Institute for Mathematical and Molecular BiomedicineActivity Report 2012-16

Institute for Mathematical and Molecular BiomedicineActivity Report 2012-16

Contents

1 Overview 1

2 Core membership 2

3 Affiliate Members and Research Partners 2

4 Research Description 4

5 Research Output and Communication 6

6 Teaching and Outreach 8

7 Facts and Figures 11

1 Overview

The Institute for Mathematical and Molecular Biomedicine (IMMB) at KCL was established as a virtual institutein 2012 to spearhead the College’s research and teaching activities at the interface between biology, medicine,mathematics, physics, and computation.

Progress in post-genomic biomedicine requires scientists from traditional disciplines to work together in re-search teams that combine the distinct and complementary methods, skills, and knowledge from each. TheIMMB aims to develop advanced quantitative tools for addressing biomedical problems, and to be an efficientsource of mathematical and computational expertise for biomedical researchers. It serves as an interdisci-plinary hub for interaction between mathematical, physical, and computational scientists and biological re-searchers and clinicians. It contributes to the education of a new generation of scientists with the backgroundknowledge and skills to work at the forefront of multidisciplinary research.

The IMMB is a joint initiative of the Faculty of Natural and Mathematical Sciences (NMS) and the Faculty ofLife Sciences and Medicine (FoLSM).

Research Themes

Complex biological Analysis of signalling processes in molecular networks (e.g. genomic, proteomic,networks and metabolic), and inter-cellular networks (e.g. neural networks and immune net-

works). Information-theoretic and statistical analysis of network topologies.

Computational Systems biology and functional genomics, analysis of complex biological networks.systems biology Inference of signalling and regulation pathways. Modelling of cellular control and

regulation networks. Mathematical immunology. Bioinformatics.

Statistical methods Innovation of tools for medical statistics, epidemiology and precision medicine. Latentin medicine class models for heterogeneous cohorts and data-driven stratification. Bayesian out-

come prediction for high-dimensional data. Overfitting correction in survival analysis.

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Institute for Mathematical and Molecular BiomedicineActivity Report 2012-16

2 Core membership

The IMMB was formed by academics whose work benefits from and contributes to cross-departmental inter-disciplinary research and teaching activity, but who have teaching and research responsibilities elsewhere intheir home departments. Though it has no core staff of its own, we have benefited from co-location in theHodgkin Building of the Guys Campus.

Principal Investigators

Prof Anthony CC Coolen Mathematics & Randall Division 0.5 FTE NMS 2012-present0.5 FTE FoLSM 2012-present

Dr Attila Csikasz-Nagy Randall Division 0.25 FTE FoLSM 2012-20160.5 FTE FoLSM 2016-present

Postdoctoral Research Associates

Dr Katherine Lawler Cancer Cell Biology & ImagingDr Alexander Mozeika Mathematics & Randall DivisionDr Mark Rowley Mathematics

PhD Students

Mr Zoltan Dul Randall DivisionMs Rosa Hernansaiz Ballesteros Randall DivisionMs Kirsten Jenkins Randall DivisionMr Akram Shalabi MathematicsMr Mansoor Sheikh MathematicsDr Mark Rowley Randall Division (completed 2013)Dr Kate Roberts Randall Division (completed 2014)Mr James Barrett Mathematics (completed 2014)

3 Affiliate Members and Research Partners

Affiliate Members

Dr Alessia Annibale MathematicsProf Franca Fraternali Randall DivisionDr Francesca Ciccarelli Cancer Epidemiology & Population Health

Principal KCL/KHP Partners and Collaborators

Dr Simon Ameer-Beg Cancer Cell Biology & ImagingDr Hans Garmo Cancer EpidemiologyDr Cheryl Gillett Research OncologyDr Anita Grigoriadis Research OncologyDr Maarten Grootendorst Research OncologyProf Reimer Kuhn MathematicsProf Cathryn Lewis Medical & Molecular GeneticsDr Chris Lorenz PhysicsDr Mariam Molokhia Primary Care & Public Health SciencesDr Kalnisha Naidoo Medicine

