A synthetic biology approach to analyze evolution of programmed bacterial death Programmed death is commonly associated with a bacterial response to stressful conditions, such as starvation, presence of competitors, and antibiotic treatment. As death offers no benefit to its actor, evolution of programmed bacterial death is a fundamental, unresolved problem in biology. A popular explanation is that the death is """"""""altruistic"""""""": it can provide direct or indirect benefitsto the survivors. In other words, death may represent the ultimate form of cooperation. By making this assumption, evolution of programmed death can be analyzed under the general framework of public-good cooperation. Using this framework, studies have suggested possible public goods resulting from death in various bacterial pathogens. However, there remains a fundamental gap in the definitive understanding of microbial social behavior in general and programmed bacterial death in particular. Indeed, advantage of altruistic death has never been unequivocally demonstrated in an experimental system. A major challenge in tackling this problem is the complexity of natural biological processes, where numerous confounding factors obscure interpretation and quantitative analysis of the benefits associated with death. For example, previous work has been criticized because gene manipulations involved led to multiple effects and so it is hard to tease apart different fitness consequences. These issues make the results open to alternative explanations, such as PCD representing a maladaptive response to stress. We propose to use a combination of synthetic-biology techniques and microfluidics to overcome these limitations. In particular, using a set of synthetic gene circuits in bacterium Escherichia coli to implement tunable altruistic death, we will quantitatively define the condition under which altruistic death can become advantageous at the population level and examine their evolutionary dynamics in the presence of cheating. To enable such analysis, we will develop a novel droplet-based platform to examine the evolutionary dynamics under different conditions. Building on such understanding, we will develop and evaluate new treatment strategies that will exploit the evolutionary dynamics. It is our vision that the proposed research will have several broad impacts. First, it will fill the critical conceptual gap in our understandig of the evolution of programmed death. Second, it will generate novel insights into how bacteria respond to antibiotic-mediated stress, which has implications for designing novel therapeutic strategies against bacterial pathogens.

Public Health Relevance

to Public Health The proposed research approach will provide insights into evolution of bacterial cooperation, which is critical for development and virulence of bacterial pathogens. Furthermore, it will develop and evaluate of novel, effective treatment strategies against two clinically relevant pathogens.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
Project #
Application #
Study Section
Modeling and Analysis of Biological Systems Study Section (MABS)
Program Officer
Gerratana, Barbara
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Duke University
Biomedical Engineering
Biomed Engr/Col Engr/Engr Sta
United States
Zip Code
Huang, Shuqiang; Lee, Anna Jisu; Tsoi, Ryan et al. (2016) Coupling spatial segregation with synthetic circuits to control bacterial survival. Mol Syst Biol 12:859
Cao, Yangxiaolu; Ryser, Marc D; Payne, Stephen et al. (2016) Collective Space-Sensing Coordinates Pattern Scaling in Engineered Bacteria. Cell 165:620-30
Ji, HaYeun; Atchison, Leigh; Chen, Zaozao et al. (2016) Transdifferentiation of human endothelial progenitors into smooth muscle cells. Biomaterials 85:180-94
Zhang, Carolyn; Tsoi, Ryan; You, Lingchong (2016) Addressing biological uncertainties in engineering gene circuits. Integr Biol (Camb) 8:456-64
Jiang, Weiqian; Li, Mingqiang; Chen, Zaozao et al. (2016) Cell-laden microfluidic microgels for tissue regeneration. Lab Chip 16:4482-4506
Lopatkin, Allison J; Huang, Shuqiang; Smith, Robert P et al. (2016) Antibiotics as a selective driver for conjugation dynamics. Nat Microbiol 1:16044
Meredith, Hannah R; Lopatkin, Allison J; Anderson, Deverick J et al. (2015) Bacterial temporal dynamics enable optimal design of antibiotic treatment. PLoS Comput Biol 11:e1004201
Tanouchi, Yu; Pai, Anand; Park, Heungwon et al. (2015) A noisy linear map underlies oscillations in cell size and gene expression in bacteria. Nature 523:357-60
Meredith, Hannah R; Srimani, Jaydeep K; Lee, Anna J et al. (2015) Collective antibiotic tolerance: mechanisms, dynamics and intervention. Nat Chem Biol 11:182-8
Huang, Shuqiang; Srimani, Jaydeep K; Lee, Anna J et al. (2015) Dynamic control and quantification of bacterial population dynamics in droplets. Biomaterials 61:239-45