Professor Andrzej M. Kierzek

Professor of Systems Biology

Qualifications: MSc, PhD, PGCAP

Phone: Work: 01483 68 3775
Room no: 19 AY 04

Further information

Research Interests

I have over 15 years of experience in computational biology. My research goal is to predict the dynamic behaviour of the living cell by computer simulation of the genome scale network models representing experimental data on interaction between molecules.

I am convinced that we can fully exploit information about full genomic sequence of human and other organisms only if we use legacy of molecular biology data to build predictive mechanistic models of genotype-phenotype relationship. Due to the number of molecular components in the cell and non-linearity of their interactions this goal can only be achieved by computer simulation. The successful computer simulation of the molecular cell biology will enable prediction of the individual genetic differences on the trajectories of major diseases providing foundation for predictive and personalized medicine of the future. Likewise, industrial biotechnology is being revolutionized by increasing ability to computer simulate the effects of genetic engineering in commercial cell lines and therefore rationally design industrial fermentation processes.I developed novel Quasi Steady State Petri Net (QSSPN) algorithm (Bioinformatics, doi: 10.1093/bioinformatics/btt552) integrating Petri nets and constraint-based analysis to predict the feasibility of qualitative dynamic behaviours in models of gene regulation, signalling and whole-cell metabolism. We presented the first dynamic simulations including regulatory mechanisms and a genome scale metabolic network in human cell, using bile acid homeostasis in human hepatocytes as a case study. QSSPN simulations reproduced experimentally determined qualitative dynamic behaviours and permitted mechanistic analysis of genotype-phenotype relationships. (QSSPN Project website:

I have performed computational part of the project leading to the first reconstruction of the Genome Scale Metabolic Reaction Network of Mycobacterium tuberculosis, causative agent of Tuberculosis disease (Genome Biology, 2007). The tools developed for this project motivated have been matured into SurreyFBA software recently published by my group (Bioinformatics, 2011). I have also been working on analysis of gene expression data in the context of genome scale metabolic networks (PLoS Computational Biology, 2011) and development of software for web based computation with FBA models (BMC Bioinformatics, 2011). Industrial biotechnology is an important application area for genome scale metabolic modeling; I worked on FBA simulations in the context of bioprocess feed development for antibiotic production in Streptomyces coelicolor (Metabolic Engineering, 2008).

I have been modeling stochastic effects in molecular interaction network dynamics for 10 years. I have constructed detailed model of prokaryotic gene expression and investigated dependence between accuracy of gene expression and transcription and translation initiation rates (J. Biol. Chem, 2001). This work has also lead to the publication of STOCKS software for stochastic simulation of molecular interaction network (Bioinformatics, 2002). Subsequently, we have developed Maximal Timestep Method, a hybrid algorithm enabling stochastic simulation of systems with reaction rates varying by many orders of magnitude. The method has been applied to investigate propagation of gene expression noise to the level of metabolic processes leading to epigenetically inherited changes in single cell physiology (Biophysical Journal 2004). More recently, I was working the influence of RNA regulators on gene expression noise (Biophysical Journal 2009) and constructed stochastic kinetic model of Two Component System Signalling (Molecular Biosystems 2010).

In have past bioinformatics experience in the field of homology modeling of protein structure (Nucl. Acids. Research 2003, Nature Immunology 2003), regulatory sequence analysis (J. Biol. Chem 2005) and annotation of genome sequences (Nature 2004). I did my PhD in the area of Biophysics and worked on the agent-based simulations of protein crystal growth (Biophysical Journal 1997). I have also performed molecular dynamics simulations and analysed light scaterring spectra (J. Phys. Chem. 1999).


Journal articles

  • Mendum TA, Schuenemann VJ, Roffey S, Taylor GM, Wu H, Singh P, Tucker K, Hinds J, Cole ST, Kierzek AM, Nieselt K, Krause J, Stewart GR. (2014) 'Mycobacterium leprae genomes from a British medieval leprosy hospital: towards understanding an ancient epidemic.'. BMC Genomics, England: 15 (1)
  • Fisher CP, Plant NJ, Moore JB, Kierzek AM. (2013) 'QSSPN: dynamic simulation of molecular interaction networks describing gene regulation, signalling and whole-cell metabolism in human cells.'. Oxford university Press Bioinformatics, England: 29 (24), pp. 3181-3190.


