About

José L. Avalos is an Associate Professor of Chemical and Biological Engineering at Princeton University and a member of the Omenn-Darling Bioengineering Institute. He earned his Ph.D. in Biochemistry, Cellular and Molecular Biology from Johns Hopkins University School of Medicine in 2004, his M.Sc. in Biochemical Research from Imperial College London in 1998, and his B.S.E. in Chemical Engineering/Biotechnology from Universidad Iberoamericana Ciudad de México in 1996. His research focuses on using biotechnology to address critical issues in sustainable energy, the environment, industry, and human health through the fields of synthetic biology and metabolic engineering. His lab develops biological pathways, systems, and organisms with synthetic behaviors to engineer microorganisms with desirable traits for applications such as biofuel production, commodity and specialty chemical synthesis, bioplastics, drug discovery platforms, and bioremediation. The central area of his research involves metabolic engineering, supported by mitochondrial engineering, biosensors, genetic circuits, systems biology, structural biology, and protein engineering. Avalos's work includes engineering microorganisms for the production of advanced biofuels, biodegradable plastics, and environmentally friendly chemicals, as well as developing tools like biosensors and genetic switches to enhance strain development and optimization. His contributions extend to understanding and manipulating cellular systems at the molecular level, including the structure and function of key proteins, to improve metabolic pathways and develop innovative biotechnological solutions.

Research topics

• Biology
• Biochemistry
• Cell biology
• Computational biology
• Chemistry
• Biotechnology
• Biophysics
• Genetics
• Biochemical engineering
• Engineering
• Neuroscience

Selected publications

• Enhanced GPP synthesis by Erg20p-peptide fusions and biomolecular condensates boosts monoterpene production in yeast

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-30

  articleSenior author

  Abstract Monoterpenes are a diverse class of natural products with broad industrial and pharmaceutical applications. While there is great interest in transitioning their production from chemical synthesis and natural source extraction to yeast bioprocesses, this approach remains limited by the dual functionality of the endogenous farnesyl diphosphate synthase Erg20p, which produces the monoterpene precursor geranyl diphosphate (GPP) but favors its subsequent conversion to farnesyl diphosphate (FPP). To address this limitation, we recruited Erg20p and monoterpene synthases into synthetic membraneless organelles, improving production. In doing so, we found that short C-terminal peptide fusions used for recruitment also significantly enhance GPP synthase activity relative to FPP synthase activity. The combined effects of metabolic spatial organization and GPP synthase activity enhancement significantly boost production of different monoterpenes, including geraniol titers exceeding 4 g/L. The strategies presented here can be readily integrated with other traditional metabolic engineering approaches to build yeast strains with high levels of monoterpene production.

  PublisherDOI

• Robust Multiplicative Control in Chemical Reaction Networks Extended Version

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-16

  articleOpen accessSenior author

  Abstract Achieving complex multi-species control objectives is essential for engineering advanced autoregulated biomolecular devices. This paper addresses the problem of robust steady-state tracking for outputs defined as multiplicative combinations of biomolecular species concentrations. We first introduce a control architecture realized via chemical reaction networks that steers the product of two target species concentrations in the controlled network to a prescribed value. A robust stability analysis is provided for closed-loop system families with distinct structural characteristics. The proposed framework is also extended to a more general formulation capable of regulating arbitrary monomial outputs involving multiple species. Numerical simulations of representative examples corroborate the theoretical results and illustrate the effectiveness of our approach.

  PublisherOA PDFDOI

• Spatial organization of enzymes and cofactors in synthetic membrane-associated condensates (sMACs)

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-27

  articleOpen accessSenior authorCorresponding

  Abstract Cells spatially organize metabolic enzymes to optimize flux through complex biochemical networks. Here, we engineered s ynthetic M embrane- A ssociated C ondensates (sMACs), artificial assemblies that form on intracellular membranes through liquid-liquid phase separation, to couple metabolic compartmentalization with cofactor partitioning. Using intrinsically disordered regions (IDRs) characterized by simulations and validated by experiments, we identified an IDR sequence, Dbp1n, that selectively enrich NADH and NADPH, establishing distinct local redox microenvironments. At the endoplasmic reticulum (ER) membrane, NADPH-enriched sMACs enhanced cytochrome P450 activity by improving electron transfer and supporting in situ regeneration. At the plasma membrane, NADH-enriched sMACs provided scaffolds for a xylose uptake and assimilation pathway that exploits localized NADH/NAD⁺ recycling to improve flux. These results collectively demonstrated that condensate composition and membrane context can be engineered to enhance metabolic functions. sMACs thus provide a modular framework for creating programmable, membrane-associated microreactors, enabling new strategies for metabolic engineering and offering insights into how phase separation can be exploited in living cells.

