Opportunities at the Intersection of Synthetic Biology, Machine Learning, and Automation.

@article{Carbonell2019OpportunitiesAT,
  title={Opportunities at the Intersection of Synthetic Biology, Machine Learning, and Automation.},
  author={Pablo Carbonell and Tijana Radivojevi{\'c} and H{\'e}ctor Garc{\'i}a Mart{\'i}n},
  journal={ACS synthetic biology},
  year={2019},
  volume={8 7},
  pages={
          1474-1477
        }
}
Our inability to predict the behavior of biological systems severely hampers progress in bioengineering and biomedical applications. We cannot predict the effect of genotype changes on phenotype, nor extrapolate the large-scale behavior from small-scale experiments. Machine learning techniques recently reached a new level of maturity, and are capable of providing the needed predictive power without a detailed mechanistic understanding. However, they require large amounts of data to be trained… 

Figures from this paper

A machine learning Automated Recommendation Tool for synthetic biology

The Automated Recommendation Tool (ART) is presented, a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system.

Machine learning for metabolic engineering: A review.

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism

This study uses a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms.

A versatile active learning workflow for optimization of genetic and metabolic networks

METIS is described, a modular and versatile active machine learning workflow with a simple online interface for the optimization of biological target functions with minimal experimental datasets that opens the way for convenient optimization and prototyping of genetic and metabolic networks with customizable adjustments according to user experience, experimental setup, and laboratory facilities.

Predictive engineering and optimization of tryptophan metabolism in yeast through a combination of mechanistic and machine learning models

This study uses a genome-scale model to pinpoint engineering targets and produce a large combinatorial library of metabolic pathway designs with different promoters which provide the basis for machine learning algorithms to be trained and used for new design recommendations.

Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering

This work presents a set of tools that, combined, provide the ability to store, visualize and leverage these data to predict the outcome of bioengineering efforts.

Artificial Metabolic Networks: enabling neural computation with metabolic networks

It is shown how Recurrent Neural Networks can surrogate constraint-based modeling and make metabolic networks suitable for backpropagation and consequently be used as an architecture for machine learning.

Learning from learning machines: a new generation of AI technology to meet the needs of science

If the fundamental challenges associated with “bridging the gap” between domain-driven scientific models and data-driven AI learning machines are addressed, then it is expected that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself.

Combining Automated Organoid Workflows with Artificial Intelligence‐Based Analyses: Opportunities to Build a New Generation of Interdisciplinary High‐Throughput Screens for Parkinson's Disease and Beyond

The opportunities and challenges resulting from the convergence of organoid HTS and AI‐driven data analytics are explored and potential future avenues toward the discovery of novel mechanisms and drugs in PD research are outlined.

References

SHOWING 1-10 OF 10 REFERENCES

A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data

It is shown that the combination of machine learning and abundant multiomics data (proteomics and metabolomics) can be used to effectively predict pathway dynamics in an automated fashion.

Synthetic biology: from hype to impact.

A brief history of synthetic biology

This Timeline article charts the technological and cultural lifetime of synthetic biology, with an emphasis on key breakthroughs and future challenges.

Dermatologist-level classification of skin cancer with deep neural networks

This work demonstrates an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists, trained end-to-end from images directly, using only pixels and disease labels as inputs.

On-chip integration of droplet microfluidics and nanostructure-initiator mass spectrometry for enzyme screening.

NIMS nanostructures can be fabricated into arrays for microfluidic droplet deposition, NIMS is compatible with droplet and digitalmicrofluidics, and can be used on-chip to assay glycoside hydrolase enzyme in vitro.

The $2.6 billion pill--methodologic and policy considerations.

  • J. Avorn
  • Business
    The New England journal of medicine
  • 2015
A broader-based and more transparent reckoning of research-and-development costs is needed to inform discussions about fostering innovation and paying for medications.

Machine Learning of Designed Translational Control Allows Predictive Pathway Optimization in Escherichia coli.

The implementation of machine learning algorithms to model the RBS sequence-phenotype relationship from representative subsets of large combinatorial RBS libraries allowing the accurate prediction of optimal high-producers is presented.

Machine learning

Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.