An important question in neuroscience is whether and how temporal patterns and fluctuations in neuronal spike trains contribute to information processing in the cortex. We have addressed this issue in the memory-related circuits of the prefrontal cortex by analyzing spike trains from a database of 229 neurons recorded in the dorsolateral prefrontal cortex of 4 macaque monkeys during the performance of an oculomotor delayed-response task. For each task epoch, we have estimated their power spectrum together with interspike interval histograms and autocorrelograms. We find that 1) the properties of most (about 60%) neurons approximated the characteristics of a Poisson process. For about 25% of cells, with characteristics typical of interneurons, the power spectrum showed a trough at low frequencies (<20 Hz) and the autocorrelogram a dip near zero time lag. About 15% of neurons had a peak at <20 Hz in the power spectrum, associated with the burstiness of the spike train, 2) a small but significant task dependency of spike-train temporal structure: delay responses to preferred locations were characterized not only by elevated firing, but also by suppressed power at low (<20 Hz) frequencies, and 3) the variability of interspike intervals is typically higher during the mnemonic delay period than during the fixation period, regardless of the remembered cue. The high irregularity of neural persistent activity during the delay period is likely to be a characteristic signature of recurrent prefrontal network dynamics underlying working memory.

Working memory capacity, the maximum number of items that we can transiently store in working memory, is a good predictor of our general cognitive abilities. Neural activity in both dorsolateral prefrontal cortex and posterior parietal cortex has been associated with memory retention during visuospatial working memory tasks. The parietal cortex is thought to store the memories. However, the role of the dorsolateral prefrontal cortex, a top-down control area, during pure information retention is debated, and the mechanisms regulating capacity are unknown. Here, we propose that a major role of the dorsolateral prefrontal cortex in working memory is to boost parietal memory capacity. Furthermore, we formulate the boosting mechanism computationally in a biophysical cortical microcircuit model and derive a simple, explicit mathematical formula relating memory capacity to prefrontal and parietal model parameters. For physiologically realistic parameter values, lateral inhibition in the parietal cortex limits mnemonic capacity to a maximum of 2-7 items. However, at high loads inhibition can be counteracted by excitatory prefrontal input, thus boosting parietal capacity. Predictions from the model were confirmed in an fMRI study. Our results show that although memories are stored in the parietal cortex, interindividual differences in memory capacity are partly determined by the strength of prefrontal top-down control. The model provides a mechanistic framework for understanding top-down control of working memory and specifies two different contributions of prefrontal and parietal cortex to working memory capacity.

The cellular maturational processes behind cognitive development during childhood, including the development of working memory capacity, are still unknown. By using the most standard computational model of visuospatial working memory, we investigated the consequences of cellular maturational processes, including myelination, synaptic strengthening, and synaptic pruning, on working memory-related brain activity and performance. We implemented five structural developmental changes occurring as a result of the cellular maturational processes in the biophysically based computational network model. The developmental changes in memory activity predicted from the simulations of the model were then compared to brain activity measured with functional magnetic resonance imaging in children and adults. We found that networks with stronger fronto-parietal synaptic connectivity between cells coding for similar stimuli, but not those with faster conduction, stronger connectivity within a region, or increased coding specificity, predict measured developmental increases in both working memory-related brain activity and in correlations of activity between regions. Stronger fronto-parietal synaptic connectivity between cells coding for similar stimuli was thus the only developmental process that accounted for the observed changes in brain activity associated with development of working memory during childhood. © 2007 Massachusetts Institute of Technology.

Cognitive functions, including working memory capacity, improve during childhood and early adulthood. Several maturational processes take place during that time, most importantly the myelination of axons, pruning of synapses and strengthening of the remaining synapses. However, it has not yet been shown how to directly relate these cellular changes to working memory development and associated changes in brain activity. Here, we bridge this gap by integrating biophysically-based computational modelling and functional MRI of the visuospatial working memory. Cellular mechanisms corresponding to different maturational processes were implemented in in silico 'child' networks, and the predicted difference in activity between 'child' and a reference 'adult' network was then compared to measured brain activity in children and adults. Network models with stronger connectivity between brain areas, but not networks with faster conduction or increased neuronal specificity, were supported by measured developmental increases in brain activity and correlations between frontal and parietal areas. The 'adult' networks with stronger fronto-parietal connections also exhibited greater stability during distraction, which was consistent with the developmental improvement in working memory performance.

[No abstract available]

Recently, important insights into static network topology for biological systems have been obtained, but still global dynamical network properties determining stability and system responsiveness have not been accessible for analysis. Herein, we explore a genome-wide gene-to-gene regulatory network based on expression data from the cell cycle in Saccharomyces cerevisae (budding yeast). We recover static properties like hubs (genes having several out-going connections), network motifs and modules, which have previously been derived from multiple data sources such as whole-genome expression measurements, literature mining, protein-protein and transcription factor binding data. Further, our analysis uncovers some novel dynamical design principles; hubs are both repressed and repressors, and the intra-modular dynamics are either strongly activating or repressing whereas inter-modular couplings are weak. Finally, taking advantage of the inferred strength and direction of all interactions, we perform a global dynamical systems analysis of the network. Our inferred dynamics of hubs, motifs and modules produce a more stable network than what is expected given randomised versions. The main contribution of the repressed hubs is to increase system stability, while higher order dynamic effects (e.g. module dynamics) mainly increase system flexibility. Altogether, the presence of hubs, motifs and modules induce few flexible modes, to which the network is extra sensitive to an external signal. We believe that our approach, and the inferred biological mode of strong flexibility and stability, will also apply to other cellular networks and adaptive systems.

