Systems Biology by the Rules: Hybrid Intelligent Systems for Pathway Modeling and Discovery

Background: Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication. Results: A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet. Conclusion: This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer

Systems Biology by the Rules: Hybrid Intelligent Systems for Pathway Modeling and Discovery

Background: Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication. Results: A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet. Conclusion: This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer

Development of an open loop fuzzy logic urea dosage controller for use with an SCR equipped HDD engine

Selective Catalytic Reduction (SCR) has been shown to be the most promising exhaust aftertreatment system for reducing oxides of nitrogen in near term in-use applications. SCRs use the ammonia containing compound urea, as a reducing agent. In order to control the urea dosage during transient operation of the engine, sophisticated control strategies are needed. The goal of this study was to design a controller to achieve the maximum NO x emission reduction possible in the transient mode of engine operation, without causing ammonia slip. The development of an open loop, non-sensor based fuzzy logic urea dosage controller is discussed in this thesis. Urea injection values were controlled with \u27maps\u27 based upon the engine speed and engine load, and fuzzy logic was employed as a robust artificial intelligence technique to allow for the development of these maps. Fuzzy logic was utilized to model the complex SCR system and predict the efficiency of NOx conversion. In order to aid in the development of the fuzzy logic SCR model, other methods for generating urea maps were investigated, as well. The first method was an optimization technique, which involved manual testing of the engine to find the optimal urea injection amount. The other method involved injection of urea based upon the average NOx produced. A correction factor was developed and applied to this map to account for losses of ammonia.;The open loop urea map control strategy was implemented without the use of NOx or NH3 sensors. The final fuzzy logic urea map created was able to reduce NOx by 57% over the FTP cycle and 60% over the ETC cycle. This reduction was achieved without causing any significant ammonia slip. The optimized and average NOx urea maps reduced NO x by 67% and 66% over the FTP cycle, but also resulted in large peaks of ammonia slip during the LAFY section. The average NH3 slip seen during the FTP was less than 10 ppm, which was deemed acceptable. The optimized map was also used on the ETC cycle and NOx was reduced by 65% with no significant NH3 slip. The urea maps created for this study appeared to be cycle independent and could be used to control NOx emissions for any transient mode of engine operation

On the development of decision-making systems based on fuzzy models to assess water quality in rivers

