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

Rule-Based Cell Systems Model of Aging using Feedback Loop Motifs Mediated by Stress Responses

Investigating the complex systems dynamics of the aging process requires integration of a broad range of cellular processes describing damage and functional decline co-existing with adaptive and protective regulatory mechanisms. We evolve an integrated generic cell network to represent the connectivity of key cellular mechanisms structured into positive and negative feedback loop motifs centrally important for aging. The conceptual network is casted into a fuzzy-logic, hybrid-intelligent framework based on interaction rules assembled from a priori knowledge. Based upon a classical homeostatic representation of cellular energy metabolism, we first demonstrate how positive-feedback loops accelerate damage and decline consistent with a vicious cycle. This model is iteratively extended towards an adaptive response model by incorporating protective negative-feedback loop circuits. Time-lapse simulations of the adaptive response model uncover how transcriptional and translational changes, mediated by stress sensors NF-κB and mTOR, counteract accumulating damage and dysfunction by modulating mitochondrial respiration, metabolic fluxes, biosynthesis, and autophagy, crucial for cellular survival. The model allows consideration of lifespan optimization scenarios with respect to fitness criteria using a sensitivity analysis. Our work establishes a novel extendable and scalable computational approach capable to connect tractable molecular mechanisms with cellular network dynamics underlying the emerging aging phenotype

J-Net: An Adaptive System for Computer-Aided Diagnosis in Lung Nodule Characterization

© Springer Science+Business Media New York 2013. All rights are reserved. The aim of this study is to evaluate the capability of improved artificial adaptive systems and additional novel training methods in order to distinguish between benign and malignant lung nodules in Multi Detector Computed Tomography. A total of 90 nodules belonging to 88 patients are analyzed. A set of adjacent slices representing the lesion selected from the CT Image analysis by the experts are collected and stored in a database. Features extracted by an assembly of adaptive algorithms working in sequence [Active Connection Fusion (ACF): a new set of ANNs for image fusion; J-Net Active Connections Matrix (J-Net): a new ANN for dynamic image segmentation; Population (Pop): a new and fast multidimensional scaling algorithm] are divided into several groups using random or experimental methods to train and test different Artificial Neural Networks. Best results are obtained with Adaptive Learning Quantization (AVQ) and Meta-Consensus, two new supervised ANNs, experts in rapid classification and not sensitive to over fitting. After optimization of the distribution of cases among the training and testing sets the following results are achieved: sensitivity (recognition of malignant nodules) ranging from 93.33% to 100%; specificity (recognition of benign nodules) stable on 95.56% and overall accuracy ranging from 94.44% to 97.78%. These results represent the highest predictive values ever recorded in lung CAD literature. Benchmarking analysis, with advanced mathematical algorithms using simpler approaches, show that complex processing systems, composed of different steps and sub processing systems, are clearly superior and probably needed to reach excellent predictive performances in lung nodule characterization