TY - JOUR
A1 - Leifeld, Thomas
A1 - Zhang, Zhihua
A1 - Zhang, Ping
T1 - Identification of Boolean Network Models from Time Series Data Incorporating Prior Knowledge
T2 - Frontiers in Physiology
N2 - Motivation: Mathematical models take an important place in science and engineering.
A model can help scientists to explain dynamic behavior of a system and to understand
the functionality of system components. Since length of a time series and number of
replicates is limited by the cost of experiments, Boolean networks as a structurally simple
and parameter-free logical model for gene regulatory networks have attracted interests
of many scientists. In order to fit into the biological contexts and to lower the data
requirements, biological prior knowledge is taken into consideration during the inference
procedure. In the literature, the existing identification approaches can only deal with a
subset of possible types of prior knowledge.
Results: We propose a new approach to identify Boolean networks fromtime series data
incorporating prior knowledge, such as partial network structure, canalizing property,
positive and negative unateness. Using vector form of Boolean variables and applying
a generalized matrix multiplication called the semi-tensor product (STP), each Boolean
function can be equivalently converted into a matrix expression. Based on this, the
identification problem is reformulated as an integer linear programming problem to
reveal the system matrix of Boolean model in a computationally efficient way, whose
dynamics are consistent with the important dynamics captured in the data. By using
prior knowledge the number of candidate functions can be reduced during the inference.
Hence, identification incorporating prior knowledge is especially suitable for the case of
small size time series data and data without sufficient stimuli. The proposed approach is
illustrated with the help of a biological model of the network of oxidative stress response.
Conclusions: The combination of efficient reformulation of the identification problem
with the possibility to incorporate various types of prior knowledge enables the
application of computational model inference to systems with limited amount of time
series data. The general applicability of thismethodological approachmakes it suitable for
a variety of biological systems and of general interest for biological and medical research.
KW - Boolean networks, identification, prior knowledge, time series data, network inference
Y1 - 2018
UR - https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/5292
UR - https://nbn-resolving.org/urn:nbn:de:hbz:386-kluedo-52929
IS - 2018 / 9
ER -