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Prof Tony Ng Cancer Cell Biology & ImagingDr Snezhana Oliferenko Cell BiologyDr Steve Phelps InformaticsProf Arnie Purushotham Research OncologyDr Emanuele de Rinaldis Translational Bioinformatics CoreProf Anne Ridley Randall DivisionDr Edina Rosta ChemistryDr Michael Simpson Medical & Molecular GeneticsProf Mahvash Tavassoli Molecular OncologyProf Shaun Thomas Haemato-OncologyProf Andy Tutt Research OncologyDr Mieke Van Hemelrijck Cancer EpidemiologyProf Fiona Watt Centre for Stem Cells & Regenerative Medicine

Principal External Partners and Collaborators (UK)

Dr Richard Adams Cardiff University, OncologyDr Paul Barber University of Oxford, OncologyDr James Barrett University College London, Medical GenomicsDr Rafael Carazo Salas University of Cambridge, Systems BiologyProf Luca Cardelli Microsoft Research Cambridge, Systems BiologyProf Deborah Dunn-Walters University of Surrey, ImmunologyDr Lindsay Edwards GlaxoSmithKline, Systems BiologyProf Allan Hackshaw University College London, Epidemiology & Medical StatisticsDr Jens Kleinjung The Francis Crick Institute, BioinformaticsProf Tim Maughan University of Oxford, OncologyDr Tunde Peto Moorfields Eye Hospital London, Ophthalmic Image AnalysisDr Peter Thorpe The Francis Crick Institute, Systems BiologyProf Boris Vojnovic University of Oxford, Oncology

Principal External Partners and Collaborators (international)

Dr Elena Agliari La Sapienza Universita di Roma, Italy, PhysicsProf Martı Aldea Institut de Biologia Molecular de Barcelona, Spain, Systems BiologyDr Adriano Barra La Sapienza Universita di Roma, Italy, PhysicsDr Christel Haggstrom Umea Universitet, Sweden, EpidemiologyProf Niklas Hammar AstraZeneca R&D, Sweden, EpidemiologyProf Lars Holmberg Uppsala Universitet, Sweden, EpidemiologyDr Christian I Hong University of Cincinnati, USA, Molecular/Cellular PhysiologyProf Masato Inoue Waseda University, Japan, Engineering & BioscienceProf Lars Juhl Jensen University of Copenhagen, Denmark, Systems BiologyProf Caroline Klaver Erasmus University Rotterdam, Netherlands, EpidemiologyProf Hans Lehrach Max Planck Institute Berlin, Germany, Molecular GeneticsDr Kazushi Mimura Hiroshima City University, Japan, Information SciencesProf Conrad Perez-Vicente Universitat de Barcelona, Spain, PhysicsProf Paola Picotti ETH Zurich, Switzerland, BiochemistryProf Corrado Priami University of Trento, Italy, BioinformaticsProf Daniel Sargent Mayo Clinic Rochester, USA, Biostatistics & OncologyDr Qian Shi Mayo Clinic Rochester, USA, Cancer StatisticsProf Goran Walldius Karolinska Institutet, Sweden, Cardiovascular Epidemiology

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Academic Visitors

Dr Daniele Tantari La Sapienza Universita di Roma 05/2012 – 07/2012Dr Ayaka Sakata Tokyo Institute of Technology 09/2012Dr Kazuhisha Nagashima Waseda University, Tokyo 01/2013 – 03/2013Dr Zoulikha Zaidi Daphne Jackson Fellow 05/2013 – 05/2015Dr Thomas Thurnherr National University of Singapore 08/2013 – 02/2014Prof Masato Inoue Waseda University, Tokyo 04/2014 – 07/2014Dr Raimund Pechlaner Medical University Innsbruck 01/2015 – 07/2015Dr Morten Hansen Moorfields Eye Hospital, London 03/2015 – 02/2016Dr Kazushi Mimura Hiroshima City University 11/2015 – 12/2015Prof Conrad Prez-Vicente Universitat de Barcelona 03/2016