The quest to determine cause from effect is often referred to as reverse engineering in the context of cellular networks. Here we propose and evaluate an algorithm for reverse engineering a gene regulatory network from time-series kind steady-state data. Our algorithmic pipeline, which is rather standard in its parts but not in its integrative composition, combines ordinary differential equations, parameter estimations by least angle regression, and cross-validation procedures for determining the in-degrees and selection of nonlinear transfer functions. The result of the algorithm is a complete directed net-work, in which each edge has been assigned a score front it bootstrap procedure. To evaluate the performance, we submitted the outcome of the algorithm to the reverse engineering assessment competition DREAM2, where we used the data corresponding to the InSillico1 and InSilico2 networks as input. Our algorithm outperformed all other algorithms when inferring one of the directed gene-to-gene networks.

Using deep sequencing (deepCAGE), the FANTOM4 study measured the genome-wide dynamics of transcription-start-site usage in the human monocytic cell line THP-1 throughout a time course of growth arrest and differentiation. Modeling the expression dynamics in terms of predicted cis-regulatory sites, we identified the key transcription regulators, their time-dependent activities and target genes. Systematic siRNA knockdown of 52 transcription factors confirmed the roles of individual factors in the regulatory network. Our results indicate that cellular states are constrained by complex networks involving both positive and negative regulatory interactions among substantial numbers of transcription factors and that no single transcription factor is both necessary and sufficient to drive the differentiation process.

Most diseases are caused by a mixture of environmental and genetic components. The genetic component is mainly inherited but can also induced by the environment. Cancer and cardiovascular diseases are not affected by a single gene but more often by a number of genes a nd also the complex system that the interactions between these genes form. To understand and treat these complex diseases we need a better understanding of the underlying gene networks and what parts of the network the diseases target. In the first study presented here we show that text mining of the biological literature together with whole-genome expression data can be used to identify gene networks and that the resulting network edges can be ranked according to their biological reliability. In the second study we present a novel algorithm, CutTree, that can identify the genetic targets of compounds using only a small number of whole-genome expression experiments. Computational tools like these will facilitate the exploration of gene networks in health and disease.

Background

A key problem of drug development is to decide which compounds to evaluate further in expensive clinical trials (Phase I- III). This decision is primarily based on the primary targets and mechanisms of action of the chemical compounds under consideration. Whole-genome expression measurements have shown to be useful for this process but current approaches suffer from requiring either a large number of mutant experiments or a detailed understanding of the regulatory networks.

Results

We have designed an algorithm, CutTree that when applied to whole-genome expression datasets identifies the primary affected genes (PAGs) of a chemical compound by separating them from downstream, indirectly affected genes. Unlike previous methods requiring whole-genome deletion libraries or a complete map of gene network architecture, CutTree identifies PAGs from a limited set of experimental perturbations without requiring any prior information about the underlying pathways. The principle for CutTree is to iteratively filter out PAGs from other recurrently active genes (RAGs) that are not PAGs. The in silico validation predicted that CutTree should be able to identify 3–4 out of 5 known PAGs (~70%). In accordance, when we applied CutTree to whole-genome expression profiles from 17 genetic perturbations in the presence of galactose in Yeast, CutTree identified four out of five known primary galactose targets (80%). Using an exhaustive search strategy to detect these PAGs would not have been feasible (>10¹² combinations).

Conclusion

In combination with genetic perturbation techniques like short interfering RNA (siRNA) followed by whole-genome expression measurements, CutTree sets the stage for compound target identification in less well-characterized but more disease-relevant mammalian cell systems.

Slow Ca²⁺ oscillations caused by release from intracellular stores have been observed in neurons in the lamprey spinal cord. These oscillations are triggered by activation of metabotropic glutamate receptors on the cell surface. The pathway leading from receptor activation to the inositol triphosphate-mediated release of Ca²⁺ from the endoplasmatic reticulum has been modelled in order to facilitate further understanding of the nature of these oscillations. The model generates Ca²⁺ oscillations with a frequency range of 0.01–0.09 Hz. A prediction of the model is that the frequency will increase with a stronger extracellular glutamate signal.

Background: Since biological networks are believed to govern the cellular behavior under normal and diseased conditions there is a large interest in developing methods that can identify the underlying structure of those networks There has been an explosion of studies using text mining to extract useful biological information from the published biomedical literature as accessed through PubMed. Co-occurrence of gene symbols in abstracts have been proposed as a method to reconstruct gene networks. On the other hand, rapid progress in micro-array technology have produced extensive data-sets of the activity of the entire genome under different biological conditions. Yet, it is not clear how to validate and assess the quality of these inferred networks beyond visual inspection and case studies and it is not feasible to reconstruct gene networks directly from whole genome wide expression data . Here we present a novel method which integrates prior knowledge in the form of published articles with whole-genome wide expression measurements.

Results: We have developed a benchmark system, using a Yeast gene network as a reference network. which enables us to determine the optimal parameters for how to integrate the information from both abstracts and full texts of published articles with whole genome wide expression data sets. We investigate how the quality of the network reconstruction depends on the number of articles used, whether only using abstracts as compared to full text articles. We develop a comprehensive network reconstruction algorithm that utilizes several criteria, including the frequency of co-occurrences in abstracts and full texts, to rank which edges that are most likely to be present in the network.

