Tsinghua Center for Synthetic and Systems Biology

Engineering customised cell signalling circuits and their applications

Time: Dec 18, 10:30am
Place: FIT 1-312
Title: Engineering customised cell signalling circuits and their applications
Presenter: Dr Baojun Wang, Chancellor’s Fellow in Synthetic Biology at the University of Edinburgh

Dr Baojun Wang is a Chancellor’s Fellow in Synthetic Biology at the University of Edinburgh and works at the interfaces between biology and engineering. He is a Principal Investigator in the School of Biological Sciencs as well as the cross disciplinary Centre for Synthetic and Systems Biology (SynthSys). He received a PhD in bioengineering (2011) from Imperial College London and a BEng in biomedical engineering (2005) from Zhejiang University China. He was a Research Associate at Imperial College London before joined the University of Edinburgh in 2013 to establish a research group in synthetic biology. His research interests include designing and building novel customised gene circuits for sensing, information processing and computing of multiple cellular and environmental signals with applications ranging from biosensing, biocomputing to bioproduction.

Cells live in an ever-changing environment and continuously sense, process and react to environmental signals using their inherent signalling and gene regulatory networks. In this talk, I will introduce the construction of synthetic gene circuits to customise cellular information processing and responses by harnessing the inherent modularity of signalling networks. A set of modular and orthogonal genetic logic gates, e.g. AND and NAND, and analogue circuits such as a tunable genetic amplifier were engineered to modulate multiple in vivo transcriptional signals in either digital-like or bespoke analogue manner. I will then show that how these gene circuits can be used to enhance the specificity and sensitivity of synthetic cell-based biosensors for a range of purposes. Finally, I will discuss the construction of chimeric transcriptional control proteins as a tool to decouple the native inter-crosslinked cell omics in the wildtype regulatory network for studying the nitrogen stress response in bacteria.

Baojun Wang, Richard Kitney, Nicolas Joly and Martin Buck, “Engineering modular and orthogonal genetic logic gates for robust digital-like synthetic biology”, Nature Communications, 2:508, (2011) Baojun Wang, and Martin Buck, “Customizing cell signalling using engineered genetic logic circuits”, Trends in Microbiology, 20(8): 376-384, (2012) Baojun Wang, Mauricio Barahona and Martin Buck, “A modular cell-based biosensor using engineered genetic logic circuits to detect and integrate multiple environmental signals”, Biosensors and Bioelectronics, 40, 368-376, (2013)

Unsupervised phenotyping of Severe Asthma Research Program participants using expanded lung data

Time: Dec.17 (next Tuesday) 10:30-11:30am
Place: FIT 1-312
Speaker: Dr. Wei Wu
Title: Unsupervised phenotyping of Severe Asthma Research Program participants using expanded lung data
Previous clinical studies have identified asthma phenotypes based on small numbers of clinical, physiologic or inflammatory characteristics. However, no studies have utilized a wide range of variables using machine learning approaches.
In this work, our goal is to identifysubphenotypes of asthma utilizing blood, bronchoscopic, exhaled nitric oxide and clinical data from the Severe Asthma Research Program using unsupervised clustering, and then characterize them using supervised learning approaches. In order to achieve this goal, we appliedunsupervised clustering approaches to 112 clinical, physiologic and inflammatory variables from 378 subjects. Variable selection and supervised learning techniques were employed to select relevant and nonredundant variables, address their predictive values, as well as the predictive value of the full variable set.
We identified ten variable clusters and six subject clusters, which differed and overlapped with previous clusters identified by clinicians. Traditionally defined severe asthmatics distributed through subject Clusters 3-6. Cluster 4 identified early onset allergic asthmatics with low lung function and eosinophilic inflammation. Later onset, mostly severe asthmatics with nasal polyps and eosinophilia characterized Cluster 5. Cluster 6 asthmatics manifested persistent inflammation in blood and bronchoalveolar lavageand exacerbations despite high systemic corticosteroiduse and side effects. Age of asthma onset, quality of life, symptoms, medications and health care utilization were some of the 51 nonredundant variables distinguishing subject clusters. These 51 variables classified test cases with 88% accuracy, compared to 93% accuracy with all 112 variables.
The machine learning approaches used here provide unique insights into asthma pathogenesis while revealing novel but readily clinically recognizable phenotypes.