There are many situations where a linguistic description of complex phenomena allows better assessments. It is well known that the assessment of water quality continues depending heavily upon subjective judgments and interpretation, despite the huge datasets available nowadays. In that sense, the aim of this study has been to introduce intelligent linguistic operations to analyze databases, and produce self interpretable water quality indicators, which tolerate both imprecision and linguistic uncertainty. Such imprecision typically reflects the ambiguity of human thinking when perceptions need to be expressed. Environmental management concepts such as: "water quality", "level of risk", or "ecological status" are ideally dealt with linguistic variables. In the present Thesis, the flexibility of computing with words offered by fuzzy logic has been considered in these management issues. Firstly, a multipurpose hierarchical water quality index has been designed with fuzzy reasoning. It integrates a wide set of indicators including: organic pollution, nutrients, pathogens, physicochemical macro-variables, and priority micro-contaminants. Likewise, the relative importance of the water quality indicators has been dealt with the analytic hierarchy process, a decision-aiding method. Secondly, a methodology based on a hybrid approach that combines fuzzy inference systems and artificial neural networks has been used to classify ecological status in surface waters according to the Water Framework Directive. This methodology has allowed dealing efficiently with the non-linearity and subjective nature of variables involved in this classification problem. The complexity of inference systems, the appropriate choice of linguistic rules, and the influence of the functions that transform numerical variables into linguistic variables have been studied. Thirdly, a concurrent neuro-fuzzy model based on screening ecological risk assessment has been developed. It has considered the presence of hazardous substances in rivers, and incorporates an innovative ranking and scoring system, based on a self-organizing map, to account for the likely ecological hazards posed by the presence of chemical substances in freshwater ecosystems. Hazard factors are combined with environmental concentrations within fuzzy inference systems to compute ecological risk potentials under linguistic uncertainty. The estimation of ecological risk potentials allows identifying those substances requiring stricter controls and further rigorous risk assessment. Likewise, the aggregation of ecological risk potentials, by means of empirical cumulative distribution functions, has allowed estimating changes in water quality over time. The neuro-fuzzy approach has been validated by comparison with biological monitoring. Finally, a hierarchical fuzzy inference system to deal with sediment based ecological risk assessment has been designed. The study was centered in sediments, since they produce complementary findings to water quality analysis, especially when temporal trends are required. Results from chemical and eco-toxicological analyses have been used as inputs to two parallel inference systems which assess levels of contamination and toxicity, respectively. Results from both inference engines are then treated in a third inference engine which provides a final risk characterization, where the risk is provided in linguistic terms, with their respective degrees of certitude. Inputs to the risk system have been the levels of potentially toxic substances, mainly metals and chlorinated organic compounds, and the toxicity measured with a screening test which uses the photo-luminescent bacteria Vibrio fischeri. The Ebro river basin has been selected as case study, although the methodologies here explained can easily be applied to other rivers. In conclusion, this study has broadly demonstrated that the design of water quality indexes, based on fuzzy logic, emerges as suitable and alternative tool to support decision makers involved in effective sustainable river basin management plans.Existen diversas situaciones en las cuales la descripción en términos lingüísticos de fenómenos complejos permite mejores resultados. A pesar de los volúmenes de información cuantitativa que se manejan actualmente, es bien sabido que la gestión de la calidad del agua todavía obedece a juicios subjetivos y de interpretación de los expertos. Por tanto, el reto en este trabajo ha sido la introducción de operaciones lógicas que computen con palabras durante el análisis de los datos, para la elaboración de indicadores auto-interpretables de calidad del agua, que toleren la imprecisión e incertidumbre lingüística. Esta imprecisión típicamente refleja la ambigüedad del pensamiento humano para expresar percepciones. De allí que las variables lingüísticas se presenten como muy atractivas para el manejo de conceptos de la gestión medioambiental, como es el caso de la "calidad del agua", el "nivel de riesgo" o el "estado ecológico". Por tanto, en la presente Tesis, la flexibilidad de la lógica difusa para computar con palabras se ha adaptado a diversos tópicos en la gestión de la calidad del agua. Primero, se desarrolló un índice jerárquico multipropósito de calidad del agua que se obtuvo mediante razonamiento difuso. El índice integra un extenso grupo de indicadores que incluyen: contaminación orgánica, nutrientes, patógenos, variables macroscópicas, así como sustancias prioritarias micro-contaminantes. La importancia relativa de los indicadores al interior del sistema de inferencia se estimó con un método de análisis de decisiones, llamado proceso jerárquico analítico. En una segunda fase, se utilizó una metodología híbrida que combina los sistemas de inferencia difusos y las redes neuronales artificiales, conocida como neuro-fuzzy, para el estudio de la clasificación del estado ecológico de los ríos, de acuerdo con los lineamientos de la Directiva Marco de Aguas. Esta metodología permitió un manejo adecuado de la no-linealidad y naturaleza subjetiva de las variables involucradas en este problema clasificatorio. Con ella, se estudió la complejidad de los sistemas de inferencia, la selección apropiada de reglas lingüísticas y la influencia de las funciones que transforman las variables numéricas en lingüísticas. En una tercera fase, se desarrolló un modelo conceptual neuro-fuzzy concurrente basado en la metodología de evaluación de riesgo ecológico preliminar. Este modelo consideró la presencia de sustancias peligrosas en los ríos, e incorporó un mapa auto-organizativo para clasificar las sustancias químicas, en términos de su peligrosidad hacia los ecosistemas acuáticos. Con este modelo se estimaron potenciales de riesgo ecológico por combinación de factores de peligrosidad y de concentraciones de las sustancias químicas en el agua. Debido a la alta imprecisión e incertidumbre lingüística, estos potenciales se obtuvieron mediante sistemas de inferencia difusos, y se integraron por medio de distribuciones empíricas acumuladas, con las cuales se pueden analizar cambios espacio-temporales en la calidad del agua. Finalmente, se diseñó un sistema jerárquico de inferencia difuso para la evaluación del riesgo ecológico en sedimentos de ribera. Este sistema estima los grados de contaminación, toxicidad y riesgo en los sedimentos en términos lingüísticos, con sus respectivos niveles de certeza. El sistema se alimenta con información proveniente de análisis químicos, que detectan la presencia de sustancias micro-contaminantes, y de ensayos eco-toxicológicos tipo "screening" que usan la bacteria Vibrio fischeri. Como caso de estudio se seleccionó la cuenca del río Ebro, aunque las metodologías aquí desarrolladas pueden aplicarse fácilmente a otros ríos. En conclusión, este trabajo demuestra ampliamente que el diseño y aplicación de indicadores de calidad de las aguas, basados en la metodología de la lógica difusa, constituyen una herramienta sencilla y útil para los tomadores de decisiones encargados de la gestión sostenible de las cuencas hidrográficas

