Group Members

Dr Alessandro Pandini My research activity focuses on the development and application of computational methods to study protein dynamics and its role in protein-ligand binding, protein-protein interactions, and protein design. I obtained my PhD in Computational Chemistry at the University of Milan-Bicocca under the supervision of Prof. Laura Bonati. As part of her research group I contributed to the unveil the molecular mechanism of toxic response mediated by binding of dioxins to the Aryl hydrocarbon Receptor. In 2008 I was awarded a Marie Curie Inter European Fellowship to work at the MRC National Institute for Medical Research (NIMR) under the supervision of Dr. Willie R. Taylor and Dr. Jens Kleinjung. From 2011 to 2014 he was a BBSRC-funded postdoctoral research assistant in the group of Prof. Franca Fraternali at King’s College London working on methods to investigate allosteric regulation, and to analyse protein-protein interaction interfaces and networks. During my career I developed and applied novel approaches combining structural bioinformatics and molecular simulation to address challenging biological questions, especially in relation to protein function, allosteric regulation and drug design. I introduced novel points of view in the definition of the limits and potential of molecular docking on theoretical models and in the use of molecular dynamics for drug design and medicinal chemistry. In particular, I developed an innovative computational method to detect local functional motions and to describe allosteric transmission in protein structures. Most recently, in collaboration with Dr. Arianna Fornili (QMUL), I contributed to the development of a novel strategy for biasing the sampling of local states to drive the global conformational transitions in proteins. In collaboration with Dr. Shahid Khan (LBNL – Berkeley Lab) and Dr. Willie Taylor, I have contributed to explain the relationships between residue coevolution and molecular dynamics in two bacterial ring assemblies.

Professor David Gilbert Professor David Gilbert is Leverhulme Emeritus Fellow, Emeritus Professor of Computing and member of the Computational Biology group in the Department of Computer Science. Previously he was Head of School of Information Systems, Computing and Mathematics, and first Dean of the College of Engineering, Design and Physical Sciences, leading its establishment in 2014-15. In his previous post he was Professor of Bioinformatics at the University of Glasgow where he set up and was Director of the Bioinformatics Research Centre. He holds a BSc in experimental Pyschology and a Masters in Education from the University of Bristol, and a Masters and PhD in Computing from Imperial College, London where his research was into modelling concurrent systems using computational logic. David was an EPSRC Research Fellow at the European Bioinformatics Institute, and a Leverhulme Research Fellow in the Department of Biochemistry and Molecular Biology, University College London when designed and developed the TOPS protein topology computational system. Bioinformatics, Computational Systems Biology, Computational Synthetic Biology; multiscale modeling, model checking, computational methods for design of biological systems. Personalised Health Care / Systems Medicine; Systems Toxicology. Disease epidemics and pandemics. Computational Linguistics. Co-leader of the Computational Biology Group, and member of the Centre for Intelligent Data Analysis I no longer teach, but previously I was Module leader for Level 1 CS1005 Logic & Computation (BSc Computer Science; BSc Business Computing). Tutor for Level 1 group projects. Supervisor for Final Year BSc Computer Science projects. Supervisor for projects in MSc Data Science & Analytics. Giving guest lectures on Intracellular Signalling and Cancer (BB5514) - biosciences

Dr Lorraine Ayad Lorraine Ayad is currently a Lecturer in the Department of Computer Science, starting in August 2020. She completed her PhD at King's College London in 2019 with a thesis titled Efficient sequence comparison via combining alignment and alignment-free techniques. Lorraine Ayad's current work lies in the creation of algorithms for Computational Molecular Biology. Specifically, this involves identifying and creating algorithms which can aid biological research and also find ways to improve existing algorithms through the reduction of time or space. Tools implemented by Lorraine Ayad: SMART - a tool to identify supermaximal approximate repeats in a sequence. MARS - a heuristic tool for multiple circular sequence alignment given a set of circular (DNA or Protein) sequences. hCED - a heuristic tool for cyclic edit distance computation given a pair of circular sequences. IsoXpressor - a tool to assess transcriptional activity within isochores. CNEFinder - a tool for the identification of conserved non-coding elements in genomes. String algorithms Algorithms for computational molecular biology Bioinformatics 2023 - 2024 An Introduction to Python (Level 7) CS1701 - Group Project (Level 4) CS1702 - Introductory Programming (Level 4) CS1005 - Logic and Computation (Level 4) Membership I am a Fellow of the Higher Education Academy.

