Author

Tijana Radivojević

United States Department of EnergyORCID 0000-0002-7165-2909Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioengineering· 1 Synthetic Biology· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 2 Assay:metabolomics· 2 Approach:Bayesian inference· 1 Approach:deep learning· 1 Assay:DNA methylation· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1 Assay:proteomics· 1

Frequent Co-Authors

Héctor García MartíniD· 3 shared Jose Manuel MartíiD· 2 shared Mark Forrer· 2 shared Tyler W. H. BackmaniD· 2 shared Andrew B. ConleyiD· 1 shared Apostolos ZournasiD· 1 shared Azadeh Alikhani· 1 shared Christina SchenkiD· 1 shared

Activity by Year

2021-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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3 shared papersBioengineering

Héctor García MartínORCID

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3 papers

Recent·Most cited

Prediction of metabolic dynamics through deep learning and high-throughput multiomics data

Jose Manuel Martí, Patrick C. Kinnunen, Mark Held, Azadeh Alikhani, Andrew B. Conley, Yan Chen, Tijana Radivojević, Jennifer W. Gin, Apostolos Zournas, Rebecca M. Lennen, Hooman Hefzi, Mustafa Baniodeh, Peter Harrington, Mark Forrer, David Melis, , Héctor García Martín

This paper trains a deep learning model on multiomics data to predict metabolic dynamics in synthetic biology, achieving high accuracy in predicting product and metabolite levels.

bioRxiv · May 27, 2025 · PreprintBioengineering

BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale

Tyler W. H. Backman, Christina Schenk, Tijana Radivojević, David Ando, Janavi Singh, Jeffrey J. Czajka, Zak Costello, Jay D. Keasling, Yinjie Tang, Elena V. Akhmatskaya, Héctor García Martín

BayFlux quantifies metabolic fluxes and their uncertainty using Bayesian inference, showing genome-scale models reduce flux uncertainty compared to core models and improve knockout predictions.

bioRxiv · Apr 19, 2023 · PreprintSystems Biology

Multiomics data collection, visualization, and utilization for guiding metabolic engineering

Somtirtha Roy, Tijana Radivojević, Mark Forrer, Jose Manuel Martí, Vamshi Jonnalagadda, Tyler W. H. Backman, William C. Morrell, Hector Plahar, Joonhoon Kim, Nathan J. Hillson, Héctor García Martín

The paper presents tools for storing, visualizing, and leveraging multiomics data to predict outcomes of bioengineering efforts.

Frontiers in Bioengineering and Biotechnology · Feb 9, 2021Peer-reviewedSynthetic Biology