4 Research Description

Complex Biological Networks

Networks have become the lingua franca of many complex many-variable systems, especially in biology andmedicine. Our quantitative work in this domain ranges from development of mathematical analysis tools tovery applied biomedical studies, and involve both molecular networks (proteomic, gene regulation, metabolic)and cellular networks (neuronal, immunological). Some project examples are:

• Quantifying and decontaminating for experimental bias in signalling networksWe develop and apply information-theoretic and statistical tools for more precise characterisation ofprotein interaction networks (via bi-partite graphs), and for quantifying and removing bias in proteininteraction data induced by experimental protocols (collaboration with the Randall Division).

• Revealing key coupling factors in protein interaction networksWe develop network analysis techniques to identify the key molecular components which channel infor-mation between distinct biological functions. Several of the predictions emerging from the analysis weresubsequently experimentally tested and verified (at King’s and elsewhere).

• Analysis of signalling and regulation processes on networks with many short loopsAll currently available mathematical tools for analysing processes on large networks demand that thesenetworks have only a few short loops, in contrast to proteomic and gene regulation networks where suchloops are not only frequent but also highly functional. We develop new mathematical methods (basedon so-called replica techniques) that are applicable to networks with many short loops.

• Modelling and analysis of immune networksAs part of an MRC Flagship Consortium (also involving experimental immunologists and bioinformati-cians at King’s) we develop, analyse and apply large-scale mathematical models of the adaptive immunesystem (including B-T interactions, BCR and TCR promiscuity effects, and hypersomatic mutation).

Computational Systems Biology

To gain a basic understanding of biological regulatory networks we need to turn current knowledge into math-ematical models and test in-silico whether it matches experimental findings. This helps us to understandunderlying generic biological principles and highlights where our current knowledge is incomplete, which canhelp the design of informative experiments. Computational Systems Biology methods provide a way to turnour knowledge on molecular interactions into mathematical models, which can be used to explain and predictthe behaviour of cells upon perturbations:

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• Biological switches as algorithmsFollowing inspiration from theoretical computer science, we develop computational models in order toexplain how biological regulatory systems implement and perform all-or-none decision making tasks.

• Predictions of protein complexesWe develop computational tools that can simulate how individual proteins bind together and form proteincomplexes in various compositions. These tools can be used to predict the types and quantities of allprotein complexes that can be formed in various tissue types, and how this complexome is perturbedupon the addition of drugs.

• Mathematical models of cell growth regulationWe investigate the molecular regulatory networks which control the cell cycle, polarized cell growth andthe circadian rhythm, and predict which are their key components. We also study how these systemsare connected to each other.

• Cell-cell interaction modellingBy combining ideas from game theory and graph theory we investigate how communities of epithelialcells interact, and we predict what type of interactions are crucial for the homeostatic maintenance ofproper tissue structure (and the consequences of their failure or removal).

Statistical Methods in Medicine

The clinical and epidemiological data of post-genome medicine are very different in complexity and scalefrom those of the previous century, yet we often continue to rely on statistical tools from the 1970s. Applying‘previous generation’ methods to ‘next generation’ data can lead to non-reproducible claims, poor clinicaloutcome prediction, and missed therapeutic opportunities. We seek to address this unmet need for statisticalinnovation. The problems and their solutions are generic and our tools can be rolled out broadly within FoLSM:

• Survival analysis for heterogeneous cohorts and competing risksWe develop new Bayesian mathematical models for survival analysis and clinical outcome prediction,which can handle and map latent cohort heterogeneity and undo the interference effects of co-morbidities.This research underpins a long-standing productive partnership with the College’s Cancer Division (ProfNg), and several international collaborations (including with the Mayo Clinic in the USA). Application toclinical trial data leads to identification of latent subgroups of patients (objective Bayesian stratification),and the rescuing of ‘fallen angles’ (i.e. previously failed expensive drugs that showed no benefit in clinicaltrials – Lancet manuscript in preparation, as part of a consortium involving KCL and Oxford).

