Systems Biology Approaches and Personal Genomics for Health and Disease Treatment

Systems biology is an integrated quantitative analysis of the manner in which all components of a biological system interact functionally over space and time. The holistic claim of systems biology is progressively influencing science and leading to the emergence of previously largely separated fields of experimental investigation and mathematical-computational modeling. Systems biology requires interdisciplinary approaches that are also capable of developing new technologies and computational tools. In turn this new technology is transformed into infrastructure that revolutionizes biology.

The comprehensive understanding of complex biological system functions requires holistic and detailed data collection, using post-genomic technologies relating to the analysis of all constituent levels of organisms. The recording and processing of all data and the comparison of various levels of living matter organization (from genes to transcripts, proteins, the molecular complexes and their metabolites) provide the ability to identify relationships between components involved in various mechanisms (molecular pathways, interaction networks, regulatory changes) that are responsible for particularly complex states and behaviors of biological systems, including those relevant to pathology.

The systems concept is the one that leads today to the rationally designed genetic medicine and personalized medical care. Delivering personalized therapeutic options to patients based on the genetic and molecular profiles of the various diseases offers great promise to improve the outcomes of therapy. The main requirement to realize genomic-based personalized medicine is to collect and include genomic information into the medical record of the patients where physicians will have ready access to it. Today genomic information in patients’ medical records is still limited to results from targeted genetic testing. However, as soon as whole exome or genome sequencing are becoming readily accessible and affordable to the average person, it will be more cost-effective for patients to have their whole exome (or genome) sequenced than to undergo targeted genetic testing. The development of other high throughput methodologies provides today tens of thousands of “omic” data points across multiple levels (DNA, RNA protein, small molecules-metabolites, (oligo)peptides) in a reasonable time frame for making clinical decisions. With this data in hand, the challenge from the bioinformatics and systems biology point of view is how one can convert data into information and knowledge that can improve the delivery of personalized therapy to the patient.

To this end, a variety of methodological approaches are used: (a) novel clustering, classification and feature selection algorithms, (b) machine learning algorithms such as artificial neural networks, hidden Markov models etc, (c) detailed biophysical and/or simplified models of cells and tissues, d) theoretical analysis and abstract mathematical modeling, (e) meta-databases and software for protein interaction and metabolic network reconstruction and analysis, (f) hierarchical simulation methodologies for studying biomolecular systems across spatial and temporal scales, (g) tools for the visualization, analysis and integration of -omic data at different levels of cellular function, (h) workflows for the efficient annotation and clinical interpretation of whole genome analysis, and  (i) methods for computationally intensive whole genome-wide association studies.

Research interests in the field of computational biology focus mainly on the discrimination of different disease subtypes for more accurate and detailed diagnosis, the identification of molecular markers for disease prognosis and prediction of therapy, and the development of in computo modelling techniques for the investigation of cellular and gene functions at multiple length and time scales. The Stavros Niarchos Foundation will partially support computational methods and tools developed for:

  • analyzing large-scale gene expression data related to various disease in search for gene markers and disease sub-categories,
  • building theoretical models of gene regulatory networks, reconstructing and integrating the active protein interaction and metabolic networks in human and animal models of disease,
  • studying disease-associated self-organized structures at the molecular level, and
  • planning medication by modeling pharmacodynamics (drug action at target) and pharmacokinetics (drug fate in body) parameters.