Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

A Knowledge-based Integrative Modeling Approach for <em>In-Silico</em> Identification of Mechanistic Targets in Neurodegeneration with Focus on Alzheimer’s Disease

Dementia is the progressive decline in cognitive function due to damage or disease in the body beyond what might be expected from normal aging. Based on neuropathological and clinical criteria, dementia includes a spectrum of diseases, namely Alzheimer's dementia, Parkinson's dementia, Lewy Body disease, Alzheimer's dementia with Parkinson's, Pick's disease, Semantic dementia, and large and small vessel disease. It is thought that these disorders result from a combination of genetic and environmental risk factors. Despite accumulating knowledge that has been gained about pathophysiological and clinical characteristics of the disease, no coherent and integrative picture of molecular mechanisms underlying neurodegeneration in Alzheimer’s disease is available. Existing drugs only offer symptomatic relief to the patients and lack any efficient disease-modifying effects. The present research proposes a knowledge-based rationale towards integrative modeling of disease mechanism for identifying potential candidate targets and biomarkers in Alzheimer’s disease. Integrative disease modeling is an emerging knowledge-based paradigm in translational research that exploits the power of computational methods to collect, store, integrate, model and interpret accumulated disease information across different biological scales from molecules to phenotypes. It prepares the ground for transitioning from ‘descriptive’ to “mechanistic” representation of disease processes. The proposed approach was used to introduce an integrative framework, which integrates, on one hand, extracted knowledge from the literature using semantically supported text-mining technologies and, on the other hand, primary experimental data such as gene/protein expression or imaging readouts. The aim of such a hybrid integrative modeling approach was not only to provide a consolidated systems view on the disease mechanism as a whole but also to increase specificity and sensitivity of the mechanistic model by providing disease-specific context. This approach was successfully used for correlating clinical manifestations of the disease to their corresponding molecular events and led to the identification and modeling of three important mechanistic components underlying Alzheimer’s dementia, namely the CNS, the immune system and the endocrine components. These models were validated using a novel in-silico validation method, namely biomarker-guided pathway analysis and a pathway-based target identification approach was introduced, which resulted in the identification of the MAPK signaling pathway as a potential candidate target at the crossroad of the triad components underlying disease mechanism in Alzheimer’s dementia

【研究分野別】シーズ集 [英語版]

[英語版

The Cellular and Physiological Basis for Lung Repair and Regeneration: Past, Present, and Future.

The respiratory system, which includes the trachea, airways, and distal alveoli, is a complex multi-cellular organ that intimately links with the cardiovascular system to accomplish gas exchange. In this review and as members of the NIH/NHLBI-supported Progenitor Cell Translational Consortium, we discuss key aspects of lung repair and regeneration. We focus on the cellular compositions within functional niches, cell-cell signaling in homeostatic health, the responses to injury, and new methods to study lung repair and regeneration. We also provide future directions for an improved understanding of the cell biology of the respiratory system, as well as new therapeutic avenues