    Dynamic simulation of genome-scale molecular interaction networks will enable the mechanistic prediction of genotype-phenotype relationships. Despite advances in quantitative biology, full parameterization of whole-cell models is not yet possible. Simulation methods capable of using available qualitative data are required to develop dynamic whole-cell models through an iterative process of modelling and experimental validation.

  • Rocco A, Kierzek AM, McFadden J. (2013) 'Slow Protein Fluctuations Explain the Emergence of Growth Phenotypes and Persistence in Clonal Bacterial Populations'. Public Library of Science PLoS One, 8 (1) Article number e54272


    One of the most challenging problems in microbiology is to understand how a small fraction of microbes that resists killing by antibiotics can emerge in a population of genetically identical cells, the phenomenon known as persistence or drug tolerance. Its characteristic signature is the biphasic kill curve, whereby microbes exposed to a bactericidal agent are initially killed very rapidly but then much more slowly. Here we relate this problem to the more general problem of understanding the emergence of distinct growth phenotypes in clonal populations. We address the problem mathematically by adopting the framework of the phenomenon of so-called weak ergodicity breaking, well known in dynamical physical systems, which we extend to the biological context. We show analytically and by direct stochastic simulations that distinct growth phenotypes can emerge as a consequence of slow-down of stochastic fluctuations in the expression of a gene controlling growth rate. In the regime of fast gene transcription, the system is ergodic, the growth rate distribution is unimodal, and accounts for one phenotype only. In contrast, at slow transcription and fast translation, weakly non-ergodic components emerge, the population distribution of growth rates becomes bimodal, and two distinct growth phenotypes are identified. When coupled to the well-established growth rate dependence of antibiotic killing, this model describes the observed fast and slow killing phases, and reproduces much of the phenomenology of bacterial persistence. The model has major implications for efforts to develop control strategies for persistent infections.

  • Lofthouse EK, Wheeler PR, Beste DJV, Khatri BL, Wu H, Mendum TA, Kierzek AM, McFadden J. (2013) 'Systems-Based Approaches to Probing Metabolic Variation within the Mycobacterium tuberculosis Complex'. PUBLIC LIBRARY SCIENCE PLOS ONE, 8 (9) Article number ARTN e75913
  • Zakrzewski P, Medema MH, Takano E, Zakrzewski P, Medema MH, Breitling R, Gevorgyan A, Kierzek AM, Breitling R. (2012) 'MultiMetEval: Comparative and Multi-Objective Analysis of Genome-Scale Metabolic Models'. PLoS ONE, 7 (12)
  • Allenby NE, Laing E, Bucca G, Kierzek AM, Smith CP. (2012) 'Diverse control of metabolism and other cellular processes in Streptomyces coelicolor by the PhoP transcription factor: genome-wide identification of in vivo targets.'. Oxford University Press Nucleic Acids Res,


    Streptomycetes sense and respond to the stress of phosphate starvation via the two-component PhoR-PhoP signal transduction system. To identify the in vivo targets of PhoP we have undertaken a chromatin-immunoprecipitation-on-microarray analysis of wild-type and phoP mutant cultures and, in parallel, have quantified their transcriptomes. Most (ca. 80%) of the previously in vitro characterized PhoP targets were identified in this study among several hundred other putative novel PhoP targets. In addition to activating genes for phosphate scavenging systems PhoP was shown to target two gene clusters for cell wall/extracellular polymer biosynthesis. Furthermore PhoP was found to repress an unprecedented range of pathways upon entering phosphate limitation including nitrogen assimilation, oxidative phosphorylation, nucleotide biosynthesis and glycogen catabolism. Moreover, PhoP was shown to target many key genes involved in antibiotic production and morphological differentiation, including afsS, atrA, bldA, bldC, bldD, bldK, bldM, cdaR, cdgA, cdgB and scbR-scbA. Intriguingly, in the PhoP-dependent cpk polyketide gene cluster, PhoP accumulates substantially at three specific sites within the giant polyketide synthase-encoding genes. This study suggests that, following phosphate limitation, Streptomyces coelicolor PhoP functions as a 'master' regulator, suppressing central metabolism, secondary metabolism and developmental pathways until sufficient phosphate is salvaged to support further growth and, ultimately, morphological development.

