Gene regulatory network analysis defines transcriptome landscape with alternative splicing of human umbilical vein endothelial cells during replicative senescence.

BACKGROUND: Endothelial cell senescence is the state of permanent cell cycle arrest and plays a critical role in the pathogenesis of age-related diseases. However, a comprehensive understanding of the gene regulatory network, including genome-wide alternative splicing machinery, involved in endothelial cell senescence is lacking. RESULTS: We thoroughly described the transcriptome landscape of replicative senescent human umbilical vein endothelial cells. Genes with high connectivity showing a monotonic expression increase or decrease with the culture period were defined as hub genes in the co-expression network. Computational network analysis of these genes led to the identification of canonical and non-canonical senescence pathways, such as E2F and SIRT2 signaling, which were down-regulated in lipid metabolism, and chromosome organization processes pathways. Additionally, we showed that endothelial cell senescence involves alternative splicing. Importantly, the first and last exon types of splicing, as observed in FLT1 and ACACA, were preferentially altered among the alternatively spliced genes during endothelial senescence. We further identified novel microexons in PRUNE2 and PSAP, each containing 9 nt, which were altered within the specific domain during endothelial senescence. CONCLUSIONS: These findings unveil the comprehensive transcriptome pathway and novel signaling regulated by RNA processing, including gene expression and splicing, in replicative endothelial senescence.

Deep Neural Network for Reducing the Screening Workload in Systematic Reviews for Clinical Guidelines: Algorithm Validation Study.

BACKGROUND: Performing systematic reviews is a time-consuming and resource-intensive process. OBJECTIVE: We investigated whether a machine learning system could perform systematic reviews more efficiently. METHODS: All systematic reviews and meta-analyses of interventional randomized controlled trials cited in recent clinical guidelines from the American Diabetes Association, American College of Cardiology, American Heart Association (2 guidelines), and American Stroke Association were assessed. After reproducing the primary screening data set according to the published search strategy of each, we extracted correct articles (those actually reviewed) and incorrect articles (those not reviewed) from the data set. These 2 sets of articles were used to train a neural network-based artificial intelligence engine (Concept Encoder, Fronteo Inc). The primary endpoint was work saved over sampling at 95% recall (WSS@95%). RESULTS: Among 145 candidate reviews of randomized controlled trials, 8 reviews fulfilled the inclusion criteria. For these 8 reviews, the machine learning system significantly reduced the literature screening workload by at least 6-fold versus that of manual screening based on WSS@95%. When machine learning was initiated using 2 correct articles that were randomly selected by a researcher, a 10-fold reduction in workload was achieved versus that of manual screening based on the WSS@95% value, with high sensitivity for eligible studies. The area under the receiver operating characteristic curve increased dramatically every time the algorithm learned a correct article. CONCLUSIONS: Concept Encoder achieved a 10-fold reduction of the screening workload for systematic review after learning from 2 randomly selected studies on the target topic. However, few meta-analyses of randomized controlled trials were included. Concept Encoder could facilitate the acquisition of evidence for clinical guidelines.

Multiomic disease signatures converge to cytotoxic CD8 T cells in primary Sjögren's syndrome.

OBJECTIVES: Multiomics study was conducted to elucidate the crucial molecular mechanisms of primary Sjögren's syndrome (SS) pathology. METHODS: We generated multiple data set from well-defined patients with SS, which includes whole-blood transcriptomes, serum proteomes and peripheral immunophenotyping. Based on our newly generated data, we performed an extensive bioinformatic investigation. RESULTS: Our integrative analysis identified SS gene signatures (SGS) dysregulated in widespread omics layers, including epigenomes, mRNAs and proteins. SGS predominantly involved the interferon signature and ADAMs substrates. Besides, SGS was significantly overlapped with SS-causing genes indicated by a genome-wide association study and expression trait loci analyses. Combining the molecular signatures with immunophenotypic profiles revealed that cytotoxic CD8 -T cells- were associated with SGS. Further, we observed the activation of SGS in cytotoxic CD8 T cells isolated from patients with SS. CONCLUSIONS: Our multiomics investigation identified gene signatures deeply associated with SS pathology and showed the involvement of cytotoxic CD8 T cells. These integrative relations across multiple layers will facilitate our understanding of SS at the system level.

Multi-parametric profiling network based on gene expression and phenotype data: a novel approach to developmental neurotoxicity testing.