• Bayesian clinical outcome prediction from high-dimensional dataThere are no rigorous statistical methods yet for clinical outcome prediction from high-dimensional (e.g.genomic) data. The problem demands that we use Bayesian methods, but the required computationsare prohibitively slow. In association with the Japanese HD3-consortium (High-Dimensional Data DrivenScience) we develop methods for outcome prediction in which the Bayesian integrations are done ana-lytically, and apply these to (genomic and imaging) data in collaboration with the Cancer Division.

• Optimised statistical analysis of cellular imaging dataWe develop novel Bayesian methods for the analysis of fluorescence lifetime imaging microscopy (FLIM)data that exploit the evidence carried by every detected photon. Our analysis can provide reliable es-timates of biological parameters even for relatively low photon numbers, offering up to a factor of twoimprovement in accuracy compared to previous popular techniques.

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• Analysis and decontamination for overfitting in multivariate Cox regressionEven for Cox’s proportional hazards method – still the main work horse of medical statisticians – onefinds in literature only rules of thumb on the minimum ratio samples/covariates that is required to preventoverfitting from invalidating results. A new line of research at IMMB aims to develop a quantitativetheory of overfitting in Cox-type regression models. It is based on replica analysis, a mathematicalmethodology that has been used successfully for several decades to model complex many-variableproblems in physics, biology, and computer science.

5 Research Output and Communication

Selected Publications

• Shalabi A, Inoue M, Watkins J, De Rinaldis E and Coolen ACC (2016) Bayesian clinical classification from high-dimensional data: signatures versus variability Stat. Meth. Med. Res. DOI 10.1177//0962280216628901

• Cardelli L, Csikasz-Nagy A, Dalchau N, Tribastone M, Tschaikowski M (2016) Noise reduction in complex biologicalswitches. Scientific Reports. 6:20214. doi: 10.1038/srep20214

• Wulaningsih W, Vahdaninia, Rowley M, Holmberg L, Garmo H, Malmstrom H, Lambe M, Hammar N, Walldius G,Jungner I, Coolen ACC, Van Hemelrijck M (2015) Prediagnostic serum glucose and lipids in relation to survival inbreast cancer patients: a competing risk analysis. BMC Cancer. DOI 10.1186/s12885-015-1928-z

• Rizzetto S, Priami C, Csikasz-Nagy A (2015) Qualitative and quantitative protein complex prediction throughproteome-wide simulations. PLoS Comput Biol. 11(10):e1004424. doi: 10.1371/journal.pcbi.1004424

• Suh Y-E, Raulf, N, Gaken J, Lawler K, Urbano TG, Bullenkamp J, Gobeil S, Huot J, Odell E, Tavassoli M (2015).MicroRNA-196a promotes an oncogenic effect in head and neck cancer cells by suppressing annexin A1 andenhancing radioresistance. International Journal of Cancer. 137(5), 102134. doi:10.1002/ijc.29397

• Barrett JE, Coolen ACC (2015) Covariate dimension reduction for survival data via the Gaussian process latentvariable model. Statistics in Medicine. DOI: 10.1002/sim.6784

• Chung SS, Pandini A, Annibale A, Coolen ACC, Thomas NSB and Fraternali F (2015) Bridging topological andfunctional information in protein interaction networks by short loops profiling. Science Reports. 5: 8540

• Annibale A, Coolen ACC and Planell-Morell N (2015) Quantifying noise in mass spectrometry and yeast two-hybridprotein interaction experiments. J. Roy. Soc. Interface. 12: 20150573

• Honeth G, Schiavinotto T, Vaggi F, Marlow R, Kanno T, Shinomiya I, Lombardi S, Buchupalli B, Graham R, Gazin-ska P, Ramalingam V, Burchell J, Purushotham AD, Pinder SE, Csikasz-Nagy A, Dontu G (2015) Models of breastmorphogenesis based on localization of stem cells in the developing mammary lobule. Stem Cell Reports. 4:699-711.

