Kaamini Dhanabalan is a postdoctoral researcher at the University of California, Santa Barbara. Her work focuses on developing smarter, self-reporting therapies to guide precision treatment using real-time biological feedback. In this Postdoc Portrait interview, she shares her motivations and hopes for the utility of her work.
Bridging Benches and Bedsides
Q | What drew you to bioengineering?
What drew me in was a frustration at the bedside I could never ignore. In clinics, patients are still treated with drugs discovered 20–30 years ago. Meanwhile, in research labs, we are constantly publishing and shelving many breakthroughs. While there is an abundance of discoveries, very little of them reach patients.
During my clinical training, I observed how limited our bedside artillery really is. Diagnoses are often late. Treatments are generalized. Monitoring is crude. Then, I stepped into research and realized we understand so much more at the molecular level than we can use in the clinics.
I found a potential gap that intrigued me. I did not want to remain on just one side. My goal was clear. Why not build tools that translate complex molecular biology into something clinicians can actually measure, monitor, and act on? I see myself as a bridge, designing technologies that mobilize ideas from bench to bedside, not just papers to shelves.
Q | What scientific problem are you trying to solve?
I am trying to solve a core problem in medicine: we treat disease without truly seeing it respond in real time. Most therapies are administered at fixed doses, with fixed schedules, and prognosis takes weeks to be established. By then, most of the precious time in early disease recovery gets lost.
My long-term goal is to merge therapeutic delivery and bioimaging into true theranostic systems that can both treat disease and report back on prognosis. I want therapies that not just release a drug, but also sense inflammation, enzyme activity, or cellular stress and communicate that information non-invasively. This would enable us to map and model complex disease progression and adjust treatment dynamically for each patient.
My current foundation is building genetic circuits that use bioimaging to sense biological activity. My bigger vision is to develop smarter, self-reporting therapies. Instead of reactive medicine, I aim to move toward adaptive, precision treatment guided by real-time biological feedback.
Creating Therapeutic Sensors
Q | What’s one thing you learned from your research that you didn’t expect?
While I set out to build a bridge, I learned that most of the work is reinforcing it against unexpected biological earthquakes. One thing I never expected was how hard it is to make biology behave predictably. While on paper, a genetic sensor or drug delivery system looks obvious and logical, inside living cells, everything is stochastic. I observed a butterfly effect where even perturbations in protein expression, timing, or cell state can completely shift the outcome.
Second, I learned that translation is less about ‘Eureka!’ moments and more about engineering discipline into systems. For translational pipelines to smoothly flow without bottlenecks, multiple iterations, failure analysis, reproducibility, and simplification mattered more than novelty. The systems that survive are the ones that are stable, controllable, and clinically realistic.
My biggest surprise was building something that works in principle is easy compared to building something that works reliably, safely, and repeatedly in a complex organism. That shifted my mindset. I stopped chasing novel ideas and started thinking like an engineer, building tools that clinicians could actually trust and would stay for years to come.
Q | If your research succeeds, what could it change for science or society?
For science, it would create a new class of tools, called therapeutic sensors, that measure not just endpoints but continuously monitor molecular processes inside living systems. For society, it means earlier diagnosis, fewer ineffective treatments, and more personalized care. Patients would not be treated based on generalized data, but instead on their own dynamic biological signals. It may sound ambitious, but progress in medicine has always started with someone imagining a future that did not yet exist. If we are going to build the next generation of healthcare, we might as well think boldly. If this research succeeds, then instead of giving a drug and waiting months to see if it worked, we could monitor disease activity in real time and adjust treatment based on biological perturbations. The therapeutic sensors could help close the gap between laboratory discovery and clinical impact, making research not just something we publish and shelf, but something that actively guides how disease is treated in real time.
Q | What question are you most excited to answer next?
The question I am most excited to answer next is this: can we build smart therapeutics that know when to act and tell us whether it is working? Right now, treatment and monitoring are separate steps. I want to test whether we can engineer systems that sense a specific disease signal like inflammation, enzyme activation, or cellular stress, release a therapeutic response, and simultaneously report that activity non-invasively. The deeper question behind that is whether disease can be managed as a dynamic system rather than a static diagnosis. If we can model how biological signals change over time and integrate that with AI, we could begin predicting when a flare, relapse, or progression will occur and intervene earlier. That is the shift I am excited about: moving from treating snapshots of disease to managing living, evolving biology with continuous feedback.
Responses have been edited for length and clarity.
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