The establishment of more efficient approaches for developmental neurotoxicity testing (DNT) has been an emerging issue for children's environmental health. Here we describe a systematic approach for DNT using the neuronal differentiation of mouse embryonic stem cells (mESCs) as a model of fetal programming. During embryoid body (EB) formation, mESCs were exposed to 12 chemicals for 24 h and then global gene expression profiling was performed using whole genome microarray analysis. Gene expression signatures for seven kinds of gene sets related to neuronal development and neuronal diseases were selected for further analysis. At the later stages of neuronal cell differentiation from EBs, neuronal phenotypic parameters were determined using a high-content image analyzer. Bayesian network analysis was then performed based on global gene expression and neuronal phenotypic data to generate comprehensive networks with a linkage between early events and later effects. Furthermore, the probability distribution values for the strength of the linkage between parameters in each network was calculated and then used in principal component analysis. The characterization of chemicals according to their neurotoxic potential reveals that the multi-parametric analysis based on phenotype and gene expression profiling during neuronal differentiation of mESCs can provide a useful tool to monitor fetal programming and to predict developmentally neurotoxic compounds.

[Drug repositioning to combat COVID-19 using artificial intelligence system].

Although months have passed since WHO declared COVID-19 a global pandemic, only a limited number of clinically effective drugs are available, and the development of drugs to treat COVID-19 has become an urgent issue worldwide. The pace of new research on COVID-19 is extremely high and it is impossible to read every report. In order to tackle these problems, we leveraged our artificial intelligence (AI) system, Concept Encoder, to accelerate the process of drug repositioning. Concept Encoder is a patented AI system based on natural language processing technology and by deeply learning papers on COVID-19, the system identified a large group of genes implicated in COVID-19 pathogenesis. The AI system then generated a molecular linkage map for COVID-19, connecting the genes by learning the molecular relationship comprehensively. By thoroughly reviewing the resulting map and list of the genes with rankings, we found potential key players for disease progression and existing drugs that might improve COVID-19 survival. Here, we focus on potential targets and discuss the perspective of our approach.

Identifying neurocognitive disorder using vector representation of free conversation.

In recent years, studies on the use of natural language processing (NLP) approaches to identify dementia have been reported. Most of these studies used picture description tasks or other similar tasks to encourage spontaneous speech, but the use of free conversation without requiring a task might be easier to perform in a clinical setting. Moreover, free conversation is unlikely to induce a learning effect. Therefore, the purpose of this study was to develop a machine learning model to discriminate subjects with and without dementia by extracting features from unstructured free conversation data using NLP. We recruited patients who visited a specialized outpatient clinic for dementia and healthy volunteers. Participants' conversation was transcribed and the text data was decomposed from natural sentences into morphemes by performing a morphological analysis using NLP, and then converted into real-valued vectors that were used as features for machine learning. A total of 432 datasets were used, and the resulting machine learning model classified the data for dementia and non-dementia subjects with an accuracy of 0.900, sensitivity of 0.881, and a specificity of 0.916. Using sentence vector information, it was possible to develop a machine-learning algorithm capable of discriminating dementia from non-dementia subjects with a high accuracy based on free conversation.

Predicting Inpatient Falls Using Natural Language Processing of Nursing Records Obtained From Japanese Electronic Medical Records: Case-Control Study.

BACKGROUND: Falls in hospitals are the most common risk factor that affects the safety of inpatients and can result in severe harm. Therefore, preventing falls is one of the most important areas of risk management for health care organizations. However, existing methods for predicting falls are laborious and costly. OBJECTIVE: The objective of this study is to verify whether hospital inpatient falls can be predicted through the analysis of a single input-unstructured nursing records obtained from Japanese electronic medical records (EMRs)-using a natural language processing (NLP) algorithm and machine learning. METHODS: The nursing records of 335 fallers and 408 nonfallers for a 12-month period were extracted from the EMRs of an acute care hospital and randomly divided into a learning data set and test data set. The former data set was subjected to NLP and machine learning to extract morphemes that contributed to separating fallers from nonfallers to construct a model for predicting falls. Then, the latter data set was used to determine the predictive value of the model using receiver operating characteristic (ROC) analysis. RESULTS: The prediction of falls using the test data set showed high accuracy, with an area under the ROC curve, sensitivity, specificity, and odds ratio of mean 0.834 (SD 0.005), mean 0.769 (SD 0.013), mean 0.785 (SD 0.020), and mean 12.27 (SD 1.11) for five independent experiments, respectively. The morphemes incorporated into the final model included many words closely related to known risk factors for falls, such as the use of psychotropic drugs, state of consciousness, and mobility, thereby demonstrating that an NLP algorithm combined with machine learning can effectively extract risk factors for falls from nursing records. CONCLUSIONS: We successfully established that falls among hospital inpatients can be predicted by analyzing nursing records using an NLP algorithm and machine learning. Therefore, it may be possible to develop a fall risk monitoring system that analyzes nursing records daily and alerts health care professionals when the fall risk of an inpatient is increased.

