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

Carbonell, P.; Radivojevic, T.; Garcia-Martin, H. (2019-07)

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 ...

A concerted systems biology analysis of phenol metabolism in Rhodococcus opacus PD630

Roell, G.W.; Carr, R.R.; Campbell, T.; Shang, Z.; Henson, W.R.; Czajka, J.J.; Garcia-Martin, H.; Zhang, F.; Foston, M.; Dantas, G.; Moon, T.S.; Tang, Y.J. (2019-06)

Rhodococcus opacus PD630 metabolizes aromatic substrates and naturally produces branched-chain lipids, which are advantageous traits for lignin valorization. To provide insights into its lignocellulose hydrolysate utilization, ...

ART: A machine learning Automated Recommendation Tool for synthetic biology

Radivojević, T.; Costello, Z.; Workman, K.; Garcia Martin, H. (2019)

Biology has changed radically in the last two decades, transitioning from a descriptive science into a design science. Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable ...

Chemoinformatic-guided engineering of polyketide synthases

Zargar, A.; Lal, R.; Valencia, L.; Wang, J.; Backman, T.; Cruz-Morales, P.; Kothari, A.; Werts, M.; Wong, A.; Bailey, C.; Loubat, A.; Liu, Y.; Benites, V.; Chang, S.; Hernández, A.; Barajas, J.; Thompson, M.; Barcelos, C.; Anayah, R.; Garcia Martin, H.; Mukhopadhyay, A.; Baidoo, E.; Katz, L.; Keasling, J. (2019)

Polyketide synthase (PKS) engineering is an attractive method to generate new molecules such as commodity, fine and specialty chemicals. A central challenge in PKS design is replacing a partially reductive module with a ...

Lessons from Two Design–Build–Test–Learn Cycles of Dodecanol Production in Escherichia coli Aided by Machine Learning

Opgenorth, P.; Costello, Z.; Okada, T.; Goyal, G.; Chen, Y.; Gin, J.; Benites, V.; de Raad, M.; Northen, T.R.; Deng, K.; Deutsch, S.; Baidoo, E.E.K.; Petzold, C.J.; Hillson, N.J.; Garcia-Martin, H.; Beller, H.R. (2019-01-01)

The Design–Build–Test–Learn (DBTL) cycle, facilitated by exponentially improving capabilities in synthetic biology, is an increasingly adopted metabolic engineering framework that represents a more systematic and efficient ...

Machine learning framework for assessment of microbial factory performance

Oyetunde, T.; Liu, D.; Garcia-Martin, H.; Tang, Y.J. (2019-01-01)

Metabolic models can estimate intrinsic product yields for microbial factories, but such frameworks struggle to predict cell performance (including product titer or rate) under suboptimal metabolism and complex bioprocess ...

Common principles and best practices for engineering microbiomes

Lawson, C.E.; Harcombe, W.R.; Hatzenpichler, R.; Noguera, D.R.; McMahon, K.D.; Lindemann, S.R.; Loffler, F.E.; O'Malley, M.A.; Garcia-Martin, H.; Pfleger, B.F.; Raskin, L.; Venturelli, O.S.; Weissbrodt, D.G. (2019)

Despite broad scientific interest in harnessing the power of Earth's microbiomes, knowledge gaps hinder their efficient use for addressing urgent societal and environmental challenges. We argue hat structuring research ...

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

Zhang, J.; Petersen, S.; Radivojevic, T.; Ramirez, A.; Pérez, A.

; Abeliuk, E.; Sánchez, B.; Costello, Z.; Chen, Y.; Fero, M.; Garcia Martin, H.; Nielsen, J.; Keasling, J.; Jensen, M. (2019)

In combination with advanced mechanistic modeling and the generation of high-quality multi-dimensional data sets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show ...

A Machine Learning Approach to Predict Metabolic Pathway Dynamics from Time Series Multiomics Data

Costello, Z.; Garcia-Martin, H. (2018-05)

New synthetic biology capabilities hold the promise of dramatically improving our ability to engineer biological systems. However, a fundamental hurdle in realizing this potential is our inability to accurately predict ...

The Experiment Data Depot: A Web-Based Software Tool for Biological Experimental Data Storage, Sharing, and Visualization

Morrell, W.C.; Birkel, G.W.; Forrer, M.; Lopez, T.; Backman, T.W.H.; Dussault, M.; Petzold, C.J.; Baidoo, E.E.K.; Costello, Z.; Ando, D.; Alonso-Gutierrez, J.; George, K.W.; Mukhopadhyay, A.; Vaino, I.; Keasling, J.D.; Adams, P.D.; Hillson, N.J.; Garcia-Martin, H. (2017-09-30)

Although recent advances in synthetic biology allow us to produce biological designs more efficiently than ever, our ability to predict the end result of these designs is still nascent. Predictive models require large ...