Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

Optimization-based Inference for Temporally Evolving Boolean Networks with Applications in Biology

Young-Hwan Chang, Joe Gray and Claire Tomlin

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2010-133
October 26, 2010

http://www.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS-2010-133.pdf

Modeling of biological genetic networks forms the basis of systems biology. In this paper, we present an optimization-based inference scheme to identify temporally evolving Boolean network representations of genetic networks from data. In the formulation of the optimization problem, we use an adjacency map as a priori information, and define a cost function which both drives the connectivity of the graph to match biological data as well as generates a sparse and robust network at corresponding time intervals. Throughout simulation studies on simple examples, it is shown that this optimization scheme can help to understand the structure and dynamics of biological genetic networks.


BibTeX citation:

@techreport{Chang:EECS-2010-133,
    Author = {Chang, Young-Hwan and Gray, Joe and Tomlin, Claire},
    Title = {Optimization-based Inference for Temporally Evolving Boolean Networks with Applications in Biology},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {2010},
    Month = {Oct},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS-2010-133.html},
    Number = {UCB/EECS-2010-133},
    Abstract = {Modeling of biological genetic networks forms the
basis of systems biology. In this paper, we present an
optimization-based inference scheme to identify temporally
evolving Boolean network representations of genetic networks
from data. In the formulation of the optimization problem, we
use an adjacency map as a priori information, and define a cost
function which both drives the connectivity of the graph to
match biological data as well as generates a sparse and robust
network at corresponding time intervals. Throughout
simulation studies on simple examples, it is shown that this
optimization scheme can help to understand the structure and
dynamics of biological genetic networks.}
}

EndNote citation:

%0 Report
%A Chang, Young-Hwan
%A Gray, Joe
%A Tomlin, Claire
%T Optimization-based Inference for Temporally Evolving Boolean Networks with Applications in Biology
%I EECS Department, University of California, Berkeley
%D 2010
%8 October 26
%@ UCB/EECS-2010-133
%U http://www.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS-2010-133.html
%F Chang:EECS-2010-133