Gene complexity in eukaryotic genomes
One of the most challenging issues of molecular biology is the concept of gene. Although remarkable technological advances and improved knowledge of eukaryotic genomes, we do not know yet the exact number of genes for any available eukaryotic organism. As a consequence, it is basic to adopt an operative gene definition in order to uncover unexplored genomic information. Bioinformatics and computational techniques are indispensable to pursue such challenging task. They enable researchers to detect and improve gene structures as well as explore and investigate post-transcriptional phenomena as alternative splicing and RNA editing that greatly increase the complexity of eukaryotic genomes. Here I present an overview of the actual concept of gene as well as computational methodologies to investigate alternative splicing and RNA editing by recent sequencing technologies.
Techniques for the inference of large-scale gene networks
Gene expression regulation is a complex process involving many actors at different stages (chromatin structure, mRNA transcription and degradation, post-transcriptional modifications, translation). Therefore, only small subsystems have been presently characterized with a detailed model, and to analyse the overall combination of regulations a high-level simplification is used, called gene network, where each gene-gene interaction may represent a complex cascade of molecular events. A variety of methods to infer gene networks from gene expression datasets will be described, together with established tools to validate their performances.
Atrial fibrillation is an abnormal rhythm originating in the upper chambers of the heart afflicting 2–3 million people in the US alone and whose incidence rises with increasing age. Due to the “graying of our population”, an estimated 12–16 million Americans will be affected by 2050. Not only is its incidence of epidemic proportions, its morbidity is also significant because of its association with increased risk of both thromboembolism and stroke. The heartbeat under normal conditions is initiated by an electrical impulse that propagates diffusively through the heart and elevates the voltage at each cell, producing an action potential. The electrical waves that propagate, without damping, through the heart can exhibit complex dynamics and instabilities such as period-doubling bifurcations, alternans, two-dimensional spiral waves, three-dimensional scroll waves, and spatiotemporal turbulence (fibrillation) that can compromise the heart’s ability to contract and pump blood efficiently. In this talk I will present an overview of my research in this field.
Multi-level modeling of molecular biology systems
Most systems biology models focus on analizing the behavior of pathways or networks. To have a model that can be analyzed and useful to generate new hypothesis, one starts by describing its structure and then defines the parameters necessary to obtain temporal behaviors of the entities in the model. A less explored dimension is the organizational level at which the system is studied. In this presentation I investigated the effect of vaccination on a cancer hierarchical structure through a multi-level model representing both population and molecular aspects. The population level is modeled by a system of Ordinary Differential Equations (ODEs) describing the cancer population’s dynamics. The molecular level is modeled using the Petri Net (PN) formalism to detail part of the proliferation pathway. Moreover, i describe a new methodology which exploits the temporal behavior derived from the molecular level to parameterize the ODE system modeling populations. A multi-level model describes the inter-dependencies between population and genetic levels, and it can be efficiently used to estimate the efficacy of drug and vaccine therapies in cancer models, given the availability of molecular data on the cancer driving force.