Conclusions: Our method is a practical tool to effectively identify as many reliable edges as possible in a gene network combining text mining and whole-genome expression data. Our scheme could easily be integrated with other methods and other data types, such as sequence information, in order to find putative interactions between genes.

Atherosclerosis is the main underlying cause of coronary artery disease (CAD) and stroke. These two diseases are leading causes of death in the developed world. The socio-economical cost for treatments and absence of work is enormous. Recent numbers from the US show no tendency of decline in the spread of atherosclerosis.

Common risk factors for premature atherosclerosis are obesity, diabetes, smoking, physical inactivity and high blood pressure. Medications have been developed for treatment of risk factors and a breakthrough was the release of statins, an effective lipid-lowering drug. Nevertheless, atherosclerosis is multi-factorial and all the different players in the pathological process are not yet identified. New large-scale studies, such as gene expression profiling, are needed to understand the interplay between risk factors and pathways in atherosclerosis and with that reveal new therapeutic targets.

In this thesis two studies are presented where gene expression profiling in well-characterized patients suffering from severe atherosclerosis have been performed. In study number one, five types of tissues (atherosclerotic aortic root, unatherosclerotic mammary artery, mediastinal fat, skeletal muscle and liver) were collected from a total of 66 patients undergoing coronary by-pass surgery. Subjects were also screened for cardiovascular risk factors. RNA was isolated from the biopsies and gene expression analysis using Affymetrix GeneChip system was performed. The main result showed that mediastinal fat appears to be central in CAD.

In study number two, gene expression analysis of plaques from patients undergoing carotid endorectomy was performed. Again, all patients were screened for conventional risk factors and RNA was isolated and expression profiles were obtained using Affymetrix technology. Cluster analyses identified genes associated to intima-media thickness in these patients.

Taken together, expression analyses of clinical whole-genome expression datasets can be used to identify novel pathways and individual genes with possible importance for atherosclerosis development.

Atherosclerosis is a progressive inflammatory disease that causes lipid accumulation in the arterial wall, leading to the formation of plaques. The clinical manifestations of plaque rupture—stroke and myocardial infarction—are increasing worldwide and pose an enormous economic burden for society. Atherosclerosis development reflects a complex interaction between environmental exposures and genetic predisposition. To understand this complexity, we hypothesized that a top-down approach—one in which all molecular activities that drive atherosclerosis are examined simultaneously—is necessary to highlight those that are clinically relevant. To this end, we performed whole-genome expression profiling in multiple tissues isolated from patients with coronary artery disease (CAD).

In the Stockholm Atherosclerosis Gene Expression (STAGE) study, biopsies of five tissues (arterial wall with and without atherosclerotic lesions, liver, skeletal muscle and visceral fat) were isolated from 124 CAD patients undergoing coronary artery bypass grafting surgery (CABG) at the Karolinska University Hospital, Solna and carotid lesions from 39 patients undergoing carotid artery surgery at Stockholm Söder Hospital. Detailed clinical characteristics of these patients were assembled together with a total of 303 global gene expression profiles obtained with the Affymetrix GeneChip platform.

In paper 1, a two-way clustering analysis of the data identified 60 tissue clusters of functionally related genes. One cluster, partly present in both visceral fat and atherosclerotic lesions, related to atherosclerosis severity as judged by coronary angiograms. Many of the genes in that cluster were also present in a carotid lesion cluster relating to intima-media thickness (IMT) in the carotid patients. The union of all three clusters relating to extent of atherosclerosis—referred to as the “A-module”—was overrepresented with genes belonging to the transendothelial migration of leukocyte (TEML) pathway. The transcription co-factor, Lim domain binding 2 (LDB2), was identified as putative regulator of the A-module and TEML pathway in validation studies including Ldb2-/- mice.

In paper 2, we investigated the increased incidence of postoperative complications in CABG patients with diabetes. Using the STAGE compendium, we identified an anti-inflammatory marker, dual-specificity phosphatase 1 (DUSP1), as a novel preoperative blood marker of risk for a prolonged hospital stay after CABG.

In paper 3, plaque age was determined with C14-dating in the carotid patients. Interestingly, the strongest correlation with plaque age was not the age of the patients or IMT. Rather, the strongest correlations were with plasma insulin levels and inflammatory gene expression.

Taken together, the findings in this thesis show that a top-down approach using multi-tissue gene expression profiling in CAD and C14-dating of plaques can contribute to a better understanding of the molecular processes underlying atherosclerosis development and to the identification of clinically useful biomarkers.

Objectives: Perform multi-organ expression profiling to identify gene markers predicting postoperative complications and hospitalization after coronary artery by-pass grafting (CABG) surgery.

Background: Identifying patients who are at increased risk of morbidity and prolonged post-operative stay is of interest from both health-economic and individual patient perspectives. Patients with diabetes often present with inflammatory conditions and have prolonged hospitalization after CABG. The recent development of technologies to generate high-dimensional data provides an opportunity to identify preoperative markers that can be used to help optimize preoperative planning to minimize postoperative complications.

Methods: We analyzed 198 whole-genome expression profiles of liver, skeletal muscle, and visceral fat isolated from 66 patients undergoing CABG in the Stockholm Atherosclerosis Gene Expression (STAGE) study. The findings were validated in pre-operative blood samples isolated from 181 patients undergoing CABG at Tartu University Hospital.

