Mindy Liu Perkins

I'm an engineer and scientist with expertise in mathematical modeling and simulation.

My passion for interdisciplinary research and innovation has led me to collaborate with engineers, physicists, and biologists on a wide variety of topics in the life sciences, including bat echolocation and insect development. Most recently I worked as a postdoctoral fellow at the European Molecular Biology Laboratory in Heidelberg, Germany, where I used a combination of dynamical systems and stochastic processes to study how quickly genes turn on and off.

I received my Ph.D. in Electrical Engineering at the University of California, Berkeley, advised by Murat Arcak. My dissertation focused on developing and applying control theory and signal processing to analyze pattern formation in biological systems, both real and synthetic. For my graduate research I was awarded the Leon O. Chua Award for outstanding achievement in nonlinear science. Prior to that, I completed my B.S. in Electrical Engineering with a minor in Biology at Stanford University.

When I'm not taking Fourier transforms or wrangling spaghetti code, I enjoy reading, writing, drawing, and spending time outdoors.

Highlights

Theory-driven designs

I've developed mathematical frameworks based on signal processing, control theory, and biophysics to propose new ways in which networked or coupled systems may support multicellular life. For example, networks of interacting cells can behave like image processing filters to generate spatial patterns, and DNA packaging helps cells optimize their performance on different tasks when coupling between actuators and controllers is unavoidable. These theoretical results can inform human-engineered biological systems as well as provide insight into naturally evolved organisms.

Predictive mathematical models

Gene expression is regulated by complex processes whose behavior can be difficult to predict. I've teamed up with experimental biologists to design predictive dynamical systems models that are descriptive and quantitative. This means that all relevant model parameters are physically interpretable and can be accurately quantitated through empirical measurements. The model's predictive power therefore derives from an understanding of how the process works, rather than on phenomenology alone.

My models have guided the design of genetically engineered "communities" of cells, and have also helped us understand how identical cells in a young embryo become different types of cells in the adult organism.

Experience

2020 - 2024 Postdoctoral Fellow (EIPOD4/Marie Curie Fellowship)
European Molecular Biology Laboratory (Heidelberg, Germany)
  • Designed and simulated mathematical models to compare optimal gene expression accuracy in "simple" vs. "complex" organisms (e.g., bacteria vs. animals)
2015 - 2020 Ph.D. in Electrical Engineering (May 2020)
University of California, Berkeley (Berkeley, CA)
Advisor: Murat Arcak
Dissertation: Biological patterning in networks of interacting cells
  • Developed networked dynamical systems theory to predict spatial patterning of gene expression in living tissues or colonies of cells
2011 - 2015 B.S. in Electrical Engineering (with honors), Minor in Biology
Stanford University (Stanford, CA)
Honors thesis: Acoustic interference in bat echolocation
  • Created a metric based on sonar signal processing to analyze how calls from one echolocating bat interfere with another

Misc. Writing


Contact

mindy.liuperkins (at) gmail (dot) com