About

Eric Klavins is the Professor and Chair of the Electrical & Computer Engineering Department at the University of Washington in Seattle. He received a B.M. in Music in 1992 and a B.S. in computer science in 1996 from San Francisco State University. He received M.S. and Ph.D. degrees in computer science and engineering in 1999 and 2001 from the University of Michigan, Ann Arbor. From 2001 to 2003 he was a postdoctoral scholar in the Control and Dynamical Systems Department at the California Institute of Technology where he worked with Richard Murray. In 2003 Eric was hired in Electrical Engineering at the University of Washington in Seattle; he received tenure in 2009. He holds adjunct appointments in Computer Science and Engineering and in Bioengineering. Until approximately 2008, Klavins’ research was primarily in computer science and control systems, focusing on stochastic processes, robotics and self-assembly. At about this time, he learned the basics of genetic engineering. In the n

Research topics

• Computer Science
• Computational biology
• Biology
• Artificial Intelligence
• Operating system
• Data science
• Software engineering
• Database
• Biochemistry
• Genetics
• Chemistry
• Computational chemistry
• Biological system
• Mathematics
• Embedded system
• Engineering
• Engineering management

Selected publications

• Using experimental results of protein design to guide biomolecular energy-function development

  PLoS Computational Biology · 2026-04-22

  articleOpen access

  Computational models of macromolecules have many applications in biochemistry, but physical inaccuracies limit their utility. One class of models uses energy functions rooted in classical mechanics. The standard datasets used to train these models are limited in diversity, pointing to a need for new training data. Here, we sought to explore a new paradigm for training an energy function, where the Rosetta energy function was used to design de novo proteins. Experimental results on these designs were then used to identify failure modes of design, which were subsequently used as a "guiding principle" to retrain the energy function. Specifically, we examined a diverse set of de novo protein designs experimentally tested for their ability to stably fold, identifying unstable designs that were predicted to be stable by the Rosetta energy function. Using deep mutational scanning, we identified single amino-acid mutations that rescued the stability of these designs, providing insight into common failure modes of the energy function. We identified one key failure mode, involving steric clashing in protein cores. We identified similar overpacking when using Rosetta to refine high-resolution protein crystal structures, quantified the degree of overpacking, and refit a small set of energy-function parameters to better recapitulate native-like packing. Following fitting, we largely eliminated the failure mode in the refinement task, while retaining performance on other benchmarks, resulting in an updated version of the Rosetta energy function. This work shows how learning from protein designs can guide energy-function development.

  PublisherDOI

• Perturbing the energy landscape for improved packing during computational protein design

  UNC Libraries · 2026-04-14

  articleOpen access

  The FastDesign protocol in the molecular modeling program Rosetta iterates between sequence optimization and structure refinement to stabilize de novo designed protein structures and complexes. FastDesign has been used previously to design novel protein folds and assemblies with important applications in research and medicine. To promote sampling of alternative conformations and sequences, FastDesign includes stages where the energy landscape is smoothened by reducing repulsive forces. Here, we discover that this process disfavors larger amino acids in the protein core because the protein compresses in the early stages of refinement. By testing alternative ramping strategies for the repulsive weight, we arrive at a scheme that produces lower energy designs with more native-like sequence composition in the protein core. We further validate the protocol by designing and experimentally characterizing over 4000 proteins and show that the new protocol produces higher stability proteins.

  PublisherDOI

• Oops: A Language for Formalizing Fallible Biological Protocols

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-06-18 · 1 citations

  preprintOpen access

  Abstract Formalizing protocols used in wetlab biological research as programs improves reproducibility by making protocols replicable and standardized. However, existing protocol languages have limited capacity for codifying error sources and standardizing error handling. When protocols inevitably go wrong, debugging must still be done by technicians, a process which is challenging for unfamiliar protocols and a scalability constraint in automated facilities. We introduce O ops , a programming language for describing biological protocols along with how they can fail. O ops makes expression of how errors affect outcomes concise through probabilistic semantics, an extensible abstraction of the lab, and metaprogramming. Given an outcome, O ops enables analyses of what errors occurred by mapping lab observations to enforced program conditions and applying existing probabilistic inference algorithms. We formalize a collection of molecular cloning protocols and present case studies demonstrating how O ops can explain errors and assess diagnostic capabilities.

