Humans can now construct and piece together DNA sequences in order to design new biological systems and organisms. We can do this more quickly and less expensively than ever. Applications abound for our synthetic biological constructs, from sensors of biochemical and chemical weapons, to devices that will remove environmental pollutants, to gene therapies. Synthetic biology is the discipline that focuses on the construction of these novel biological systems. It has all the characteristic features of an engineering discipline: applying technical and scientific knowledge to design and implement devices, systems, and processes that safely realize a desired objective. Mathematical modeling has always been an important component of engineering disciplines. It can play an important role in synthetic biology the same way modeling helps in aircraft or architecture design: models and computer simulations can quickly provide a clear picture of how different components influence the behavior of the whole, reaching objectives quickly. The proposed activities will result in modeling tools that will help scientists and engineers to construct complex synthetic biological systems. These tools will be standardized, so that they are applicable to any synthetic biological system. The activities will also produce novel synthetic gene regulatory networks that can find applications in pharmaceutical production and gene therapies. We will develop sophisticated mathematical models of synthetic biological systems that connect the targeted biological phenotype (what we want the synthetic biological system to do) to the DNA sequence (that we need to physically construct to realize the synthetic biological system). We will conduct simulations of many alternate designs to decide on the optimum set of molecular components, before we go into the wet laboratory. We will then construct these designs in E. coli and optimize them for performance. We propose to work with synthetic tetracycline inducible networks because they have significant biomedical applications, mainly as gene therapy expression vectors. Tetracycline is a small antibiotic molecule that can safely turn on the production of any protein, when this protein is expressed under the control of a tetracycline-responsive DNA promoter. We will model, design, build and test these promoters to determine how to best control protein expression with tetracycline induction.

Public Health Relevance

Synthetic biologists and engineers can now quickly construct and piece together non-naturally ocuring DNA sequences to design new, synthetic biological systems and organisms. Thus scientists are now afforded more precise control of biological systems and their functions than ever before. Applications abound, such as detectors for biochemical and chemical weapons, devices that will remove environmental pollutants, and gene therapies. Modeling tools can play an important role in synthetic biology the same way modeling helps in aeronautical or architectural design: simulations can quickly provide a clear picture of how different components influence the behavior of the whole. The proposed activities wil result in modeling tools that will help engineers to construct complex synthetic biological systems with new functions.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM086865-02
Application #
7945291
Study Section
Special Emphasis Panel (ZRG1-BST-Q (02))
Program Officer
Lyster, Peter
Project Start
2009-09-30
Project End
2012-08-31
Budget Start
2010-09-01
Budget End
2012-08-31
Support Year
2
Fiscal Year
2010
Total Cost
$247,072
Indirect Cost
Name
University of Minnesota Twin Cities
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
555917996
City
Minneapolis
State
MN
Country
United States
Zip Code
55455
Afroz, Taliman; Biliouris, Konstantinos; Boykin, Kelsey E et al. (2015) Trade-offs in engineering sugar utilization pathways for titratable control. ACS Synth Biol 4:141-9
Smadbeck, Patrick; Kaznessis, Yiannis N (2015) Chemical master equation closure for computer-aided synthetic biology. Methods Mol Biol 1244:179-91
Borrero, Juan; Chen, Yuqing; Dunny, Gary M et al. (2015) Modified lactic acid bacteria detect and inhibit multiresistant enterococci. ACS Synth Biol 4:299-306
Kaznessis, Yiannis N (2014) Multiscale Models of Antibiotic Probiotics. Curr Opin Chem Eng 6:18-24
Smadbeck, Patrick; Kaznessis, Yiannis N (2014) Solution of Chemical Master Equations for Nonlinear Stochastic Reaction Networks. Curr Opin Chem Eng 5:90-95
Afroz, Taliman; Biliouris, Konstantinos; Kaznessis, Yiannis et al. (2014) Bacterial sugar utilization gives rise to distinct single-cell behaviours. Mol Microbiol 93:1093-1103
Smadbeck, Patrick; Kaznessis, Yiannis N (2013) A closure scheme for chemical master equations. Proc Natl Acad Sci U S A 110:14261-5
Volzing, Katherine; Borrero, Juan; Sadowsky, Michael J et al. (2013) Antimicrobial peptides targeting Gram-negative pathogens, produced and delivered by lactic acid bacteria. ACS Synth Biol 2:643-50
Smadbeck, Patrick; Kaznessis, Yiannis (2012) Stochastic model reduction using a modified Hill-type kinetic rate law. J Chem Phys 137:234109
Smadbeck, P; Kaznessis, Y N (2012) Efficient Moment Matrix Generation for Arbitrary Chemical Networks. Chem Eng Sci 84:612-618

Showing the most recent 10 out of 17 publications