Nucleus accumbens pathways control cell-specific gene expression in the medial prefrontal cortex.

The medial prefrontal cortex (mPFC) is a critical component of a cortico-basal ganglia-thalamo-cortical loop regulating limbic and cognitive functions. Within this circuit, two distinct nucleus accumbens (NAc) output neuron types, dopamine D1 or D2 receptor-expressing neurons, dynamically control the flow of information through basal ganglia nuclei that eventually project back to the mPFC to complete the loop. Thus, chronic dysfunction of the NAc may result in mPFC transcriptomal changes, which in turn contribute to disease conditions associated with the mPFC and basal ganglia. Here, we used RNA sequencing to analyse differentially expressed genes (DEGs) in the mPFC following a reversible neurotransmission blocking technique in D1 or D2 receptor-expressing NAc neurons, respectively (D1-RNB, or D2-RNB). Gene Set Enrichment Analysis revealed that gene sets of layer 5b and 6 pyramidal neurons were enriched in DEGs of the mPFC downregulated in both NAc D1- and D2-RNB mice. In contrast, gene sets of layer 5a pyramidal neurons were enriched in upregulated DEGs of the mPFC in D1-RNB mice, and downregulated DEGs of the mPFC in D2-RNB mice. These findings reveal for the first time that NAc output pathways play an important role in controlling mPFC gene expression.

The project for objective measures using computational psychiatry technology (PROMPT): Rationale, design, and methodology.

INTRODUCTION: Depressive and neurocognitive disorders are debilitating conditions that account for the leading causes of years lived with disability worldwide. However, there are no biomarkers that are objective or easy-to-obtain in daily clinical practice, which leads to difficulties in assessing treatment response and developing new drugs. New technology allows quantification of features that clinicians perceive as reflective of disorder severity, such as facial expressions, phonic/speech information, body motion, daily activity, and sleep. METHODS: Major depressive disorder, bipolar disorder, and major and minor neurocognitive disorders as well as healthy controls are recruited for the study. A psychiatrist/psychologist conducts conversational 10-min interviews with participants ≤10 times within up to five years of follow-up. Interviews are recorded using RGB and infrared cameras, and an array microphone. As an option, participants are asked to wear wrist-band type devices during the observational period. Various software is used to process the raw video, voice, infrared, and wearable device data. A machine learning approach is used to predict the presence of symptoms, severity, and the improvement/deterioration of symptoms. DISCUSSION: The overall goal of this proposed study, the Project for Objective Measures Using Computational Psychiatry Technology (PROMPT), is to develop objective, noninvasive, and easy-to-use biomarkers for assessing the severity of depressive and neurocognitive disorders in the hopes of guiding decision-making in clinical settings as well as reducing the risk of clinical trial failure. Challenges may include the large variability of samples, which makes it difficult to extract the features that commonly reflect disorder severity. TRIAL REGISTRATION: UMIN000021396, University Hospital Medical Information Network (UMIN).

Gene expression profiling of functional murine embryonic stem cell-derived cardiomyocytes and comparison with adult heart: profiling of murine ESC-derived cardiomyocytes.

Although embryonic stem cell (ESC)-derived cardiomyocytes may be a powerful tool in drug discovery, their potential has not yet been fully explored. Nor has a detailed comparison with adult heart tissue been performed. We have developed a method for efficient production of cardiomyocyte-rich embryoid bodies (EBs) from murine ESCs. Analysis of global gene expression profiles showed that EBs on day 7 and/or 21 of differentiation (d7CMs and d21CMs, respectively) were similar to adult heart tissue for genes categorized as regulators of muscle contraction or voltage-gated ion channel activity, although d21CMs were more mature than d7CMs for contractile components related to morphological structures. Calcium and sodium channel blockers altered Ca2+ transients, and isoproterenol, a beta-adrenergic compound, increased the rate of beating in d7CMs and d21CMs. Our gene analytic system therefore enabled us to identify genes that are expressed in the physiological pathways associated with ion channels and structural components in d7CMs and d21CMs. We conclude that EBs might be of use for the basic screening of drugs that might affect contractile function through ion channels.