UNDERSTANDING CONDITIONAL MODES OF ACTIONS IN CHEMICAL-INDUCED TOXICITY USING RULE MODELS

It is estimated that 115 million animals are used in experimental testing each year. Hence, shifting efforts toward alternative methods for toxicity assessment is essential. However, slow regulatory acceptance of new approaches is governed by knowledge gaps in toxicity modes of action. In this thesis, I describe these challenges and the use of in vitro screening as an alternative of animal testing. I also discuss common data-based methods to derive hypotheses about toxicity modes of actions, and the associated limitations in capturing multiple biological perturbations. I applied novel data-based workflows, using rule models, to prioritize in vitro assays predictive of toxicity as well as to detect significant polypharmacology profiles. I explain how constraints were applied to rule-based models to inform meaningful mechanistic interpretation for two toxicity endpoints: rat hepatotoxicity and acute toxicity. I compared assays selected, by rules, for predicting hepatotoxicity with endpoints used in in vitro models from commercial sources. An overlap was observed including cytochrome activity, mitochondrial toxicity and immunological responses. However, nuclear receptor activity, identified in rules, is not currently covered in commercial setups. I also demonstrate that endocrine disruption endpoints extrapolate better into in vivo toxicity when a set of specific conditions are met, such as physicochemical properties associated with good bioavailability. Next, I examined synergistic interactions between conditions in rules describing acute toxicity. I gained novel insights into how specific stressors potentiate the perturbation by known key events, such as acetylcholinesterase inhibition and neuro-signalling disruption. I show that examining polypharmacology profiles is particularly important at low bioactive potencies. Further, the overall predictive performance of rules describing acute toxicity was tested against a benchmark Random Forest model in a conformal prediction framework. Irrespective to the data type used in the training, the models were prone to bias over compounds promiscuity, by which high promiscuous compounds were more likely to be predicted as toxic. Overall, the studies conducted in this thesis provide novel insights into molecular mechanisms of toxicity, namely hepatotoxicity and acute toxicity, and with regards to chemical properties and polypharmacology. This knowledge can be used to improve the utility and design of alternative methods for toxicity, and hence, accelerate the regulatory acceptance.Islamic Development Bank Cambridge Trust Fun

Toxicological profile for cobalt : draft for public comment

Version historyDate DescriptionJanuary 2023 Draft for public comment releasedApril 2004 Final toxicological profile releasedJuly 1992 Final toxicological profile releasedtp33.pd

Systems biology of breast cancer

Breast cancer, with an alarming incidence rate throughout the globe, has attracted significant investigations to identify disease specific biomarkers. Among these, oestrogen receptor (ER) occupies a central role where overexpression is a prognostic indication for breast cancer. The cross-talk between the responsible contenders of ER-associated genes potentially play an important role in the disease aetiology. Investigation of such cross talk is the focus of this thesis. The development of high throughput technologies such as expression microarrays has paved the way for investigating thousands of genes at a time. Microarrays with their high data volume, multivariate nature and non-linearity pose challenges for analysing using conventional statistical approaches. To combat these challenges, computational researchers have developed machine learning approaches such as Artificial Neural Networks (ANNs). This thesis evaluates ANNs based methodologies and their application to the analysis of microarray data generated for breast cancer cases of differing oestrogen receptor status. Furthermore they are used for network inferencing to identify interactions between ER-associated markers and for the subsequent identification of putative pathway elements. The present thesis shows that it is possible to identify some ER-associated breast cancer relevant markers using ANNs. These have been subsequently validated on clinical breast tumour samples highlighting the promise of this approach

Reverse Engineering of Gene Regulatory Networks for Discovery of Novel Interactions in Pathways Using Gene Expression Data