  • Hoyle RB, Avitabile D, Kierzek AM. (2012) 'Equation-free analysis of two-component system signalling model reveals the emergence of co-existing phenotypes in the absence of multistationarity.'. Public Library of Science PLoS Computational Biology, United States: 8 (6)


    Phenotypic differences of genetically identical cells under the same environmental conditions have been attributed to the inherent stochasticity of biochemical processes. Various mechanisms have been suggested, including the existence of alternative steady states in regulatory networks that are reached by means of stochastic fluctuations, long transient excursions from a stable state to an unstable excited state, and the switching on and off of a reaction network according to the availability of a constituent chemical species. Here we analyse a detailed stochastic kinetic model of two-component system signalling in bacteria, and show that alternative phenotypes emerge in the absence of these features. We perform a bifurcation analysis of deterministic reaction rate equations derived from the model, and find that they cannot reproduce the whole range of qualitative responses to external signals demonstrated by direct stochastic simulations. In particular, the mixed mode, where stochastic switching and a graded response are seen simultaneously, is absent. However, probabilistic and equation-free analyses of the stochastic model that calculate stationary states for the mean of an ensemble of stochastic trajectories reveal that slow transcription of either response regulator or histidine kinase leads to the coexistence of an approximate basal solution and a graded response that combine to produce the mixed mode, thus establishing its essential stochastic nature. The same techniques also show that stochasticity results in the observation of an all-or-none bistable response over a much wider range of external signals than would be expected on deterministic grounds. Thus we demonstrate the application of numerical equation-free methods to a detailed biochemical reaction network model, and show that it can provide new insight into the role of stochasticity in the emergence of phenotypic diversity.

  • Stoy N, Chen S, Kierzek AM. (2012) 'Studying prostate cancer as a network disease by qualitative computer simulation with Stochastic Petri Nets'. CEUR Workshop Proceedings, 852, pp. 76-80.
  • Mendum TA, Newcombe J, Mannan AA, Kierzek AA, McFadden J. (2011) 'Interrogation of global mutagenesis data with a genome scale model of Neisseria meningitidis to assess gene fitness in vitro and in sera.'. BioMed Central Ltd Genome Biol, 12 (12)


    BACKGROUND: Neisseria meningitidis is an important human commensal and pathogen that causes several thousand deaths each year, mostly in young children. How the pathogen replicates and causes disease in the host is largely unknown, particularly the role of metabolism in colonization and disease. Completed genome sequences are available for several strains but our understanding of how these data relate to phenotype remains limited. RESULTS: To investigate the metabolism of N. meningitidis we generated and selected a representative Tn5 library on rich medium, a minimal defined medium and in human serum to identify genes essential for growth under these conditions. To relate these data to a systems-wide understanding of the pathogen's biology we constructed a genome-scale metabolic network: Nmb_iTM560. This model was able to distinguish essential and non-essential genes as predicted by the global mutagenesis. These essentiality data, the library and the Nmb_iTM560 model are powerful and widely applicable resources for the study of meningococcal metabolism and physiology. We demonstrate the utility of these resources by predicting and demonstrating metabolic requirements on minimal medium such as a requirement for PEP carboxylase, and by describing the nutritional and biochemical status of N. meningitidis when grown in serum, including a requirement for both the synthesis and transport of amino acids. CONCLUSIONS: This study describes the application of a genome scale transposon library combined with an experimentally validated genome-scale metabolic network of N. meningitidis to identify essential genes and provide novel insight to the pathogen's metabolism both in vitro and during infection.