Dr Yasoda Jayaweera Yasoda Jayaweera joined the Department of Computer Science, Brunel University in 2020. She completed her PhD in 2022 with a thesis titled "Methodology for identifying alternative solutions in a population based data generation approach applied to synthetic biology". Her research is mainly focused on application of optimisation methods and machine learing to improve design in synthetic biology. Optimisation for alternative design System design Machine learning to improve system design She completed her PhD in 2022 with a thesis titled "Methodology for identifying alternative solutions in a population based data generation approach applied to synthetic biology". Her research is mainly focused on application of optimisation methods and machine learing to improve design in synthetic biology. Module leader of CS1701 - Level 1 Group Project and SAS bootcamp (for MSc) Partial duties in CS1703 Data and Information and CS1702 Introductory Programming Tutoring and project supervision in CS1701 Group Project, CS2555 Placement, CS3072/CS3605 Level 6 Final year project, CS5500 MSc dissertation Module reviewer of CS5702/CS5802

Dr Valeriia Haberland I have been appointed as Lecturer in Computer Science in 2023. My research interests lie in the intersection of computing and biological sciences. Currently, I'm interested in modelling and predicting cancer aggressiveness based on the available molecular profiles using the state-of-the-art machine learning techniques. My other interests also include epidemiological causal inference to find associations between genetically translatable factors and disease outcomes. Developing the web applications and underlying infrastructure to support further research in these areas are also of interest. Cancer genetics AI in Healthcare Epidemiology Postgraduate: Modern Data (CS5702/CS5802) - supporting this course MSc dissertation supervision Undergraduate: Logic and Computation (CS1005) - supporting this course Group project Level 2 (CS2001) - supervision and support Computer Science-Business Computing final year project (CS3072-CS3605) - supervision Placement tutor (CS2555)

Dr Leila Ghanbar I am a Computational Biologist with a background in Microbiology and Biomedical Engineering. I am interested in modelling biological behaviours in silicon in order to understand and predict them. I attained my PhD from Brunel University London under the supervision of Professor David Gilbert and Alessandro Pandini. During my PhD, I created a library of models with different levels of complexity in order to portray different biological behaviours such as movement (chemotaxis), communication, response, reproduction and death. These models can be used individually to for simpler behaviours and could be combined for more complex ones. I have also created a detailed model of biofilm formation, a biological response activity to population density, and combined it with a quorum sensing model. All these models were created in Petri nets, simulated in Spike and analysed using R. Currently, I am interested in developing new modelling approaches for biological systems. I am looking at novel methods that are simple and yet powerful enough to be efficient and useful in creating models that help with understanding and predicting biological systems. Data Analysis Software development for modelling biological networks Computational Biology Systems Biology Bio-model Engineering Modelling biological systems in silico - Quantitative Data Analysis (CS5701/CS5801) Level: Postgraduate Date: September 2023 - Logic and Computation (CS1005) Level: Undergraduate Date: September 2023

Dr Ferdoos Hossein Nezhad Dr. Ferdoos Hossein Nezhad is an Associate Lecturer in the Department of Computer Science at Brunel University London. Prior to this, she was a Research Fellow working on the Privacy Assessment of Synthetic Patient Data and Trustworthy machine learning. Her main research interests are Computational biology, Graph learning and embedding, machine learning in healthcare, and Trustworthy machine learning and data privacy. Computational biology, Graph learning, Trustworthy machine learning, and data privacy, Machine learning in healthcare. PG Level: Modern Data NLP and text mining, NLP and sentiment analysis UG Level: Cybersecurity Threats and attacks, Cybersecurity Analytics

