About

Bernhard Palsson is the Y.C. Fung Endowed Professor in Bioengineering, Professor of Pediatrics, and the Principal Investigator of the Systems Biology Research Group in the Department of Bioengineering at the University of California, San Diego. He has co-authored more than 700 peer-reviewed research articles and authored four textbooks, with more in preparation. Dr. Palsson is an inventor on over 40 U.S. patents and a co-founder of several biotechnology companies. He has received several major biotechnology awards throughout his career. Dr. Palsson earned his PhD in Chemical Engineering from the University of Wisconsin, Madison in 1984. He is a member of the National Academy of Engineering and National Academy of Inventors, and a Fellow of the AIChE, AIMBE, AAAS, and the AAM. Since 2014, Dr. Palsson has been recognized as a Clarivate Highly Cited Researcher.

Research topics

• Computer Science
• Biology
• Computational biology
• Genetics
• Programming language
• Biochemistry
• World Wide Web
• Anatomy
• Botany
• Management science
• Mathematics
• Library science
• Engineering
• Biotechnology
• Physiology
• Data science
• Cell biology

Selected publications

• Gut microbial ethanol metabolism contributes to auto-brewery syndrome in an observational cohort

  Nature Microbiology · 2026-01-08 · 3 citations

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  PublisherDOI

• Approaches for accelerating microbial gene function discovery using artificial intelligence

  Nature Microbiology · 2026-01-07 · 3 citations

  article1st author
  PublisherDOI

• 30: GUT MICROBIOTA IN AUTO-BREWERY SYNDROME: PATHOLOGIC MICROBIAL ETHANOL PRODUCTION AND TREATMENT WITH FECAL MICROBIOTA TRANSPLANTATION

  Gastroenterology · 2025-05-01

  article
  PublisherDOI

• Laboratory Evolution Reveals Transcriptional Mechanisms Underlying Thermal Adaptation of <i>Escherichia coli</i>

  Genome Biology and Evolution · 2025-09-30 · 5 citations

  articleOpen accessSenior author

  Adaptive laboratory evolution is able to generate microbial strains, which exhibit extreme phenotypes, revealing fundamental biological adaptation mechanisms. Here, we use adaptive laboratory evolution to evolve Escherichia coli strains that grow at temperatures as high as 45.3 °C, a temperature lethal to wild-type cells. The strains adopted a hypermutator phenotype and employed multiple systems-level adaptations that made global analysis of the DNA mutations difficult. Given the challenge at the genomic level, we were motivated to uncover high-temperature tolerance adaptation mechanisms at the transcriptomic level. We employed independently modulated gene set (iModulon) analysis to reveal five transcriptional mechanisms underlying growth at high temperatures. These mechanisms were connected to acquired mutations, changes in transcriptome composition, sensory inputs, phenotypes, and protein structures. They are as follows: (i) downregulation of general stress responses while upregulating the specific heat stress responses, (ii) upregulation of flagellar basal bodies without upregulating motility and upregulation fimbriae, (iii) shift toward anaerobic metabolism, (iv) shift in regulation of iron uptake away from siderophore production, and (v) upregulation of yjfIJKL, a novel heat tolerance operon whose structures we predicted with AlphaFold. iModulons associated with these five mechanisms explain nearly half of all variance in the gene expression in the adapted strains. These thermotolerance strategies reveal that optimal coordination of known stress responses and metabolism can be achieved with a small number of regulatory mutations and may suggest a new role for large protein export systems. Adaptive laboratory evolution with transcriptomic characterization is a productive approach for elucidating and interpreting adaptation to otherwise lethal stresses.

  PublisherDOI

• Deep Red Blood Cell Proteome Defines the Band 3 N-Terminus Interactome as a Regulator of Hypoxic Adaptation via BLVRB-Dependent <i>S</i> -Nitroso Transfer

