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PubMed2004

Systems level insights into the stress response to UV radiation in the halophilic archaeon Halobacterium NRC-1.

We report a remarkably high UV-radiation resistance in the extremely halophilic archaeon Halobacterium NRC-1 withstanding up to 110 J/m2 with no loss of viability. Gene knockout analysis in two putative photolyase-like genes (phr1 and phr2) implicated only phr2 in photoreactivation. The UV-response was further characterized by analyzing simultaneously, along with gene function and protein interactions inferred through comparative genomics approaches, mRNA changes for all 2400 genes during light and dark repair. In addition to photoreactivation, three other putative repair mechanisms were identified including d(CTAG) methylation-directed mismatch repair, four oxidative damage repair enzymes, and two proteases for eliminating damaged proteins. Moreover, a UV-induced down-regulation of many important metabolic functions was observed during light repair and seems to be a phenomenon shared by all three domains of life. The systems analysis has facilitated the assignment of putative functions to 26 of 33 key proteins in the UV response through sequence-based methods and/or similarities of their predicted three-dimensional structures to known structures in the PDB. Finally, the systems analysis has raised, through the integration of experimentally determined and computationally inferred data, many experimentally testable hypotheses that describe the metabolic and regulatory networks of Halobacterium NRC-1.

Animals↗

PubMed2005

Using large-scale perturbations in gene network reconstruction.

BACKGROUND: Recent analysis of the yeast gene network shows that most genes have few inputs, indicating that enumerative gene reconstruction methods are both useful and computationally feasible. A simple enumerative reconstruction method based on a discrete dynamical system model is used to study how microarray experiments involving modulated global perturbations can be designed to obtain reasonably accurate reconstructions. The method is tested on artificial gene networks with biologically realistic in/out degree characteristics. RESULTS: It was found that a relatively small number of perturbations significantly improve inference accuracy, particularly for low-order inputs of one or two genes. The perturbations themselves should alter the expression level of approximately 50-60% of the genes in the network. CONCLUSIONS: Time-series obtained from perturbations are a common form of expression data. This study illustrates how gene networks can be significantly reconstructed from such time-series while requiring only a relatively small number of calibrated perturbations, even for large networks, thus reducing experimental costs.

Algorithms↗

PubMed2026

Hepatic metabolic adaptation to endurance exercise: temporal and sex differences by multiomics integration and validation.

BACKGROUND: Although endurance exercise benefits liver health, sex-specific adaptive trajectories remain unclear. This study mapped dynamic liver adaptation in males and females during prolonged training and identified underlying molecular programs. METHODS: Using publicly available time-resolved liver multi-omics data generated by the Molecular Transducers of Physical Activity Consortium (MoTrPAC), we established a computational pipeline for differential analysis of transcriptomic, proteomic, phosphoproteomic, and metabolomic data with FDR correction, followed by FGSEA pathway enrichment. Kinase activities were inferred through ortholog mapping and PhosphoSitePlus. Cross-omics co-expression networks were constructed using WGCNA and topological overlap to link omics features with physiological phenotypes. For experimental validation, liver tissues were collected from endurance-trained Sprague-Dawley rats, and key nodes were confirmed by Western blotting, qRT-PCR, and immunofluorescence/immunohistochemical staining. Public scRNA-seq data were further integrated to map multi-omics signals to single-cell resolution and assess functional changes in specific cell types. RESULTS: The hepatic response to exercise stress was stage-specific, shifting from early transcriptional activation to later proteomic and metabolic remodeling. Multi-omics integration revealed distinct sex-associated adaptive trajectories: males were more strongly associated with energy metabolism, redox-related programs, and amino acid/organic acid catabolism, whereas females showed prominent membrane lipid remodeling, proteostasis -related programs, and mitochondrial/ribosomal translational features. Single-cell analysis showed that tissue remodeling occurred without major lineage turnover, instead involving altered communication among pre-existing cell communities. Validation of PPP1R3G identified a protein-dominant exercise-responsive marker, supporting the contribution of post-transcriptional or protein-level regulation. CONCLUSIONS: Hepatic adaptation to endurance stress follows a cross-omics evolutionary pattern with sex-specific reprogramming of energy supply and homeostatic maintenance. This time-resolved framework clarifies how exercise improves liver function and supports sex-oriented metabolic interventions and therapeutic target discovery.

Animals↗

PubMed1986

Application of the EXPERT consultation system to accelerated laboratory testing and interpretation.