• Kiuchi, T, Ortiz-Zapater E, Monypenny J, Matthews D R, Nguyen L K, Barbeau J, Ng T (2014). The ErbB4 CYT2variant protects EGFR from ligand-induced degradation to enhance cancer cell motility. Science Signaling. 7(339), ra78. doi:10.1126/scisignal.2005157

• Fernandes de Abreu DA, Caballero A, Fardel P, Stroustrup N, Chen Z, Lee K, Keyes WD, Nash ZM, Lopez-Moyado IF, Vaggi F, Cornils A, Regenass M, Neagu A, Ostojic I, Liu C, Cho Y, Sifoglu D, Shen Y, Fontana W, LuH, Csikasz-Nagy A, Murphy CT, Antebi A, Blanc E, Apfeld J, Zhang Y, Alcedo J, and Ch’ng Q (2014). An insulin-to-insulin regulatory network orchestrates phenotypic specificity in development and physiology. PLoS Genet. 10:e1004225.

• Chowdhury R, Ganeshan B, Irshad S, Lawler K, Eisenblatter M, Milewicz H, Ng T (2014). The use of molecularimaging combined with genomic techniques to understand the heterogeneity in cancer metastasis. The BritishJournal of Radiology. 87 (1038), 20140065. doi:10.1259/bjr.20140065

• Weitsman G, Lawler K, Kelleher M T, Barrett J E, Barber P R, Shamil E, Ng T (2014). Imaging tumour het-erogeneity of the consequences of a PKC?-substrate interaction in breast cancer patients. Biochemical SocietyTransactions. 42 (6), 1498505. doi:10.1042/BST20140165

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• Hong CI, Zmborszky J, Baek M, Labiscsak L, Ju K, Lee H, Larrondo LF, Goity A, Chong HS, Belden WJ, Csiksz-Nagy A (2014) Circadian rhythms synchronize mitosis in neurospora crassa. Proc Natl Acad Sci USA. 111(4):1397-402

• Bajpai A, Feoktistova A, Chen JS, McCollum D, Sato M, Carazo-Salas RE, Gould KL, Csikasz-Nagy A (2013)Dynamics of SIN asymmetry establishment. PLOS Computational Biology.9(7):e1003147

• Csikasz-Nagy A, Escudero LM, Guillaud M, Sedwards S, Baum B, Cavaliere M (2013) Cooperation and compe-tition in the dynamics of tissue architecture during homeostasis and tumorigenesis. Seminars in Cancer Biology.23:293-298

• Dodgson J, Chessel A, Yamamoto M, Vaggi F, Cox S, Rosten E, Albrecht D, Geymonat M, Csikasz-Nagy A, SatoM, Carazo-Salas RE (2013) Spatial segregation of polarity factors into distinct cortical clusters is required for cellpolarity control. Nature Communications. 4:1834

• Agliari E, Annibale A, Barra A, Coolen ACC and Tantari D (2013) Immune networks: multi-tasking capabilities nearsaturation. J Phys A. 46: 415003

• Agliari E, Annibale A, Barra A, Coolen ACC and Tantari D (2013) Immune networks: multi-tasking capabilities atmedium load. J Phys A. 46: 335101

• Irshad S, Grigoriadis A, Lawler K, Ng T, Tutt A. (2012). Profiling the immune stromal interface in breast cancer andits potential for clinical impact. Breast Care. 7(5), 273280. doi:10.1159/000341529