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-11-30

  preprintOpen access

  Red blood cells (RBCs) have long been regarded as passive oxygen carriers, yet growing evidence reveals a complex, dynamic proteome independent of de novo gene expression. Here, we define the erythrocyte as an oxygen-responsive system organized around a Band 3 (SLC4A1)-centered metabolon. Using deep proteomics of ultra-pure RBCs and cross-linking interactomics, we identify biliverdin reductase B (BLVRB) as a previously unrecognized Band 3 interactor that binds the N-terminal cytosolic domain under normoxia and dissociates under hypoxia, when band 3-deoxyhemoglobin interactions increase threefold. This reversible interaction forms an oxygen-sensitive switch coupling structural, redox, and metabolic remodeling. In humanized mice, truncation of the Band 3 N-terminus disrupted glycolytic activation, reduced 2,3-bisphosphoglycerate synthesis, and impaired exercise tolerance despite preserved cardiopulmonary function, establishing the physiological relevance of this module. Population-scale proteome quantitative trait locus (pQTL) analyses revealed coordinated variation of SLC4A1 and BLVRB abundance but minimal association of biliverdin levels with BLVRB genotype, suggesting alternative functions beyond heme catabolism. Mechanistically, BLVRB Cys109 acts as a nitric oxide (NO) relay, trans-nitrosating glycolytic enzymes such as GAPDH at active site Cys152, transiently inhibiting glycolysis. This S-nitrosation-mediated feedback mirrors conserved mechanisms in plants, where GAPDH-SNO redirects carbon flow toward the Calvin-Benson cycle under nitrosative stress, revealing an evolutionary convergence in gas-responsive metabolic control. Collectively, our findings define a Band 3-BLVRB-hemoglobin axis that links oxygen sensing, NO signaling, and redox homeostasis, providing a unifying model for how an anucleate cell achieves environmental adaptability through reversible protein-protein interactions and post-translational chemistry. Graphic abstract: Issaian et al. define the most comprehensive proteome of ultra-pure human red blood cells (3,775 proteins) and map the O₂-dependent interactome, revealing a Band 3-BLVRB-hemoglobin module that links oxygen sensing to metabolic remodeling via reversible inhibitory S-nitrosation of GAPDH C152. In plants this redirects carbon toward photosynthesis, illustrating a conserved NO-dependent metabolic reprogramming mechanism across oxygen-regulated systems. Highlights: Deep proteomics defines a complete, contamination-free RBC proteome (3,775 proteins)Cross-linking proteomics maps an oxygen-sensitive Band 3-centered interactomeO2-dependent BLVRB-Band 3 binding regulates metabolism via S-nitrosation of GAPDHBand 3 N-terminus is required for hypoxic remodeling and exercise tolerance in vivo.

  PublisherOA PDFDOI

• Reframing the role of the objective function in its proper context for metabolic network modeling

  Cell Systems · 2025-06-01 · 6 citations

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  PublisherDOI

• Revealing Transcriptomic Responses in <i>Escherichia coli</i> During Early Antibiotic Exposure

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-09-20

  preprintOpen accessSenior authorCorresponding

  Abstract The earliest responses of pathogenic bacteria to antibiotics can affect the outcome of an infection. While long-term adaptations have been extensively studied, the immediate transcriptional changes that unfold immediately following antibiotic exposure remain poorly understood. Here, we applied iModulon analysis to time-resolved transcriptomic data from Escherichia coli exposed to subinhibitory concentrations of two antibiotics (ampicillin and ciprofloxacin), capturing transcriptional regulatory changes occurring within the first 30 minutes of exposure. This analysis reveals an integrated, three-phase response: an immediate and sustained primary response that broadly activates stress programs, a transient secondary response that restores redox balance, and a tertiary response that supports long-term survival through metabolic remodeling and antibiotic-specific defenses. These results highlight a coordinated and dynamic regulatory strategy describing how metabolic, redox, and stress responses are integrated to manage the physiological challenges of antibiotic stress. By disentangling these overlapping transcriptional regulatory programs, this work offers a genome-scale understanding of how survival mechanisms unfold during the critical moments following antibiotic exposure. The study opens new directions for investigating early survival mechanisms and the possible identification of new targets to disrupt the initial adaptation process. Importance Initial bacterial responses to antibiotics are important for survival and can influence the development of tolerance and resistance. Yet this period remains poorly understood, in part because the transcriptional responses that unfold within minutes of antibiotic exposure are complex and difficult to interpret. In this study, we applied novel data generation and data analytics approaches to untangle the complexity of the initial response of Escherichia coliI to two antibiotics. We reveal a three-phase process that explains how E. coli coordinates stress responses, maintains redox homeostasis, and establishes longer-term defenses. The novel transcriptomic analytics elucidate independently regulated sets of genes that constitute cellular processes. By identifying all such cellular processes that react over the initial time scale, we can deconvolute the response based on first principles of cellular physiology.