The EXPERT consultation system-building tool, a knowledge-based artificial intelligence program developed at Rutgers University, has been applied to the development of a laboratory consultation system facilitating sequential laboratory testing and interpretation. Depending on the results of a basic panel of laboratory tests, the system requests that specific secondary tests be performed. Input of these secondary findings can result in requests for tertiary testing, to complete the database necessary for interpretation. Interpretation of all results is based upon final inferences from the collected findings through a series of rules, a hierarchical network that yields an efficient production system not easily obtained through conventional programming. The rules included in this model are based upon initial results for total protein, calcium, glucose, total bilirubin, alkaline phosphatase, lactate dehydrogenase, aspartate aminotransferase, thyroxin, hemoglobin, mean corpuscular volume, and the concentrations of four drugs. Pertinent clinical history items included are jaundice, diabetes, thyroid disease, medications, and ethanol. Implementing this system in a laboratory-based accelerated testing program involving outpatients maximized the effective use of laboratory resources, eliminated useless testing, and provided the patient with low-cost laboratory information.

Adolescent↗

PubMed1993

Evaluating the quality of a probabilistic diagnostic system using different inferencing strategies.

In this paper we describe the evaluation of a probabilistic diagnostic system for patients with renal mass. Three inference models: Multi-membership Bayesian (MB), Minimal Diagnosis (MD) and Bayesian Network (BN), and 72 patients are used to illustrate three interrelated measures of system performance: accuracy, reliability and discriminating power. The inferencing strategies we tested demonstrated the kind of trade-offs in the performance measures that can be expected from imperfect systems. Ultimately, the purpose and expected use of a system should dictate the relative importance ascribed to different aspects of system performance.

Adolescent↗

PubMed2003

Transitivity, flexibility, conjunctive representations, and the hippocampus. II. A computational analysis.

A computational neural network model is presented that explains how the hippocampus can contribute to transitive inference performance observed in rats (Dusek and Eichenbaum, 1997. Proc Natl Acad Sci U S A 94:7109-7114; Van Elzakker et al., 2003. Hippocampus 12:this issue). In contrast to existing theories that emphasize the idea that the hippocampus contributes by flexibly relating previously encoded memories, we find that the hippocampus contributes by altering the elemental associative weights of individual stimulus elements during learning. We use this model to account for a range of existing data and to make a number of distinctive predictions that clearly contrast these two views.

Animals↗

PubMed2005

Application of a neuro-fuzzy network for gait event detection using electromyography in the child with cerebral palsy.

An adaptive neuro-fuzzy inference system (ANFIS) with a supervisory control system (SCS) was used to predict the occurrence of gait events using the electromyographic (EMG) activity of lower extremity muscles in the child with cerebral palsy (CP). This is anticipated to form the basis of a control algorithm for the application of electrical stimulation (ES) to leg or ankle muscles in an attempt to improve walking ability. Either surface or percutaneous intramuscular electrodes were used to record the muscle activity from the quadriceps muscles, with concurrent recording of the gait cycle performed using a VICON motion analysis system for validation of the ANFIS with SCS. Using one EMG signal and its derivative from each leg as its inputs, the ANFIS with SCS was able to predict all gait events in seven out of the eight children, with an average absolute time differential between the VICON recording and the ANFIS prediction of less than 30 ms. Overall accuracy in predicting gait events ranged from 98.6% to 95.3% (root mean-squared error between 0.7 and 1.5). Application of the ANFIS with the SCS to the prediction of gait events using EMG data collected two months after the initial data demonstrated comparable results, with no significant differences between gait event detection times. The accuracy rate and robustness of the ANFIS with SCS with two EMG signals suggests its applicability to ES control.

Adolescent↗

PubMed2006

Protein-protein interactions more conserved within species than across species.

