Ultra-fast organismic physics, biological soft matter, frugal science and global health
The BhamlaLab explores fundamental and applied research questions through the development of new experimental tools and techniques at the intersection of soft matter, organismic physics and global health.
Ultra-fast Organismic Physics
Biologists are just starting to systematically examine ultrafast motion across species (jellyfish, mantis shrimp, trap-jaw ants), some of which achieve accelerations exceeding a million g-forces in nanoseconds. At the single-cell level, the physical biology of ultra-fast motility remains poorly understood. What is the fastest motion a single cell can achieve? How do single-cell organisms amplify power and survive repeated high accelerations? These fundamental questions guide our exploration of several non-model unicellular and multicellular organisms to uncover the principles of extreme motility at cellular scales.
Biological Soft Matter
Our bodies are composed almost entirely of soft, wet, squishy materials. How do the fundamental principles of soft matter and complex fluids enable us to grasp dynamic processes, from the self-assembly of proteins to the stretching of a spider web? We study a spectrum of biological soft matter, from the tears on our eyes to biological foams from insects, with the goal of connecting the microscale structures (lipids, proteins) to their consequences for macroscale biological function (contact lens-eye interaction, microbiome health). As engineers, we leverage this understanding for human-health applications, ranging from diagnostics and monitoring to artificial therapeutic replacements and biomedical devices.
Frugal Science and Global Health
Today, although information is free to anyone with internet, access to scientific tools and healthcare devices still has many barriers. How do we design and build tools that are scientifically rigorous, but cost a few cents on the dollar? Driven by the spirit of doing “frugal science”, we box ourselves in to find out of the box solutions for global challenges in science education, agriculture, and healthcare. Projects in this area include field-work, science outreach, and citizen-science initiatives.
Quantitative fluorescence microscopy and image analysis
Computational models of gene regulatory networks
Transcriptional regulation and developmental biology of plants
The past fifteen years has seen dramatic advancements in genome sequencing and editing. The cost of sequencing a genome has decreased by two orders of magnitude, giving rise to new systems-level approaches to biology research that aim to understand life as an emerging property of all the molecular interactions in an organism. At the same time, technologies that allow site-specific modifications of the genome are enabling researchers to manipulate multicellular organisms in unprecedented ways.
From reductionist approaches to systems biology, and from conventional plant breeding to synthetic biology, the future of plant biology research relies on the adoption of computational methods to analyze experimental data and develop predictive models. In biomedicine, mathematical models are already revolutionizing drug discovery; in agriculture, they have the potential to generate more efficient, faster growing crop varieties.
The goal of the Cheung lab is to bring quantitative techniques and mathematical modeling to plants in order to gain systems-level insight into their physiology and development – particularly to understanding how metabolic and gene regulatory networks interact to control homeostasis and growth.
Modelling and controlling metabolic dynamics and regulation (metabolic engineering)
Systems biology-based experimental and bioinformatics analysis of metabolism
Synthetic biology for the development of biosensors and diagnostics
The main focus of the Styczynski group is the experimental and computational study of the dynamics and regulation of metabolism, with ultimate applications in metabolic engineering, biotechnology, and biosensors/diagnostics.
Develop microneedle patches for vaccination that is simpler and more effective than conventional injection.
Design microneedle patches for rapid and slow-release delivery of drugs.
Translate microneedle technology from the laboratory into human clinical studies and advanced manufacturing.
Study targeted drug delivery to the eye using microneedle injection into the suprachoroidal space.
Examine the effects of laser-activated nanoparticles on delivery of molecules into cells to manipulate cellular behavior
Dr. Prausnitz and his colleagues carry out research on biophysical methods of drug delivery, which employ microneedles, ultrasound, lasers, electric fields, heat, convective forces and other physical means to control the transport of drugs, proteins, genes and vaccines into and within the body. A major area of focus involves the use of microneedle patches to administer vaccines to the skin in a painless, minimally invasive manner that improves vaccine effectiveness by targeting delivery to the skin’s immune cells. In collaboration with Emory University, the Centers for Disease Control and Prevention and other organizations, Dr. Prausnitz’s group is advancing microneedles from device design and fabrication through pharmaceutical formulation and preclinical animal studies through studies in human subjects. In addition to developing a self-administered influenza vaccine using microneedles, Dr. Prausnitz is translating microneedles technology especially to make vaccination in developing countries more effective.
Dr. Lu’s research lies at the interface of engineering and biology. The lab engineers microfluidic devices and BioMEMS (Bio Micro-Electro-Mechanical Systems) to study neuroscience, genetics, cancer biology, systems biology, and biotechnology. These miniaturized Lab-on-a-chip tools enable us to study biology in a unique way unavailable to conventional techniques. Applied to the study of fundamental biological questions, these new techniques allow us to gather large-scale quantitative data about complex systems. Microfluidic devices are especially suitable for solving these problems because of the many advantages associated with shrinking the devices down to a scale comparable to typical biological systems. Furthermore, unique phenomena at the micro and nano length scale, such as enhanced surface effects and transport phenomena, can be exploited in designing novel techniques and devices.
In neuroscience, we are interested in how the nervous system develops and functions, and how genes and environment influence behavior. In cancer biology, we are interested in the roll of extra cellular matrix and soluble factors in cell migrations. In cancer therapy, we are interested in signal transductions for adoptive transfer. For systems biology, we are interested in large-scale experimentation and automation, and applications in neuroscience and cell biology. In general, we bring together molecular and genetic techniques and the micro devices to further our understanding of the complex biological systems. We make micro devices to investigate molecular events and signaling networks, cellular behavior, connectivity and activities of populations of cells, and the resulting complex behaviors of the animals. The ultimate goal is to bring new technologies to understand natural and dysfunctional states of biological systems and ultimately bring cures to diseases.
Discrete atoms and molecules interact to form macromolecules and even larger mesoscale assemblies, ultimately yielding macroscopic structures and properties. A quantitative relationship between the nanoscale discrete interactions and the macroscale properties is required to design, optimize, and control such systems; yet in many applications, predictive models do not exist or are computationally intractable. The Grover group is dedicated to the development of tractable and practical approaches for the engineering of macroscale behavior via explicit consideration of molecular and atomic scale interactions. We focus on applications involving the kinetics of self-assembly, specific those in which methods from non-equilibrium statistical mechanics do not provide closed form solutions. General approaches employed include stochastic modeling, model reduction, machine learning, experimental design, robust parameter design, estimation, and optimal control.