About

The Biodynamics Laboratory at UCSD, part of the UCSD Synthetic Biology Institute, focuses on the use of engineering methods in the theoretical design and experimental construction of synthetic gene networks. The laboratory's work involves applying engineering principles to understand and manipulate biological systems, particularly through the development of synthetic gene networks. The research aims to advance the field of synthetic biology by designing and constructing gene networks with specific functions, contributing to the broader understanding of biological systems and their engineering.

Research topics

• Genetics
• Biology
• Computational biology
• Computer Science
• Cell biology
• Microbiology
• Evolutionary biology
• Mathematics
• Combinatorics
• Cancer research
• Bioinformatics

Selected publications

• Rational engineering of combinatorial bacterial therapies for cancer

  Genome biology · 2026-01-28

  articleOpen accessSenior authorCorresponding

  Engineered bacteria are emerging as a transformative class of cancer therapeutics. Recent advances in synthetic biology have expanded the genetic circuit toolbox, enabling the programmable control of attenuation, payload release, and immunomodulation. These developments have transformed bacteria from simple, colonizing agents into a versatile chassis for complex therapeutic functions. In this review, we examine recent circuit-based strategies for enhancing tumor specificity, regulating therapeutic delivery and engaging the host immune system, with emphasis on programming spatiotemporal control and consortia behavior. We consider current barriers to clinical translational and discuss how rational engineering can guide the next generation of microbial therapeutics.

  PublisherDOI

• BudFinder: A Masked Auto-Encoder vision transformer framework for yeast budding detection and lifespan quantification

  PLoS Computational Biology · 2026-05-18

  articleOpen access

  Studying replicative aging in yeast is a central component of aging research. Recent advances in time-lapse microscopy and microfluidics now enable continuous, high-resolution tracking of individual yeast cells throughout their lifespan. However, quantifying replicative lifespan from microscopy data remains labor-intensive, as it traditionally requires manual counting of cell division events for each cell. Recent deep learning-based approaches have begun to address this challenge by automating lifespan quantification. Here, we present a versatile image analysis framework that accurately detects yeast cell division events during replicative aging. To reduce the need for large, manually annotated datasets, we pretrain a Masked Autoencoder on large-scale (~250K), unlabeled yeast cell image crops. This self-supervised pretraining substantially lowers the amount of annotated data required to train a transformer model for division event detection. Moreover, our model is trained to directly identify budding events, eliminating dependence on arbitrary heuristics such as changes in cell area. By leveraging self-supervised learning, our approach only requires training data with fewer than 50 mother cells (~1,000 division events, which is significantly lower than reported in previous methods), while maintaining high detection accuracy.

  PublisherDOI

• BudFinder: A Masked Auto-Encoder Vision Transformer Framework for Yeast Budding Detection

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-11-05

  preprintOpen access

  Abstract Yeast replicative lifespan is a crucial part of aging research, yet its quantification remains labor-intensive and time-consuming, particularly when using time-lapse imaging and microfluidics. Manual counting methods for cell division events are prone to bias and inefficiency, while existing automated approaches often require extensive annotated datasets. These limitations hinder the adaptability of such tools across different microfluidic setups. To address these challenges, we propose a versatile image analysis approach that accurately detects yeast cell division events. To reduce the burden of requiring a large cell division annotated dataset, we pretrained a Masked Auto-Encoder on large-scale segmented yeast cell images. This substantially reduced the annotated data needed to train the transformer model for detecting cellular division events. Additionally, the model is trained directly on budding event detection, circumventing reliance on arbitrary heuristics, such as changes in cell area. By leveraging self-supervised pretraining, we reduced the training data requirement to fewer than 50 mother cells (∼1,000 divisions), representing a >5-fold reduction compared to prior methods while maintaining comparable accuracy. Author Summary Our work addresses a longstanding challenge in in live-cell time-lapse microscopy analysis, namely, automating cellular division tracking while minimizing the amount of training data required. Traditionally, scientists identify each division event by manually inspecting thousands of time-lapse images, a process that is both tedious and prone to bias. While automated tools exist, they often require large amounts of annotated data to work effectively, limiting their use across different experimental setups. To overcome these barriers, we developed BudFinder, which can recognize and track cell divisions with far less training data. Using yeast replicative aging data as an example, we first trained a model to understand what a yeast cell “looks like”, using tens of thousands of segmented yeast cell images entrapped in our custom-built microfluidic device. Then, we taught it to detect budding events directly from time-lapse movies. This approach reduces the need for manual labeling by more than five-fold compared to previous structures in place, while maintaining accuracy comparable to existing methods. By making high-throughput analysis of cellular division more accessible, our work paves the way for faster and more scalable quantification of cellular dynamics.

