Agents in Bioinformatics

The scope of the Technical Forum Group (TFG) on Agents in Bioinformatics (BIOAGENTS) was to inspire collaboration between the agent and bioinformatics communities with the aim of creating an opportunity to propose a different (agent-based) approach to the development of computational frameworks both for data analysis in bioinformatics and for system modelling in computational biology. During the day, the participants examined the future of research on agents in bioinformatics primarily through 12 invited talks selected to cover the most relevant topics. From the discussions, it became clear that there are many perspectives to the field, ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages for use by information agents, and to the use of Grid agents, each of which requires further exploration. The interactions between participants encouraged the development of applications that describe a way of creating agent-based simulation models of biological systems, starting from an hypothesis and inferring new knowledge (or relations) by mining and analysing the huge amount of public biological data. In this report we summarise and reflect on the presentations and discussions

Mean-Field Theory of Meta-Learning

We discuss here the mean-field theory for a cellular automata model of meta-learning. The meta-learning is the process of combining outcomes of individual learning procedures in order to determine the final decision with higher accuracy than any single learning method. Our method is constructed from an ensemble of interacting, learning agents, that acquire and process incoming information using various types, or different versions of machine learning algorithms. The abstract learning space, where all agents are located, is constructed here using a fully connected model that couples all agents with random strength values. The cellular automata network simulates the higher level integration of information acquired from the independent learning trials. The final classification of incoming input data is therefore defined as the stationary state of the meta-learning system using simple majority rule, yet the minority clusters that share opposite classification outcome can be observed in the system. Therefore, the probability of selecting proper class for a given input data, can be estimated even without the prior knowledge of its affiliation. The fuzzy logic can be easily introduced into the system, even if learning agents are build from simple binary classification machine learning algorithms by calculating the percentage of agreeing agents.Comment: 23 page

New Scientific and Technological Developments of Relevance to the Fifth Review Conference

Ye

Analogy, Mind, and Life

I'll show that the kind of analogy between life and information [argue for by authors such as Davies (2000), Walker and Davies (2013), Dyson (1979), Gleick (2011), Kurzweil (2012), Ward (2009)] – that seems to be central to the effect that artificial mind may represents an expected advance in the life evolution in Universe – is like the design argument and that if the design argument is unfounded and invalid, the argument to the effect that artificial mind may represents an expected advance in the life evolution in Universe is also unfounded and invalid. However, if we are prepared to admit (though we should not do) this method of reasoning as valid, I'll show that the analogy between life and information to the effect that artificial mind may represents an expected advance in the life evolution in Universe seems suggest some type of reductionism of life to information, but biology respectively chemistry or physics are not reductionist, contrary to what seems to be suggested by the analogy between life and information

Viral pathogen discovery.

Viral pathogen discovery is of critical importance to clinical microbiology, infectious diseases, and public health. Genomic approaches for pathogen discovery, including consensus polymerase chain reaction (PCR), microarrays, and unbiased next-generation sequencing (NGS), have the capacity to comprehensively identify novel microbes present in clinical samples. Although numerous challenges remain to be addressed, including the bioinformatics analysis and interpretation of large datasets, these technologies have been successful in rapidly identifying emerging outbreak threats, screening vaccines and other biological products for microbial contamination, and discovering novel viruses associated with both acute and chronic illnesses. Downstream studies such as genome assembly, epidemiologic screening, and a culture system or animal model of infection are necessary to establish an association of a candidate pathogen with disease

Bioinformatics Approach for Pattern of Myelin-Specific Proteins and Related Human Disorders

