Deep Learning Concepts and Applications for Synthetic Biology

Abstract

Synthetic biology has a natural synergy with deep learning. It can be used to generate large data sets to train models, for example by using DNA synthesis, and deep learning models can be used to inform design, such as by generating novel parts or suggesting optimal experiments to conduct. Recently, research at the interface of engineering biology and deep learning has highlighted this potential through successes including the design of novel biological parts, protein structure prediction, automated analysis of microscopy data, optimal experimental design, and biomolecular implementations of artificial neural networks. In this review, we present an overview of synthetic biology-relevant classes of data and deep learning architectures. We also highlight emerging studies in synthetic biology that capitalize on deep learning to enable novel understanding and design, and discuss challenges and future opportunities in this space.

The whack-a-mole governance challenge for AI-enabled synthetic biology: literature review and emerging frameworks

“…This is expected to increase dramatically quite soon, given the success of this latest wave of AI technology and the platform aspects of its spread. However, as one paper put it, synthetic biology has a natural synergy with deep learning ( Beardall et al, 2022 ). The use cases discussed in various papers include: as a classifying text, generic search engine, generating ideas, helping with access to scientific knowledge, coding, patient care ( Clusmann et al, 2023 ), protein folding, proofreading, sequence analysis, summarizing knowledge, text mining of biomedical data, translation ( Clusmann et al, 2023 ), workflow optimization, foresight of future research directions ( Yan et al, 2023 ); collection of related synthetic biology data, and more ( Beardall et al, 2022 ; Clusmann et al, 2023 ; Tarasava, 2023 ).…”

“…However, as one paper put it, synthetic biology has a natural synergy with deep learning ( Beardall et al, 2022 ). The use cases discussed in various papers include: as a classifying text, generic search engine, generating ideas, helping with access to scientific knowledge, coding, patient care ( Clusmann et al, 2023 ), protein folding, proofreading, sequence analysis, summarizing knowledge, text mining of biomedical data, translation ( Clusmann et al, 2023 ), workflow optimization, foresight of future research directions ( Yan et al, 2023 ); collection of related synthetic biology data, and more ( Beardall et al, 2022 ; Clusmann et al, 2023 ; Tarasava, 2023 ). The promise of AI-enabled cell-free synbio systems ( Lee and Kim, 2023 ), which use molecular machinery extracted from cells, is particularly significant for automation and scale-up of biosensors among other things.…”

Deep model predictive control of gene expression in thousands of single cells

“…Adapting our methodology to other systems could further pave the way towards orthogonal, multiple-input multiple-output optogenetic control. Beyond investigating natural cell processes, our platform could also be used for synthetic biology and metabolic engineering applications 54 . Others have already used similar cell-machine interfaces to simulate the impact of different genetic circuit topologies 18 , as a test bench to characterize gene circuit responses 29 , or to control methionine metabolism 43 .…”

Deep model predictive control of gene expression in thousands of single cells

“…Further, with an additional fluorescent reporter, we could also quantify signal propagation between related genes, or even map out and model gene regulation networks. Beyond investigating natural cell processes, our platform can also be used for synthetic biology and metabolic engineering applications (46): Others have already used similar cell-machine interfaces to simulate the impact of different genetic circuits topologies (18), as a test bench to characterize gene circuit responses (29), or to control methionine metabolism (42). We envision that this control approach can dramatically expand the scale of these types of studies.…”