Cells function through complicated interactions of multiple components (genes, proteins, metabolites, etc.). Using a system-level approach, we investigate how interactions of these components at the molecular level lead to the function of cells at the cellular level. Experimentally, we employ advanced biophysical techniques as well as conventional microbial techniques to characterize biological processes at the molecular and cellular levels. To establish a quantitative bridge between the two levels, we employ mathematical modeling. Our research is highly interdisciplinary, at the intersection of physics, microbiology, synthetic biology, and theoretical biology. Below is more detailed description of several different areas of on-going research.

1. Systems biology
When a system operates through interactions of multiple components, it often shows properties that cannot be predicted from a simple sum of the properties of individual components. These new, distinct properties (termed emergent properties) arise through collective interactions among the components, and can be understood only when the system is studied in its entirety.

The same is true in biology. Cells function through complex biochemical interactions of multiple molecular components. Often, a function of cells at the cellular level is much more elaborate and sophisticated than a simple sum of functions of molecular components. In other words, the whole is greater than the sum of its parts. Currently, our knowledge about molecular components in biological systems is rapidly expanding. And yet, we have little understanding of how their interactions at the molecular level lead to elaborate functions we see at the cellular level.

We aim to fill this gap using quantitative experiments and mathematical modeling. Experimentally, we characterize a biological network (consisting of genes, proteins, and metabolites) responsible for a cellular function of bacteria employing advanced biophysical techniques and conventional microbial techniques. We perturb the network using various environmental conditions, or employing a synthetic biology approach. Analyzing the data quantitatively and modeling the network in a mathematical framework, we characterize system-level properties, such as the connectivity of components, network dynamics etc., to establish a quantitative bridge between biological processes at the molecular and cellular levels.

2. Single cell microbiology

How alike are two microbial cells in a culture? Although cells in clonal population generally behave similarly in homogeneous environments, some environments (often stressful environments) induce heterogeneous behaviors among individual cells. We seek to understand the origin of this heterogeneity at the systems level and characterize its consequences at the population level.

We are particularly interested in the following topics:
1) cooperation among genetically identical cells with different phenotypes,
2) evolution and propagation of advantageous mutants, and
3) interaction of individual cells in heterogeneous environments.

We culture bacteria in microfluidic devices in which a microenvironment can be precisely controlled. Combining it with single cell fluorescence microscopy or other optical techniques, we probe physiology, metabolism, and gene regulation of bacteria at the single cell level. Using mathematical modeling at the systems level, we characterize the origin of the heterogeneity. Further, we investigate how the cellular heterogeneity contributes to the survival and proliferation of a whole population. The findings from this study, integrating a wide range of biological processes from the molecular level to the population level, will provide an integral picture of the origin and consequences of cellular heterogeneity.

3. Pattern formation by Proteus mirabilis

Bacterial cells of about a micron in size can interact together and form intriguing patterns at much larger length scales (e.g., centimeter). Such pattern formation is a nontrivial task. It is analogous of individual persons in crowded Time Square, NY, follow a set of rules and form certain patterns in the length scale of mile. We focus on understanding the rules that bacteria follow in the bacterial pattern formation and how those rules are implemented at the molecular level.

Here, we are particularly interested in P. mirabilis. This bacterium is an agile swarmer and can move on tough surfaces that other bacteria cannot move. This superior motility underlies the pathogenesis of this bacterium as it enables P. mirabilis cells to reach across the urethra and colonize the bladder and kidneys, and leads to rapid surface fouling of newly inserted catheters. Interestingly, they form beautiful concentric rings on surfaces. We are investigating how and why they form such beautiful patterns.

4. Phenotypic switching in Acinetobacter baumannii
Acinetobacter baumannii is an opportunistic pathogen responsible for numerous outbreaks across the globe. Recently, it emerged as one of the most serious threats to public health due to the prevalence of antibiotic resistance. Interestingly, it switches between virulence and avirulence phenotypes. Working together with Phil Rather (bacterial genetist), Nic Vega (C. elegans expert), and Daniel Weissman (poplation biologist) at Emory Univ, we are investigating how this phentypic switching promotes community-based spread of A. baumannii infection. Our ultimate goal is to manipulate this switching to control diseases caused by A. baumannii.

5. Phenotypic resistance to antibiotics

In recent years, we have observed a rapid increase in antibiotic treatment failure due to widespread antibiotic-resistant bacteria. Previous studies of antibiotic resistance have focused on identifying individual genes whose products confer cells the antibiotic resistance and characterizing their biochemical functions. However, our studies have found that the expression of the antibiotic-resistant genes significantly alter the molecular interaction network, as well as cell physiology, giving rise to various emergent phenomena. One of such phenomena is phenotypic resistance, which bacteria can express without modifying their genomes through mutations. We found that its expression is stochastically, which makes it challenging to predict the outcome of antibiotic treatment determinstically. In our lab, we take a quantitative and systems-level approach to characterize the origin and consequence of phenotypic resistance.