A variety of chemicals in the environment have the potential to adversely affect the biological systems. We examined the responses of Rat (Rattus norvegicus) to the RDX exposure and female fathead minnows (FHM, Pimephales promelas) to a model aromatase inhibitor, fadrozole, using a transcriptional network inference approach. Rats were exposed to RDX and fish were exposed to 0 or 30mg/L fadrozole for 8 days. We analyzed gene expression changes using 8000 probes microarrays for rat experiment and 15,000 probe microarrays for fish. We used these changes to infer a transcriptional network. The central nervous system is remarkably plastic in its ability to recover from trauma. We examined recovery from chemicals in rats and fish through changes in transcriptional networks. Transcriptional networks from time series experiments provide a good basis for organizing and studying the dynamic behavior of biological processes. The goal of this work was to identify networks affected by chemical exposure and track changes in these networks as animals recover. The top 1254 significantly changed genes based upon 1.5-fold change and P\u3c 0.05 across all the time points from the fish data and 937 significantly changed genes from rat data were chosen for network modeling using either a Mutual Information network (MIN) or a Graphical Gaussian Model (GGM) or a Dynamic Bayesian Network (DBN) approach. The top interacting genes were queried to find sub-networks, possible biological networks, biochemical pathways, and network topologies impacted after exposure to fadrozole. The methods were able to reconstruct transcriptional networks with few hub structures, some of which were found to be involved in major biological process and molecular function. The resulting network from rat experiment exhibited a clear hub (central in terms of connections and direction) connectivity structure. Genes such as Ania-7, Hnrpdl, Alad, Gapdh, etc. (all CNS related), GAT-2, Gabra6, Gabbrl, Gabbr2 (GABA, neurotransmitter transporters and receptors), SLC2A1 (glucose transporter), NCX3 (Na-Ca exchanger), Gnal (Olfactory related), skn-la were showed up in our network as the \u27hub\u27 genes while some of the known transcription factors Msx3, Cacngl, Brs3, NGF1 etc. were also matched with our network model. Aromatase in the fish experiment was a highly connected gene in a sub-network along with other genes involved in steroidogenesis. Many of the sub-networks were involved in fatty acid metabolism, gamma-hexachlorocyclohexane degradation, and phospholipase activating pathways. Aromatase was a highly connected gene in a sub-network along with the genes LDLR, StAR, KRT18, HER1, CEBPB, ESR2A, and ACVRL1. Many of the subnetworks were involved in fatty acid metabolism, gamma-hexachlorocyclohexane degradation, and phospholipase activating pathways. A credible transcriptional network was recovered from both the time series data and the static data. The network included transcription factors and genes with roles in brain function, neurotransmission and sex hormone synthesis. Examination of the dynamic changes in expression within this network over time provided insight into recovery from traumas and chemical exposures

Autism spectrum disorder: molecular profiling analysis and identification of candidate genes through complex Systems Biology approaches

Los trastornos del espectro autista (TEA) engloban una amplia gama de afecciones neurológicas y del desarrollo caracterizadas por alteraciones en las habilidades sociales, conductas repetitivas, habla y comunicación no verbal. Existen muchos subtipos de autismo, influenciados por una combinación de factores genéticos, neurológicos, inmunológicos y ambientales y frecuentemente acompañados de una carga sustancial de comorbilidad. La gran variabilidad clínica y etiológica entre los individuos con TEA hace que la biología de sistemas sea el enfoque más prometedor en la búsqueda de tratamientos eficaces. En esta tesis doctoral se exploran diferentes estrategias de biología de sistemas para descifrar la heterogeneidad clínica y neurobiológica del autismo mediante la búsqueda de genes candidatos. Nuestro objetivo es desentrañar la complejidad de los mecanismos neurológicos subyacentes a los TEA, sus comorbilidades y las posibles limitaciones evolutivas diferenciadoras, para identificar nuevos genes y rutas biológicas clave en los resultados funcionales, contribuyendo al avance de la medicina personalizada.Autism spectrum disorders (ASD) encompass a wide range of neurological and developmental conditions characterized by alterations in social skills, repetitive behaviors, speech and nonverbal communication. There are many subtypes of autism, influenced by a combination of genetic, neurological, immunological and environmental factors and often accompanied by a substantial burden of comorbidity. The enormous clinical and etiological variability among individuals with ASD makes systems biology the most promising approach in the search for effective treatments. In this doctoral thesis different strategies of the emerging field of systems biology are explored to better understand the clinical and neurobiological heterogeneity of autism by using genome-wide search for autism candidate genes. Our goal is to disentangle the complexity of ASD underlying neurological mechanisms, overlapping genes, comorbidities and differential evolutionary constraints, in order to identify novel genes and biological pathways that may specifically impact functional outcomes, contributing to advance in the field of personalized medicine.Tesis Univ. Jaén. Departamento de Biología Experimental. Leída el 24 de junio de 2021

Toxicological profile for mercury : draft for public comment : April 2022

VERSION HISTORYDate DescriptionFinal toxicological profile releasedApril 2022 Draft for public comment toxicological profile releasedMarch 2013 Addendum to the toxicological profile releasedMarch 1999 Final toxicological profile releasedMay 1994 Final toxicological profile releasedDecember 1989 Final toxicological profile releasedtp46.pdf20221136