TY - GEN
T1 - A theoretical approach to gene network identification
AU - Birget, Jean Camille
AU - Lun, Desmond S.
AU - Wirth, Anthony
AU - Hong, Dawei
PY - 2012
Y1 - 2012
N2 - We take a theoretical approach to the problem of identification, or 'reverse engineering', of gene regulatory networks. Through a mathematical model of a gene regulatory network, we examine fundamental questions on the limits and achievability of network identification. We apply simplifying assumptions to construct an acyclic binary model, and we assume that the identification strategy is restricted to perturbing the network by gene expression assignments, followed by expression profile measurements at steady-state. Further, we assume the presence of side information, which we call sensitivity, that is likely to be present in actual gene networks. We show that with sensitivity side information and realistic topology assumptions we can identify the topology of acyclic binary networks using O(n) assignments and measurements, n being the number of genes in the network. Our work establishes a theoretical framework for examining an important technological problem where a number of significant questions remain open.
AB - We take a theoretical approach to the problem of identification, or 'reverse engineering', of gene regulatory networks. Through a mathematical model of a gene regulatory network, we examine fundamental questions on the limits and achievability of network identification. We apply simplifying assumptions to construct an acyclic binary model, and we assume that the identification strategy is restricted to perturbing the network by gene expression assignments, followed by expression profile measurements at steady-state. Further, we assume the presence of side information, which we call sensitivity, that is likely to be present in actual gene networks. We show that with sensitivity side information and realistic topology assumptions we can identify the topology of acyclic binary networks using O(n) assignments and measurements, n being the number of genes in the network. Our work establishes a theoretical framework for examining an important technological problem where a number of significant questions remain open.
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U2 - https://doi.org/10.1109/ITW.2012.6404709
DO - https://doi.org/10.1109/ITW.2012.6404709
M3 - Conference contribution
SN - 9781467302234
T3 - 2012 IEEE Information Theory Workshop, ITW 2012
SP - 432
EP - 436
BT - 2012 IEEE Information Theory Workshop, ITW 2012
T2 - 2012 IEEE Information Theory Workshop, ITW 2012
Y2 - 3 September 2012 through 7 September 2012
ER -