• Vaggi F, Dodgson J, Bajpai A, Chessel A, Jordan F, Sato M, Carazo-Salas RE, Csikasz-Nagy A (2012) Linkers ofcell polarity and cell cycle regulation in the fission yeast protein interaction network. PLoS Computational Biology.8(10): e1002732

• Cardelli L, Csikasz-Nagy A (2012) The cell cycle switch computes approximate majority. Scientific Reports. 2:656

• Roberts ES and Coolen ACC (2012) Unbiased degree-preserving randomization of directed binary networks. PhysRev E. 85 046103

• Ferrezuelo F, Colomina N, Palmisano A, Gar E, Gallego C, Csikasz-Nagy A, Aldea M (2012) The critical size is setat a single-cell level by growth rate to attain homeostasis and adaptation. Nature Communications. 3:1012

• Shayeghi N, Ng T, Coolen ACC (2012) Direct Response Analysis in cellular signalling networks. J Theor Biol.304:219-225

Selected Invited Oral Presentations by PIs

10/05/16 Institut de Biologie de l’Ecole Normale Superieure ENS, France:Protein complex prediction through proteome-wide simulations

Dr Attila Csikasz-Nagy

20/04/16 Mayo Clinic, Rochester, USA: Give me six hours to chop down atree... Statistical tools to meet the challenges of modern medicaldata

Prof Ton Coolen

18/04/16 Columbia University, New York, USA: Towards a theory of overfit-ting in proportional hazards regression for survival data

Prof Ton Coolen

15/12/15 HD3-2015 Conference, Kyoto, Japan: Replica methods for loopysparse random graphs

Prof Ton Coolen

30/06/15 Stem Cells Lunch - Centre for Stem Cells & RegenerativeMedicine, London: Epithelial topology dynamics in tissue home-ostasis and disease

Dr Attila Csikasz-Nagy

20/05/15 Random Graphs, Simplicial Complexes, and their ApplicationsConference, Northeastern University, USA: New analytical toolsfor loopy sparse random graphs

Prof Ton Coolen

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Institute for Mathematical and Molecular BiomedicineActivity Report 2012-16

04/03/15 Genes to Cells Seminar Series, Cancer Research UK ResearchInstitute, London: Systems Level investigations of cell size regula-tion

Dr Attila Csikasz-Nagy

02/02/15 Bardonecchia Conference, Torino, Italy: Tools for stochastic pro-cesses on loopy networks

Prof Ton Coolen

14/01/15 Max Planck Institute, Berlin, Germany: Mathematics for cancerresearch - making optimal use of cancer data

Prof Ton Coolen

08/12/14 7th ERCIM Conference, Pisa, Italy: Bayesian clinical classificationfrom high-dimensional data: signatures versus variability

Prof Ton Coolen

21-25/07/14 Interdisciplinary Signalling Workshop, Visegrad, Hungary: Epithe-lial topology dynamics in tissue homeostasis and tumorigenesis

Dr Attila Csikasz-Nagy

05/06/14 The Francis Crick Institute (NIMR), London: Immune networks:multi-tasking capabilities near saturation

Prof Ton Coolen

01/05/14 Cancer Research UK, London: Maths for intelligent personalisedcancer medicine

Prof Ton Coolen

11/03/14 Crick Seminar Series: Modelling and Computation in Biology andMedicine, London: Models and experiments to understand cellsize control

Dr Attila Csikasz-Nagy

05/03/14 Instituto Gulbenkian de Ciencia, Oeiras, Portugal: Models and ex-periments to understand cell size control

Dr Attila Csikasz-Nagy

11/12/13 Mathematical Modelling of Complex Systems Conference, Paris:Solvable immune network models on finitely connected graphswith many short loops

Prof Ton Coolen

12/06/13 Nordic-Baltic Biometric Conference, Stockholm, Sweden: Genericsolution of the competing risk problem in survival analysis

Prof Ton Coolen

6 Teaching and Outreach

Systems Biomedicine Graduate Programme (SBGP)

The SBGP is a series of multidisciplinary lectures and seminars curated by the IMMB, covering the broad fieldof systems biomedicine. It is designed to give an overview of work at the intersection of medicine, biology,mathematics, informatics, bioinformatics, and biophysics, and to foster a spirit of discussion and collaborationbetween students from different disciplines.