  PublisherDOI

• Noise analysis of derivative-action biomolecular topologies

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-05-08

  articleOpen accessSenior author

  Abstract Temporal gradient sensing is a fundamental capability observed across diverse natural biological systems, contributing to the coordination of their functions. Harnessing this ability is also of significant interest in synthetic biology, particularly for sensing and control applications. In this work, we focus on a biomolecular topology that exemplifies a broader class of signal-differentiating architectures, while introducing a structural variant of it. We examine their behavior under both nominal and non-ideal conditions, accounting for stochastic noise arising from different sources. Our investigation includes scenarios where these topologies operate independently, as well as when embedded within minimal regulatory architectures based on negative as well as positive feedback. We analyze the stability of the resulting macroscopic dynamics—a prerequisite for practical deployment—and quantify stochastic fluctuations in system output, providing comparisons with the corresponding input/unregulated process. Importantly, our results demonstrate that signal differentiation can be effectively implemented in a biomolecular setting without incurring deleterious noise amplification—a major concern in the utilization of derivative action across disciplines.

  PublisherDOI

• BPS2026 – Development of a multiscale framework for engineering protein condensates as microreactors

  Biophysical Journal · 2026-02-01

  article
  PublisherDOI

• Growth inhibition of Saccharomyces cerevisiae by SUMO-specific nanobodies

  Scientific Reports · 2025-05-26 · 2 citations

  articleOpen access

  Four nanobodies (VHH1-4SMT3) that target the yeast SUMO protein Smt3p were isolated and characterized. VHH1-4SMT3 bind to Smt3p and Smt3p-tagged proteins with high affinity (Kd: low nM). NMR analysis shows that the four nanobodies all bind near the C-terminus of Smt3p, partially overlapping with the binding site for the SUMO protease Ulp1p. Binding of Smt3p-specific nanobodies impairs Ulp1-mediated cleavage of Smt3p-tagged proteins, with VHH1SMT3 showing complete inhibition. The use of immobilized VHH2SMT3 enabled efficient purification of Smt3p-tagged proteins, while VHH1SMT3 can be used for immunoblotting and detects both Smt3p-tagged and free Smt3p. When expressed in yeast, VHH1SMT3 causes significant growth defects, particularly when targeted to the nucleus or fused with GFP, indicative of interference with essential SUMOylation-dependent processes.

  PublisherOA PDFDOI

• Biochemical implementation of acceleration sensing and PIDA control

  npj Systems Biology and Applications · 2025-04-26

  articleOpen accessSenior author

  This work introduces a realization of a proportional-integral-derivative-acceleration control scheme as a chemical reaction network governed by mass action kinetics. A central feature of this architecture is a speed and acceleration biosensing mechanism integrated into a feedback configuration. Our control scheme provides enhanced dynamic performance and robust steady-state tracking. In addition to our theoretical analysis, this is practically highlighted in-silico in both the deterministic and stochastic settings.

  PublisherOA PDFDOI

• Anti-Pdc1p Nanobody as a Genetically Encoded Inhibitor of Ethanol Production Enables Dual Transcriptional and Post-translational Controls of Yeast Fermentations

  ACS Synthetic Biology · 2025-03-18 · 1 citations

  articleSenior authorCorresponding

  Microbial fermentation provides a sustainable method of producing valuable chemicals. Adding dynamic control to fermentations can significantly improve titers, but most systems rely on transcriptional controls of metabolic enzymes, leaving existing intracellular enzymes unregulated. This limits the ability of transcriptional controls to switch off metabolic pathways, especially when metabolic enzymes have long half-lives. We developed a two-layer transcriptional/post-translational control system for yeast fermentations. Specifically, the system uses blue light to transcriptionally activate the major pyruvate decarboxylase PDC1, required for cell growth and concomitant ethanol production. Switching to darkness transcriptionally inactivates PDC1 and instead activates the anti-Pdc1p nanobody, NbJRI, to act as a genetically encoded inhibitor of Pdc1p accumulated during the growth phase. This dual transcriptional/post-translational control improves the production of 2,3-BDO and citramalate by up to 100 and 92% compared to using transcriptional controls alone in dynamic two-phase fermentations. This study establishes the NbJRI nanobody as an effective genetically encoded inhibitor of Pdc1p that can enhance the production of pyruvate-derived chemicals.