  PublisherOA PDFDOI

• Calculating fast differential genome coverages among metagenomic sources using micov

  Communications Biology · 2025-11-20 · 5 citations

  articleOpen access

  Breadth of coverage, the proportion of a reference genome covered by at least one sequencing read, is critical for interpreting metagenomic data, informing analyses from genome assembly to taxonomic profiling. However, existing tools typically summarize coverage breadth at the whole-genome or aggregate-sample level, missing informative variation along genomes and between sample groups. Here we introduce MIcrobiome COVerage (micov), a tool that computes and compares per-sample breadth of coverage across many genomes and samples. micov offers two key advances: (1) rapid cumulative coverage breadth calculations specific to each sample type, and (2) detection of differential coverage breadth along genomes. Applying micov to three metagenomic datasets, we show that it identifies a genomic region in Prevotella copri that explains variation in community composition independent of host country of origin, uncovers dietary association with a partially annotated region in an uncharacterized Lachnospiraceae genome, enabling hypothesis generation for genes of unknown function, and improves sensitivity in low-biomass settings by detecting a single genomic copy of enteropathogenic Escherichia coli (EPEC) in wastewater and distinguishing Mediterraneibacter gnavus across specimen types.

  PublisherOA PDFDOI

• Evolved microbial diversity enables combinatoric biosensing in complex environments

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-24 · 1 citations

  preprintOpen accessSenior authorCorresponding

  Whole-cell biosensors (WCBs) offer rapid, cost-effective monitoring of environmental contamination and human disease. Current WCB efforts to optimize detection of single target analytes under laboratory conditions have achieved vastly improved performance, setting the stage for WCB deployment in complex environments. We propose a framework that leverages the cross-specificity of single-target WCBs to quantify multiple targets using supervised machine learning. Specifically, we engineer six sensors for heavy metal contaminants in laboratory E. coli. We then evolve the strain to generate five chassis with improved growth in seawater conditions. We transform the chassis with the sensors, creating a set of 30 variants. The variant dynamic responses are characterized with microfluidics, revealing significant diversity. Leveraging this diversity, we construct a consortium to combinatorically quantify multiple analytes with machine learning, outperforming single-target biosensors in over 90% of our test samples. These results form a generalizable framework that facilitates WCB translation toward settings beyond the laboratory.

  PublisherOA PDFDOI

• SARS-CoV-2 infectivity can be modulated through bacterial grooming of the glycocalyx