  PublisherDOI

• Slow release of a synthetic auxin induces formation of adventitious roots in recalcitrant woody plants

  Nature Biotechnology · 2024-01-24 · 25 citations

  articleOpen access
  PublisherOA PDFDOI

• Slow release of a synthetic auxin induces formation of adventitious roots in recalcitrant woody plants

  bioRxiv (Cold Spring Harbor Laboratory) · 2023-03-13 · 5 citations

  preprintOpen access

  Abstract Clonal propagation of plants by induction of adventitious roots (ARs) from stem cuttings is a requisite step in breeding programs. A major barrier exists for propagating valuable plants that naturally have low capacity to form ARs. Due to the central role of auxin in organogenesis, indole-3-butyric acid (IBA) is often used as part of commercial rooting mixtures, yet many recalcitrant plants do not form ARs in response to this treatment. Here, we describe the synthesis and screening of a focused library of synthetic auxin conjugates in Eucalyptus grandis cuttings and identify 4-chlorophenoxyacetic acid-L-tryptophan-OMe as a competent enhancer of adventitious rooting in a number of recalcitrant woody plants, including apple and argan. Comprehensive metabolic and functional analyses reveal that this activity is engendered by prolonged auxin signaling due to initial fast uptake and slow release and clearance of the free auxin 4-chlorophenoxyacetic acid. This work highlights the utility of a slow-release strategy for bioactive compounds for more effective plant growth regulation.

  PublisherOA PDFDOI

• Introduction to the Special Issue on BioFoundries and Cloud Laboratories

  ACM Journal on Emerging Technologies in Computing Systems · 2023-07-31 · 6 citations

  articleOpen access

  No abstract available.

  PublisherOA PDFDOI

• Open-source workflow design and management software to interrogate duckweed growth conditions and stress responses

  Plant Methods · 2023-08-31 · 2 citations

  articleOpen accessSenior author

  Duckweeds, a family of floating aquatic plants, are ideal model plants for laboratory experiments because they are small, easy to cultivate, and reproduce quickly. Duckweed cultivation, for the purposes of scientific research, requires that lineages are maintained as continuous populations of asexually propagating fronds, so research teams need to develop optimized cultivation conditions and coordinate maintenance tasks for duckweed stocks. Additionally, computational image analysis is proving to be a powerful duckweed research tool, but researchers lack software tools to assist with data collection and storage in a way that can feed into scripted data analysis. We set out to support these processes using a laboratory management software called Aquarium, an open-source application developed to manage laboratory inventory and plan experiments. We developed a suite of duckweed cultivation and experimentation operation types in Aquarium, which we then integrated with novel data analysis scripts. We then demonstrated the efficacy of our system with a series of image-based growth assays, and explored how our framework could be used to develop optimized cultivation protocols. We discuss the unexpected advantages and the limitations of this approach, suggesting areas for future software tool development. In its current state, our approach helps to bridge the gap between laboratory implementation and data analytical software for duckweed biologists and builds a foundation for future development of end-to-end computational tools in plant science.

  PublisherOA PDFDOI

• Modular, robust, and extendible multicellular circuit design in yeast

  eLife · 2022-03-21 · 13 citations

  articleOpen accessSenior author

  Division of labor between cells is ubiquitous in biology but the use of multicellular consortia for engineering applications is only beginning to be explored. A significant advantage of multicellular circuits is their potential to be modular with respect to composition but this claim has not yet been extensively tested using experiments and quantitative modeling. Here, we construct a library of 24 yeast strains capable of sending, receiving or responding to three molecular signals, characterize them experimentally and build quantitative models of their input-output relationships. We then compose these strains into two- and three-strain cascades as well as a four-strain bistable switch and show that experimentally measured consortia dynamics can be predicted from the models of the constituent parts. To further explore the achievable range of behaviors, we perform a fully automated computational search over all two-, three-, and four-strain consortia to identify combinations that realize target behaviors including logic gates, band-pass filters, and time pulses. Strain combinations that are predicted to map onto a target behavior are further computationally optimized and then experimentally tested. Experiments closely track computational predictions. The high reliability of these model descriptions further strengthens the feasibility and highlights the potential for distributed computing in synthetic biology.