Results: As shown in other studies, diabetic CABG patients in the STAGE cohort also had prolonged hospitalization time (P<0.02). Out of ~50 000 mRNAs measures in the liver, skeletal muscle and visceral fat in 66 STAGE patients, the mRNA levels of anti-inflammatory gene dual specificity phosphatase-1 (DUSP1) correlated independently with post-operative rehabilitation and separated the patients into those with normal (8 days) and prolonged hospitalization (>8 days). In the validation cohort, preoperative blood levels of DUSP1 separated patients with short and long hospitalization stay (P=9x10^-10).

Conclusions: From genome scans in three separate organs, we identified the anti-inflammatory gene DUSP1 as a pre-operative marker indicating risk for prolonged postoperative stay after CABG.

BACKGROUND

By offering a comprehensive view of the molecular underpinnings of pathology, high-dimensional data have the potential to revolutionize the diagnosis and management of complex disorders such as coronary artery disease (CAD). To identify molecular phenotypes of CAD, we performed multi organ gene expression profiling of subjects enrolled in the Stockholm Atherosclerosis Gene Expression (STAGE) study.

METHODS

Atherosclerotic and unaffected arterial wall, liver, skeletal muscle, and mediastinal fat biopsies were obtained during coronary artery bypass grafting from 114 well-characterized CAD patients. RNA samples were isolated, and 278 transcription profiles were obtained using Affymetrix HG-U133_Plus_2 GeneChips.

RESULTS

The most prominent molecular phenotype of the CAD patients was represented by 733 genes in mediastinal fat, which were involved in extracellular matrix organization, response to stress and regulation of programmed cell death. Other aspects of this phenotype were shared with liver (e.g., oxidoreductase activity), skeletal muscle (insulin-like growth factor binding), and atherosclerotic arterial wall (cell motility and adhesion, fatty acid metabolism). In addition, the activity of 400 genes exclusively in mediastinal fat was associated with the extent of coronary stenosis and atherosclerosis. Immune-cell activation in mediastinal fat defined CAD patients with poor blood glucose control and prolonged hospitalization.

CONCLUSIONS

The molecular phenotype of mediastinal fat appears to be central in CAD and should be useful for early identification of CAD risk.

Rationale: The exact nature of atherosclerotic plaque development and the molecular mechanisms that lead to clinical manifestations of carotid stenosis are unclear. After nuclear bomb tests in the 1950s, atmospheric ¹⁴C concentrations rapidly increased. Since then, the concentrations have been declining, and the curve of declination can be used to date biological samples synthesized during the last five to six decades.

Objective: To investigate plaque age as a novel characteristic of atherosclerotic plaques in patients with carotid stenosis.

Methods and Results: Carotid plaques from 29 well-characterized endarterectomy patients with symptomatic carotid stenosis were analyzed by accelerator mass spectrometry, and global gene expression of 25 plaque samples was profiled with HG-U133 Plus 2.0 arrays. The average plaque age was 9.3 years, and inter- and intrasample standard variations were low (1–3.5 years); thus, most of the plaques were generated 5–15 years before surgery. Plaque age was not associated with patient age or plaque size, determined by intima-media thickness, but was inversely related to plasma insulin levels (P=0.0014). A cluster of functionally related genes enriched with genes involved in immune responses was activated in plaques with low plaque age, as were oxidative phosphorylation genes.

Conclusion: Patients with mild insulin resistance have increased immune and inflammatory gene activity in their carotid plaques causing them to become instable, rapidly progressing into clinical manifestations at a relatively young age. These results show that plaque age, determined by ¹⁴C dating, is a novel and important characteristic of atherosclerotic plaques that will improve our understanding of the clinical significance and molecular underpinnings of atherosclerosis.

Environmental exposures filtered through the genetic make-up of each individual alter the transcriptional repertoire in organs central to metabolic homeostasis, thereby affecting arterial lipid accumulation, inflammation, and the development of coronary artery disease (CAD). The primary aim of the Stockholm Atherosclerosis Gene Expression (STAGE) study was to determine whether there are functionally associated genes (rather than individual genes) important for CAD development. To this end, two-way clustering was used on 278 transcriptional profiles of liver, skeletal muscle, and visceral fat (n=66/tissue) and atherosclerotic and unaffected arterial wall (n=40/tissue) isolated from CAD patients during coronary artery bypass surgery. The first step, across all mRNA signals (n=15,042/12,621 RefSeqs/genes) in each tissue, resulted in a total of 60 tissue clusters (n=3958 genes). In the second step (performed within tissue clusters), one atherosclerotic lesion (n=49/48) and one visceral fat (n=59) cluster segregated the patients into two groups that differed in the extent of coronary stenosis (P=0.008 and P=0.00015). The associations of these clusters with coronary atherosclerosis were validated by analyzing carotid atherosclerosis expression profiles. Remarkably, in one cluster (n=55/54) relating to carotid stenosis (P=0.04), 27 genes in the two clusters relating to coronary stenosis were confirmed (n=16/17, P<10^-27and-30). Genes in the transendothelial migration of leukocytes (TEML) pathway were overrepresented in all three clusters, referred to as the atherosclerosis module (A-module). In a second validation step, using three independent cohorts, the A-module was found to be genetically enriched with CAD risk by 1.8-fold (P<0.004). The transcription co-factor LIM domain binding 2 (LDB2) was identified as a potential high-hierarchy regulator of the A-module, a notion supported by subnetwork analysis, cellular and lesion expression of LDB2, and the expression of 13 TEML genes in Ldb2-deficient arterial wall. Thus, the A-module appears to be important for atherosclerosis development and together with LDB2 merits further attention in CAD research.

