Resource description framework technologies in chemistry

ISSN:1758-294

Towards a unifying, systems biology understanding of large-scale cellular death and destruction caused by poorly liganded iron: Parkinson’s, Huntington’s, Alzheimer’s, prions, bactericides, chemical toxicology and others as examples

Exposure to a variety of toxins and/or infectious agents leads to disease, degeneration and death, often characterised by circumstances in which cells or tissues do not merely die and cease to function but may be more or less entirely obliterated. It is then legitimate to ask the question as to whether, despite the many kinds of agent involved, there may be at least some unifying mechanisms of such cell death and destruction. I summarise the evidence that in a great many cases, one underlying mechanism, providing major stresses of this type, entails continuing and autocatalytic production (based on positive feedback mechanisms) of hydroxyl radicals via Fenton chemistry involving poorly liganded iron, leading to cell death via apoptosis (probably including via pathways induced by changes in the NF-κB system). While every pathway is in some sense connected to every other one, I highlight the literature evidence suggesting that the degenerative effects of many diseases and toxicological insults converge on iron dysregulation. This highlights specifically the role of iron metabolism, and the detailed speciation of iron, in chemical and other toxicology, and has significant implications for the use of iron chelating substances (probably in partnership with appropriate anti-oxidants) as nutritional or therapeutic agents in inhibiting both the progression of these mainly degenerative diseases and the sequelae of both chronic and acute toxin exposure. The complexity of biochemical networks, especially those involving autocatalytic behaviour and positive feedbacks, means that multiple interventions (e.g. of iron chelators plus antioxidants) are likely to prove most effective. A variety of systems biology approaches, that I summarise, can predict both the mechanisms involved in these cell death pathways and the optimal sites of action for nutritional or pharmacological interventions

Investigating Combining Quantitative And Textual Causal Knowledge In Learning Causal Structure

The study of causes and effects in large systems such as meteorology, biochemistry, finance, and sociology plays a critical role in predicting future developments and possible interventions. In the last decades, several new techniques and algorithms have been developed to discover causal structures in multivariate quantitative datasets. Yet, solely determining causal structure from observations is challenging and often yields ambiguous results. Additional knowledge from other sources is likely to be beneficial. Recently emerging large-scale language models are showing impressive results in the field of natural language processing (NLP). One task in the field of NLP is to extract causal relations from text. Combining these with causal discovery algorithms could be advantageous. This bachelor thesis investigates the combination of causal structures from quantitative and qualitative sources. A feasibility study was conducted on two datasets; (1) a biochemistry flow cytometry dataset and (2) a self-collected financial dataset. During this process, a common framework was developed that enables the combination of both sources. Considerations and problems were monitored and improvements suggested. A focus laid upon visualizing the evidences with different Python and R libraries. In principle, it is possible to combine both domains. However, it was found, that a lack of training data for causal relation extraction exists. Knowledge graphs with an underlying ontology need to be leveraged to account for lexically different terms of the same entity. To improve the results from the qualitative data, it would be advantageous to extract events rather than causal relations. This thesis makes a valuable contribution to the study of integrating quantitative and qualitative causal knowledge by applying various methods to two distinct datasets from different domains. Furthermore, it addresses a research gap, as there is limited existing literature in this specific area to the best of my knowledge

Pharmacolgical and biological annotations enhance functional residues prediction

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Biología Molecular. Fecha de lectura: 15-09-201