  PublisherOA PDFDOI

• Structure of the <i>Enterobacter</i> pan-genome is revealed using machine learning

  Microbiology Spectrum · 2025-12-15 · 1 citations

  articleOpen accessSenior author

  ABSTRACT The growing availability of publicly accessible Enterobacter genomes offers an opportunity to reveal the structure of its pangenome, uncovering the catalog of genes across the genus and their distribution across the different species and subspecies of the genus. In this study, we analyze 777 high-quality complete Enterobacter genomes using a pangenome matrix. The accessory genome, consisting of the genes found in many, but not all strains, was decomposed using non-negative matrix factorization (NMF) to identify groups of genes, called Phylons, that are found to be present across the subgroups of the genomes analyzed. The Phylons are representative of major modes of inheritance, both lineage-associated and horizontal, found across the pangenome. Using NMF, we defined 31 Phylons, representative of 21 lineage-associated gene sets, and 10 Phylons containing genes associated with mobile genetic elements. Six mobile Phylons were extrachromosomal, representing plasmids, and four associated with chromosomal DNA. These 31 Phylons define the structure of the Enterobacter pangenome. This structure is consistent with the classification of an additional 2,291 fragmented genome sequences. This structure enables the pangenome-wide mapping of genetic traits, such as motility genes, biosynthetic gene clusters, antimicrobial resistance genes, and virulence factors. NMF thus enabled phylogenetic and functional classification of genomes based on the pangenome-scale assessment of a genome’s gene portfolio. A robust classification of Enterobacter spp . enhances the understanding of the evolution of this clinically significant pathogen. IMPORTANCE Enterobacter spp . represent a vital member of the Enterococcus faecium , Staphylococcus aureus , Klebsiella pneumoniae , Acinetobacter baumannii , Pseudomonas aeruginosa , Enterobacter species, and Escherichia coli pathogens relevant for their nosocomial pathogenicity and antimicrobial resistance. Understanding the genomic diversity of the genus is vital for further study of its evolution and resistance potential. We constructed a pangenome of 777 Enterobacter complete genomes. Machine learning techniques were used to mathematically define major subpopulations of Enterobacter based on their accessory gene content, which for the first time defined dominant modes of lineage-associated and horizontal inheritance. This analysis provides insights into the distribution of traits related to antimicrobial resistance, biosynthetic gene clusters, and virulence factors. This study provides robust classification of Enterobacter isolates identifying differential genetic traits across the species and subspecies of the genus, overcoming some of the ambiguity in its taxonomy.

  PublisherDOI

• How to Utilize a Genome-Scale Metabolic Model and iModulon in the Research of Streptococcus pyogenes M1 Serotype

  2025-08-06

  articleOpen access

  <i>Streptococcus pyogenes</i> can cause a wide variety of acute infections throughout the body of its human host [...]

  PublisherOA PDFDOI

• QSProteome: a community-driven interactive platform for large-scale exploration and evaluation of predicted protein complex structures

  Nucleic Acids Research · 2025-10-08 · 2 citations

  articleOpen accessSenior author

  QSProteome (https://QSProteome.org) is a community-scale platform for modeling, evaluating, and refining quaternary protein structure. The resource hosts 35 528 unique modeled assemblies spanning over 42 000 genes, covering nearly all curated complexes in BioCyc and ComplexPortal databases. Each model is displayed on an interactive page with 3D visualization, chain-level confidence metrics, structural alignments, functional annotations, and automated stoichiometry checks against database expectations. A cloud-based server supports continuous user uploads and automated processing pipelines-enabling submission and validation of >54 000 models within 14 weeks. To promote iterative refinement, QSProteome includes a gamified re-curation workflow that transitions users from training modules into live curation, enabling the community-led assessment of 1547 ABC transporter complexes. Together, these components form a dynamic, scalable infrastructure for proteome-scale structural biology. By unifying modeling, validation, and annotation in a reusable, searchable, and community-extensible framework, QSProteome enables proteome-scale structure accessibility and reuse-powering discovery, annotation, and collaborative refinement across the structural biology community.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R01GM068837

  NIH · $2.4M · 2014

• NIH Grant R01HL060398

  NIH · $1.1M · 2002

• Systems Biology Approach to Redefine Susceptibility Testing and Treatment of MDR Pathogens in the Context of Host Immunity

  NIH · $2.0M · 2016–2021

• NIH Grant R01GM062791

  NIH · $4.4M · 2010

• Systems Biology Approach to Redefine Susceptibility Testing and Treatment of MDR Pathogens in the Context of Host Immunity

  NIH · $6.0M · 2016–2022

Frequent coauthors

• Adam M. Feist

  Novo Nordisk Foundation

  832 shared

• Richard Szubin

  La Jolla Bioengineering Institute

  320 shared

• Jonathan M. Monk

  University of California, San Diego

  256 shared

• Nathan E. Lewis

  University of California, San Diego

  255 shared

• Anand V. Sastry

  University of California, San Diego

  255 shared

• Byung‐Kwan Cho

  Korea Advanced Institute of Science and Technology

  243 shared

• Daniel C. Zielinski

  La Jolla Bioengineering Institute

  213 shared

• Patrick V. Phaneuf

  Technical University of Denmark

  178 shared

Labs

• Systems Biology Research GroupPI

  Not provided

Awards & honors

• Induction into the National Academy of Inventors (2025)