Xue-Heng Zhang

Detecting drug targets with minimum side effects in metabolic networks

High-throughput techniques produce massive data on a genome-wide scale which facilitate pharmaceutical research. Drug target discovery is a crucial step in the drug discovery process and also plays a vital role in therapeutics. In this study, the problem of detecting drug targets was addressed, which finds a set of enzymes whose inhibition stops the production of a given set of target compounds and meanwhile minimally eliminates non-target compounds in the context of metabolic networks. The model aims to make the side effects of drugs as small as possible and thus has practical significance of potential pharmaceutical applications. Specifically, by exploiting special features of metabolic systems, a novel approach was proposed to exactly formulate this drug target detection problem as an integer linear programming model, which ensures that optimal solutions can be found efficiently without any heuristic manipulations. To verify the effectiveness of our approach, computational experiments on both Escherichia coli and Homo sapiens metabolic pathways were conducted. The results show that our approach can identify the optimal drug targets in an exact and efficient manner. In particular, it can be applied to large-scale networks including the whole metabolic networks from most organisms.

Identifying differentially expressed pathways via a mixed integer linear programming model

The identification of genes and pathways involved in biological processes is a central problem in systems biology. Recent microarray technologies and other high-throughput experiments provide information which sheds light on this problem. In this article, the authors propose a new computational method to detect active pathways, or identify differentially expressed pathways via integration of gene expression and interactomic data in a sophisticated and efficient manner. Specifically, by using signal-to-noise ratio to measure the differentially expressed level of networks, this problem is formulated as a mixed integer linear programming problem (MILP). The results on yeast and human data demonstrate that the proposed method is more accurate and robust than existing approaches.

Cross-platform method for identifying candidate network biomarkers for prostate cancer

Discovering biomarkers using mass spectrometry (MS) and microarray expression profiles is a promising strategy in molecular diagnosis. Here, the authors proposed a new pipeline for biomarker discovery that integrates disease information for proteins and genes, expression profiles in both genomic and proteomic levels, and protein-protein interactions (PPIs) to discover high confidence network biomarkers. Using this pipeline, a total of 474 molecules (genes and proteins) related to prostate cancer were identified and a prostate-cancer-related network (PCRN) was derived from the integrative information. Thus, a set of candidate network biomarkers were identified from multiple expression profiles composed by eight microarray datasets and one proteomics dataset. The network biomarkers with PPIs can accurately distinguish the prostate patients from the normal ones, which potentially provide more reliable hits of biomarker candidates than conventional biomarker discovery methods.

Predicting cooperative drug effects through the quantitative cellular profiling of response to individual drugs

Quantitative prediction of cellular responses to drugs and drug combinations is a challenging and valuable topic in pharmaceutical research. In the past decade, microarray technology has become a routine tool for monitoring genome‐wide expression changes and has been widely adopted for exploring drug response in the pharmaceutical field. However, how to predict the synergistic effect of drug combinations using microarray data is a challenging task. In this article, we report a simple prediction framework based on the genome‐wide and quantitative profiling of cellular responses to individual drugs. By exploring the differential expression profiles, our correlation‐based strategy can reveal the synergistic effects of drug combinations. The comparison with gold‐standard experimental results demonstrates the strengths and weaknesses in relation to prediction based only on cellular response to individual drugs. Specifically, the prediction strategy may work for a drug combination whose individual drugs show related transcriptomic mechanisms but not for others.

Growth, abscisic acid content, and carbon isotope composition in wheat cultivars grown under different soil moisture

Changes in dry matter accumulation and allocation, abscisic acid content and carbon isotope composition of three wheat cultivars from dry, middle and wet climate regions were recorded at full maturity after exposure to different watering regimes (100, 50 and 25 % field capacity). Compared with the wet climate cultivar, the dry climate cultivar showed lower stem height, total leaf area, total dry biomass and total grain dry mass, and higher root/shoot ratio, abscisic acid content and carbon isotope composition under all watering regimes. Both water-limited treatments significantly reduced leaf growth and increased dry matter allocation into the roots leading to a significant raise of root/shoot ratio in all cultivars tested. In addition, drought affected morphological and physiological properties more in the dry climate cultivar than in the wet climate cultivar.

Different responses of two contrasting wheat genotypes to abscisic acid application

Purpose of this study was to investigate different responses of two wheat genotypes (Triticum aestivum L.) from the wet and dry climate regions to exogenous abscisic acid (ABA) application under well-watered and water-stressed conditions. Exogenous ABA was applied to the leaves by spraying and changes in dry matter accumulation and allocation, endogenous ABA content and carbon isotope ratio (δ13C) were monitored. The ABA application significantly decreased stem height, total biomass, total leaf area, total grain mass and leaf area/mass ratio, and significantly increased root/aboveground biomass ratio, endogenous ABA content and δ13C under well-watered and water-stressed conditions. Compared with the wet climate genotype, the dry climate genotype was more responsive to exogenous ABA application, resulting in lower stem height, total biomass, total leaf area, total grain mass and leaf area/mass ratio, and higher root/aboveground biomass ratio, endogenous ABA content and δ13C under all experimental treatments.

Plastic scintillation detectors for precision Time-of-Flight measurements of relativistic heavy ions

Plastic scintillation detectors for Time-of-Flight (TOF) measurements are almost essential for event-by-event identification of relativistic rare isotopes. In this work, a pair of plastic scintillation detectors of dimensions 50 × 50 × 3t mm3 and 80 × 100 × 3t mm3 have been set up at the External Target Facility (ETF), Institute of Modern Physics (IMP). Their time, energy and position responses are measured with the 18O primary beam at 400 MeV/nucleon. After off-line corrections for walk effect and position, the time resolutions of the two detectors are determined to be 27 ps (σ) and 36 ps (σ), respectively. Both detectors have nearly the same energy resolution of 3.1% (σ) and position resolution of about 3.4 mm (σ). The detectors have been used successfully in nuclear reaction cross section measurements, and will be be employed for upgrading the RIBLL2 beam line at IMP as well as for the high energy branch at HIAF.

A large area plastic scintillation detector with 4-corner-readout

A 760 mm × 760 mm × 30 mm plastic scintillation detector viewed by photomultiplier tubes(PMTs)from four corners has been developed, and the detector has been tested with cosmic rays and γ rays. A positionindependent effective time Teff has been found, indicating this detector can be used as a TOF detector. The hit position can also be reconstructed by the time from the four corners. A TOF resolution of 236 ps and a position resolution of 48 mm have been achieved, and the detection efficiency has also been investigated.A 760 mm × 760 mm × 30 mm plastic scintillation detector viewed by photomultiplier tubes(PMTs)from four corners has been developed, and the detector has been tested with cosmic rays and γ rays. A positionindependent effective time Teff has been found, indicating this detector can be used as a TOF detector. The hit position can also be reconstructed by the time from the four corners. A TOF resolution of 236 ps and a position resolution of 48 mm have been achieved, and the detection efficiency has also been investigated.

Study of the data analysis process of the neutron wall with simulation

We study the response function of the neutron wall for 300 MeV neutrons with GEANT4 simulations. The methods to find the correct neutron incident position and time are discussed, and the neutron emission angle and energy are reconstructed and compared with the simulation.