Experimental high-throughput studies of protein-protein interactions are beginning to provide enough data for comprehensive computational studies. Today, about ten large data sets, each with thousands of interacting pairs, coarsely sample the interactions in fly, human, worm, and yeast. Another about 55,000 pairs of interacting proteins have been identified by more careful, detailed biochemical experiments. Most interactions are experimentally observed in prokaryotes and simple eukaryotes; very few interactions are observed in higher eukaryotes such as mammals. It is commonly assumed that pathways in mammals can be inferred through homology to model organisms, e.g. the experimental observation that two yeast proteins interact is transferred to infer that the two corresponding proteins in human also interact. Two pairs for which the interaction is conserved are often described as interologs. The goal of this investigation was a large-scale comprehensive analysis of such inferences, i.e. of the evolutionary conservation of interologs. Here, we introduced a novel score for measuring the overlap between protein-protein interaction data sets. This measure appeared to reflect the overall quality of the data and was the basis for our two surprising results from our large-scale analysis. Firstly, homology-based inferences of physical protein-protein interactions appeared far less successful than expected. In fact, such inferences were accurate only for extremely high levels of sequence similarity. Secondly, and most surprisingly, the identification of interacting partners through sequence similarity was significantly more reliable for protein pairs within the same organism than for pairs between species. Our analysis underlined that the discrepancies between different datasets are large, even when using the same type of experiment on the same organism. This reality considerably constrains the power of homology-based transfer of interactions. In particular, the experimental probing of interactions in distant model organisms has to be undertaken with some caution. More comprehensive images of protein-protein networks will require the combination of many high-throughput methods, including in silico inferences and predictions. http://www.rostlab.org/results/2006/ppi_homology/

Animals↗

PubMed1999

Neural network modeling for on-line estimation of nutrient dynamics in a sequentially-operated batch reactor.

In monitoring and controlling wastewater treatment processes, on-line information of nutrient dynamics is very important. However, these variables are determined with a significant time delay. Although the final effluent quality can be analyzed after this delay, it is often too late to make proper adjustments. In this paper, a neural network approach, a software sensor, was proposed to overcome this problem. Software sensor refers to a modeling approach inferring hard-to-measure process variables from other on-line measurable process variables. A bench-scale sequentially-operated batch reactor (SBR) used for advanced wastewater treatment (BOD plus nutrient removal) was employed to develop the neural network model. In order to improve the network performance, the structure of neural network was arranged in such a way of reflecting the change of operational conditions within a cycle. Real-time estimation of PO3-(4), NO-3, and NH+4 concentrations was successfully carried out with the on-line information of the SBR system only.

Anaerobiosis↗

PubMed2026

Endothelial cell-specific DNA methylation alterations in breast cancer.

DNA methylation alterations are well-established contributors to carcinogenesis, yet, in the tumor microenvironment (TME), patterns of lineage and cell-specific methylation alterations are not well understood. Single-cell DNA methylation profiling in the TME is limited by technical challenges and high costs. Here, we use bulk DNA methylation, cell type deconvolution (HiTIMED), and an interaction testing framework (CellDMC) to identify reproducible, computationally inferred lineage-specific epigenetic alterations in the TME supported by orthogonal data sources. Tumor endothelial cells (TECs), critical regulators of angiogenesis, vascular permeability, and immune cell trafficking, acquire structural and functional abnormalities that promote tumor growth. We hypothesize that TECs have altered DNA methylation compared with endothelial cells in non-tumor tissues. In genome-scale methylation data from discovery and validation datasets (tumor n = 1071; non-tumor n = 415), we identify and validate >4500 TEC-specific CpGs with altered methylation, many mapping to genes involved in angiogenesis and endothelial function. Integration with gene expression data indicates that TEC-specific methylation alterations may reprogram transcriptional networks controlling angiogenesis. High-resolution, cell lineage-specific epigenetic landscapes can be inferred from bulk methylation data, implicating TEC-specific DNA methylation alterations as potential drivers of cancer angiogenesis and vascular dysfunction and providing a framework for future mechanistic and translational studies of the tumor vasculature.

DNA Methylation↗

PubMed1997

Estrogen and testosterone, but not a nonaromatizable androgen, direct network integration of the hypothalamo-somatotrope (growth hormone)-insulin-like growth factor I axis in the human: evidence from pubertal pathophysiology and sex-steroid hormone replacement.

