# In Vitro Simulation of Human Colonic Fermentation:
## 4 Available Colonic In Vitro Fermentation Models
### Dynamic Colonic Fermentation Models  
Dynamic colonic models are far more complex and expensive than the static ones. Recently, [PERSON] et al. (2020) presented the available dynamic colonic models according to their design, technical features, and main applicability [61]. Among them, SHIME and TIM-2 stand out for being widely applied in the food science and technology fields although SIMGI and PolyFermS are also commonly used.  
These simulators have different designs so that the fermentation process can include a single (mono-stage) or several compartments (multi-stage). While only a segment of the colon is simulated (i.e., the proximal colon which is considered as ascandant and transversal) in mono-stage systems [62; 63; 64], dynamic multi-stage models can simulate up to the three regions of the colon: the ascending, transverse, and descending colon, reproducing the different in vivo environmental conditions (in terms of pH, retention time, characteristic volume, and availability of substrates) in each of them [65; 66; 67]. Moreover, some of the dynamic in vitro models also include compartments to simulate part of the upper gastrointestinal digestion: gastric and small intestine [68; 69] or a dialysis system, which simulate the absorption of the metabolites produced during the fermentation process [62]. Some others allow for the simulation of the mucosal environment to colonize and mimic the gut mucosa microbiota [64; 70], and finally, others are able to run two or three samples at the same time in order to have a control reference or replicates [71].  
In general terms, dynamic models enable a continuous regime (substrates are continuously coming in the system while the products of the reactions are removed), allowing for the stabilization of the microbiota to the environmental conditions and reaching the steady state [72]. Once the steady state is achieved, the observed changes in the composition and function of the microbiota can be attributed to those experimental conditions, rather than to the adaptation to the environmental conditions. The stabilization period is essential in dynamic models, and it is performed before starting the experiment. The followed protocol depends on the system characteristics [62; 73; 74]. Furthermore, achieving the physiological conditions of the environment is a key aspect for reaching the proper simulated microbiota in each colonic region. Consequently, these models work at 37 \({}^{\circ}\)C, maintain anaerobic conditions (e.g., by periodically flushing nitrogen), control the pH by automatically adding NaOH or HCl to each vessel, and mixing the compartment contents (e.g., by magnetic stirring or peristaltic movements) [73; 75]. Finally, colonic variables such as pH, retention time, and volume not only depend on the utilized model but also on the aim or purpose of the study. For instance, in studies with PolyFermS focused on assaying both, the colonic fermentation of both the elderly [76] and children [77], the parameters in terms of pH, retention time, and volume were set up according to an age model.  
Altogether, dynamic models allow for the study of the effects of food on gut microbiota in a more complex environment that simulates the in vivo conditions better than static models and allow for the assessment of longer periods of time (e.g., one or several weeks) [61; 65; 66]. The main research objectives when using these models have been the evaluation of the prebiotic, probiotic, and symbiotic effect of some compounds on gut microbiota: Table 2 gathers the objectives and references of published studies reporting the applicability of dynamic colonic models in food science and technology fields over the last 10 years. As in the case for the in vitro simulation of digestion in the upper gut, most of the studies have focused on a food extract rather than a complete food matrix, probably due to the increased complexity of the experimental procedure and analytical determinations that food matrices imply.