  • Sroka J, Krupa Ł, Kierzek AM, Tyszkiewicz J. (2011) 'CalcTav--integration of a spreadsheet and Taverna workbench.'. Bioinformatics, England: 27 (18), pp. 2618-2619.
  • Bonde BK, Beste DJV, Laing E, Kierzek AM, McFadden J. (2011) 'Differential Producibility Analysis (DPA) of Transcriptomic Data with Metabolic Networks: Deconstructing the Metabolic Response of M. tuberculosis'. PUBLIC LIBRARY SCIENCE PLOS COMPUTATIONAL BIOLOGY, 7 (6) Article number ARTN e1002060
  • Sroka J, Bieniasz-Krzywiec L, Gwóźdź S, Leniowski D, Lącki J, Markowski M, Avignone-Rossa C, Bushell ME, McFadden J, Kierzek AM. (2011) 'Acorn: a grid computing system for constraint based modeling and visualization of the genome scale metabolic reaction networks via a web interface.'. BioMed Central BMC Bioinformatics, England: 12 (196)


    Constraint-based approaches facilitate the prediction of cellular metabolic capabilities, based, in turn on predictions of the repertoire of enzymes encoded in the genome. Recently, genome annotations have been used to reconstruct genome scale metabolic reaction networks for numerous species, including Homo sapiens, which allow simulations that provide valuable insights into topics, including predictions of gene essentiality of pathogens, interpretation of genetic polymorphism in metabolic disease syndromes and suggestions for novel approaches to microbial metabolic engineering. These constraint-based simulations are being integrated with the functional genomics portals, an activity that requires efficient implementation of the constraint-based simulations in the web-based environment.

  • Gevorgyan A, Bushell ME, Avignone-Rossa C, Kierzek AM. (2010) 'SurreyFBA: a command line tool and graphics user interface for constraint-based modeling of genome-scale metabolic reaction networks'. OXFORD UNIV PRESS BIOINFORMATICS, 27 (3), pp. 433-434.
  • Kadir TAA, Mannan AA, Kierzek AM, McFadden J, Shimizu K. (2010) 'Modeling and simulation of the main metabolism in Escherichia coli and its several single-gene knockout mutants with experimental verification'. BIOMED CENTRAL LTD MICROBIAL CELL FACTORIES, 9 Article number ARTN 88
  • Estorninho M, Smith H, Thole J, Harders-Westerveen J, Kierzek A, Butler RE, Neyrolles O, Stewart GR. (2010) 'ClgR regulation of chaperone and protease systems is essential for Mycobacterium tuberculosis parasitism of the macrophage'. SOC GENERAL MICROBIOLOGY MICROBIOLOGY-SGM, 156, pp. 3445-3455.
  • Kierzek AM, Zhou L, Wanner BL. (2010) 'Stochastic kinetic model of two component system signalling reveals all-or-none, graded and mixed mode stochastic switching responses'. ROYAL SOC CHEMISTRY MOLECULAR BIOSYSTEMS, 6 (3), pp. 531-542.
  • Lewis RA, Laing E, Allenby N, Bucca G, Brenner V, Harrison M, Kierzek AM, Smith CP. (2010) 'Metabolic and evolutionary insights into the closely-related species Streptomyces coelicolor and Streptomyces lividans deduced from high-resolution comparative genomic hybridization'. BIOMED CENTRAL LTD BMC GENOMICS, 11 Article number ARTN 682
  • Beste DJV, Espasa M, Bonde B, Kierzek AM, Stewart GR, McFadden J. (2009) 'The Genetic Requirements for Fast and Slow Growth in Mycobacteria'. PUBLIC LIBRARY SCIENCE PLOS ONE, 4 (4) Article number ARTN e5349
  • Komorowski M, Miekisz J, Kierzek AM. (2009) 'Translational Repression Contributes Greater Noise to Gene Expression than Transcriptional Repression'. ELSEVIER SCI LTD BIOPHYS J, 96 (2), pp. 372-384.
  • Khannapho C, Zhao H, Bonde BK, Kierzek AM, Avignone-Rossa CA, Bushell ME. (2008) 'Selection of objective function in genome scale flux balance analysis for process feed development in antibiotic production'. ACADEMIC PRESS INC ELSEVIER SCIENCE METABOLIC ENGINEERING, 10 (5), pp. 227-233.
  • Jacewicz A, Makiela K, Kierzek A, Drake JW, Bebenek A. (2007) 'The roles of Tyr391 and Tyr619 in RB69 DNA polymerase replication fidelity'. ACADEMIC PRESS LTD ELSEVIER SCIENCE LTD JOURNAL OF MOLECULAR BIOLOGY, 368 (1), pp. 18-29.
  • Beste DJV, Hooper T, Stewart G, Bonde B, Avignone-Rossa C, Bushell M, Wheeler P, Klamt S, Kierzek AM, McFadden J. (2007) 'GSMN-TB: a web-based genome scale network model of Mycobacterium tuberculosis metabolism'. BIOMED CENTRAL LTD GENOME BIOLOGY, 8 (5) Article number ARTN r89
  • Bushell ME, Sequeira SIP, Khannapho C, Zhao H, Chater KF, Butler MJ, Kierzek AM, Avignone-Rossa CA. (2006) 'The use of genome scale metabolic flux variability analysis for process feed formulation based on an investigation of the effects of the zwf mutation on antibiotic production in Streptomyces coelicolor'. ELSEVIER SCIENCE INC ENZYME AND MICROBIAL TECHNOLOGY, 39 (6), pp. 1347-1353.
  • Sroka J, Kaczor G, Tyszkiewicz J, Kierzek AM. (2006) 'XQTav: An XQuery processor for Taverna environment'. OXFORD UNIV PRESS BIOINFORMATICS, 22 (10), pp. 1280-1281.
  • Caldwell RB, Kierzek AM. (2006) 'Genome resources for the DT40 community.'. Subcell Biochem, England: 40, pp. 25-37.
  • Hamimes S, Arakawa H, Stasiak AZ, Kierzek AM, Hirano S, Yang YG, Takata M, Stasiak A, Buerstedde JM, Van Dyck E. (2005) 'RDM1, a novel RNA recognition motif (RRM)-containing protein involved in the cell response to cisplatin in vertebrates'. AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC JOURNAL OF BIOLOGICAL CHEMISTRY, 280 (10), pp. 9225-9235.
  • Zaim J, Speina E, Kierzek AM. (2005) 'Identification of new genes regulated by the Crt1 transcription factor, an effector of the DNA damage checkpoint pathway in Saccharomyces cerevisiae'. AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC JOURNAL OF BIOLOGICAL CHEMISTRY, 280 (1), pp. 28-37.
  • Caldwell RB, Kierzek AM, Arakawa H, Bezzubov Y, Zaim J, Fiedler P, Kutter S, Blagodatski A, Kostovska D, Koter M, Plachy J, Carninci P, Hayashizaki Y, Buerstedde JM. (2005) 'Full-length cDNAs from chicken bursal lymphocytes to facilitate gene function analysis'. BIOMED CENTRAL LTD GENOME BIOL, 6 (1) Article number R6


