Dr. Kirill A. Veselkov (BSc, MSc, PhD, MRSC) is a Lecturer and Principal Investigator n Computational Medicine at the Department of Surgery and Cancer. He received a 1st class honours combined B.Sc. and M.Sc. in Physics of Biological Systems, supported by a Supreme Council of Ukraine scholarships. He then obtained Overseas Research Students Award Scholarship from the British government to undertake a PhD in chemo-informatics at Imperial College, which he conducted within the Section of Biomolecular Medicine (BMM). During his PhD studies, he introduced a new concept of metabolic entropy for measuring disruptions in metabolism and developed a number of innovative chemo-informatics approaches for improved information recovery, pattern recognition analysis and predictive modelling of biological datasets generated by post-genomic analytical technologies. After successful completion of his PhD studies, he was awarded a Junior Research Fellowship from Imperial College to advance translational chemo-informatics approaches to integrate metabolic profiling technologies into a clinical domain. Dr Veselkov subsequently won an internationally advertised Lectureship position in Computational Medicine at Imperial College, funded by Waters Corporation. His role is to design, conduct and lead research developments in computational systems medicine for the streamlined interpretation of large volumes of molecular data to address unmet medical needs in areas including cancer and sepsis. His recent works include a translational bioinformatics platform for mass spectrometry imaging in augmented systems histology and an integrated bioinformatic solution for bacterial identification using rapid evaporative ionization mass spectrometry, which targets an unmet clinical need of prediction of personalized sepsis outcomes. Dr Veselkov is a Member of Royal Society of Chemistry.
et al., 2014, Chemo-informatic strategy for imaging mass spectrometry-based hyperspectral profiling of lipid signatures in colorectal cancer, Proceedings of the National Academy of Sciences of the United States of America, Vol:111, ISSN:0027-8424, Pages:1216-1221
et al., 2013, Analysis of intact bacteria using rapid evaporative ionisation mass spectrometry, Chemical Communications, Vol:49, ISSN:1359-7345, Pages:6188-6190
et al., 2008, Human metabolic phenotype diversity and its association with diet and blood pressure, Nature, Vol:453, ISSN:0028-0836, Pages:396-U50
et al., 2009, Recursive Segment-Wise Peak Alignment of Biological H-1 NMR Spectra for Improved Metabolic Biomarker Recovery, Analytical Chemistry, Vol:81, ISSN:0003-2700, Pages:56-66
et al., 2010, A Metabolic Entropy Approach for Measurements of Systemic Metabolic Disruptions in Patho-Physiological States, Journal of Proteome Research, Vol:9, ISSN:1535-3893, Pages:3537-3544
et al., 2011, Optimized Preprocessing of Ultra-Performance Liquid Chromatography/Mass Spectrometry Urinary Metabolic Profiles for Improved Information Recovery, Analytical Chemistry, Vol:83, ISSN:0003-2700, Pages:5864-5872