Similar compounds searching system by using the gene expression microarray database.

Numbers of microarrays have been examined and several public and commercial databases have been developed. However, it is not easy to compare in-house microarray data with those in a database because of insufficient reproducibility due to differences in the experimental conditions. As one of the approach to use these databases, we developed the similar compounds searching system (SCSS) on a toxicogenomics database. The datasets of 55 compounds administered to rats in the Toxicogenomics Project (TGP) database in Japan were used in this study. Using the fold-change ranking method developed by Lamb et al. [Lamb, J., Crawford, E.D., Peck, D., Modell, J.W., Blat, I.C., Wrobel, M.J., Lerner, J., Brunet, J.P., Subramanian, A., Ross, K.N., Reich, M., Hieronymus, H., Wei, G., Armstrong, S.A., Haggarty, S.J., Clemons, P.A., Wei, R., Carr, S.A., Lander, E.S., Golub, T.R., 2006. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929-1935] and criteria called hit ratio, the system let us compare in-house microarray data and those in the database. In-house generated data for clofibrate, phenobarbital, and a proprietary compound were tested to evaluate the performance of the SCSS method. Phenobarbital and clofibrate, which were included in the TGP database, scored highest by the SCSS method. Other high scoring compounds had effects similar to either phenobarbital (a cytochrome P450s inducer) or clofibrate (a peroxisome proliferator). Some of high scoring compounds identified using the proprietary compound-administered rats have been known to cause similar toxicological changes in different species. Our results suggest that the SCSS method could be used in drug discovery and development. Moreover, this method may be a powerful tool to understand the mechanisms by which biological systems respond to various chemical compounds and may also predict adverse effects of new compounds.

Identification of evolutionarily conserved gene networks mediating neurodegenerative dementia.

Identifying the mechanisms through which genetic risk causes dementia is an imperative for new therapeutic development. Here, we apply a multistage, systems biology approach to elucidate the disease mechanisms in frontotemporal dementia. We identify two gene coexpression modules that are preserved in mice harboring mutations in MAPT, GRN and other dementia mutations on diverse genetic backgrounds. We bridge the species divide via integration with proteomic and transcriptomic data from the human brain to identify evolutionarily conserved, disease-relevant networks. We find that overexpression of miR-203, a hub of a putative regulatory microRNA (miRNA) module, recapitulates mRNA coexpression patterns associated with disease state and induces neuronal cell death, establishing this miRNA as a regulator of neurodegeneration. Using a database of drug-mediated gene expression changes, we identify small molecules that can normalize the disease-associated modules and validate this experimentally. Our results highlight the utility of an integrative, cross-species network approach to drug discovery.

Differential contributions of spinal and cortical motoneurons to input-output properties of human small hand muscle.

OBJECTIVE: It is not fully understood how the stimulus/response curves obtained by transcranial magnetic stimulation (TMS) reflect the function of the cortico-motoneuronal (CM) and spinal motoneuronal (SM) systems in healthy subjects. To understand these response functions, we studied patients with amyotrophic lateral sclerosis (ALS) whose upper but not lower motor neurons were affected. METHODS: First, we determined the effects of voluntary muscle contraction and intensity of TMS on the motor evoked potentials (MEPs) of the first dorsal interossei (FDI) muscle in ten healthy control subjects (mean age: 35.1 +/- 6.7 years) and two older female subjects (60 and 64 years old). Second, we investigated whether this relationship was altered in two ALS patients (60-year-old woman and 69-year-old man). The MEPs were recorded at different degrees of voluntary contraction by threshold stimulus intensities (TSIs) or at different levels of TMS with the muscle at rest. RESULTS: In the controls, the MEP amplitudes of the FDI muscle elicited by TSI increased linearly with muscle contraction (right: Y = 0.068X + 0.754; left: Y = 0.0670X + 0.807), whereas the MEP amplitudes elicited by different TMS intensities increased sigmoidally with a flexion point at 1.10-1.15 TSI: right: Amp = 2.21/[1 + exp[(S50-Stim)/K]], S50 = 10.73, K = 4.70; left: Amp = 2.90/[1 + exp[(S(50)-Stim)/K]], S50 = 13.64, K = 5.98. The latter was abnormal only on one side of each ALS patient. DISCUSSION: From these results, we suggest that the sigmoidal TMS intensity-size curves reflect mainly CM activities, while the linear contraction-size curves reflect SM activities.