    A large number of cDNA inserts were sequenced from a high-quality library of chicken bursal lymphocyte cDNAs. Comparisons to public gene databases indicate that the cDNA collection represents more than 2,000 new, full-length transcripts. This resource defines the structure and the coding potential of a large fraction of B-cell specific and housekeeping genes whose function can be analyzed by disruption in the chicken DT40 B-cell line.

  • Wahl MB, Caldwell RB, Kierzek AM, Arakawa H, Eyras E, Hubner N, Jung C, Soeldenwagner M, Cervelli M, Wang YD, Liebscher V, Buerstedde JM. (2004) 'Evaluation of the chicken transcriptome by SAGE of B cells and the DT40 cell line'. BIOMED CENTRAL LTD BMC GENOMICS, 5 Article number ARTN 98
  • Hillier LW, Miller W, Birney E, Warren W, Hardison RC, Ponting CP, Bork P, Burt DW, Groenen MAM, Delany ME, Dodgson JB, Chinwalla AT, Cliften PF, Clifton SW, Delehaunty KD, Fronick C, Fulton RS, Graves TA, Kremitzki C, Layman D, Magrini V, McPherson JD, Miner TL, Minx P, Nash WE, Nhan MN, Nelson JO, Oddy LG, Pohl CS, Randall-Maher J, Smith SM, Wallis JW, Yang SP, Romanov MN, Rondelli CM, Paton B, Smith J, Morrice D, Daniels L, Tempest HG, Robertson L, Masabanda JS, Griffin DK, Vignal A, Fillon V, Jacobbson L, Kerje S, Andersson L, Crooijmans RPM, Aerts J, van der Poel JJ, Ellegren H, Caldwell RB, Hubbard SJ, Grafham DV, Kierzek AM, McLaren SR, Overton IM, Arakawa H, Beattie KJ, Bezzubov Y, Boardman PE, Bonfield JK, Croning MDR, Davies RM, Francis MD, Humphray SJ, Scott CE, Taylor RG, Tickle C, Brown WRA, Rogers J, Buerstedde JM, Wilson SA, Stubbs L, Ovcharenko I, Gordon L, Lucas S, Miller MM, Inoko H, Shiina T, Kaufman J, Salomonsen J, Skjoedt K, Wong GKS, Wang J, Liu B, Wang J, Yu J, Yang HM, Nefedov M, Koriabine M, deJong PJ, Goodstadt L, Webber C, Dickens NJ, Letunic I, Suyama M, Torrents D, von Mering C, Zdobnov EM, Makova K, Nekrutenko A, Elnitski L, Eswara P, King DC, Yang S, Tyekucheva S, Radakrishnan A, Harris RS, Chiaromonte F, Taylor J, He JB, Rijnkels M, Griffiths-Jones S, Ureta-Vidal A, Hoffman MM, Severin J, Searle SMJ, Law AS, Speed D, Waddington D, Cheng Z, Tuzun E, Eichler E, Bao ZR, Flicek P, Shteynberg DD, Brent MR, Bye JM, Huckle EJ, Chatterji S, Dewey C, Pachter L, Kouranov A, Mourelatos Z, Hatzigeorgiou AG, Paterson AH, Ivarie R, Brandstrom M, Axelsson E, Backstrom N, Berlin S, Webster MT, Pourquie O, Reymond A, Ucla C, Antonarakis SE, Long MY, Emerson JJ, Betran E, Dupanloup I, Kaessmann H, Hinrichs AS, Bejerano G, Furey TS, Harte RA, Raney B, Siepel A, Kent WJ, Haussler D, Eyras E, Castelo R, Abril JF, Castellano S, Camara F, Parra G, Guigo R, Bourque G, Tesler G, Pevzner PA, Smit A, Fulton LA, Mardis ER, Wilson RK. (2004) 'Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution'. NATURE PUBLISHING GROUP NATURE, 432 (7018), pp. 695-716.
