In many organisms the expression levels of each gene are controlled by the
activation levels of known "Transcription Factors" (TF). A problem of
considerable interest is that of estimating the "Transcription Regulation
Networks" (TRN) relating the TFs and genes. While the expression levels of
genes can be observed, the activation levels of the corresponding TFs are
usually unknown, greatly increasing the difficulty of the problem. Based on
previous experimental work, it is often the case that partial information about
the TRN is available. For example, certain TFs may be known to regulate a given
gene or in other cases a connection may be predicted with a certain
probability. In general, the biology of the problem indicates there will be
very few connections between TFs and genes. Several methods have been proposed
for estimating TRNs. However, they all suffer from problems such as unrealistic
assumptions about prior knowledge of the network structure or computational
limitations. We propose a new approach that can directly utilize prior
information about the network structure in conjunction with observed gene
expression data to estimate the TRN. Our approach uses $L_1$ penalties on the
network to ensure a sparse structure. This has the advantage of being
computationally efficient as well as making many fewer assumptions about the
network structure. We use our methodology to construct the TRN for E. coli and
show that the estimate is biologically sensible and compares favorably with
previous estimates.

该论文提出了一种新的方法，结合观察到的基因表达数据和先前关于网络结构的信息，使用 $L_1$ 惩罚对网络进行稀疏化，从而估计转录调节网络。该方法在计算效率和对网络结构的假设方面都具有优点。该方法被用于构建大肠杆菌的 TRN，并且该估计是生物上合理的，并且与先前的估计相比有优越性。