Our research is supported by NSF (CBET/CAREER), NSF (DMS/NIGMS), and UTD.

The laboratory invites applications for graduate students and postdoctoral associates in the area of synthetic biology and genome editing. Please email your cover letter and CV to [email protected]

Suggested Reading:

CRISPR-based editing reveals edge-specific effects in biological networks.
The CRISPR Journal, 2018.
Networks consist of functional elements (nodes) that form a variety of diverse connections (edges), with each node being a hub for multiple edges. Herein, in contrast to node-centric network perturbation and analysis approaches, we present a high-throughput CRISPR-based methodology for delineating the role of network edges.

Mapping the operational landscape of microRNAs in synthetic gene circuits.
NPJ Systems Biology and Applications, 2018.
We combined experiments and mathematical modeling to study the microRNA operational landscape. We engineered custom genetic circuits that contain microRNA-based regulation and introduced an analytical strategy, that includes clustering and superposition of discrete experiments, to produce a “bird’s-eye view” perspective.

Exploiting the CRISPR/Cas9 PAM constraint for single-nucleotide resolution interventions.
PLOS ONE, 2016.
We demonstrate that the PAM requirement can be exploited to specifically target single-nucleotide heterozygous mutations while exerting no aberrant effects on the wild-type alleles. Specifically, we target the heterozygous G13A activating mutation of KRAS in colorectal cancer cells and we show reversal of drug resistance to a MEK small-molecule inhibitor.

Discriminating direct and indirect connectivities in biological networks.
Proceedings of the National Academy of Sciences (PNAS), 2015.
We used a combination of computational and theoretical approaches coupled to synthetic biology experimentation in mammalian cells to study direct and indirect connectivities in biological networks.

CRISPR-based self-cleaving mechanism for controllable gene delivery in human cells.
Nucleic Acids Research, 2015.
We introduce a methodology to control the copies and residence time of a gene product delivered in host human cells but also selectively disrupt fragments of the delivery vehicle. We envision future applications in complex synthetic biology architectures, gene therapy and trace-free delivery.

Assembly and validation of versatile transcription activator-like effector libraries.
Scientific Reports, 2014.
Here we introduce, describe the assembly, and demonstrate the use of comprehensive and versatile transcription activator-like effector (TALE) libraries. Considering the highly modular nature of TALEs and the versatility and ease of constructing these libraries we envision broad implications for high-throughput genomic assays.

Biological 2-input decoder circuit in human cells.
ACS Synthetic Biology, 2014.
Decoders are combinational circuits that convert information from n inputs to a maximum of 2n outputs. We present the first implementation of a genetic decoder in human kidney cells.

Synthetic mammalian transgene negative autoregulation.
Nature/EMBO Molecular Systems Biology, 2013.
The effect of negative feedback on global and local sources of uncertainty is studied with synthetic circuits stably integrated in human cells. Negative feedback is shown to be the most efficient way to mitigate the effects of global fluctuations by introducing a single additional regulatory link.

Reverse engineering validation using a benchmark synthetic gene circuit in human cells.
ACS Synthetic Biology, 2013.
We stably integrate a synthetic gene network in human kidney cells and use it as a benchmark for validating reverse engineering methodologies.

Transcripts for combined synthetic microRNA and gene delivery.
Molecular BioSystems, 2013.
We engineer and study in mammalian cells a range of synthetic intragenic miRNAs co-expressed with their host genes.

Transcription activator-like effector hybrids for conditional control and rewiring of chromosomal transgene expression.
Scientific Reports, Nature Publishing Group, 2012.
We introduce a general purpose two-hybrid approach that can be customized to regulate the function of any TALE either using effector molecules or a heterodimerization reaction. We demonstrate the successful interface of TALEs to specific endogenous signals, namely hypoxia signaling and microRNAs, essentially closing the loop between cellular information and chromosomal transgene expression.

Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template.
Nature/EMBO Molecular Systems Biology, 2011.
News and Views
Variable gene dosage is a major source of fluctuations in gene product levels in both endogenous and synthetic circuits. To mitigate gene expression variability, we designed, simulated, constructed, and tested a range of regulatory circuits. Feedforward regulation displayed better adaptation than negative feedback, and circuits based on RNA interference were the most robust to variation in DNA template amounts.

Older Work:

Rationally designed logic integration of regulatory signals in mammalian cells.
Nature Nanotechnology, 2010.
Multiple-transcription-factor proteins are used to build complex logic circuits inside mammalian cells, offering a platform for intelligent therapeutics that interact with biological environments.

Logic integration of mRNA signals by an RNAi-based molecular computer.
Nucleic Acids Research, 2010.
We report the construction and implementation of biosensors that ‘transduce’ mRNA levels into bioactive, small interfering RNA molecules via RNA strand exchange in a cell-free Drosophila embryo lysate, a step beyond simple buffered environments.

A universal RNAi-based logic evaluator that operates in mammalian cells.
Nature Biotechnology, 2007.
We use RNA interference (RNAi) in human kidney cells to construct a molecular computing core that implements general Boolean logic to make decisions based on endogenous molecular inputs. The state of an endogenous input is encoded by the presence or absence of 'mediator' small interfering RNAs (siRNAs).