  PublisherDOI

• Large-scale design and refinement of stable proteins using sequence-only models

  PLoS ONE · 2022-03-14 · 29 citations

  articleOpen accessSenior author

  Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be tested computationally and experimentally in order to find stable ones, which is expensive in terms of time and resources. Here we use a high-throughput, low-fidelity assay to experimentally evaluate the stability of approximately 200,000 novel proteins. These include a wide range of sequence perturbations, providing a baseline for future work in the field. We build a neural network model that predicts protein stability given only sequences of amino acids, and compare its performance to the assayed values. We also report another network model that is able to generate the amino acid sequences of novel stable proteins given requested secondary sequences. Finally, we show that the predictive model-despite weaknesses including a noisy data set-can be used to substantially increase the stability of both expert-designed and model-generated proteins.

  PublisherDOI

• Open-Source Workflow Design and Management Software to Interrogate Duckweed Growth Conditions and Stress Responses

  Research Square · 2022-06-16 · 1 citations

  preprintOpen accessSenior author

  Abstract Duckweeds, a group of floating aquatic plants, are ideal model plants for laboratory experiments because they are small, easy to cultivate, and reproduce quickly. Duckweed laboratory biology, however, requires that lineages are maintained as continuous populations of asexually propagating fronds, so research teams need to calibrate cultivation conditions and coordinate maintenance tasks for duckweed stocks. Computational image data analysis is proving a powerful duckweed research tool, but researchers lack software tools to assist with data collection and storage in a way that can feed into scripted data analysis. We set out to support these processes — cultivation, and subsequent integration with data analysis tools — using a laboratory management software called Aquarium, an open-source application developed to manage laboratory inventory and plan experiments. We developed a suite of duckweed cultivation and experimentation operation types in Aquarium, which we then integrated with novel data analysis scripts. We then demonstrated the efficacy of our system with a series of image-based growth assays, and explored how our framework could be used to calibrate cultivation protocols. We discuss the unexpected advantages and the limitations of this approach, suggesting areas for future software tool development. In its current state, our approach helps to bridge the gap between laboratory implementation and data analytical software for duckweed biologists and builds a foundation for future development of end-to-end computational tools in plant science.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Molecular Programming Architectures, Abstractions, Algorithms, and Applications

  NSF · $2.1M · 2013–2018

• RoL: FELS: EAGER: Exploring the adaptive possibilities of 'redundancy' in a plant defense hormone signaling pathway

  NSF · $300k · 2018–2021

• Estimation & Observation of Stochastic Biochemical Networks

  NSF · $335k · 2010–2013

• CAREER: Programmed Robotic Self Assembly

  NSF · $524k · 2004–2009

• SemiSynBio: Collaborative Research: YeastOns: Neural Networks Implemented in Communicating Yeast Cells

  NSF · $338k · 2018–2021

Frequent coauthors

• Jennifer L. Nemhauser

  University of Washington

  15 shared

• David Baker

  University of Washington

  14 shared

• Nils Napp

  Cornell University

  13 shared

• Alberto Carignano

  University of Washington

  10 shared

• David Thorsley

  Samsung (United States)

  10 shared

• John-Michael McNew

  9 shared

• Arjun Khakhar

  Colorado State University

  9 shared

• Joshua D. Bishop

  University of Washington

  9 shared

Education

• PhD, EECS

  University of Michigan

  2001

Awards & honors

• 8 faculty named 2017 Amazon Catalyst Fellows
• Klavins and Javidi latest CAREER recipients
• Klavins and Seelig Win NSF Award for Molecular Programming
• Eric Klavins Wins Faculty Innovator Award
• Two EE Faculty Receive CoMotion Innovation Fund Awards