Scientific Program

Conference Series Ltd invites all the participants across the globe to attend 13th International Conference on Metabolomics and Systems Biology Hilton Zurich Airport Hohenbuehlstrasse 10, 8152, Opfikon, Switzerland.

Day 1 :

Keynote Forum

Tao Huang

Shanghai Institutes for Biological Sciences, China

Keynote: Deciphering the mechanisms of complex diseases using network and machine learning approaches

Time : 09:40-10:25

OMICS International Euro Metabolomics 2018 International Conference Keynote Speaker Tao Huang photo

Tao Huang has completed his Postdoctoral Fellowship in the Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York City, USA. He is currently a Professor and the Director of Bioinformatics Core Facility at the Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences ( P R China). His research interests include bioinformatics, computational biology, systems genetics and big data research. He has published over 100 articles in peer-reviewed journals. His works have been cited for 3138 times with an h-index of 26 and an i10-index of 64. He has been an Independent Reviewer for 30 journals. He is the Editor of the book: “Computational Systems Biology - Methods and Protocols” published in Springer; Guest Editor for BBA Molecular Basis of Disease, BBA General Subjects, Artificial Intelligence in Medicine, BioMed Research International, Combinatorial Chemistry & High Throughput Screening and Computational and Mathematical Methods in Medicine.


The complex diseases, such as cancer, involves dysfunctions on multiple levels. There have been several methods to identify the dysfunctions on each level. On genomics level, GWAS (Genome-wide association study) can detect the disease phenotype associated SNPs (single nucleotide polymorphisms). But many GWAS identified SNPs locate in intergenic region and can't be annotated to specific genes. With the help of eQTL (expression quantitative trait loci) method, the downstream genes of the GWAS SNPs can be found. And based on the co-expression network, regulatory network, protein-protein interaction network, or even protein-chemical interaction network of these genes, the possible cascade of the signal transduction from the genetic perturbations can be discovered. By integrating all these omics data, we can establish a systems biology model of how the GWAS SNPs affect the expression of their direct target genes, how these direct targets affect secondary target genes, or proteins, or metabolites, and eventually cause the catastrophic pathological changes on network or pathway. With the comprehensive network, we can not only study the mechanism of one disease, but also the relationship of several traits. For example, schizophrenia and anti-tuberculosis drug-induced hepatotoxicity co-occurred frequently but they do not share common genes. We mapped their genes onto the network, the common drivers of these two diseases were revealed through two-way RWR analysis. Another example is that the lung cancer dysfunctions on several levels but how the different level dysfunctions connect are still unknown. We investigated the mutation, methylation, mRNA and microRNA expression difference between cancer and normal tissues using machine learning methods, then mapped the dysfunction of each level onto the comprehensive network. By connecting each other with shortest paths, the highly frequent shortest path genes that can cause the tumorigenesis on multiple levels were discovered. Overall, the network and machine learning approaches are effective to dissect the complex system and reveal the mechanisms of diseases.

Break: Networking & Refreshments 10:25-10:45 @ Europa Foyer

Keynote Forum

Andrea Ragusa

University of Salento, Italy

Keynote: Metabolomics and lipidomic profile of clinical serum samples from patients with liver disease

Time : 10:45-11:30

OMICS International Euro Metabolomics 2018 International Conference Keynote Speaker Andrea Ragusa photo

Andrea Ragusa pursued his PhD from the University of Southampton (UK) in 2005. He dedicated himself to nanomedicine and metabolomics by developing novel smart nanovectors for the targeted delivery of drugs and studying their effects in vitro through NMR and HPLC-MS investigations. He is an Organic Chemist with an expertise in analytical techniques. More recently, he shifted his efforts towards the analysis of clinical samples for better understanding the molecular mechanisms involved in several liver-related diseases, with particular attention to HCC (Hepatocellular carcinoma), as well as for identifying novel biomarkers and their exploitation, in combination with multivariate statistical analysis, in the early diagnosis and treatment of the disease.


Statement of the Problem: Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the third leading cause of cancer deaths worldwide. Unfortunately, early-stage HCC is asymptomatic and most patients already have an advanced stage at the time of diagnosis. Alpha fetal protein (AFP) is the current clinical biomarker used for the diagnosis and follow-up of HCC treatments. Nevertheless, its sensitivity is not satisfactory and there is an urgent need for the identification of novel more efficient biomarkers. In this regard, metabolomics can provide unique clinical information and it offers the possibility to understand the system-level effects of metabolites through its integration into multi-functional approaches. Nuclear magnetic resonance (NMR) spectroscopy already showed to be a valuable tool for metabolomic investigations, providing a system capable of studying and identifying novel biomarkers, as well as for yielding a metabolic fingerprint useful for the classification of several pathologies.
Methodology: 1D and 2D 1H NMR spectroscopy, in combination with univariate and multivariate statistical analysis (MVA), was used to investigate the metabolic profile of serum samples from HCC, type-2 diabetes mellitus (T2DM), and control subjects. The patients’ response to metformin or insulin treatment was also investigated. A similar analysis was also performed on the corresponding serum lipid extracts, yielding a more global view of the metabolic alterations occurring.
Findings: Several metabolites directly or indirectly related to energy pathways were found to be either over- or under-expressed in pathological patients with respect to control patients. Among them, a substantial increase in lactate production, with the concomitant decrease of branched amino acids, was observed. Similarly, an increase in the total fatty acids (FA) with a concomitant decrease of unsaturated FA, phosphocholines, phospholipids, and cholesterol was also detected.
Conclusion & Significance: NMR spectroscopy, in combination with MVA, allowed to gain a better insight into the metabolic alterations involved in HCC disease. The obtained metabolic fingerprints could be exploited in the early diagnosis of the disease
as well as in its follow-up upon treatment.

OMICS International Euro Metabolomics 2018 International Conference Keynote Speaker Gerald C Hsu photo

Gerald C Hsu received an honorable PhD in mathematics and majored in engineering at MIT. He attended different universities over 17 years and studied seven academic disciplines. He has spent 20,000 hours in T2D research. First, he studied six metabolic diseases and food nutrition during 2010-2013, then conducted research during 2014-2018. His approach is “math-physics and quantitative medicine” based on mathematics, physics, engineering modeling, signal processing, computer science, big data analytics, statistics, machine learning, and AI. His main focus is on preventive medicine using prediction tools. He believes that the better the prediction, the more control you have. The author has not received any financial assistance from any organization.


Introduction: By using the big data on one patient (author), this clinical paper describes the relationship between the metabolism state and medical conditions, including obesity, diabetes, and cardiovascular risk.
Methodology: The obese patient was diagnosed with type 2 diabetes (T2D), hyperlipidemia, hypertension for over 25 years along with suffering five cardiac episodes during 1994-2006. The study processed ~1.5M detailed metabolic conditions and lifestyle data (2012-2018) based on a math-physical medicine approach (mathematics, physics, engineering modeling, artificial intelligence or AI), rather than the traditional biochemical medicine method. The author defined two new terms: Metabolism Index (MI) and General Health Status Unit (GHSU) to evaluate a person’s overall metabolism and associated chronic diseases. The research steps are 1. observing the patient’s physical phenomena and metabolic changes, collecting relevant big data. 2. building up engineering models and deriving inter-relationship equations, applying statistics tools for variance study. 3. using machine learning and AI to predict important metabolic changes.
Results: Metabolic factors and health symptoms show some key results within eight years.
Conclusions: This math-physical medicine approach has proven the close relationship between metabolic changes quantitatively
due to the improvement of lifestyle management and chronic disease conditions.