Dr Ismail Adeniran

Research expertise

The application of scientific machine learning (computational science and machine learning) to solve complex, real-world problems at all levels of scale.

• Computational Physiology: The use of novel and innovative physiologically based scientific machine learning to:
• understand the functional consequences of sarcomeric protein mutations in Hypertrophic Cardiomyopathy and Dilated Cardiomyopathy, linking genotype to phenotype and possible novel therapeutic interventions.
• understand the pathophysiological mechanisms underlying co-existent Atrial Fibrillation and Heart Failure and possible novel therapeutic interventions.
• understand the pathophysiological mechanisms underlying Heart Failure with Preserved Ejection Fraction and possible novel therapeutic interventions.
• understand the pathophysiological mechanisms underlying neuromusculo-skeletal diseases and possible novel therapeutic interventions.
• understand the common pathways between neuromusculo-skeletal diseases and cardiomyopathies.
• Scientific software development
• Optimal Control, Deep Reinforcement Learning, PDE-Constrained Optimisation, Operations Research, Mathematical Optimisation/Mathematical Programming
• Quantum Computing
• Grassmann Algebra and its derivatives including Clifford/Geometric Algebra.
• Cryptoassets: Specifically, the development of novel classical and quantum algorithms for fundamental analysis of the Cryptoasset class. The primary objective is to adress the central problems of volatility, instability and uncertainty, which presently plague cryptoassets by providing a valuation and regulatory framework that imbues cryptoassets with intrinsic value.

Publications

• Books (authored/edited/special issues)

  I. Adeniran (2014). Modelling the Short QT Syndrome Gene Mutations. Springer International Publishing.

• Journal articles

  I. Adeniran, DG. Whittaker, A. El Harchi, JC. Hancox, H. Zhang (2017). In silico investigation of a KCNQ1 mutation associated with short QT syndrome. Scientific Reports. 7(1),

  H. Ni, I. Adeniran, H. Zhang (2017). In-silico investigations of the functional impact of KCNA5 mutations on atrial mechanical dynamics. Journal of Molecular and Cellular Cardiology. 111, pp.86-95.

  JC. Hancox, I. Adeniran, DG. Whittaker, H. Zhang (2015). To the Editor--Altered in vivo systolic function in the short QT syndrome anticipated in silico. Heart Rhythm. 12(9), pp.e115-e115.

  I. Adeniran, JCH. Hancox, H. Zhang (2013). In silico investigation of the electromechanical consequences of the short QT syndrome. Journal of Electrocardiology. 46(4), pp.e16-e16.

  I. Adeniran, JC. Hancox, H. Zhang (2013). Effect of cardiac ventricular mechanical contraction on the characteristics of the ECG: A simulation study. Journal of Biomedical Science and Engineering. 06(12), pp.47-60.

  TR. Gaunt, S. Shah, CP. Nelson, F. Drenos, PS. Braund, et al. I. Adeniran, L. Folkersen, DA. Lawlor, J-P. Casas, A. Amuzu, M. Kivimaki, J. Whittaker, P. Eriksson, H. Zhang, JC. Hancox, M. Tomaszewski, PR. Burton, MD. Tobin, SE. Humphries, PJ. Talmud, PW. Macfarlane, AD. Hingorani, NJ. Samani, M. Kumari, INM. Day. (2012). Integration of genetics into a systems model of electrocardiographic traits using HumanCVD BeadChip. Circ Cardiovasc Genet. 5(6), pp.630-638.

  S. Kharche, I. Adeniran, J. Stott, P. Law, MR. Boyett, et al. JC. Hancox, H. Zhang. (2012). Pro-arrhythmogenic effects of the S140G KCNQ1 mutation in human atrial fibrillation - insights from modelling. J Physiol. 590(18), pp.4501-4514.

  I. Adeniran, A. El Harchi, JC. Hancox, H. Zhang (2012). Proarrhythmia in KCNJ2-linked short QT syndrome: insights from modelling. Cardiovasc Res. 94(1), pp.66-76.

  CY. Du, I. Adeniran, H. Cheng, YH. Zhang, A. El Harchi, et al. MJ. McPate, H. Zhang, CH. Orchard, JC. Hancox. (2010). Acidosis impairs the protective role of hERG K(+) channels against premature stimulation. J Cardiovasc Electrophysiol. 21(10), pp.1160-1169.

  MJ. McPate, H. Zhang, I. Adeniran, JM. Cordeiro, HJ. Witchel, et al. JC. Hancox. (2009). Comparative effects of the short QT N588K mutation at 37 degrees C on hERG K+ channel current during ventricular, Purkinje fibre and atrial action potentials: an action potential clamp study. J Physiol Pharmacol. 60(1), pp.23-41.

• Chapters in books

  JC. Hancox, MJ. McPate, A. El Harchi, AS. Butler, Y. Zhang, et al. DG. Whittaker, CE. Dempsey, HJ. Witchel, I. Adeniran, AG. Stuart, H. Zhang. (2023). The Short QT Syndrome. In: Heart Rate and Rhythm. Springer International Publishing, pp.517-538.

  JC. Hancox, MJ. McPate, A. El Harchi, RS. Duncan, CE. Dempsey, et al. HJ. Witchel, I. Adeniran, H. Zhang. (2011). The Short QT Syndrome. In: Heart Rate and Rhythm. Springer Berlin Heidelberg, pp.431-449.

• Conferences

  I. Adeniran, DH. MaIver, H. Zhang (2014). Myocardial Electrophysiological, Contractile and Metabolic Properties of Hypertrophic Cardiomyopathy: Insights from Modelling. In: 2014 COMPUTING IN CARDIOLOGY CONFERENCE (CINC), VOL 41. Cambridge, MA, 7/9/2014. pp.1037-1040.

  G. Jones, BD. Spencer, I. Adeniran, H. Zhang (2012). Development of biophysically detailed electrophysiological models for pacemaking and non-pacemaking human pulmonary vein cardiomyocytes. In: Annu Int Conf IEEE Eng Med Biol Soc. United States, 28/8/2012. pp.199-202.