Three-dimensionally printed constructs are static and do not recapitulate the dynamic nature of tissues. Four-dimensional (4D) bioprinting has emerged to include conformational changes in printed structures in a predetermined fashion using stimuli-responsive biomaterials and/or cells. The ability to make such dynamic constructs would enable an individual to fabricate tissue structures that can undergo morphological changes. Furthermore, other fields (bioactuation, biorobotics, and biosensing) will benefit from developments in 4D bioprinting. Here, the authors discuss stimuli-responsive biomaterials as potential bioinks for 4D bioprinting. Natural cell forces can also be incorporated into 4D bioprinted structures. The authors introduce mathematical modeling to predict the transition and final state of 4D printed constructs. Different potential applications of 4D bioprinting are also described. Finally, the authors highlight future perspectives for this emerging technology in biomedicine.
Renewable energy from lignocellulosic biomass has been deemed an alternative to depleting fossil fuels. In order to improve this technology, we aim to develop robust mathematical models for the enzymatic lignocellulose degradation process. By analyzing 96 groups of previously published and newly obtained lignocellulose saccharification results and fitting them to Weibull distribution, we discovered Weibull statistics can accurately predict lignocellulose saccharification data, regardless of the type of substrates, enzymes and saccharification conditions. A mathematical model for enzymatic lignocellulose degradation was subsequently constructed based on Weibull statistics. Further analysis of the mathematical structure of the model and experimental saccharification data showed the significance of the two parameters in this model. In particular, the λ value, defined the characteristic time, represents the overall performance of the saccharification system. This suggestion was further supported by statistical analysis of experimental saccharification data and analysis of the glucose production levels when λ and n values change. In conclusion, the constructed Weibull statistics-based model can accurately predict lignocellulose hydrolysis behavior and we can use the λ parameter to assess the overall performance of enzymatic lignocellulose degradation. Advantages and potential applications of the model and the λ value in saccharification performance assessment were discussed.