Author

Mark Forrer

Sandia National Laboratories California

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

Click to filter author papers

Bioengineering· 1 Synthetic Biology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

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

Frequent Co-Authors

Héctor García MartíniD· 2 shared Jose Manuel MartíiD· 2 shared Tijana RadivojevićiD· 2 shared Andrew B. ConleyiD· 1 shared Apostolos ZournasiD· 1 shared Azadeh Alikhani· 1 shared Christopher J. PetzoldiD· 1 shared David Melis· 1 shared

Activity by Year

2021-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 39 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBioengineering

Héctor García MartínORCID

Mutual co-authors in cluster

Author profile →

2 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

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