Biological Networking (BoNe)

 

Biological systems can be envisioned to be a complex network of physical objects (example: molecules, ions, and bacteria) through the different signaling methods (example: reactions/binding through diffusions and transportation). This "networking" is responsible for driving the wide variety of structure and form found in living organisms, spreading of diseases and evolution of the species. The network can be extended from the interaction of the genes, to the proteins, moving into higher scales - interaction between cells, tissues and finally to that of organs manifesting itself in 'Life'.

A holistic understanding of this complex biological network can play a key role in comprehending Natures' choreography of 'Life' as well as help us emulate them for improving the human/physical networks. In this research group, we are focused on a network-centric systems engineering approach to unravel the dynamics of this complex biological network. A biological process and system can be abstracted as a robust and optimal deployment of a multi-layered physical network. This abstraction can be realized by modeling a cell or gene in the biological network as a node and any interactions between them as links. In turn, a gene network can be considered as a distributed network. Thus, we should be able to extract these multi-layered biological systems to design distributed network architectures.

Our work is centered around three key areas :

  • Knowledge Extraction and Pathway Construction: In this work, we are focused on extracting knowledge from existing databases and literatures to build the molecular interaction maps of various biological processes.

  • Stochastic Modeling of biological processes: One of the primary component of our research is focused on developing stochastic models for various biological processes and sub-processes. We have developed stochastic models for some key cellular processes.

  • Discrete Event Simulation: The dynamics of a cellular process are captured based on an 'in silico' discrete event based simulation. The simulation platform uses pathway intelligence in conjunction with stochastic modeling knowledge for simulating the temporal dynamics of various sub-processes in a complex biological system. Details on our simulation methodology and software are available here.

  • Evolutions of Gene Regulatory Networks (GRNs): Genes are a collection of DNA segments and the GRNs model them as well as their dynamic interactions. In this work, we are studying the evolution of these robust networks, to design mechanisms for optimal creation and deployment of physical networks, specifically wireless sensor networks.

Project

People

Faculty

Students

  • Armita Abedijaberi

Collaborators

  • Prof. Preetam Ghosh, Virginia Commonwealth University