About this Research Topic
Establishing a bio-based economy is a vital component of our transition to a more sustainable and resource-efficient society. This biological transformation of conventional manufacturing is rooted in biotechnological disciplines such as Metabolic Engineering, Synthetic Biology, and Bioprocess Development. In their journey towards robustness and reliability, these fields have evolved towards certain goals such as standardization, reproducibility, simplification, automation, rapid prototyping, and scalability of biological processes. This is achieved by adhering to engineering principles and enabling technologies such as gene editing, DNA synthesis, directed evolution, etc. In recent years, a thorough framework called the design-build-test-learn (DBTL) cycle has been adopted to discover, design, and improve bioengineered systems and bioprocesses. This cycle accelerates and reduces the cost of exploration, analysis, optimization, and exploitation of desired target solutions through iterations of its four phases.
Ideally, after each DBTL cycle, new knowledge is generated which feeds back into a subsequent cycle by means of alternative approaches, reformulated problems, or forward specifications for future designs. In this way, the multi-dimensional solution space of the initial problem becomes narrowed, leading to a reduced set of optimal options. The current state of DBTL approaches allows almost complete automation of the cycle and integrates strong computational components such as biological modeling, machine learning, and artificial intelligence (AI). However, while iterative procedures are routinely performed for pathway, strain, and bioprocess development, the DBTL is often not fully closed due to scarce or basic testing methods or omission of the learning step. Advanced computational methods can be synergistically used to overcome bottlenecks of biological complexity. Altogether, they facilitate accurate predictions, enhance the assessment of system performance, and enable hybrid learning. These “biointelligent” DBTL approaches to enhance the progress of modern biotechnology by strengthening the last two phases of the cycle, Test and Learn.
The focus of this Research Topic is to explore advances in bioengineered systems and bioprocesses that have adopted a "biointelligent" design-build-test-learn (DBTL) approach, with an emphasis on the testing and learning phases. We invite submissions that are relevant to or inspired by the following topics:
• Testing: a novel, automatable tools and devices for bioengineering and assessment; novel in vivo, standardized, and quantitative readouts; innovative metrologies for quantification and qualification; biosensors for in-line monitoring of key features; adaptive laboratory evolution studies of cellular reprogramming; -omics methodologies, data integration, and analysis; high-throughput screening of biological libraries; valorization cases; rapid prototyping; etc.
• Learning: hybrid learning to speed up the development of novel strains and microbially engrafted features; application of digital twinning to yield actionable knowledge; mechanistic models linked to AI fed by experimental DBT stages; biology/biotech/biochem-guided AI; self-driving laboratories; autonomous biomanufacturing; etc.
• Representation and organization of generated experimental data through data format standards, ontologies, FAIRness, etc. as well as sharing of data in a decentralized manner.
The aforementioned topics do not necessarily need to be presented as the standalone focus of the manuscripts and can be part of overarching methodologies that attempt to fulfill the whole iteration of a DBTL cycle.
We appreciate contributions in the shape of Original Research, Reviews, Mini-reviews, and Perspectives.
Ideally, after each DBTL cycle, new knowledge is generated which feeds back into a subsequent cycle by means of alternative approaches, reformulated problems, or forward specifications for future designs. In this way, the multi-dimensional solution space of the initial problem becomes narrowed, leading to a reduced set of optimal options. The current state of DBTL approaches allows almost complete automation of the cycle and integrates strong computational components such as biological modeling, machine learning, and artificial intelligence (AI). However, while iterative procedures are routinely performed for pathway, strain, and bioprocess development, the DBTL is often not fully closed due to scarce or basic testing methods or omission of the learning step. Advanced computational methods can be synergistically used to overcome bottlenecks of biological complexity. Altogether, they facilitate accurate predictions, enhance the assessment of system performance, and enable hybrid learning. These “biointelligent” DBTL approaches to enhance the progress of modern biotechnology by strengthening the last two phases of the cycle, Test and Learn.
The focus of this Research Topic is to explore advances in bioengineered systems and bioprocesses that have adopted a "biointelligent" design-build-test-learn (DBTL) approach, with an emphasis on the testing and learning phases. We invite submissions that are relevant to or inspired by the following topics:
• Testing: a novel, automatable tools and devices for bioengineering and assessment; novel in vivo, standardized, and quantitative readouts; innovative metrologies for quantification and qualification; biosensors for in-line monitoring of key features; adaptive laboratory evolution studies of cellular reprogramming; -omics methodologies, data integration, and analysis; high-throughput screening of biological libraries; valorization cases; rapid prototyping; etc.
• Learning: hybrid learning to speed up the development of novel strains and microbially engrafted features; application of digital twinning to yield actionable knowledge; mechanistic models linked to AI fed by experimental DBT stages; biology/biotech/biochem-guided AI; self-driving laboratories; autonomous biomanufacturing; etc.
• Representation and organization of generated experimental data through data format standards, ontologies, FAIRness, etc. as well as sharing of data in a decentralized manner.
The aforementioned topics do not necessarily need to be presented as the standalone focus of the manuscripts and can be part of overarching methodologies that attempt to fulfill the whole iteration of a DBTL cycle.
We appreciate contributions in the shape of Original Research, Reviews, Mini-reviews, and Perspectives.
Keywords: Design-build-test-learn cycle, biomanufacturing; automation, hybrid learning, strain optimization, bioprocess optimization
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