The programme is intended for PhD and Master’s students who wish to equip themselves with a broad under-standing of research in other, complementary disciplines, in order to develop the background knowledge andskills to work at the forefront of multidisciplinary research. Lectures are delivered by experts in their field whogenerously donate their time, and are grouped in thematic blocks over Semester 1. In Semester 2, studentsare invited to make short presentations about their own research.

Participation is voluntary, and was initially via nomination from PhD supervisors. In recent years, we havewidened the entry requirements to self-nomination by interested PGT and PGR students, and publicise widelyacross NMS, FoLSM, the Institute of Psychiatry, Psychology, and Neuroscience. We plan to extend access tothe Dental Institute, the Florence Nightingale School of Nursing and Midwifery, and KHP in future years.

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Participation has grown from a modest 7 students in 2012 to 24 in 2015.

2012/13 2013/14 2014/15 2015/16

Student Numbers 7 16 21 24Lectures 12 14 15 16

Contributions by IMMB PIs to Taught Programmes

Dr Attila Csikasz-Nagy Chemistry for the Biosciences 4 practicals/annumCell Biology A 2 lectures/annumAdvanced Topics in Non-Equilibrium Systems 4 lectures/annum

Prof Ton Coolen Theory of Complex Networks (UG & PG Maths) 11 lectures/annumLondon Interdisciplinary Doctoral Programme (LIDO) 2 lectures/annumMSc Gens, Environment & Development 1 lecture/annumSystems Biomedicine Graduate programme 4 lectures/annumMSci & MSc research project supervision 3-4 projects/annum

Core members of IMMB have taken active roles in promoting interdisciplinary and interfaculty teaching initia-tives at King’s. We have developed blueprints for: (i) a possible MSci programme in Systems Biology (Dr AttilaCsikasz-Nagy), (ii) Joint Honours BSc Programmes in Mathematics with Biology (Prof ACC Coolen), and (iii)BSc/MSci Streams in Biomedical Mathematics (Prof ACC Coolen).

If and when these teaching initiatives materialise, IMMB will have greatly increased opportunities to recruitmore undergraduate students to work on research projects focusing on the interface between the quantitativesciences and biomedicine.

Conferences, Workshops and Courses Organised by IMMB

04/06/14 One-day Conference on Fundamental Problems in Survival Analysis:Heterogeneity, Dimension Mismatch, and Competing Risks

08-29/01/14 Short course: The Replica Method and its Applications in Biomedical Modelling

19/09/13 One-day Conference on Modelling the Complexity of the Immune System

05-06/13 Short course: Principles of Survival Analysis

06/02/13 Open-house – Unsolved Mathematical Problems in Biomedicine

01-31/01/13 KCL-La Sapienza Workshop on Models of Cytokine Signalling in the Immune System

Selected Seminars Hosted by IMMB

09/03/16 Meta-analysis of cell cycle models to understand Dr Daniel Seatonresponses to dynamically changing environments University of Edinburgh

03/03/16 A novel perspective on mathematical modelling of Dr Adriano Barrainformation processing occurring in biological La Sapienza Universita di Romareaction kinetics

29/04/15 Emerging social behaviour during aggregation in Dr Robert EndresDictyostelium discoideum Imperial College London

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12/12/14 Bayesian adaptive clinical trials: Dr James Barretta selective-recruitment design UCL

09/01/14 Systems biology of the cell cycle Dr Attila Csikasz-NagyKCL

27/06/13 Computational and Optical Approaches to Dr Kevin EliceiriMultidimensional Live Cell Imaging University of Wisconsin-Madison

Biomedical Software Development

ALPACA Bayesian software for survival analysis in the presence of latent cohort het-erogeneity and possible competing risks. The program (i) detects nature andcharacteristics of latent patient subclasses (data-driven Bayesian stratifica-tion), (ii) decontaminates survival curves for the impact of informative censor-ing, and (iii) determinines retrospective class membership probabilities for alldata samples (for guided biomarker discovery). It forms the basis of severalnational and international collaborations (e.g. Moorfields Eye Hospital, Upp-sala and Umea Universities, Mayo Clinic USA).