Recent experimental observations of spike-timing-dependent synaptic plasticity (STDP) have revitalized the study of synaptic learning rules. The most surprising aspect of these experiments lies in the observation that synapses activated shortly after the occurrence of a postsynaptic spike are weakened. Thus, synaptic plasticity is sensitive to the temporal ordering of pre- and postsynaptic activation. This temporal asymmetry has been suggested to underlie a range of learning tasks. In the first part of this review we highlight some of the common themes from a range of findings in the framework of predictive coding. As an example of how this principle can be used in a learning task, we discuss a recent model of cortical map formation. In the second part of the review, we point out some of the differences in STDP models and their functional consequences. We discuss how differences in the weight-dependence, the time-constants and the non-linear properties of learning rules give rise to distinct computational functions. In light of these computational issues raised, we review current experimental findings and suggest further experiments to resolve some controversies.

Increased baseline values of the acute-phase reactant C-reactive protein (CRP) are significantly associated with future cardiovascular disease, and some in vitro studies have claimed that human CRP (hCRP) has proatherogenic effects. In vivo studies in apolipoprotein E-deficient mouse models, however, have given conflicting results. We bred atherosclerosis-prone mice (Apob 100/100Ldlr-/-), which have human-like hypercholesterolemia, with hCRP transgenic mice (hCRP+/0) and studied lesion development at 15, 30, 40, and 50 weeks of age. Atherosclerotic lesions were smaller in hCRP+/0 Apob100/100Ldlr-/- mice than in hCRP0/0Apob100/100Ldlr-/- controls, as judged from the lesion surface areas of pinned-out aortas from mice at 40 and 50 weeks of age. In lesions from 40-week-old mice, mRNA expression levels of several genes in the proteasome degradation pathway were higher in hCRP +/0Apob100/100Ldlr-/- mice than in littermate controls, as shown by global gene expression profiles. These results were confirmed by real-time PCR, which also indicated that the activities of those genes were the same at 30 and 40 weeks in hCRP+/0Apob 100/100Ldlr-/- mice but were significantly lower at 40 weeks than at 30 weeks in controls. Our results show that hCRP is not proatherogenic but instead slows atherogenesis, possibly through proteasome-mediated protein degradation. © 2007 by The National Academy of Sciences of the USA.

The performance on various cognitive tasks, from language to selective attention and guiding future actions depends on working memory (WM), the capacity to hold and manipulate limited items of information. However, neural basis of WM and the capacity limitation are still unclear. The present work includes behavioral, functional magnetic resonance imaging (fMRI) and computational modeling studies of the visuospatial WM in order to identify the neural correlates of WM capacity. In the first study we used behavioral distracting stimuli in order to identify cellular mechanisms that accounts for the observed behavioral decrease in mnemonic accuracy as a function of distractor distance. The study provided theoretical support that independently of the cellular and synaptic properties, increased neuronal firing rates accounted for higher mnemonic accuracy and resistance against distractors. In the second study we performed fMRI experiments on adults and children to monitor brain activity during a WM task. We isolated the delay-related activity and analyzed group differences and the distractor influence both behaviorally and in terms of changed brain activity. The fMRJ study showed higher brain activity in inferior frontal and intraparietal cortex in adults compared to children during the delay periods of WM tasks. Furthermore, adults were more accurate and less distractible than children. In a subsequent study we addressed the cellular changes during WM development. The study combined a computational analysis with fMRl in order to establish putative maturational processes governing developmental changes in brain activity. We found that the increase in activit' together with higher resistance against distractors could be explained by computational models having stronger connectivity between network areas. Our studies suggest that increased firing rates of the cortical areas involved in the maintenance of visuospatial information accounts for the developmental related increase of activity in areas associated with WM processes as well as for the higher resistance against distractors. Therefore, increasing the neural activity of the WM circuitry using either psychophysiological training protocols or pharmacological manipulation may have a beneficial effect on the WM capacity and distractibility.

Biophysically based computational models have successfully accounted for the persistent neural activity underlying the maintenance of single items of information in working memory. The aim of the present study was to extend previous models in order to retain multiple items, in agreement with the observed human storage capacity. This was done by implementing cellular mechanisms known to occur during the childhood development of working memory, such as an increased synaptic strength and improved contrast and specificity of the neural response. Our computational study shows that these mechanisms are sufficient to create a neural network which can store information about multiple items through sustained neural activity. Furthermore, by using functional magnetic resonance imaging, we found that the information-activity curve predicted by the model corresponds to that in the human posterior parietal cortex during performance of working memory tasks, which is consistent with previous studies of brain activity related to working memory capacity in humans. © 2006 IBRO.