  PublisherDOI

• HP1-enhanced chromatin compaction stabilizes a synthetic metabolic circuit in yeast

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-06

  preprintOpen access

  Abstract Chromatin compaction defines genome topology, evolution, and function. The Saccharomycotina subphylum, including the fermenting yeast Saccharomyces cerevisiae have a decompacted genome, possibly because they lost two genes mediating a specific histone lysine methylation and histone binding protein heterochromatin protein 1 (HP1). This decompaction may result in the higher-than-expected mutation and meiotic recombination rates observed in this species. To test this hypothesis, we retro-engineered S. cerevisiae to compact the genome by expressing the HP1 homologue of Schizosaccharomyces pombe Sp Swi6 and H3K9 methyltransferase Sp Clr4. The resulting strain had significantly more compact chromatin and reduced rates of mutation and meiotic recombination. The increased genomic stability significantly prolongs the optogenetic control of an engineered strain designed to grow only in blue light. This result demonstrates the potential of our approach to enhance the stability of strains for metabolic engineering and other synthetic biology applications, which are prone to lose activities due to genetic instability.

  PublisherDOI

• Reinforcement learning for efficient and robust multi-setpoint and multi-trajectory tracking in bioprocesses

  Computers & Chemical Engineering · 2025-07-25 · 4 citations

  articleOpen access

  Efficient and robust bioprocess control is essential for maximizing performance and adaptability in advanced biotechnological systems. In this work, we present a reinforcement-learning framework for multi-setpoint and multi-trajectory tracking. Tracking multiple setpoints and time-varying trajectories in reinforcement learning is challenging due to the complexity of balancing multiple objectives, a difficulty further exacerbated by system uncertainties such as uncertain initial conditions and stochastic dynamics. This challenge is relevant, e.g., in bioprocesses involving microbial consortia, where precise control over population compositions is required. We introduce a novel return function based on multiplicative reciprocal saturation functions, which explicitly couples reward gains to the simultaneous satisfaction of multiple references. Through a case study involving light-mediated cybergenetic growth control in microbial consortia, we demonstrate via computational experiments that our approach achieves faster convergence, improved stability, and superior control compliance compared to conventional quadratic-cost-based return functions. Moreover, our method enables tuning of the saturation function’s parameters, shaping the learning process and policy updates. By incorporating system uncertainties, our framework also demonstrates robustness, a key requirement in industrial bioprocessing. Overall, this work advances reinforcement-learning-based control strategies in bioprocess engineering, with implications in the broader field of process and systems engineering. • Reinforcement learning tailored for multi-setpoint and multi-trajectory tracking. • A novel return function enhances learning stability, convergence, and control. • Proposed return function based on multiplicative reciprocal saturation functions. • Framework accounts for system uncertainties, ensuring robust bioprocess control. • Computational experiments involving cybergenetic growth control in consortia.

  PublisherDOI

Recent grants

• CAREER: Closed-loop control of engineered metabolism using biosensors and optogenetics

  NSF · $525k · 2018–2023

• NIH Grant F32GM098022

  NIH · $90k · 2013

Frequent coauthors

• Cynthia Wolberger

  Johns Hopkins University

  33 shared

• Jef D. Boeke

  Institute for Systems Biology

  25 shared

• Shabazz Muhammad

  Soran University

  23 shared

• Ivana Celic

  Tulane University

  23 shared

• Margaret A. Kenna

  Boston Children's Hospital

  20 shared

• Carrie Baker Brachmann

  20 shared

• Leire San-José

  University of the Basque Country

  17 shared

• Jeffrey S. Smith

  17 shared

Labs

• Avalos Research GroupPI

Education

• Ph.D., Biophysics and Biophysical Chemistry

  Johns Hopkins School of Medicine

  2004

• M.Sc., Biochemistry

  Imperial College London

  1998

• B.Eng., Chemical Engineering

  Universidad Iberoamericana

  1996

Awards & honors

• ACS Biochemical Technology (BIOT) Young Investigator Award,…
• HHMI Gilliam Fellowship, 2021
• Camille Dreyfus Teacher-Scholar Award, 2019
• NSF-CAREER Award, 2018
• The Pew Scholarship in Biomedical Research, The Pew Charitab…