Implemented on dedicated multi-core hardware at the IMMB.

FractalMammaryLobule Computational tool developedto visualize mammary lobuleformation in 3D and supportthe identification of stem cellpositions in 3D from 2D med-ical images.

Available on https://github.com/FedericoV/FractalMammaryLobule.

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SciComPre Simulation-based, qualitative and quantitative prediction of protein complexes.Integrating different datasets, like protein-protein interactions, domain-domaininteractions and protein abundance, SiComPre simulates the interactions ofproteins identifying protein complexes, their variation and their abundance.

Available on http://www.cosbi.eu/research/prototypes/sicompre.

Other Contributions

IMMB’s PIs provide computational, mathematical, programming, and data analysis support to several exper-imental and biomedical groups at other departments of King’s and other institutions, and have hosted andsupported their PhD students and postdocs. These interactions have led to several joint publications andresearch proposals.

Between 2012 and 2015 we invested significant time in detailed planning, costing, and blueprinting a large-scale philanthropically funded quantitative medicine research hub at King’s – including research pilot studies.Due to diverging views this initiative unfortunately had to be abandoned.

Finally, the IMMB PIs are members of national and international panels and committees relating to quantita-tive biomedical research (e.g. BBSRC’s Systems Biology Panel, MRC’s Methodology for Stratified MedicineGroup, and Research Councils in Germany and Norway), and have actively promoted the College’s presenceand long-term ambitions in quantitative biomedicine within the UK and abroad.

7 Facts and Figures

2012/13 2013/14 2014/15 2015/16

PIs (FTE) 1.25 1.25 1.25 1.37

PDRAs (FTE) 1 1 3 3

PhD Students (FTE) 4 3 3.5 4.5

Professional Services Staff (FTE) 0 1 1 1

Academic visitors (FTE) 2 3 4 3

Journal publications 9 11 13 4

Invited Oral Presentations 3 9 10 5

Research Grants

2010-13 Rigorous information-theoretic tools for comparative interactomics £276,720ACC Coolen (joint applicant with F Fraternali) BBRSC

2009-14 Supra-molecular rules in signalling networks: £2,015,000A single molecule comparative study in cells and tissues BBSRCACC Coolen (joint applicant in a consortium)

2010-15 HER imaging and molecular interaction mapping in breast cancer Eu 5,998,993ACC Coolen (joint applicant in a consortium) EU FP-7

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2011-15 Development of novel mathematical approaches for the integrative £60,000analysis of NGS data and application to breast cancer studies EPSRC/IDBSACC Coolen (CASE grant with E Di Rinaldis)

2013-15 Philanthropic support for Quantitative Medicine Research £126,000ACC Coolen Ana Leaf Foundation

2014-17 Fondazione Edmund Mach PhD Studentship £71,000A Csikasz-Nagy Mach Foundation

2014-17 Microsoft Research PhD Studentship £71,650A Csikasz-Nagy Microsoft

2016-17 The impact of mathematical innovation in translational medicine £45,500ACC Coolen (joint applicant with M Molokhia) EPSRC

2014-19 Multi-scale analysis of B cell responses in ageing £1,730,000ACC Coolen (joint applicant with F Fraternali and D Dunn-Walters) MRC

2015-19 Meeting the systems challenges in drug discovery £135,000ACC Coolen (CASE grant with Dr L Edwards) BBSRC/GSK

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