Welcome to the Lu Laboratory!

The Lu Laboratory is in the Division of Biomedical Informatics at the Cincinnati Children’s Hospital Medical Center (CCHMC). We are affiliated with the Department of Pediatrics of College of Medicine and Departments of Biomedical Engineering, Computer Science, and Environmental Health at the University of Cincinnati (UC).

 

Current Research Topics

Our laboratory's mission is to bring quantitative approaches from disciplines such as computer science and applied mathematics to study the molecular mechanism of human diseases. Our expertise includes biomolecular network prediction and analysis, machine learning and statistical inference, and dynamic modeling of living systems. We also provide bioinformatic / statistical service to collaborating investigators.

(1) Investigating molecular basis for High Density Lipoprotein (HDL) heterogeneity

HDLs are blood-borne complexes of protein and lipid that play critical roles in the prevention of cardiovascular disease (CVD), the major cause of mortality in the United States. Although HDL is best known for its common apolipoproteins (apo) A-I and A-II, recent proteomics studies have identified up to 50 additional proteins. The protein complement plays a critical role in defining the metabolism and potential benefit / pathology of a particular HDL particle. Our research aims to infer the HDL interactome and the synergistic functional relationships among the protein components, using computational / statistical approaches. In the long term, the resulting compositional information could provide a basis for therapeutic strategies designed to modulate certain HDL subparticles or mimic their effects, with the goal of reducing CVD.

(2) Unraveling networks and biomarkers for childhood brain disorders

Imaging techniques, such as magnetoencephalography (MEG), positron emission tomography (PET), and magnetic resonance imaging (MRI), provide opportunities for analyzing both structural and functional organization of human brains. Traditional analysis of brain images focuses on the study of individual brain regions without taking into account the interconnections among several brain regions. Our research aims to develop novel computational algorithms capable of unraveling the structural, functional and effective networks in human brains. Network-based biomarkers are expected to significantly improve the diagnosis, prognosis and treatment options for childhood brain disorders.

(3) Enabling network pharmacology for infectious diseases

The philosophy in traditional drug design, i.e., the "one gene, one drug, one disease" paradigm, focuses on the individual properties of a protein, for example, whether the deletion of a gene is lethal to an organism and if it is, then inhibition of the product of the gene by a drug should also be lethal. However, many effective drugs have been found to affect a handful of targets instead of a single protein. Recently, understanding of biological networks and molecule functionality has given rise to a new discipline, network pharmacology, that provides the opportunity to identify new drug targets in an infectious organism, while avoiding targets that cause toxicity in the host. Our group recently combined a gene's essentiality and network properties to facilitate the identification of potential drug targets in treating Pseudomonas infection.

(4) Probing developmental robustness in fruit fly Drosophila

Development is a precise and reproducible process that is insensitive (or robust) to both individual differences and environmental variations. How such robustness is achieved remains an intriguing but fundamental problem in developmental biology. We are part of an interdisciplinary team led by Dr. Jun Ma, that uses Drosophila as a model organism to study developmental robustness. Since the fundamental principles that guide developmental processes are conserved in all animals, the findings of this study will be directly relevant to the understanding of human development. This study will focus on a gradient protein called bicoid that instructs the embryo to develop the anterior structures. We aim to build a mathematical model that describes developmental robustness based on experimental data.

Open Positions

We always welcome highly motivated researchers to join our lab. Please visit the CAREER page for how to apply.

 

Recent News

04/2014: Lu Lab won a new CCTST Methodology grant to study brain disease with deep learning algorithm. (Lu, PI)

02/2014: Craig Slusher, Boyu Diao, Boyang Fu and Xinyu Guo joined our lab. Welcome!

10/2013: Dr. Lu is promoted to Associate Professor! Congratulations!

10/2013: David Moneysmith joined our lab. Welcome David!

04/2013: Lirong won a URC graduate student research fellowship! Congratulations!

04/2013: Anna received a REWU Award to join UC WISE program! Congratulations!

03/2013: For the past 5 months, Huaiyu, Sheng, Anna, Hailong, Liya joined our lab. Welcome new members!

12/2012: Dr. Lu is officially listed as an MSTP Program Faculty.

10/2012: Dr. Debi Swertfeger joined our lab. Welcome Debi!

09/2012: Zhaowei Ren joined our lab. Welcome Zhaowei!

09/2012: Lu Lab received an NIH R01 to study metabolomics in CF! (Clancy, PI). Three major grants within two months!

09/2012: Lu Lab received funding from Gates Foundation to study Candidal infection! (Hostetter, PI)

08/2012: Lu Lab won a $2.7 million NIH R01 grant to study HDL subspeciation! (Lu, PI).

07/2012: Jingyuan defended her thesis. Congratulations, Dr. Deng!

04/2012: Lu Lab won a CCTST Methodology grant to predict MRI based biomarkers in brain. (Lu, PI)

02/2012: Minlu defended his thesis. Congratulations, Dr. Zhang!

Lu Lab, Division of Biomedical Informatics,Cincinnati Children's Hospital Medical Center