Decoding Transcriptome Dynamics of Genome-Encoded Polyadenylation and Autoregulation with Small-Molecule Modulators of Alternative Polyadenylation.

Alternative polyadenylation (APA) plays a critical role in regulating gene expression. However, the balance between genome-encoded APA processing and autoregulation by APA modulating RNA binding protein (RBP) factors is not well understood. We discovered two potent small-molecule modulators of APA (T4 and T5) that promote distal-to-proximal (DtoP) APA usage in multiple transcripts. Monotonically responsive APA events, induced by short exposure to T4 or T5, were defined in the transcriptome, allowing clear isolation of the genomic sequence features and RBP motifs associated with DtoP regulation. We found that longer vulnerable introns, enriched with distinctive A-rich motifs, were preferentially affected by DtoP APA, thus defining a core set of genes with genomically encoded DtoP regulation. Through APA response pattern and compound-small interfering RNA epistasis analysis of APA-associated RBP factors, we further demonstrated that DtoP APA usage is partly modulated by altered autoregulation of polyadenylate binding nuclear protein-1 signaling.

Multi-omics monitoring of drug response in rheumatoid arthritis in pursuit of molecular remission.

Sustained clinical remission (CR) without drug treatment has not been achieved in patients with rheumatoid arthritis (RA). This implies a substantial difference between CR and the healthy state, but it has yet to be quantified. We report a longitudinal monitoring of the drug response at multi-omics levels in the peripheral blood of patients with RA. Our data reveal that drug treatments alter the molecular profile closer to that of HCs at the transcriptome, serum proteome, and immunophenotype level. Patient follow-up suggests that the molecular profile after drug treatments is associated with long-term stable CR. In addition, we identify molecular signatures that are resistant to drug treatments. These signatures are associated with RA independently of known disease severity indexes and are largely explained by the imbalance of neutrophils, monocytes, and lymphocytes. This high-dimensional phenotyping provides a quantitative measure of molecular remission and illustrates a multi-omics approach to understanding drug response.

CLK-dependent exon recognition and conjoined gene formation revealed with a novel small molecule inhibitor.

CDC-like kinase phosphorylation of serine/arginine-rich proteins is central to RNA splicing reactions. Yet, the genomic network of CDC-like kinase-dependent RNA processing events remains poorly defined. Here, we explore the connectivity of genomic CDC-like kinase splicing functions by applying graduated, short-exposure, pharmacological CDC-like kinase inhibition using a novel small molecule (T3) with very high potency, selectivity, and cell-based stability. Using RNA-Seq, we define CDC-like kinase-responsive alternative splicing events, the large majority of which monotonically increase or decrease with increasing CDC-like kinase inhibition. We show that distinct RNA-binding motifs are associated with T3 response in skipped exons. Unexpectedly, we observe dose-dependent conjoined gene transcription, which is associated with motif enrichment in the last and second exons of upstream and downstream partners, respectively. siRNA knockdown of CLK2-associated genes significantly increases conjoined gene formation. Collectively, our results reveal an unexpected role for CDC-like kinase in conjoined gene formation, via regulation of 3'-end processing and associated splicing factors.The phosphorylation of serine/arginine-rich proteins by CDC-like kinase is a central regulatory mechanism for RNA splicing reactions. Here, the authors synthesize a novel small molecule CLK inhibitor and map CLK-responsive alternative splicing events and discover an effect on conjoined gene transcription.

Comparison of the 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)-induced CYP1A1 gene expression profile in lymphocytes from mice, rats, and humans: most potent induction in humans.

