Plants are challenged by a constantly changing environment. In response to these environmental signals as well as developmental cues, plants will meticulously adjust and finetune their growth. To manipulate plant growth, it is thus key to understand the regulations that integrate these signals and elicit a timely and coordinated response. Through probabilistic and machine learning inference methods, we have identified central regulators during development in Arabidopsis, tomato, pepper, and soybean. However, studying dynamic regulations in non-model species appeared challenging as a result of poor functional annotation of regulatory proteins. To overcome such limitations, we leveraged the advantages of artificial intelligence to predict protein function or sequence domains, including transcriptional activation domains. Finally, to further dissect the cellular signals in changing environments, we have established a framework for 3D bioprinting plant cells to study cell viability, cell division, and cell identity. The framework established here paves the way for a general use of 3D bioprinting for studying cellular responses in a tunable environment.
Genome editing, cutting-edge technology for a sustainable agriculture