Stochastic systems
Biological systems are inherently noisy. Nonetheless, cells continue to carry out necessary functions reliably. We investigate fundamental questions that can explain role of stochasticity in biological systems. These questions include stochastic control analysis (parallel to metabolic control analysis) and independent control of noise and concentration. Stochastic fluctuations can also be used to measure retroactivity, or the impact of downstream reactions on upstream reactions. Theoretical work is supported by experiments when feasible.
Faculty: Eric Klavins, Herbert Sauro
image taken from http://faculty.washington.edu/klavins/papers/lowcopy_cdc2010.pdf
Synthetic Biology and Evolution
Evolution can be a negative and positive force in synthetic biology. It can cause functional genetic circuits to degrade because the functional circuits impede the growth of the cells. It can also be used to discover novel enzymes or network architectures. Both aspects of evolution are investigated. The processes by which cells degrade functional circuits is explored, revealing possible mechanisms by which cells manage to degrade synthetic circuits. Directed evolution is used to evolve cells that can perform specific tasks, such as catalyze novel reactions.
Faculty: Eric Klavins, Herbert Sauro
image taken from http://palscience.com/health-medicine/theories-aging/
RNA Logic
Nucleic acids are a good substrate for designing arbitrary networks because the interactions in the network can be programmed using the nucleic acid sequences. Nucleic acids can be programmed to act as competitive inhibitors, transcriptional regulators, substrates for another reaction, or catalysts. Using such interactions, we have constructed networks that can compute logical functions as well as mimic some of the characteristics of electronic circuits. Additionally, analog functions such as amplification has also been constructed using nucleic acids. We are also investigating the potential of using nucleic acids based networks to detect cellular states and control cellular states.
Faculty: Eric Klavins, Georg Seelig
image taken from http://www.sciencemag.org/content/314/5805/1585.abstract
Software
Computational modeling is an important aspect of systems biology and synthetic biology. It provides insight into network dynamics that can be used to understand existing networks and design new synthetic networks. Our research focuses on developing graphical user-interface tools and software libraries that can assist in designing and analyzing models of biological systems. Computer aided design tools for synthetic biology are being developed to integrate information about the synthetic construct and the mathematical model, allowing better use of predictable models in synthetic biology. The development of computer aided design tools overlaps significantly with development of computational standards.
Faculty: Herbert Sauro
Standards
Exchange of information in synthetic biology relies on software standards. At present, exchange of information primarily relies on published material, which are often not sufficient to completely reproduce or reuse a synthetic construct. Automated and computational exchange is necessary to enable synthetic biologist to exchange results from their work. To enable efficient forms of exchange, community co-operation and standards are required. These computational standards will be able to capture the results of synthetic biology experiments unambiguously. Software tools that support the standards will enable easy exchange of information. See Synthetic Biology Open Language (SBOL) for details.
Faculty: Herbert Sauro
image taken from http://uh.edu/engines/