Timothy K. Lu

Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Building NE47 -Room 221
77 Massachusetts Avenue, Cambridge, MA 02139

tel: (617) 715-4808 fax: (619) 789-4808
Email: timlu@mit.edu
Web: http://www.rle.mit.edu/sbg/

Research Interests

My research focuses on advancing the fundamentals of synthetic biology and applying synthetic biology to important biomedical diseases.  I have a diverse background in computer science, electrical engineering, and medical sciences that I bring to bear on my current research.  My previous work has involved designing tangible interfaces between humans and computers, engineering ultra-low-power cochlear implants for deaf patients, and using quantitative models to study cochlear dynamics.  For example, I answered a decades-old question in cochlear mechanics – how do slow outer hair cells, which operate up to 1 kHz in vitro, amplify fast sounds in the cochlea in vivo (up to 100 kHz)?  Using electronic circuits to create a detailed biomechanical model, I showed that negative feedback inherent in the mechanical layout of the inner ear enables the tradeoff of amplification gain for speed in outer hair cells, allowing for fast cochlear amplification at reasonable gains.

One major research thrust of my laboratory is to engineer fundamental circuits and frameworks that enable the construction of scalable biological systems.  We aim to use these circuits to study natural biological phenomena, including the phenotypic resistance of bacteria to antibiotics (i.e. persistence).  For example, we are creating and characterizing libraries of interoperable transcription factors.  We intend to use these libraries to construct synthetic models of natural networks in order to study phenomena such as bacterial persistence.  Persistence refers to the ability of a subpopulation of cells to survive antibiotic treatment without any genotypic changes and plays a major role in the ineffectiveness of antimicrobials.  As an initial case study, we will reconstruct the SOS network, which is known to influence persistence levels.  By altering network topology as well as the strength and variability of transcriptional links with our synthetic transcription factors, we will gain insights into how topology, stochasticity, and dynamics impact persistence levels.

In addition, we aim to use these synthetic frameworks to construct tools for programming and probing biological systems. I previously developed a design for extensible and stable genetic memory modules using recombinases and used these modules to create genetic counters that work in living cells.  Currently, we are applying these modules to design biological digital-to-analog and analog-to-digital converters.  Digital-to-analog converters enable digital combinations of inputs, such as inducers, to define analog levels of outputs, such as transcriptional activity.  This functionality may be useful for the reliable cellular programming – for example, in differentiating stem cells and transitioning cells between multiple states in biotechnology processes (e.g., from growth to product synthesis to product modification to self lysis).  Analog-to-digital converters translate internal analog states, such as RNA and protein levels, to digital outputs, such as combinations of fluorescent reporters.  This functionality can enable the threshold-based detection and reporting of internal signals to the outside world for studying phenomena such as cell patterning and chemotaxis and enabling reliable biosensors.

The other major thrust of my research is to apply synthetic biology to study and treat important biomedical afflictions, such as infectious diseases and amyloid-associated diseases.  For example, I previously engineered bacteriophages that efficiently remove bacterial biofilms by expressing biofilm-degrading enzymes during infection.  We have also designed phages that enhance the efficacy of antibiotics against wild-type and antibiotic-resistant bacteria in vivo and in vitro. Although phages are an attractive platform for engineering next generation antimicrobials, there are still unresolved limitations including phage resistance and host specificity.  Thus, we are currently designing new phages that exhibit decreased propensities for phage resistance by producing broad-spectrum antimicrobial agents which can kill resistant bacteria.  We are also creating phages with tunable host ranges by engineering their tail fibers with rational and combinatorial methods.

In addition, I have recently engineered synthetic bacteriophages that express and display rationally designed peptides that target amyloid nucleation sequences.  These synthetic constructs exhibit exceptional efficacy and potency at inhibiting amyloid assembly (i.e., orders of magnitude improvements over chemical inhibitors and peptides alone).  By inhibiting bacterial curli amyloids, we have been able to drastically reduce the formation of bacterial biofilms and decrease the ability of bacteria to invade into mammalian cells.  We are currently studying the structural and biophysical properties of our identified nucleation sequences.  We are also working to translate these findings into other amyloid-associated conditions, including Alzheimer’s disease, Parkinson’s disease, and systemic amyloidoses.


Selected Publications:

T.K. Lu, “Engineering Scalable Biological Systems,” Bioengineered Bugs, vol. 1, no. 6, pp. 1-6, November/ December, 2010.

T.K. Lu, A.S. Khalil, and J.J. Collins, “Next-Generation Synthetic Gene Networks,” Nature Biotechnology, vol. 27, no. 12, pp. 1139-1150, December 7, 2009.

T.K. Lu*, A.E. Friedland*, X. Wang, D. Shi, G.M. Church, and J.J. Collins, “Synthetic Gene Networks that Count,” Science, vol. 324, no. 5931, pp. 1199-1202, May 29, 2009.

T.K. Lu and J.J. Collins, “Engineered Bacteriophage Targeting Gene Networks as Adjuvants for Antibiotic Therapy,” Proceedings of the National Academy of Sciences, vol. 106, no. 12, pp. 4629-4634, March 24, 2009.

T.K. Lu and J.J. Collins, “Dispersing Biofilms with Engineered Enzymatic Bacteriophage,” Proceedings of the National Academy of Sciences, vol. 104, no. 27, pp. 11197-11202, July 3, 2007.

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