Dr Michelle Sahai Dr Michelle Sahai is a Lecturer in Biosciences (Drug Discovery) since 2024. She completed her first two degrees at the University of Toronto, before moving to the UK where she received her PhD in Computational Biochemistry from the Structural Bioinformatics and Computational Biochemistry Unit at the University of Oxford. After receiving her PhD degree, she carried out postdoctoral research at the Department of Physiology and Biophysics, at the Weill Cornell Medical College, New York, NY. She worked as a Lecturer/Senior Lecturer in Biomedical Sciences at the University of Roehampton from 2014-2023. Her research focuses on answering important questions relating to membrane proteins and the structural, dynamic and electronic determinants of biological processes underlying physiological functions. Neurological Diseases Addiction Genetic mutations Cancer Antimicrobial Resistance Membranes and Membrane Proteins Molecular and Structural Proteins AI-driven Computational Biomedicine Understanding biological processes is vital for discovering disease mechanisms and new treatment targets. The roles of membrane proteins in cell signalling, transport, and metabolism are fundamental to cellular function, and any disruptions in these processes are central to many diseases, highlighting the importance of studying these proteins for developing new therapies. Dr Sahai's research focuses on the atomistic-level study of membrane protein dynamics, developing computational models that explain the behaviours and interactions of receptors and transporters with various ligands. These in silico methodologies, underpinned by Dr Sahai's extensive expertise in the dynamics of membrane proteins, receptor-ligand interactions, and molecular simulations, have laid a strong foundation for her work across various disease models. BB1719 - Introduction to Data Analysis (Block Lead)

Miss Namir Oues I am a doctoral researcher working on designing and implementing machine learning techniques to predict the effect of mutations on protein dynamics. For my work, I use data from computational simulations of proteins generated with Molecular Dynamics methods. My academic background is in Applied Mathematics and Data Science and Analytics fields. I have professional experience working at the Medicines and Healthcare Regulatory Agency as a Research Software Applications Developer and Data Scientist UK’s Government’s primary healthcare data. While working in MHRA I was also part of the team monitoring the Covid Vaccination Surveillance program. Publications Oues, N., Dantu, S. C., Patel, R. J., Pandini, A., (2023) "MDSubSampler: a posteriori sampling of important protein conformations from biomolecular simulations", Bioinformatics, Volume 39, Issue 7, July, btad427, Available at: Benedetti, J., Oues, N., Wang, Z., Myles, P., & Tucker, A. (2020). "Practical Lessons from Generating Synthetic Healthcare Data with Bayesian Networks", Advances in Artificial Intelligence (pp. 24-34). Springer. DOI: 10.1007/978-3-030-65965-3_3. Campbell, J., Shepherd, H., Welburn, S., Barnett, R., Oyinlola, J., Oues, N., & Williams, R. (2022) "Methods to refine and extend a Pregnancy Register in the UK Clinical Practice Research Datalink primary care databases", Pharmacoepidemiology and Drug Safety, DOI: 10.1002/pds.5584. Conferences and awards CCP5 Summer School (Award for best poster presentation) Durham, United Kingdom (July 2022) Enhanced Sampling Simulations Methods University College London, United Kingdom (December 2022) Thomas Young Centre Postgraduate Day Event (Poster presentation) University College London, United Kingdom (May 2023) PLUMED Summer School Lausanne, Switzerland (July 2023) MDAnalysis User Group Meeting (Award for best presentation talk & Travel Award) Lisbon, Portugal (September 2023) Young Modellers' Forum (Research presentation talk) University of Oxford, United Kingdom (November 2023) Biophysical Society Annual Conference (Poster presentation & Travel Award) Philadelphia, USA (February 2024) Vice-Chancellor’s Conference Prize Award For attending the Biophysical Society Annual Conference Machine Learning, Biochemistry, Biophysics, Computational Biology, Statistics, Data Analysis, Bioinformatics