The proposed project is dealing with the study of cellular senescence/aging mechanisms with revolutionary computational intelligence algorithms for modelling the aging complexity. Several concepts from the Systems Biology and Bioinformatics field will be tested and new ones will be developed to analyse text-mining data and mostly high-throughput experimental ones, collected in a certain time span, with the scope of deriving dynamic rather than static biomolecular networks of the aging mechanisms, as well as elucidating their regulatory modes. For this purpose three research teams are united, each one with distinct experience, to act complementary, while significant will be the contribution of the invited researcher from abroad.

The best available model to study human aging in vitro is the replicative senescence model, based on the fact that young primary fibroblasts can perform a limited number of duplications in vitro before they enter a state of irreversible growth arrest where they are called senescent [1]. Alternatively, the stress-induced premature senescence model can also reveal many undefined pathways that govern the progression of cellular aging [2]. The ultimate goal of this proposal is to model the aging phenomenon, based on novel designed experiments that record transcriptome and interactome activity at higher time resolution in comparison to the time span stated in recent literature. The proposed work will begin with experiments regarding the dynamic response and progression of cellular aging in these two models. Biological material (RNA and proteins) will be collected from 5 time points for the replicative senescence model along with 4 time points for the stress-induced premature senescence and will be subjected to the major state of the art technologies (genomic, proteomic and phosphoproteomic) of Systems Biology. Biological samples will be also maintained for verification experiments of the in silico derived results.

The second and most crucial phase of the proposal is the development of a complete computational intelligence environment, for experimental data analysis and simulation purposes, representing a virtual functional analogue of the mechanisms governing the aging process. This approach will employ a wide range of top-down and bottom-up computational methodologies (e.g. stochastic modeling, clustering and learning algorithms, optimization methodologies, decision trees, fuzzy logic, graph theory, control theory, enzyme kinetics). The results will lead to the revelation of dynamic Gene Regulatory Networks and Protein-Protein Interaction Networks; while at the same time sets of candidate bioindicators will emerge either at molecule level (gene/proteins) or at sub-network level. The results will be compared with information gleaned from the literature and open access databases. Next, kinetic models that describe the aging mechanisms will be designed after meta-analysis of the experimental and computational data.

Finally, the predictive power of the computational analysis will be experimentally validated (by the invited researcher from abroad) in the in vivo model of C.elegans. At the same time, a local database will be constructed including the experimental data, the developed computational tools, as well as the data from open access databases used for cross-validation.

The main objectives of the proposed research are:

  1. Dynamic study of cellular aging process at the genomic, proteomic and phosphoproteomic level in two in vitro models of aging.
  2. The reveal of the Gene Regulatory Networks dynamics in aging process.
  3. Identification of the protein networks involved in the evolution of the two different aging processes and the characterization of the specialized regulatory interactions involved separately in each type of aging.
  4. Development of standard models simulating functional aspects of aging through computational methods.
  5. Derivation of the critical biological networks and interactions to reproduce effectively the functionality of the aging process through in silico testing (reduced cellular supernetwork of the logic of aging).
  6. Identification of bioindicators (molecules or molecular subnetworks) of aging mechanisms.
  7. Design and implementation of a data repository for the study of aging, compatible with international standards for biological information exchange, representation and semantic processing.

Verification of the results on in vivo organismal level, leading to the evolutionary screening of the aging process.