Short Bio:
Dr. Wei Wu is an Associate Research Professor at the Lane Center for Computational Biology at Carnegie Mellon University. Her research focuses on understanding complex human diseases by undertaking integrative approaches, which combine biology, computational and statistical learning, bioinformatics, and genomics. Dr. Wu received a Ph.D. in Computational Molecular Biology from Rutgers University. She also received a M.S. in Computer Science from the University of California at Santa Cruz, where she worked on the construction of the Human Genome Browser with Professor David Haussler. She later did a postdoctoral training at Lawrence Berkeley National Lab with Dr. Mina Bissell. Her current work involves: i) subphenotyping asthma patients using computational approaches; and ii) understanding breast cancer mechanisms using dynamic network learning approaches. Her paper on developing a tree-varying network learning approach for analyzing a breast cancer progression series of cells has won the Best Paper Award at the ISMB conference in 2012. She is a co-Principle Investigator on two NIH R01 awards.

Learning Sparse Causal Gaussian Networks With Experimental Intervention: Regularization and Coordinate Descent

Time: 10:30-12:00 AM, Dec 5
Place: FIT 1-312
Speaker: Prof. Qing Zhou, UCLA
Causal networks are graphically represented by directed acyclic graphs (DAGs). Learning causal networks from data is a challenging problem due to the size of the space of DAGs, the acyclicity constraint placed on the graphical structures and the presence of equivalence classes. In this paper, we develop an L1-penalized likelihood approach to estimate the structure of causal Gaussian networks. A blockwise coordinate descent algorithm, which takes advantage of the acyclicity constraint, is proposed for seeking a local maximizer of the penalized likelihood. We establish that model selection consistency for causal Gaussian networks can be achieved with the adaptive lasso penalty and sufficient experimental interventions. Simulation and real data examples are used to demonstrate the effectiveness of our method. In particular, our method shows satisfactory performance for DAGs with 200 nodes which have about 20,000 free parameters.

A Penalized Bayesian Approach to Predicting Sparse Protein-DNA Binding Landscapes

Time: 10:30-12:00 AM, Dec 4
Place: FIT 1-312
Speaker: Prof. Qing Zhou, UCLA
Cellular processes are controlled, directly or indirectly, by the binding of hundreds of different DNA binding factors (DBFs) to the genome. One key to deeper understanding of the cell is discovering where, when, and how strongly these DBFs bind to the DNA sequence. Direct measurement of DBF binding sites (e.g. through ChIP-Chip or ChIP-Seq experiments) is expensive, noisy, and not available for every DBF in every cell type.  Naïve and most existing computational approaches to detecting which DBFs bind in a set of genomic regions of interest often perform poorly, due to the high false discovery rates and restrictive requirements for prior knowledge. We develop SparScape, a penalized Bayesian method for identifying DBFs active in the considered regions and predicting a joint probabilistic binding landscape. Utilizing a sparsity-inducing penalization, SparScape is able to select a small subset of DBFs with enriched binding sites in a set of DNA sequences from a much larger candidate set. This substantially reduces the false positives in prediction of binding sites. Analysis of ChIP-Seq data in mouse embryonic stem cells and simulated data show that SparScape dramatically outperforms the naïve motif scanning method and the comparable computational approaches in terms of DBF identification and binding site prediction.

DNA copy number aberrations in cancer and evidence-based text mining for cancer

Time: 10:00-12:00 AM, Oct 24, Thu
Place: FIT 1-312
Speaker: Prof. Hyunju Lee, Ph.D., School of Info. & Comm., GIST, South Korea
ABSTRACT: This talk consists of two main parts. First, integrative approaches for the analysis of DNA copy number aberrations (CNAs), gene expressions, protein-protein interactions in cancer are introduced. To find cancer-related genes and pathways, we developed a voting-based cancer module identification method by combining topological and data-driven properties, and a wavelet-based method to distinguish cancer-driving genes from passenger genes. In the second part of the talk, a disease gene search engine, DigSee, is introduced. DigSee is a Web service to search MEDLINE abstracts for evidence sentences describing that 'genes' are involved in the development of 'cancer' through 'biological event. DigSee is available through http://gcancer.org/digsee.