Persistent neural activity constitutes one neuronal correlate of working memory, the ability to hold and manipulate information across time, a prerequisite for cognition. Yet, the underlying neuronal mechanisms are still elusive. Here, we design a visuo- spatial delayed-response task to identify the relationship between the cue-distractor spatial distance and mnemonic accuracy. Using a shared experimental and computational test protocol, we probe human subjects in computer experiments, and subsequently we evaluate different neural mechanisms underlying persistent activity using an in silico prefrontal network model. Five modes of action of the network were tested: weak or strong synaptic interactions, wide synaptic arborization, cellular bistability and reduced synaptic NMDA component. The five neural mechanisms and the human behavioral data, all exhibited a significant deterioration of the mnemonic accuracy with decreased spatial distance between the distractor and the cue. A subsequent computational analysis revealed that the firing rate and not the neural mechanism per se, accounted for the positive correlation between mnemonic accuracy and spatial distance. Moreover, the computational modeling predicts an inverse correlation between accuracy and distractibility. In conclusion, any pharmacological modulation, pathological condition or memory training paradigm targeting the underlying neural circuitry and altering the net population firing rate during the delay is predicted to determine the amount of influence of a visual distraction.

Background: Plasmid encoded (CTX)-C-bla-M enzymes represent an important sub-group of class A beta-lactamases causing the ESBL phenotype which is increasingly found in Enterobacteriaceae including Klebsiella spp. Molecular typing of clinical ESBL-isolates has become more and more important for prevention of the dissemination of ESBL-producers among nosocomial environment.

Methods: Multiple displacement amplified DNA derived from 20 K. pneumoniae and 34 K. oxytoca clinical isolates with an ESBL-phenotype was used in a universal CTX-M PCR amplification assay. Identification and differentiation of (CTX)-C-bla-M and (OXY)-O-bla/K1 sequences was obtained by DNA sequencing of M13-sequence-tagged CTX-M PCR-amplicons using a M13-specific sequencing primer.

Results: Nine out of 20 K. pneumoniae clinical isolates had a (CTX)-C-bla-M genotype. Interestingly, we found that the universal degenerated primers also amplified the chromosomally located K1-gene in all 34 K. oxytoca clinical isolates. Molecular identification and differentiation between (CTX)-C-bla-M and (OXY)-O-bla/K1-genes could only been achieved by sequencing of the PCR-amplicons. In silico analysis revealed that the universal degenerated CTX-M primer-pair used here might also amplify the chromosomally located (OXY)-O-bla and K1-genes in Klebsiella spp. and K1-like genes in other Enterobacteriaceae.

Conclusion: The PCR-based molecular typing method described here enables a rapid and reliable molecular identification of (CTX)-C-bla-M, and (OXY)-O-bla/K1-genes. The principles used in this study could also be applied to any situation in which antimicrobial resistance genes would need to be sequenced.

We analyze two different feature selection problems: finding a minimal feature set optimal for classification (MINIMAL-OPTIMAL) vs. finding all features relevant to the target variable (ALL-RELEVANT). The latter problem is motivated by recent applications within bioinformatics, particularly gene expression analysis. For both problems, we identify classes of data distributions for which there exist consistent, polynomial-time algorithms. We also prove that ALL-RELEVANT is much harder than MINIMAL-OPTIMAL and propose two consistent, polynomial-time algorithms. We argue that the distribution classes considered are reasonable in many practical cases, so that our results simplify feature selection in a wide range of machine learning tasks.

In order to retain information in working memory (WM) during a delay, distracting stimuli must be ignored. This important ability improves during childhood, but the neural basis for this development is not known. We measured brain activity with functional magnetic resonance imaging in adults and 13-year-old children. Data were analyzed with an event-related design to isolate activity during cue, delay, distraction, and response selection. Adults were more accurate and less distractible than children. Activity in the middle frontal gyrus and intraparietal cortex was stronger in adults than in children during the delay, when information was maintained in WM. Distraction during the delay evoked activation in parietal and occipital cortices in both adults and children. However, distraction activated frontal cortex only in children. The larger frontal activation in response to distracters presented during the delay may explain why children are more susceptible to interfering stimuli.

Motivation: For the last few years, Bayesian networks (BNs) have received increasing attention from the computational biology community as models of gene networks, though learning them from gene-expression data is problematic. Most gene-expression databases contain measurements for thousands of genes, but the existing algorithms for learning BNs from data do not scale to such high-dimensional databases. This means that the user has to decide in advance which genes are included in the learning process, typically no more than a few hundreds, and which genes are excluded from it. This is not a trivial decision. We propose an alternative approach to overcome this problem. Results: We propose a new algorithm for learning BN models of gene networks from gene-expression data. Our algorithm receives a seed gene S and a positive integer R from the user, and returns a BN for the genes that depend on S such that less than R other genes mediate the dependency. Our algorithm grows the BN, which initially only contains S, by repeating the following step R + 1 times and, then, pruning some genes, find the parents and children of all the genes in the BN and add them to it. Intuitively, our algorithm provides the user with a window of radius R around S to look at the BN model of a gene network without having to exclude any gene in advance. We prove that our algorithm is correct under the faithfulness assumption. We evaluate our algorithm on simulated and biological data (Rosetta compendium) with satisfactory results. © The Author 2005. Published by Oxford University Press. All rights reserved.

We study cross-validation as a scoring criterion for learning dynamic Bayesian network models that generalize well. We argue that cross-validation is more suitable than the Bayesian scoring criterion for one of the most common interpretations of generalization. We confirm this by carrying out an experimental comparison of cross-validation and the Bayesian scoring criterion, as implemented by the Bayesian Dirichlet metric and the Bayesian information criterion. The results show that cross-validation leads to models that generalize better for a wide range of sample sizes. © 2005 Elsevier B.V. All rights reserved.