Activation of the gonadotropic and somatotropic axes in puberty is marked by striking amplification of pulsatile neurohormone secretion. In addition, each axis, as a whole, constitutes a regulated network whose feedback relationships are likely to manifest important changes at the time of puberty. Here, we use the regularity statistic, approximate entropy (ApEn), to assess feedback activity within the somatotropic (hypothalamo-pituitary/GH-insulin-like growth factor I) axis indirectly. To this end, we studied pubertal boys and prepubertal girls or boys with sex-steroid hormone deficiency treated short-term with estrogen, testosterone, or a nonaromatizable androgen in a total of 3 paradigms. First, our cross-sectional analysis of 53 boys at various stages of puberty or young adulthood revealed that mean ApEn, taken as a measure of feedback complexity, of 24-h serum GH concentration profiles is maximal in pre- and mid-late puberty, followed by a significant decline in postpubertal adolescence and young adulthood (P = 0.0008 by ANOVA). This indicates that marked disorderliness of the GH release process occurs in mid-late puberty at or near the time of peak growth velocity, with a return to maximal orderliness thereafter at reproductive maturity. Second, oral administration of ethinyl estradiol for 5 weeks to 7 prepubertal girls with Turner's syndrome also augmented ApEn significantly (P = 0.018), thus showing that estrogen per se can induce greater irregularity of GH secretion. Third, in 5 boys with constitutionally delayed puberty, im testosterone administration also significantly increased ApEn of 24-h GH time series (P = 0.0045). In counterpoint, 5 alpha-dihydrotestosterone, a nonaromatizable androgen, failed to produce a significant ApEn increase (P > 0.43). We conclude from these three distinct experimental contexts that aromatization of testosterone to estrogen in boys, or estrogen itself in girls, is likely the proximate sex-steroid stimulus amplifying secretory activity of the GH axis in puberty. In addition, based on inferences derived from mathematical models that mechanistically link increased disorderliness (higher ApEn) to network changes, we suggest that sex-steroid hormones in normal puberty modulate feedback within, and hence network function of, the hypothalamo-pituitary/GH-insulin-like growth factor I axis.

Adolescent↗

PubMed2006

Inferring functional linkages between proteins from evolutionary scenarios.

Identifying potential protein interactions is of great importance in understanding the topologies of cellular networks, which is much needed and valued in current systematic biological studies. The development of our computational methods to predict protein-protein interactions have been spurred on by the massive sequencing efforts of the genomic revolution. Among these methods is phylogenetic profiling, which assumes that proteins under similar evolutionary pressures with similar phylogenetic profiles might be functionally related. Here, we introduce a method for inferring functional linkages between proteins from their evolutionary scenarios. The term evolutionary scenario refers to a series of events that occurred in speciation over time, which can be reconstructed given a phylogenetic profile and a species tree. Common evolutionary pressures on two proteins can then be inferred by comparing their evolutionary scenarios, which is a direct indication of their functional linkage. This scenario method has proven to have better performance compared with the classical phylogenetic profile method, when applied to the same test set. In addition, predicted results of the two methods are found to be fairly different, suggesting the possibility of merging them in order to achieve a better performance. We analyzed the influence of the topology of the phylogenetic tree on the performance of this method, and found it to be robust to perturbations in the topology of the tree. However, if a completely random tree is incorporated, performance will decline significantly. The evolutionary scenario method was used for inferring functional linkages in 67 species, and 40,006 linkages were predicted. We examine our prediction for budding yeast and find that almost all predicted linkages are supported by further evidence.

Computational Biology↗

PubMed2005

The net of life: reconstructing the microbial phylogenetic network.

It has previously been suggested that the phylogeny of microbial species might be better described as a network containing vertical and horizontal gene transfer (HGT) events. Yet, all phylogenetic reconstructions so far have presented microbial trees rather than networks. Here, we present a first attempt to reconstruct such an evolutionary network, which we term the "net of life". We use available tree reconstruction methods to infer vertical inheritance, and use an ancestral state inference algorithm to map HGT events on the tree. We also describe a weighting scheme used to estimate the number of genes exchanged between pairs of organisms. We demonstrate that vertical inheritance constitutes the bulk of gene transfer on the tree of life. We term the bulk of horizontal gene flow between tree nodes as "vines", and demonstrate that multiple but mostly tiny vines interconnect the tree. Our results strongly suggest that the HGT network is a scale-free graph, a finding with important implications for genome evolution. We propose that genes might propagate extremely rapidly across microbial species through the HGT network, using certain organisms as hubs.

Algorithms↗

PubMed2001

A real-coded genetic algorithm for training recurrent neural networks.

The use of Recurrent Neural Networks is not as extensive as Feedforward Neural Networks. Training algorithms for Recurrent Neural Networks, based on the error gradient, are very unstable in their search for a minimum and require much computational time when the number of neurons is high. The problems surrounding the application of these methods have driven us to develop new training tools. In this paper, we present a Real-Coded Genetic Algorithm that uses the appropriate operators for this encoding type to train Recurrent Neural Networks. We describe the algorithm and we also experimentally compare our Genetic Algorithm with the Real-Time Recurrent Learning algorithm to perform the fuzzy grammatical inference.

