Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

Complexity, BioComplexity, the Connectionist Conjecture and Ontology of Complexity\ud

This paper develops and integrates major ideas and concepts on complexity and biocomplexity - the connectionist conjecture, universal ontology of complexity, irreducible complexity of totality & inherent randomness, perpetual evolution of information, emergence of criticality and equivalence of symmetry & complexity. This paper introduces the Connectionist Conjecture which states that the one and only representation of Totality is the connectionist one i.e. in terms of nodes and edges. This paper also introduces an idea of Universal Ontology of Complexity and develops concepts in that direction. The paper also develops ideas and concepts on the perpetual evolution of information, irreducibility and computability of totality, all in the context of the Connectionist Conjecture. The paper indicates that the control and communication are the prime functionals that are responsible for the symmetry and complexity of complex phenomenon. The paper takes the stand that the phenomenon of life (including its evolution) is probably the nearest to what we can describe with the term “complexity”. The paper also assumes that signaling and communication within the living world and of the living world with the environment creates the connectionist structure of the biocomplexity. With life and its evolution as the substrate, the paper develops ideas towards the ontology of complexity. The paper introduces new complexity theoretic interpretations of fundamental biomolecular parameters. The paper also develops ideas on the methodology to determine the complexity of “true” complex phenomena.\u

Simons Foundation 2020 Annual Report

The Simons Foundation is pleased to present this copy of our 2020 annual report. Staying connected through Zoom, emails and conference calls, our grantees and scientists made groundbreaking advancements over the last year

Genome reconstruction and combinatoric analyses of rearrangement evolution

Cancer is often associated with a high number of large-scale, structural rearrangements. In a highly selective environment, some `driver' mutations conferring clonal growth advantage will be positively selected, accounting for further cancer development. Clarifying their nature, as well as their contribution to the pathology is a major current focus of biomedical research. Next generation sequencing technologies can be used nowadays to generate high-resolution data-sets of these alterations in cancer genomes. This project has been developed along two main lines: 1) the reconstruction of cancer aberrant karyotypes, together with their underlying evolutionary history; 2) the elucidation of some combinatorial properties associated with gene duplications. We applied graph theory to the problem of reconstructing the final cancer genome sequence; additionally, we developed an algorithmic approach for the reconstruction of a multi-step evolution consistent with read coverage and paired end data, giving insights on the possible molecular mechanisms underlying rearrangements. Looking at the combinatorics of both tandem and inverted duplication, we developed an algebraic formalism for the representation of these processes. This allowed us to both explore the geometric properties of sequences arising by Tandem Duplication (TD), and obtain a recursion for the number of tandem duplications evolutions after n events. Such results are missing for inverted duplications, whose combinatorial properties have been nevertheless deeply elucidated. Our results have allowed: 1) the identification, through an original approach, of potential rearrangement mechanisms associated with cancer development, and 2) the definition and mathematical description of the complete evolutionary space of specific rearrangement classes

BIOMOLECULE INSPIRED DATA SCIENCE

BIOMOLECULE INSPIRED DATA SCIENC

Identification and analysis of patterns in DNA sequences, the genetic code and transcriptional gene regulation

The present cumulative work consists of six articles linked by the topic ”Identification and Analysis of Patterns in DNA sequences, the Genetic Code and Transcriptional Gene Regulation”. We have applied a binary coding, to efficiently findpatterns within nucleotide sequences. In the first and second part of my work one single bit to encode all four nucleotides is used. The three possibilities of a one - bit coding are: keto (G,U) - amino (A,C) bases, strong (G,C) - weak (A,U) bases, and purines (G,A) - pyrimidines (C,U). We found out that the best pattern could be observed using the purine - pyrimidine coding. Applying this coding we have succeeded in finding a new representation of the genetic code which has been published under the title ”A New Classification Scheme of the Genetic Code” in ”Journal of Molecular Biology” and ”A Purine-Pyrimidine Classification Scheme of the Genetic Code” in ”BIOForum Europe”. This new representation enables to reduce the common table of the genetic code from 64 to 32 fields maintaining the same information content. It turned out that all known and even new patterns of the genetic code can easily be recognized in this new scheme. Furthermore, our new representation allows us for speculations about the origin and evolution of the translation machinery and the genetic code. Thus, we found a possible explanation for the contemporary codon - amino acid assignment and wide support for an early doublet code. Those explanations have been published in ”Journal of Bioinformatics and Computational Biology” under the title ”The New Classification Scheme of the Genetic Code, its Early Evolution, and tRNA Usage”. Assuming to find these purine - pyrimidine patterns at the DNA level itself, we examined DNA binding sites for the occurrence of binary patterns. A comprehensive statistic about the largest class of restriction enzymes (type II) has shown a very distinctive purine - pyrimidine pattern. Moreover, we have observed a higher G+C content for the protein binding sequences. For both observations we have provided and discussed several explanations published under the title ”Common Patterns in Type II Restriction Enzyme Binding Sites” in ”Nucleic Acid Research”. The identified patterns may help to understand how a protein finds its binding site. In the last part of my work two submitted articles about the analysis of Boolean functions are presented. Boolean functions are used for the description and analysis of complex dynamic processes and make it easier to find binary patterns within biochemical interaction networks. It is well known that not all functions are necessary to describe biologically relevant gene interaction networks. In the article entitled ”Boolean Networks with Biologically Relevant Rules Show Ordered Behavior”, submitted to ”BioSystems”, we have shown, that the class of required Boolean functions can strongly be restricted. Furthermore, we calculated the exact number of hierarchically canalizing functions which are known to be biologically relevant. In our work ”The Decomposition Tree for Analysis of Boolean Functions” submitted to ”Journal of Complexity”, we introduced an efficient data structure for the classification and analysis of Boolean functions. This permits the recognition of biologically relevant Boolean functions in polynomial time