In silico bioreactors for the computer-aided design of tissue engineering applications

Tissue engineering (TE) is an emerging multidisciplinary field that draws on expertise from medicine, biology, chemistry, genetics, engineering, computer and life science. Its mission is to discover solutions to one of the most challenging medical problems faced by humans: replace tissue and organ functions when damage is beyond natural recovery process. A precondition for successful TE is having an adequate understanding of the principles of tissue genesis. The goal is to apply that knowledge to produce functional tissue replacements suitable for clinical use. Specifically achieve biological-inspired, biocompatible, tissuemimetic structures that, when implanted in vivo, restore or improve failed or compromised human tissue and/or organ function. Impressive progress in human tissue regeneration followed development and implementation of advanced technologies that enabled better understanding and control signalling within microenvironments during growth and maturation of tissue functionalization. In particular, the latest generation of bioreactors have demonstrably improved in vitro tissue maturation prior to implantation. That achievement was made possible by two engineering advances: i) repeatable and automated bioprocesses, and ii) recapitulation of key physiologic, physicochemical and mechanical cues in vitro. Despite this progress critical, large gaps in our knowledge are slowing progress. For example, how can cell level operating principles and environmental cues be orchestrated in advanced bioreactors to enable the formation of a physiological-like functional tissue? What are those cell level operating principles? Because the tissue is developing ex vivo, will the orchestration need to be different in important ways from that occurring during organogenesis? When detailed information is limited, uncertainties are large, and feasible wet-lab experiments are limited by costs and other factors, in silico exploratory modelling and simulation can be a cost-effective adjunct strategy for answering those and related questions. It can help achieve critically needed mechanistic insight into complexities of suitable cellular responses to the conditioning procedures. Improving and experimenting upon multi-attribute, multiscale computational models is a relatively new, scientific approach for i) evaluating cellular responses to the conditioning procedures within bioreactors, ii) predicting or anticipating the dynamic modification of the cellular behaviours during culture as a function of external stimuli, and iii) discovering relevant features and protocols for optimizing bioreactor working conditions (i.e., the external stimuli for the cells), in terms of interactions between cells and the bioartificial hosting environment. Within this chapter we discuss how coupling i) computational fluid dynamics (CFD) and ii) multi agent systems (MAS) modelling methods is enabling rationale design and subsequent establishment of in silico bioreactors. Problems associated with designing TE experiments can be explored using in silico high-throughput experiments, and plausible solutions can be identified in advance by creating a software framework, which incorporates a variety of phenomena known to influence tissue growth, along with a model of cell population dynamics. Upon maturation, the approach is expected to provide exploitable insight into how tissue generation and maturation emerge and can be controlled. That insight can be leveraged to fine-tune system parameters to achieve desired cellular responses during in vitro conditioning while reducing reliance on costly physical experiments. Computational modelling and simulation in TE is presented as a diverse, active, and powerful transdisciplinary expansion of scientific and engineering methods for overcoming many of the current limitations in identifying optimal regenerative therapies for diseased and damaged tissues.