Background: Recent neuroinformatic studies, on the structure-function interaction of proteins, causative agents basis of human disease have implied that dysfunction or defect of different protein classes could be associated with several related diseases. Objectives: The aim of this study was the use of bioinformatics approaches for understanding the structure, function and relationship of myelin protein 2 (PMP2), a myelin-basic protein in the basis of neuronal disorders. Methods: A collection of databases for exploiting classification information systematically, including, protein structure, protein family and classification of human disease, based on a new approach was used. Knowledge discovery was carried out based on collections criteria and in silico integrative in vitro studies. Results: The results of the evaluation of bioinformatics comorbid proteomics studies revealed that PMP2, an intracellular andmembrane myelin protein, is specific for a neuritis disease and collaborative to other diseases. Leprosy, another neuronal disease that could be related to neuritis, consists of interferon gamma (IFNG), a secreted protein included various protein classes from what is neuritis. Conclusions: The growth rate of information in bioinformatics databases could facilitate studies of live organisms prior to observation studies. Two different protein classes could be causative agents of one disease. However, two related diseases from one disease group could consist of different protein classes. Future research in the field of proteomics could allow modern insight to reshuffling of proteins in different diseases, and lead to the discovery of the etiology of such diseases

In Vivo validation of a bioinformatics based tool to identify reduced replication capacity in HIV-1.

Although antiretroviral drug resistance is common in treated HIV infected individuals, it is not a consistent indicator of HIV morbidity and mortality. To the contrary, HIV resistance-associated mutations may lead to changes in viral fitness that are beneficial to infected individuals. Using a bioinformatics-based model to assess the effects of numerous drug resistance mutations, we determined that the D30N mutation in HIV-1 protease had the largest decrease in replication capacity among known protease resistance mutations. To test this in silico result in an in vivo environment, we constructed several drug-resistant mutant HIV-1 strains and compared their relative fitness utilizing the SCID-hu mouse model. We found HIV-1 containing the D30N mutation had a significant defect in vivo, showing impaired replication kinetics and a decreased ability to deplete CD4+ thymocytes, compared to the wild-type or virus without the D30N mutation. In comparison, virus containing the M184V mutation in reverse transcriptase, which shows decreased replication capacity in vitro, did not have an effect on viral fitness in vivo. Thus, in this study we have verified an in silico bioinformatics result with a biological assessment to identify a unique mutation in HIV-1 that has a significant fitness defect in vivo

Compressive Sensing DNA Microarrays

Compressive sensing microarrays (CSMs) are DNA-based sensors that operate using group testing and compressive sensing (CS) principles. In contrast to conventional DNA microarrays, in which each genetic sensor is designed to respond to a single target, in a CSM, each sensor responds to a set of targets. We study the problem of designing CSMs that simultaneously account for both the constraints from CS theory and the biochemistry of probe-target DNA hybridization. An appropriate cross-hybridization model is proposed for CSMs, and several methods are developed for probe design and CS signal recovery based on the new model. Lab experiments suggest that in order to achieve accurate hybridization profiling, consensus probe sequences are required to have sequence homology of at least 80% with all targets to be detected. Furthermore, out-of-equilibrium datasets are usually as accurate as those obtained from equilibrium conditions. Consequently, one can use CSMs in applications in which only short hybridization times are allowed

Bioinformatic Analysis for the Validation of Novel Biomarkers for Cancer Diagnosis and Drug Sensitivity

Background: The genetic control of tumour progression presents the opportunity for bioinformatics and gene expression data to be used as a basis for tumour grading. The development of a genetic signature based on microarray data allows for the development of personalised chemotherapeutic regimes. Method: ONCOMINE was utilised to create a genetic signature for ovarian serous adenocarcinoma and to compare the expression of genes between normal ovarian and cancerous cells. Ingenuity Pathways Analysis was also utilised to develop molecular pathways and observe interactions with exogenous molecules. Results: The gene signature demonstrated 98.6% predictive capability for the differentiation between borderline ovarian serous neoplasm and ovarian serous adenocarcinoma. The data demonstrated that many genes were related to angiogenesis. Thymidylate synthase, GLUT-3 and HSP90AA1 were related to tanespimycin sensitivity (p=0.005). Conclusions: Genetic profiling with the gene signature demonstrated potential for clinical use. The use of tanespimycin alongside overexpression of thymidylate synthase, GLUT-3 and HSP90AA1 is a novel consideration for ovarian cancer treatment