Sui Huang, an esteemed Professor at the Institute for Systems Biology (ISB), has started as a new University of Eastern Finland Distinguished Professor, UEFDiPro.
The part-time professorship focusing on Systems Medicine is hosted by the Institute of Biomedicine, where Professor Huang has long-standing collaboration with Merja Heinäniemi, Professor of Computational Biomedicine. “One specific focus of the project is plasma proteomics, which offers a clinically novel, still underappreciated useful window to systems health and disease states. Our goal is to develop automated analysis of plasma proteomics data,” Huang says.
UEFDiPro positions are geared towards developing the university’s research with strategy and competitiveness in mind. An internationally recognised and well-networked professor can be invited to a UEFDiPro position. Huang’s professorship is part of the university’s strategic profiling with focus on translational medicine, supported by the Research Council of Finland with PROFI7 funding.
Pioneers of systems medicine
Huang says he and Heinäniemi share an interest in applying new omics technologies to better understand cancer – and more generally, the very nature of a “cell type”. The term ‘omics’ refers to a comprehensive analysis of an organism using technologies, such as genomics, transcriptomics, proteomics and metabolomics. The technologies deliver tens of thousands of molecular measurements per blood sample.
“We were both attracted early on to gene expression profiles – a type of data that introduced high-dimensional thinking to biologists. But more importantly, we share the vision that all big data on gene activities are the consequence of elementary principles of complex dynamics, imposed by the gene regulatory network. The changes in gene expression profiles are a systems behaviour, emerging from molecular interactions.”
The overarching goal in Huang’s research is to help bring omics technologies and associated computational tools from the realm of basic science towards patient care. He envisions that after two decades of genomics in the forefront, the next decades will be the era of phenomics, with a strong emphasis on clinical and personalised medicine.
The phenome comprises all our measurable traits, such as traditional body weight, heart rate, blood sugar, etc. but also includes the big data from “omics”. Unlike the genome, which consists of the genes that we inherit, the phenome is also affected our lifestyle and our environment. Therefore, it is the starting point for health improvement that unites “nature and nurture”. ISB’s ambitiousinitiatives in the Human Phenome project aims at bringing the phenome to the forefront of health care and individual disease prevention.
“I am fortunate to have worked for the past 12 years in one of the birth places of systems biology, and to have witnessed closely how it became systems medicine – all under the aegis of Leroy Hood, the founder of ISB and pioneer of systems approaches. I would like to bring some of that vision of systems medicine to UEF and contribute to the training of researchers.”
Tools for personalised medicine
“The specific goal of my UEF DiPro project is to focus on making sense of plasma proteomics, a new technology that is currently spreading rapidly and being commoditised – much like gene expression profiling in the past two decades.”
According to Huang, plasma protein levels, unlike gene expression profiling, are relatively cheap and easy to measure in clinical settings. Up to 5000 circulating proteins can be measured from a small blood sample. “Plasma proteomics will finally allow us to connect a person’s unchanging genome information to their everchanging health state that is also subject to lifestyle.”
However, he points out that such data requires deep knowledge of physiology and pathophysiology to decipher. “Data are frenzily being collected but their analysis is still underdeveloped.“
Together with Heinäniemi’s team, Huang aims to build a computational tool, using large knowledge graphs and AI, to help connect plasma protein profiles to all known (patho)physiogy relationships, and ultimately the medical phenotype, in an automated and quantitative manner. “This may help the clinical adoption of this new technology for truly personalised medicine,” says Heinäniemi, whose own research focuses on rare cancers, childhood leukemias, where such solutions are needed.
“Plasma proteomics is a small part of the phenome, but a good starting point that provides standardisable data for computing that is rather readily linked to clinical phenomena,” Huang says. To take it further, he hopes to work with researchers within the Profi7 project as well as those involved in the unique FinnGen project where genome information of the Finnish population is combined with digital health care data from national health registries. “These collaborations and resources would enable us to jointly design a larger-scale project towards applying some of our approaches involving plasma proteomics and our computational approaches, to connect genome and phenome at a mechanistic level and at the resolution of the individual. “
What can researchers in training expect to learn from Huang’s lectures at UEF? “My approach to biological big data is to also consider systems dynamics and networks of mechanisms, as opposed to being just descriptive-statistical, as one is tempted to do in view of the easy access to big data.”
One concrete tool he will introduce to his students are knowledge graphs (KG). “They can link the vast networks of genes, proteins, metabolites, chemicals, clinical labs, diseases and phenotypes, et cetera, interconnected by their functional and causal relationships – all stored as one integrated network database.”
According to Huang, knowledge graphs can be used to leverage AI tools to answer highly specific biomedical questions. They also pave the way for future deep phenotype based personalised medicine and “digital twins” that contain all the information needed for our individualised health care.
