The basic mechanisms that are characteristic to life act on three different scales of biological organization: on the molecular level, the cellular level and the population level. These mechanisms also act in multiple dimensions: time (dynamics) and space (heterogeneity). All these scales and dimensions are coupled and they are equally important. However, most of the research in the field of biophysics concentrate on molecular and cellular processes.
We are trying to bring together theoretical and experimental concepts and tools in biology, physics, mathematics and engineering to better understand the importance of space, time, individuals and communities in biology: from molecules to cells and from cells to their collectives.
Today we focus on bacteria. Bacteria are among the most abundant living organisms on Earth. Many consider them as the simplest form of life, however we are far from their complete understanding and we are just recently learning more about their social interactions. Studying bacteria is not only important because of their medical or healthcare implications, but they play a fundamental role in all ecosystems and exhibit complex phenomena that have more general biological importance and which apply to all cells.
Micro- and nanofabrication techniques allow us to observe and manipulate bacteria from the single cell to the population level while maintaining well defined, precisely controlled environment which we can define with nanoscale details. This is important since the organisms and their environment cohere strongly (with a physics phrasing cells are open systems) at this scale: bacteria move around, uptake and release chemicals, communicate and signal to each other through their surroundings and react to external effects. Using microfabricated devices allow us to study these aspects of the bacterial life. These resonances between the cell’s metabolic input-output relationships (metabolic coupling) and the topology of their habitat (adaptive) landscapes (nano/micro fluidic devices) are the main subjects of our interests.
Recently, we have also begun to study the “co-evolutionary” dynamics between ecosystem machines capable of both, actuating dynamic habitat landscapes, as well as autonomous learning (using robotic time-lapse microscopy, computer vision, and machine learning), the adaptive patterns of biological cell communities inhabiting the spatio-temporal niches which are being rendered by the machine. To explore the evolutionary design of arbitrary ecosystem functions to such cell-ecosystem machine constructions is another of our main interests.