  • Puchalka J, Kierzek AM. (2004) 'Bridging the gap between stochastic and deterministic regimes in the kinetic simulations of the biochemical reaction networks'. BIOPHYSICAL SOCIETY BIOPHYSICAL JOURNAL, 86 (3), pp. 1357-1372.
  • Zaim J, Kierzek AM. (2003) 'Domain organization of activation-induced cytidine deaminase'. NATURE PUBLISHING GROUP NATURE IMMUNOLOGY, 4 (12), pp. 1153-1153.
  • Zaim J, Kierzek AM. (2003) 'The structure of full-length LysR-type transcriptional regulators. Modeling of the full-length OxyR transcription factor dimer'. OXFORD UNIV PRESS NUCLEIC ACIDS RESEARCH, 31 (5), pp. 1444-1454.
  • Kierzek AM. (2002) 'STOCKS: STOChastic kinetic Simulations of biochemical systems with gillespie algorithm'. OXFORD UNIV PRESS BIOINFORMATICS, 18 (3), pp. 470-481.
  • Kucharczyk R, Kierzek AM, Slonimski PP, Rytka J. (2001) 'The Ccz1 protein interacts with Ypt7 GTPase during fusion of multiple transport intermediates with the vacuole in S-cerevisiae'. COMPANY OF BIOLOGISTS LTD JOURNAL OF CELL SCIENCE, 114 (17), pp. 3137-3145.
  • Kucharczyk R, Kierzek AM, Slonimski PP, Rytka J. (2001) 'The Ccz1p interacts with Ypt7 GTPase in the process of fusion of multiple transport intermediates with the vacuole in S.cerevisiae.'. JOHN WILEY & SONS LTD YEAST, 18, pp. S242-S242.
  • Kierzek AM, Zielenkiewicz P. (2001) 'Models of protein crystal growth'. ELSEVIER SCIENCE BV BIOPHYSICAL CHEMISTRY, 91 (1), pp. 1-20.
  • Kierzek AM, Zaim J, Zielenkiewicz P. (2001) 'The effect of transcription and translation initiation frequencies on the stochastic fluctuations in prokaryotic gene expression'. AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC JOURNAL OF BIOLOGICAL CHEMISTRY, 276 (11), pp. 8165-8172.
  • Kierzek AM, Pokarowski P, Zielenkiewicz P. (2000) 'Microscopic model of protein crystal growth'. ELSEVIER SCIENCE BV BIOPHYSICAL CHEMISTRY, 87 (1), pp. 43-61.
  • Georgalis Y, Kierzek AM, Saenger W. (2000) 'Cluster formation in aqueous electrolyte solutions observed by dynamic light scattering'. AMER CHEMICAL SOC JOURNAL OF PHYSICAL CHEMISTRY B, 104 (15), pp. 3405-3406.
  • Kaczanowski S, Kierzek AM, Zielenkiewicz P. (1999) 'Shuffling algorhithm for protein design'. BENTHAM SCIENCE PUBL BV PROTEIN AND PEPTIDE LETTERS, 6 (2), pp. 99-104.
  • Kierzek AM, Pokarowski P, Zielenkiewicz P. (1999) 'Lattice simulations of protein crystal formation'. ELSEVIER SCIENCE BV BIOPHYSICAL CHEMISTRY, 77 (2-3), pp. 123-137.
  • Kierzek AM, Wolf WM, Zielenkiewicz P. (1997) 'Simulations of nucleation and early growth stages of protein crystals'. BIOPHYSICAL SOCIETY BIOPHYSICAL JOURNAL, 73 (2), pp. 571-580.

