Neocortical neurons can be classified into different electrical firing types (e-types). A common approach to modeling e-types involves creating detailed electrical models (e-models) using generic ion channel currents such as transient and persistent sodium, potassium channels, and high- and low-voltage activated calcium channels. While this approach accurately captures a neuron's electrical behavior, it does not establish a link between specific ion channels and observed electrophysiological properties. However, if we model the neuron using only the ion channels known to be expressed in that cell, based on single-cell gene expression data, it would be possible to trace causal events down to specific ion channel genes. The main obstacle with this approach has been the lack of ion channel models for the different genetic subtypes of ion channel genes. To this end, we systematically characterized the kinetics of all voltage-gated ion channels and then built Hodgkin-Huxley models from experimental data. In this project, we used these genetic channel models to construct e-models. We optimized model parameters, specifically ion channel conductance, by minimizing differences between the e-model and targeted experimental data across a defined set of electrophysiological features. These genetic models reproduce firing properties observed in in vitro recordings and establish a link between single-cell gene expression data and neuron modeling, leading to a better understanding of neuron electrophysiology. Ultimately, these models can be used for disease simulations and in silico drug screening.