Algorithms↗

PubMed2003

Evolutionary self-organising modelling of a municipal wastewater treatment plant.

Building predictive models for highly time varying and complex multivariable aspects of the wastewater treatment plant is important both for understanding the dynamics of this complex system, and in the development of optimal control support and management schemes. This paper presents a new approach, which is called genetic programming as a self-organising modelling tool, to model dynamic performance of municipal activated-sludge wastewater treatment plants. Genetic programming evolves several process models automatically based on methods of natural selection ('survival of the fittest'), that could predict the dynamics of MLSS and suspended solids in the effluent. The predictive accuracy of the genetic programming approach was compared with a nonlinear state-space model with neural network and a well-known IAWQ ASM2. The genetic programming system evolved some models that were an improvement over the neural network and ASM2 and showed that the transparency of the model evolved may allow inferences about underlying processes to be made. This work demonstrates that dynamic nonlinear processes in the wastewater treatment plant may be successfully modelled through the use of evolutionary model induction algorithms in GP technique. Further, our results show that genetic programming can work as a cost-effective intelligent modelling tool, enabling us to create prototype process models quickly and inexpensively instead of an engineer developing the process model.

Automation↗

PubMed1990

[Cognitive processes and neuronal networks].

It is clear that computers are but a poor brain models: the nervous system has many "processors" (neurons) in parallel, whereas von Neuman's machines work sequentially on a single processor. In complex systems, emergent properties cannot be inferred from the behaviour of single elements. Anthills display collective "meaningful" moves, while each ant seems to obey local interactions only. Likewise, large parallel networks of processing elements elicit emergent properties. Like brains, some of them are self-organizing systems. In large parallel processing networks, each unit performs an elementary computation: adding inputs from other units. Large nets display surprising spontaneous computational abilities: associative memories, classes, generalizations may be seen as emergent properties of the network. Symbols are dynamical entities, whose handing is driven by local interactions of activation/inhibition of related representations. In such models, representations (memories) are distributed in the whole network, as stable configurations. Indeed, the basic properties of representation in connectionist models seem closer to human mental objects than the classic Artificial Intelligence concepts. Connectionist models have been used in many fields, namely simulations of real neural networks, pattern recognition and artificial vision, speech recognition, language understanding and knowledge representation, problem solving... Connectionist models have been thus used in neurobiology as well as cognition. One basic structure seems indeed able to account for a range of cognitive functions, from perception to problem solving and high level cognitive tasks. Nevertheless studies about "pathological" networks are yet rare, still an open field... We explore some of these fields.

Artificial Intelligence↗

PubMed1999

Internal representation in neural networks used for classification of patient anaesthetic states and dosage.

In this study we aimed to explore the ability of artificial neural networks (ANN) to classify patient anaesthetic states and dosage. Surgical data obtained under different states of anaesthesia and dose levels were modelled via this approach. It is shown that inferential parameters can be used to determine the patient anaesthetic states and drug dosage. In addition to demonstrating the capability of ANN for classification we were interested in the internal representations that are developed automatically by networks while they are learning their processing task. An unsupervised learning procedure of clustering via which the classes are inferred from the data and a supervised learning technique of discrimination via which to construct a classification of the known categories were applied to analyse the performance of the ANN. Discriminant analysis (DA) was also utilised to optimise the network architecture.

Anesthesia↗

PubMed1996

POPFNN: A Pseudo Outer-product Based Fuzzy Neural Network.

A novel fuzzy neural network, called the pseudo outer-product based fuzzy neural network (POPFNN), is proposed in this paper. The functions performed by each layer in the proposed POPFNN strictly correspond to the inference steps in the truth value restriction method in fuzzy logic [[Mantaras (1990)] Approximate reasoning models, Ellis Horwood]. This correspondence gives it a strong theoretical basis. Similar to most of the existing fuzzy neural networks, the proposed POPFNN uses a self-organizing algorithm ([Kohonen, 1988], Self-organization and associative memories, Springer) to learn and initialize the membership functions of the input and output variables from a set of training data. However, instead of employing the popularly used competitive learning [[Kosko (1990)] IEEE Trans. Neural Networks, 3(5), 801], this paper proposes a novel pseudo outer-product (POP) learning algorithm to identify the fuzzy rules that are supported by the training data. The proposed POP learning algorithm is fast, reliable, and highly intuitive. Extensive experimental results and comparisons are presented at the end of the paper for discussion. Copyright 1996 Elsevier Science Ltd.

Journal Article↗