represents the modeling data. The means and standard deviations are determined from repetitions in each point.(11)SSNE=∑j = 1N(mj−yj)2σj2


Application of circuit simulation method for differential modeling of TIM-2 iron uptake and metabolism in mouse kidney cells.

Xie Z, Harrison SH, Torti SV, Torti FM, Han J - Front Physiol (2013)

View of ADS simulation bench for simulation of TIM-2 pathway model. (A) Simulation setup defines the time duration and resolution; (B) Variable setup defines parameters and their range for optimization or tuning; (C) Initial condition and environment (external source) as applied to the cell subcircuit through wire connections; (D) Optimization setup of optimization methods and iteration time; (E) Goals for optimization.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3675319&req=5

Figure 4: View of ADS simulation bench for simulation of TIM-2 pathway model. (A) Simulation setup defines the time duration and resolution; (B) Variable setup defines parameters and their range for optimization or tuning; (C) Initial condition and environment (external source) as applied to the cell subcircuit through wire connections; (D) Optimization setup of optimization methods and iteration time; (E) Goals for optimization.
Mentions: A simulation “bench” was constructed with Agilent ADS as shown in Figure 4 (Agilent ADS manual, http://www.agilent.com, Santa Clara, CA, USA). With proper conversion, mathematical equations can be mapped to equivalent circuits and solved through transient simulation for dynamic process or direct current (DC) simulation for stable states. Conversion consists of three parts: mathematical equation conversion, initial condition conversion, and output parameter conversion. Controlling parameters were extracted from in vitro experiments of iron uptake, storage and export as were performed in TCMK-1 vector and TIM-2 containing cells. Although the experiments were performed independently, the underlying mechanisms for iron metabolism would be the same since the same cell line was used. Therefore, the model with the same internal controlling parameters should be able to describe the underlying mechanisms for these three experiments. The goal of optimization for the overall model is to find a set of internal controlling parameters that will minimize error which is modeled by the sum of squares due to normalized error (SSNE) as shown in Equation 11. N is the total number of data points collected from three in vitro experiments. For each point j, yj represents the mean, σj represents the standard deviation, and mj represents the modeling data. The means and standard deviations are determined from repetitions in each point.(11)SSNE=∑j = 1N(mj−yj)2σj2

Bottom Line: At the end of endocytosis, about 28% HFt remained intact and the rest was degraded.Iron released from degraded HFt was in the labile iron pool (LIP) and stimulated the generation of endogenous HFt for new storage.Both experimental data and the model showed that TIM-2 was not involved in the process of iron export.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, North Carolina Agricultural and Technical State University Greensboro, NC, USA.

ABSTRACT
Circuit simulation is a powerful methodology to generate differential mathematical models. Due to its highly accurate modeling capability, circuit simulation can be used to investigate interactions between the parts and processes of a cellular system. Circuit simulation has become a core technology for the field of electrical engineering, but its application in biology has not yet been fully realized. As a case study for evaluating the more advanced features of a circuit simulation tool called Advanced Design System (ADS), we collected and modeled laboratory data for iron metabolism in mouse kidney cells for a H ferritin (HFt) receptor, T cell immunoglobulin and mucin domain-2 (TIM-2). The internal controlling parameters of TIM-2 associated iron metabolism were extracted and the ratios of iron movement among cellular compartments were quantified by ADS. The differential model processed by circuit simulation demonstrated a capability to identify variables and predict outcomes that could not be readily measured by in vitro experiments. For example, an initial rate of uptake of iron-loaded HFt (Fe-HFt) was 2.17 pmol per million cells. TIM-2 binding probability with Fe-HFt was 16.6%. An average of 8.5 min was required for the complex of TIM-2 and Fe-HFt to form an endosome. The endosome containing HFt lasted roughly 2 h. At the end of endocytosis, about 28% HFt remained intact and the rest was degraded. Iron released from degraded HFt was in the labile iron pool (LIP) and stimulated the generation of endogenous HFt for new storage. Both experimental data and the model showed that TIM-2 was not involved in the process of iron export. The extracted internal controlling parameters successfully captured the complexity of TIM-2 pathway and the use of circuit simulation-based modeling across a wider range of cellular systems is the next step for validating the significance and utility of this method.

No MeSH data available.


Related in: MedlinePlus