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) exerts its toxicity by binding a transcription factor, the aryl hydrocarbon receptor (AhR). C57BL/6 (C57) mice express AhRs that have high affinity for TCDD, and they strongly express target genes and develop severe toxic effects upon TCDD exposure. By contrast, DBA/2 (DBA) mice have a low-affinity form of AhR, weakly express target genes, and are resistant to TCDD. Although humans express low-affinity AhRs and have been assumed to be refractory to TCDD, their sensitivity to TCDD has yet to be determined. In this study we compared the TCDD-induced CYP1A1 gene expression profiles in lymphocytes from humans, C57 mice, DBA mice, and SD rats to obtain data as a basis for estimating human sensitivity to TCDD. Lymphocyte fractions prepared from the blood of individual humans and animals were cultured with TCDD. Their mRNAs for CYP1A1 and housekeeping genes were measured by RT-PCR or real-time PCR with primers designed for regions that are 100% homologous among each of the genes of all species/strains tested to obtain similar PCR efficiency. TCDD-induced CYP1A1 expression peaked at 2h in DBA mice and SD rats and at 6h in C57 mice and humans. At the peak times human lymphocytes showed the most potent CYP1A1 mRNA induction of the four species/strains tested. These results suggest that human lymphocytes are more sensitive to TCDD than the lymphocytes of mice and rats. Since the AhR-dependent gene expression did not reflect the AhR affinity for TCDD, these results also suggest that AhR-dependent gene expression in lymphocytes is modulated by an as yet unidentified mechanism in addition to the AhR affinity.

Gene expression elucidates functional impact of polygenic risk for schizophrenia.

Over 100 genetic loci harbor schizophrenia-associated variants, yet how these variants confer liability is uncertain. The CommonMind Consortium sequenced RNA from dorsolateral prefrontal cortex of people with schizophrenia (N = 258) and control subjects (N = 279), creating a resource of gene expression and its genetic regulation. Using this resource, ∼20% of schizophrenia loci have variants that could contribute to altered gene expression and liability. In five loci, only a single gene was involved: FURIN, TSNARE1, CNTN4, CLCN3 or SNAP91. Altering expression of FURIN, TSNARE1 or CNTN4 changed neurodevelopment in zebrafish; knockdown of FURIN in human neural progenitor cells yielded abnormal migration. Of 693 genes showing significant case-versus-control differential expression, their fold changes were ≤ 1.33, and an independent cohort yielded similar results. Gene co-expression implicates a network relevant for schizophrenia. Our findings show that schizophrenia is polygenic and highlight the utility of this resource for mechanistic interpretations of genetic liability for brain diseases.

Bayesian network reconstruction using systems genetics data: comparison of MCMC methods.

Reconstructing biological networks using high-throughput technologies has the potential to produce condition-specific interactomes. But are these reconstructed networks a reliable source of biological interactions? Do some network inference methods offer dramatically improved performance on certain types of networks? To facilitate the use of network inference methods in systems biology, we report a large-scale simulation study comparing the ability of Markov chain Monte Carlo (MCMC) samplers to reverse engineer Bayesian networks. The MCMC samplers we investigated included foundational and state-of-the-art Metropolis-Hastings and Gibbs sampling approaches, as well as novel samplers we have designed. To enable a comprehensive comparison, we simulated gene expression and genetics data from known network structures under a range of biologically plausible scenarios. We examine the overall quality of network inference via different methods, as well as how their performance is affected by network characteristics. Our simulations reveal that network size, edge density, and strength of gene-to-gene signaling are major parameters that differentiate the performance of various samplers. Specifically, more recent samplers including our novel methods outperform traditional samplers for highly interconnected large networks with strong gene-to-gene signaling. Our newly developed samplers show comparable or superior performance to the top existing methods. Moreover, this performance gain is strongest in networks with biologically oriented topology, which indicates that our novel samplers are suitable for inferring biological networks. The performance of MCMC samplers in this simulation framework can guide the choice of methods for network reconstruction using systems genetics data.

The TAO-Gen algorithm for identifying gene interaction networks with application to SOS repair in E. coli.

One major unresolved issue in the analysis of gene expression data is the identification and quantification of gene regulatory networks. Several methods have been proposed for identifying gene regulatory networks, but these methods predominantly focus on the use of multiple pairwise comparisons to identify the network structure. In this article, we describe a method for analyzing gene expression data to determine a regulatory structure consistent with an observed set of expression profiles. Unlike other methods this method goes beyond pairwise evaluations by using likelihood-based statistical methods to obtain the network that is most consistent with the complete data set. The proposed algorithm performs accurately for moderate-sized networks with most errors being minor additions of linkages. However, the analysis also indicates that sample sizes may need to be increased to uniquely identify even moderate-sized networks. The method is used to evaluate interactions between genes in the SOS signaling pathway in Escherichia coli using gene expression data where each gene in the network is over-expressed using plasmids inserts.