We propose a framework for learning from data and validating Bayesian network models of gene networks. The learning phase selects multiple locally optimal models of the data and reports the best of them. The validation phase assesses the confidence in the model reported by studying the different locally optimal models obtained in the learning phase. We prove that our framework is asymptotically correct under the faithfulness assumption. Experiments with real data (320 samples of the expression levels of 32 genes involved in Saccharomyces cerevisiae, i.e. baker’s yeast, pheromone response) show that our framework is reliable.

We propose an algorithm for learning the Markov boundary of a random variable from data without having to learn a complete Bayesian network. The algorithm is correct under the faithfulness assumption, scalable and data efficient. The last two properties are important because we aim to apply the algorithm to identify the minimal set of random variables that is relevant for probabilistic classification in databases with many random variables but few instances. We report experiments with synthetic and real databases with 37, 441 and 139352 random variables showing that the algorithm performs satisfactorily.

We present a sound and complete graphical criterion for reading dependencies from the minimal undirected independence map G of a graphoid M that satisfies weak transitivity. Here, complete means that it is able to read all the dependencies in M that can be derived by applying the graphoid properties and weak transitivity to the dependencies used in the construction of G and the independencies obtained from G by vertex separation. We argue that assuming weak transitivity is not too restrictive. As an intermediate step in the derivation of the graphical criterion, we prove that for any undirected graph G there exists a strictly positive discrete probability distribution with the prescribed sample spaces that is faithful to G. We also report an algorithm that implements the graphical criterion and whose running time is considered to be at most O(n(2)(e + n)) for n nodes and e edges. Finally, we illustrate how the graphical criterion can be used within bioinformatics to identify biologically meaningful gene dependencies.

We propose algorithms for learning Markov boundaries from data without having to learn a Bayesian network first. We study their correctness, scalability and data efficiency. The last two properties are important because we aim to apply the algorithms to identify the minimal set of features that is needed for probabilistic classification in databases with thousands of features but few instances, e.g. gene expression databases. We evaluate the algorithms on synthetic and real databases, including one with 139,351 features. © 2006 Elsevier Inc. All rights reserved.

Combinatorial interactions among transcription factors are critical to directing tissue-specific gene expression. To build a global atlas of these combinations, we have screened for physical interactions among the majority of human and mouse DNA-binding transcription factors (TFs). The complete networks contain 762 human and 877 mouse interactions. Analysis of the networks reveals that highly connected TFs are broadly expressed across tissues, and that roughly half of the measured interactions are conserved between mouse and human. The data highlight the importance of TF combinations for determining cell fate, and they lead to the identification of a SMAD3/FLI1 complex expressed during development of immunity. The availability of large TF combinatorial networks in both human and mouse will provide many opportunities to study gene regulation, tissue differentiation, and mammalian evolution.

Recent models of the oculomotor delayed response task have been based on the assumption that working memory is stored as a persistent activity state (a 'bump' state). The delay activity is maintained by a finely tuned synaptic weight matrix producing a line attractor. Here we present an alternative hypothesis, that fast Hebbian synaptic plasticity is the mechanism underlying working memory. A computational model demonstrates a working memory function that is more resistant to distractors and network inhomogeneity compared to previous models, and that is also capable of storing multiple memories.

Synapses on dendritic spines of pyramidal neurons show a remarkable ability to induce phosphorylation of transcription factors at the nuclear level with a short latency, incompatible with a diffusion process from the dendritic spines to the nucleus. To account for these findings, we formulated a novel extension of the classical cable theory by considering the fact that the endoplasmic reticulum (ER) is an effective charge separator, forming an intrinsic compartment that extends from the spine to the nuclear membrane. We use realistic parameters to show that an electrotonic signal may be transmitted along the ER from the dendritic spines to the nucleus. We found that this type of signal transduction can additionally account for the remarkable ability of the cell nucleus to differentiate between depolarizing synaptic signals that originate from the dendritic spines and back-propagating action potentials. This study considers a novel computational role for dendritic spines, and sheds new light on how spines and ER may jointly create an additional level of processing within the single neuron. © 2008 Shemer et al.

Background: Free fatty acids released from adipose tissue affect the synthesis of apolipoprotein B-containing lipoproteins and glucose metabolism in the liver. Whether there also exists a reciprocal metabolic arm affecting energy metabolism in white adipose tissue is unknown. Methods and Findings: We investigated the effects of apoB-containing lipoproteins on catecholamine-induced lipolysis in adipocytes from subcutaneous fat cells of obese but otherwise healthy men, fat pads from mice with plasma lipoproteins containing high or intermediate levels of apoB100 or no apoB100, primary cultured adipocytes, and 3T3-L1 cells. In subcutaneous fat cells, the rate of lipolysis was inversely related to plasma apoB levels. In human primary adipocytes, LDL inhibited lipolysis in a concentration-dependent fashion. In contrast, VLDL had no effect. Lipolysis was increased in fat pads from mice lacking plasma apoB100, reduced in apoB100-only mice, and intermediate in wild-type mice. Mice lacking apoB100 also had higher oxygen consumption and lipid oxidation. In 3T3-L1 cells, apoB100-containing lipoproteins inhibited lipolysis in a dose-dependent fashion, but lipoproteins containing apoB48 had no effect. ApoB100-LDL mediated inhibition of lipolysis was abolished in fat pads of mice deficient in the LDL receptor (Ldlr-/- Apob100/100). Conclusions: Our results show that the binding of apoB100-LDL to adipocytes via the LDL receptor inhibits intracellular noradrenaline-induced lipolysis in adipocytes. Thus, apoB100-LDL is a novel signaling molecule from the liver to peripheral fat deposits that may be an important link between atherogenic dyslipidemias and facets of the metabolic syndrome. © 2008 Skogsberg et al.

