Towards a dynamic view of genetic networks: A Kalman filtering framework for recovering temporally-rewiring stable networks from undersampled data

It is widely accepted that cellular requirements and environmental conditions dictate the architecture of genetic regulatory networks. Nonetheless, the status quo in regulatory network modeling and analysis assumes an invariant network topology over time. We refocus on a dynamic perspective of genetic networks, one that can uncover substantial topological changes in network structure during biological processes such as developmental growth and cancer progression. We propose a novel outlook on the inference of time-varying genetic networks, from a limited number of noisy observations, by formulating the networks estimation as a target tracking problem. Assuming linear dynamics, we formulate a constrained Kalman ltering framework, which recursively computes the minimum mean-square, sparse and stable estimate of the network connectivity at each time point. The sparsity constraint is enforced using the weighted l1-norm; and the stability constraint is incorporated using the Lyapounov stability condition. The proposed constrained Kalman lter is formulated to preserve the convex nature of the problem. The algorithm is applied to estimate the time-varying networks during the life cycle of the Drosophila Melanogaster (fruit fly).

Name thesis:degree_name

M.S. Electrical and Computer Engineering

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Year dc:date.available

2014

Author dc:creator

• Khan, Jehandad

Contributors dc:contributor

• Bouaynaya, Nidhal

• Gene regulatory networks; Kalman filtering
• Electrical and Computer Engineering

Repository record dc:identifier

https://rdw.rowan.edu/etd/335

OAI identifier oai:identifier

oai:rdw.rowan.edu:etd-1334