Conference papers

  • Fisher CP, Kierzek AM, Plant NJ, Moore JB. (2014) 'DYNAMIC MODELLING OF HEPATOCYTE REGULATORY NETWORKS USING NOVEL PETRI NET-LINKED FLUX BALANCE ANALYSIS'. INFORMA HEALTHCARE DRUG METABOLISM REVIEWS, Toronto, CANADA: 10th International Meeting of the International-Society-for-the-Study-of-Xenobiotics (ISSX) 45, pp. 160-161.
  • Speina E, Kierzek AM, Tudek B. (2003) 'Chemical rearrangement and repair pathways of 1,N-6-ethenoadenine'. ELSEVIER SCIENCE BV MUTATION RESEARCH-FUNDAMENTAL AND MOLECULAR MECHANISMS OF MUTAGENESIS, WARSAW, POLAND: 32nd Annual Meeting of the European-Environmental-Mutagen-Society 531 (1-2), pp. 205-217.


1. BMS3072: Systems Biology: Genomes in Action
2. BMS1023: Numeracy skills and Statistics
4. MSc Coourses: Statistics

Departmental Duties

Professor of Systems Biology

Module organiser for BMS3072


  1. EraSysBio+/BBSRC grant “Integration of modeling with transcription and gene essentiality profiling to study interaction of MTB bacillus with macrophages and dendritic cells” PI and international consortium coordinator: Dr Andrzej M. Kierzek Co-investigators: Graham Stewart (University of Surrey), Johnjoe McFadden (University of Surrey), Olivier Neyrolles (CNRS, France), Ludovic Tailleux (Institute Pasteur, France), Steffen Klamt (Max Planck Institute Germany), Maria Foti (University Milan-Biocca, Italy). Amount for University of Surrey: £495,173 Entire consortium award €1,260,501, Start: 01/03/2010, End: 28/02/2013 (36 months)
  2. BBSRC grant "Predictive Analysis of Network Activation in Response to Lipid Loading in the Liver". PI: Dr Bernadette J. Moore, Co-applicants: Dr Andrzej M.Kierzek, Dr Nick Plant. Amount: £498.809. Start: 01/04/2011 End: 01/04/2014
  3. Wellcome Trust grant “Investigation of metabolism and substrate utilization of Mycobacterium Tuberculosis”, PI: Johnjoe McFadden, Co-applicants: Andrzej M. Kierzek, Graham Stewart, Dany Beste amount: £405,329 Start: 01/09/2009, End 30/08/2011 (36 months)
  4. BBSRC grant: "In silico study of lignocellulosic biofuel processes.". PI: Professor Michael Bushell Co-applicants: Dr Andrzej M. Kierzek, Dr Claudio Avignone-Rossa. Amount: £107,059. Start: 01/05/2009. End: 01/05/2012

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