Development of an Ammonia Reduction After-Treatment Systems for Stoichiometric Natural Gas Engines

Three-way catalyst (TWC) equipped stoichiometric natural gas vehicles have proven to be an effective alternative fuel strategy that shows significant low NOx emissions characteristics. However, recent studies have shown the TWC activity to contribute to elevated levels of tailpipe ammonia (NH 3) emissions. Although a non-regulated pollutant, ammonia is a potent pre-cursor to ambient secondary PM formation. Ammonia is an inevitable byproduct of fuel rich operation that results in lowest NOx slip through the TWC after-treatment system.;The main objective of the study is to develop a passive Ammonia Reduction Catalyst (passive-ARC) based NH3 reduction strategy that results in an overall reduction of ammonia as well as NOx emissions. The study investigated the characteristics of Fe-based and Cu-based zeolites SCR catalysts in storage and desorption of ammonia at high exhaust temperature conditions, that are typical of stoichiometric natural gas engines. Continuous measurements of NOx and NH3 before and after the SCR systems were conducted using a Fourier Transform Infrared Spectrometry (FTIR) gas analyzer. Results of the investigation showed that both, the Fe- and Cu zeolite SCRs adsorbed above 90% of TWC generated NH3 emissions below 350--375 °C SCR temperatures. Desorption or slipping of NH3 was observed at exhaust gas temperatures exceeding 400 °C. In terms of NOx conversions, Fe-zeolite showed efficiency between 50--80% above temperatures of 300--350 °C while Cu-zeolite performed well at lower SCR temperature from 250 °C and above with a conversion efficiency of greater than 50%.;In order to efficiently reduce both NOx and NH3 simultaneously over longer durations it was found that an engine-based air fuel ratio operation strategy for the passive-ARC system must be developed. To this extent, the study extended its objectives to develop an engine-based control strategy that results in stoichiometric ammonia production operation followed by brief lean operation to regenerate the saturated ammonia reduction catalyst using high NOx slip through TWC. The study presents comprehensive results of ammonia storage characteristics of SCRs pertaining to stoichiometric natural gas engine exhaust as well as an advanced engine control strategy approach to simultaneously reduce both NOx and NH3 using an alternating air -fuel ratio approach

Development of Diagnostic Program for Gas Compressor using Knowledge Based Management Concept

Compressor maintenance is vital in oil and gas industry because it is an important equipment that runs continuously. Among all of the deterioration mechanisms, fouling is found to be the most common in oil and gas industry and it is relatively easier to be analyzed. Currently, plant engineers face difficulties in predicting the appropriate time for maintenance and usually they will follow the original equipment manufacturer (OEM) recommendations. Most plant engineers do not have a predictive tool to advise them on compressor maintenance and the necessary steps to be taken. Usually, the engineers will only attend to the equipment when problems or abnormalities arise from it, apart from the planned maintenance. Late decision made on compressor maintenance will sometimes cause problems to operation either due to late arrival of spare parts or staff availability. The objective of this project is to develop a software that will be able to assist engineers in determining the performance deterioration of gas compressor and deciding the optimum time to do maintenance. The maintenance history data is collected and analysed by the software regularly. The correlations between isentropic efficiency, isentropic head, and gas power and the compressor deterioration are studied based on two centrifugal gas compressors from January 2009 to December 2010. Later, a software that is able to produce maintenance advice based on the input parameters given by the user is created. The software is developed using Microsoft Excel 2010 and Microsoft Visual Basic. From the analysis conducted, it is found that due to fouling, isentropic efficiency and isentropic head decrease with time for low pressure compressors. In contrast, the gas power increases with time. Based on these findings, Performance Indicators Monitoring Program (PIMP) is developed

Intelligent Computing: The Latest Advances, Challenges and Future

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human-computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing. Intelligent computing is still in its infancy and an abundance of innovations in the theories, systems, and applications of intelligent computing are expected to occur soon. We present the first comprehensive survey of literature on intelligent computing, covering its theory fundamentals, the technological fusion of intelligence and computing, important applications, challenges, and future perspectives. We believe that this survey is highly timely and will provide a comprehensive reference and cast valuable insights into intelligent computing for academic and industrial researchers and practitioners