Despite the well-documented effects of plasma lipid lowering regimes halting atherosclerosis lesion development and reducing morbidity and mortality of coronary artery disease and stroke, the transcriptional response in the atherosclerotic lesion mediating these beneficial effects has not yet been carefully investigated. We performed transcriptional profiling at 10-week intervals in atherosclerosis-prone mice with human-like hypercholesterolemia and a genetic switch to lower plasma lipoproteins (Ldlr-/-Apo 100/100 Mttpflox/flox Mx1-Cre). Atherosclerotic lesions progressed slowly at first, then expanded rapidly, and plateaued after advanced lesions formed. Analysis of lesion expression profiles indicated that accumulation of lipid-poor macrophages reached a point that led to the rapid expansion phase with accelerated foam-cell formation and inflammation, an interpretation supported by lesion histology. Genetic lowering of plasma cholesterol (e.g., lipoproteins) at this point all together prevented the formation of advanced plaques and parallel transcriptional profiling of the atherosclerotic arterial wall identified 37 cholesterol-responsive genes mediating this effect. Validation by siRNA-inhibition in macrophages incubated with acetylated-LDL revealed a network of eight cholesterol-responsive atherosclerosis genes regulating cholesterol-ester accumulation. Taken together, we have identified a network of atherosclerosis genes that in response to plasma cholesterol-lowering prevents the formation of advanced plaques. This network should be of interest for the development of novel atherosclerosis therapies. © 2008 Skogsberg et al.

Objective

To reveal relevant genes for atherosclerosis by whole-genome expression analyses of plaques from patients undergoing carotid endorectomy.

Methods and Results

Whole-genome expression measurements (WGEM) using Affymetrix HG-U133_Plus_2 chip of carotid plaques in patients undergoing carotid endorectomy at Stockholm Söder Hospital, Sweden. Patients were screened for conventional risk factors at a three-month follow-up visit and atherosclerosis burden in the common carotid artery (CCA) was measured by intima-media thickness (IMT). An unsupervised coupled two-way clustering approach identified genderspecific genes and 55 genes associated to degree of IMT in these patients.

Conclusions

Coupled two-way clustering of carotid lesion expression profiles from a well-characterized clinical cohort is useful for identification of novel genes that may be relevant for atheroscleroris.

Integrative systems biology has emerged as an exciting research approach in molecular biology and functional genomics that involves the integration of genomics, proteomics, and metabolomics datasets. These endeavors establish a systematic paradigm by which to interrogate, model, and iteratively refine our knowledge of the regulatory events within a cell. Here we review the latest technologies available to collect high-throughput measurements of a cellular state as well as the most successful methods for the integration and interrogation of these measurements. In particular we will focus on methods available to infer transcription regulatory networks in mammals.

After the major achievements of the DNA sequencing projects, an equally important challenge now is to uncover the functional relationships among genes (i.e. gene networks). It has become increasingly clear that computational algorithms are crucial for extracting meaningful information from the massive amount of data generated by high-throughput genome-wide technologies. Here, we summarise how systems identification algorithms, originating from physics and control theory, have been adapted for use in biology. We also explain how experimental perturbations combined with genome-wide measurements are being used to uncover gene networks. Perturbation techniques could pave the way for identifying gene networks in more complex settings such as multifactorial diseases and for improving the efficacy of drug evaluation. © 2006 Elsevier Ltd. All rights reserved.

Together with computational analysis and modeling, the development of whole-genome measurement technologies holds the potential to fundamentally change research on complex disorders such as coronary artery disease. With these tools, the stage has been set to reveal the full repertoire of biological components (genes, proteins, and metabolites) in complex diseases and their interplay in modules and networks. Here we review how network identification based on reverse engineering, as applied to whole-genome datasets from simpler organisms, is now being adapted to more complex settings such as datasets from human cell lines and organs in relation to physiological and pathological states. Our focus is on the use of a systems biological approach to identify gene networks in coronary atherosclerosis. We also address how gene networks will probably play a key role in the development of early diagnostics and treatments for complex disorders in the coming era of individualized medicine. Copyright ©2007 by the American Society for Biochemistry and Molecular Biology, Inc.

Systems Biology has emerged as an exciting research approach in molecular biology and functional genomics that involves a systematic use of genomic, proteomic, and metabolomic technologies for the construction of network-based models of biological processes. These endeavors, collectively referred to as systems biology establish a paradigm by which to systematically interrogate, model, and iteratively refine our knowledge of the regulatory events within a cell. Here, we present a new systems approach, integrating DNA and transcript expression information, specifically designed to identify transcriptional networks governing the macrophage immune response to lipopolysaccharide (LPS). Using this approach, we are not only able to infer a global macrophage transcriptional network, but also time-specific sub-networks that are dynamically active across the LPS response. We believe that our system biological approach could be useful for identifying other complex networks mediating immunological responses. © 2007 Elsevier Inc. All rights reserved.