  mBio · 2025-02-25 · 2 citations

  articleOpen access

  ABSTRACT The gastrointestinal (GI) tract is a site of replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and GI symptoms are often reported by patients. SARS-CoV-2 cell entry depends upon heparan sulfate (HS) proteoglycans, which commensal bacteria that bathe the human mucosa are known to modify. To explore human gut HS-modifying bacterial abundances and how their presence may impact SARS-CoV-2 infection, we developed a task-based analysis of proteoglycan degradation on large-scale shotgun metagenomic data. We observed that gut bacteria with high predicted catabolic capacity for HS differ by age and sex, factors associated with coronavirus disease 2019 (COVID-19) severity, and directly by disease severity during/after infection, but do not vary between subjects with COVID-19 comorbidities or by diet. Gut commensal bacterial HS-modifying enzymes reduce spike protein binding and infection of authentic SARS-CoV-2, suggesting that bacterial grooming of the GI mucosa may impact viral susceptibility. IMPORTANCE Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019, can infect the gastrointestinal (GI) tract, and individuals who exhibit GI symptoms often have more severe disease. The GI tract’s glycocalyx, a component of the mucosa covering the large intestine, plays a key role in viral entry by binding SARS-CoV-2’s spike protein via heparan sulfate (HS). Here, using metabolic task analysis of multiple large microbiome sequencing data sets of the human gut microbiome, we identify a key commensal human intestinal bacteria capable of grooming glycocalyx HS and modulating SARS-CoV-2 infectivity in vitro . Moreover, we engineered the common probiotic Escherichia coli Nissle 1917 (EcN) to effectively block SARS-CoV-2 binding and infection of human cell cultures. Understanding these microbial interactions could lead to better risk assessments and novel therapies targeting viral entry mechanisms.

  PublisherDOI

• Host Evolution Improves Genetic Circuit Function in Complex Growth Environments

  ACS Synthetic Biology · 2025-05-20 · 1 citations

  articleSenior author

  The systematic design of genetic circuits with predictable behaviors in complex environments remains a significant challenge. Here, we engineered a population control circuit and used a combination of evolutionary and rational engineering approaches to enhance Escherichia coli for robust genetic circuit behavior in nontraditional growth environments. We utilized adaptive laboratory evolution (ALE) on E. coli MG1655 in minimal media with a sole carbon source and saw improved dynamics of the circuit after host evolution. Additionally, we applied ALE to E. coli Nissle, a probiotic strain, in a more complex medium environment with added reactive oxygen species (ROS) stress. In combination with directed mutagenesis and high-throughput microfluidic screening, we observed restored circuit function and improved tolerance of the circuit components. These findings serve as a framework for the optimization of relevant bacterial host strains for improved growth and gene circuit performance in complex environments.

  PublisherDOI

• Bacteria as living biosensors for DNA

  Nature Reviews Bioengineering · 2025-10-30

  articleOpen access
  PublisherOA PDFDOI

• 31: METATRANSCRIPTOMICS REVEALS BACTERIAL TRANSGENES CONFERRING THE BENEFICIAL METABOLIC EFFECTS OF TIMERESTRICTED FEEDING

  Gastroenterology · 2025-05-01

  article
  PublisherDOI

• Metatranscriptomics uncovers diurnal functional shifts in bacterial transgenes with profound metabolic effects

  Cell Host & Microbe · 2025-06-18 · 13 citations

  article
  PublisherDOI

Recent grants

• Engineered Gene Circuits for Basic Science and Biotechnology

  NIH · $13.6M · 2004–2029

• Delays and variability in single-cell NFkB signaling

  NIH · $2.0M · 2010–2015

• Engineered Selective Pressure: Challenges and Opportunities

  NSF · $1.2M · 2016–2020

• Design and characterization of bacterial population dynamics in solid tumor models

  NIH · $2.5M · 2021–2025

• Reprogramming cell-fate decisions through predictive modeling and synthetic biology

  NIH · $2.8M · 2022–2027

Frequent coauthors

• Lev S. Tsimring

  University of California, San Diego

  164 shared

• Andriy Didovyk

  Vertex Pharmaceuticals (United States)

  36 shared

• Richard O’Laughlin

  Weizmann Institute of Science

  34 shared

• Nan Hao

  University of California, San Diego

  31 shared

• Robert M. Cooper

  University of California, San Diego

  28 shared

• Philip Bittihn

  Max Planck Institute for Dynamics and Self-Organization

  27 shared

• William Mather

  Quantitative BioSciences

  24 shared

• Tal Danino

  Columbia University

  22 shared