Electrical stimulation for application in tissue engineering and regenerative medicine has received increasing attention in recent years. A variety of stimulation methods, waveforms and amplitudes have been studied. However, a clear choice of optimal stimulation parameters is still not available and is complicated by ambiguous reporting standards. In order to understand underlying cellular mechanisms affected by the electrical stimulation, the knowledge of the actual prevailing field strength or current density is required. Here, we present a comprehensive digital representation, a digital twin, of a basic electrical stimulation device for the electrical stimulation of cells in vitro. The effect of electrochemical processes at the electrode surface was experimentally characterised and integrated into a numerical model of the electrical stimulation. Uncertainty quantification techniques were used to identify the influence of model uncertainties on relevant observables. Different stimulation protocols were compared and it was assessed if the information contained in the monitored stimulation pulses could be related to the stimulation model. We found that our approach permits to model and simulate the recorded rectangular waveforms such that local electric field strengths become accessible. Moreover, we could predict stimulation voltages and currents reliably. This enabled us to define a controlled stimulation setting and to identify significant temperature changes of the cell culture in the monitored voltage data. Eventually, we give an outlook on how the presented methods can be applied in more complex situations such as the stimulation of hydrogels or tissue in vivo.

An extensive research field in regenerative medicine is electrical stimulation (ES) and its impact on tissue and cells. The mechanism of action of ES, particularly the role of electrical parameters like intensity, frequency, and duration of the electric field, is not yet fully understood. Human MG-63 osteoblasts were electrically stimulated for 10 min with a commercially available multi-channel system (IonOptix). We generated alternating current (AC) electrical fields with a voltage of 1 or 5 V and frequencies of 7.9 or 20 Hz, respectively. To exclude liquid-mediated effects, we characterized the AC-stimulated culture medium. AC stimulation did not change the medium’s pH, temperature, and oxygen content. The H₂O₂ level was comparable with the unstimulated samples except at 5 V_7.9 Hz, where a significant increase in H₂O₂ was found within the first 30 min. Pulsed electrical stimulation was beneficial for the process of attachment and initial adhesion of suspended osteoblasts. At the same time, the intracellular Ca²⁺ level was enhanced and highest for 20 Hz stimulated cells with 1 and 5 V, respectively. In addition, increased Ca²⁺ mobilization after an additional trigger (ATP) was detected at these parameters. New knowledge was provided on why electrical stimulation contributes to cell activation in bone tissue regeneration.

Treatment of cartilage lesions remains a clinical challenge. Therefore, biophysical stimuli like electric fields seem to be a promising tool for chondrocytic differentiation and treatment of cartilage lesions. In this in vitro study, we evaluated the effects of low intensity capacitively coupled electric fields with an alternating voltage of 100 mV_RMS (corresponds to 5.2 × 10⁻⁵ mV/cm) or 1 V_RMS (corresponds to 5.2 × 10⁻⁴ mV/cm) with 1 kHz, on human chondrocytes derived from osteoarthritic (OA) and non-degenerative hyaline cartilage. A reduction of metabolic activity after electrical stimulation was more pronounced in non-degenerative cells. In contrast, DNA contents in OA cells were significantly decreased after electrical stimulation. A difference between 100 mV_RMS and 1 V_RMS was not detected. However, a voltage-dependent influence on gene and protein expression was observed. Both cell types showed increased synthesis rates of collagen (Col) II, glycosaminoglycans (GAG), and Col I protein following stimulation with 100 mV_RMS, whereas this increase was clearly higher in OA cells. Our results demonstrated the sensitization of chondrocytes by alternating electric fields, especially at 100 mV_RMS, which has an impact on chondrocytic differentiation capacity. However, analysis of further electrical stimulation parameters should be done to induce optimal hyaline characteristics of ex vivo expanded human chondrocytes.

The frequency-dependent behaviour of the dielectric properties of biological tissues in the frequency range below 1 kHz has been under debate since the past century. Here, we reanalyse the raw data of the main resource of the dielectric properties of biological tissues in impedance representation. Employing a Kramers-Kronig validity test and parameter estimation techniques, we can describe the data by two physical parametric models that correspond to opposing biophysical interpretations: on the one hand the data can be explained only by intrinsic tissue properties, but on the other hand evidence for electrode-specific effects can be found for all tissues under investigation. The first interpretation would justify the continued use of a parametric model comprising four Cole-Cole dispersions, which describe the dielectric properties from extremely low to very high frequencies. As an alternative that is in accordance with the second interpretation, we suggest to omit the slowest of the four dispersions in the model and increase the static conductivity to account for a frequency-independent conductivity below 1 kHz.

Background: Electrical stimulation is used for enhanced bone fracture healing. Electrochemical processes occur during the electrical stimulation at the electrodes and influence cellular reactions. Our approach aimed to distinguish between electrochemical and electric field effects on osteoblast-like MG-63 cells. We applied 20 Hz biphasic pulses via platinum electrodes for 2 h. The electrical stimulation of the cell culture medium and subsequent application to cells was compared to directly stimulated cells. The electric field distribution was predicted using a digital twin. Results: Cyclic voltammetry and electrochemical impedance spectroscopy revealed partial electrolysis at the electrodes, which was confirmed by increased concentrations of hydrogen peroxide in the medium. While both direct stimulation and AC-conditioned medium decreased cell adhesion and spreading, only the direct stimulation enhanced the intracellular calcium ions and reactive oxygen species. Conclusion: The electrochemical by-product hydrogen peroxide is not the main contributor to the cellular effects of electrical stimulation. However, undesired effects like decreased adhesion are mediated through electrochemical products in stimulated medium. Detailed characterisation and monitoring of the stimulation set up and electrochemical reactions are necessary to find safe electrical stimulation protocols.

With the increasing complexity of simulation studies, and thus increasing complexity of simulation experiments, there is a high demand for better support for them to be conducted. Recently, model-driven approaches have been explored for facilitating the specification, execution, and reproducibility of simulation experiments. However, a more general approach that is suited for a variety of modeling and simulation areas, experiment types, and tools, which also allows for further automation, is still missing. Therefore, we present a novel model-driven engineering (MDE) framework for simulation studies that extends the state-of-the-art of conducting simulation experiments in the following ways: (a) Providing a structured representation of the various ingredients of simulation experiments in the form of meta models and collecting them in a repository improves knowledge sharing across application domains and simulation approaches. (b) Specifying simulation experiments in the quasi-standardized form of the meta models (e.g., via a GUI) and, subsequently, performing the automatic generation of experiment specifications in a language of choice increases both the productivity and quality of complex simulation experiments. (c) Automatic code transformation between specification languages via the meta models enables the reusability of simulation experiments. (d) Integrating the framework using a command-line interface allows for further automation of subprocesses within a simulation study. We demonstrate the advantages and practicality of our approach using real simulation studies from three different fields of simulation (stochastic discrete-event simulation of a cell signaling pathway, virtual prototyping of a neurostimulator, and finite element analysis of electric fields) and various experiment types (global sensitivity analysis, time course analysis, and convergence testing). The proposed framework can be the starting point for further automation of simulation experiments and, therefore, can assist in conducting simulation studies in a more systematic and effective manner. For example, based on this MDE framework, approaches for automatically selecting and parametrizing experimentation methods, or for planning follow-up activities depending on the context of the simulation study, could be developed.