Dr Conor Lawless
- Email: firstname.lastname@example.org
- Personal Website: http://www.staff.ncl.ac.uk/conor.lawless/
- Address: Institute for Cell and Molecular Biosciences
Newcastle upon Tyne
I am a computational biologist (post-doctoral research scientist) working in the Lydall lab in the Institute for Cellular and Molecular Biosciences (ICaMB) at Newcastle University.
I am particularly interested in computational and mathematical models of biological systems and the assessment and development of such models (usually dynamic, mechanistic simulation models). I develop high-throughput, robotic, growth assays for carrying out genome-wide fitness screens in the model organism S. cerevisiae (budding yeast), comparing fitnesses to search for evidence for interactions between genes on a genome-wide scale. These quantitative, genome-wide tools allow us to develop a systematic understanding of targetted areas of eukaryotic biology.
The Lydall lab is specifically interested in applying these tools to develop a systematic understanding of how the telomere cap works, which may improve our understanding of replicative senescence (relevant for ageing) and cancer. Much of my previous work has involved studying replicative senescence and ageing in human cell cultures and I enjoy drawing parallels between the two model systems.
My university website contains other things I am interested in.
Mathematical modelling, numerical simulation, stochastic simulation, systems biology, optimisation, parameter estimation, Bayesian inference, quantitative growth assays, image analysis, epistasis, automated microscopy, data handling, data visualisation, robot-assisted science.
Currently I work on genome-wide screens in S. cerevisiae to understand the function of the telomere cap. I developed an image analysis tool: Colonyzer and complementary Quantitative Fitness Analysis (QFA) workflows to infer growth rates and genetic interaction strengths from robot-assisted timelapse photography of thousands of independent microorganism cultures growing in parallel on solid agar plates. I have developed software which automates QFA and allows visualisation of genome-wide datasets. There is a video article describing QFA in more detail. I also developed the computational infrastructure for capturing, archiving and analysing data generated by the High-throughput Screening service at Newcastle (which carries out QFA screens, for example).
Introduction to Python for Scientists
As experimental datasets become bigger and more quantitative, and as computing becomes more ubiquitous, being able to read and write code has become an increasingly important component of scientific literacy. Python is a powerful general programming language, that is very clean and easy to learn and is extensible to an amazing range of applications. Each year I run a half day workshop introducing Python programming to biology researchers in the medical school. The course notes are available online.
Mathematical Modelling in Systems Biology
I lecture on the MRes in Systems Biology course run at Newcastle.
- Heydari JJ, Lawless C, Lydall DA, Wilkinson DJ. Bayesian hierarchical modelling for inferring genetic interactions in yeast. Journal of the Royal Statistics Society: Series C (Applied Statistics) 2015, epub ahead of print.
- Dubarry M, Lawless C, Banks AP, Cockell S, Lydall D. Genetic Networks Required to Coordinate Chromosome Replication by DNA Polymerases α, δ, and ε in Saccharomyces cerevisiae. G3: Genes, Genomes, Genetics 2015, 5(10), 2187-2197.
- Narayanan S, Dubarry M, Lawless C, Banks AP, Wilkinson DJ, Whitehall SK, Lydall D. Quantitative Fitness Analysis Identifies exo1∆ and Other Suppressors or Enhancers of Telomere Defects in Schizosaccharomyces pombe. PloS ONE 2015, 10(7), e0132240.
- Jurk D, Wilson C, Passos J, Oakley F, Correia-Melo C, Greaves L, Saretzki G, Fox C, Lawless C, Anderson R, Hewitt G, Pender SLF, Fullard N, Nelson G, Mann J, van de Sluis B, Mann DA, von Zglinicki T. Chronic inflammation induces telomere dysfunction and accelerates ageing in mice. Nature Communications 2014, 2, 4172.
- Heydari J, Lawless C, Lydall DA, Wilkinson DJ. Fast Bayesian parameter estimation for stochastic logistic growth models. Biosystems 2014, 122, 55-72.
- Andrew EJ, Merchan S, Lawless C, Banks AP, Wilkinson DJ, Lydall D. Pentose Phosphate Pathway Function Affects Tolerance to the G-Quadruplex Binder TMPyP4. PLoS One 2013, 8(6), e66242.
- Banks AP, Lawless C, Lydall DA. A Quantitative Fitness Analysis Workflow. Journal of Visualised Experiments 2012, 66, e4018.
- Nelson G, Wordsworth J, Wang CF, Jurk D, Lawless C, Martin-Ruiz C, von Zglinicki T. A senescent cell bystander effect: senescence-induced senescence. Aging Cell 2012, 11(2), 345-349.
- Lawless C, Jurk D, Gillespie CS, Shanley D, Saretzki G, von Zglinicki T, Passos JF. A Stochastic Step Model of Replicative Senescence Explains ROS Production Rate in Ageing Cell Populations. PLoS One 2012, 7(2), e32117.
- Chang H, Lawless C, Addinall SG, Oexle S, Taschuk M, Wipat A, Wilkinson DJ, Lydall D. Genome-Wide Analysis to Identify Pathways Affecting Telomere-Initiated Senescence in Budding Yeast. G3: Genes, Genomes, Genetics 2011, 1(3), 197-208.
- Addinall SG, Holstein EM, Lawless C, Yu M, Chapman K, Banks AP, Ngo HP, Maringele L, Taschuk M, Young A, Ciesiolka A, Lister AL, Wipat A, Wilkinson DJ, Lydall D. Quantitative Fitness Analysis Shows That NMD Proteins and Many Other Protein Complexes Suppress or Enhance Distinct Telomere Cap Defects. PLoS Genetics 2011, 7(4), e1001362.
- Chen YH, Lawless C, Gillespie CS, Wu J, Boys RJ, Wilkinson DJ. CaliBayes and BASIS: integrated tools for the calibration, simulation and storage of biological simulation models. Briefings in Bioinformatics 2010, 11(3), 278-289.
- Lawless C, Wilkinson DJ, Young A, Addinall SG, Lydall DA. Colonyzer: automated quantification of micro-organism growth characteristics on solid agar. BMC Bioinformatics 2010, 11(1), 287.
- Miwa S, Lawless C, von Zglinicki T. Correction of radiolabel pulse-chase data by a mathematical model: application to mitochondrial turnover studies. Biochemical Society Transactions 2010, 38, 1322-1328.
- Lawless C, Wang CF, Jurk D, Merz A, von Zglinicki T, Passos JF. Quantitative assessment of markers for cell senescence. Experimental Gerontology 2010, 45(10), 772-778.
- Henderson DA, Boys RJ, Krishnan KJ, Lawless C, Wilkinson DJ. Bayesian Emulation and Calibration of a Stochastic Computer Model of Mitochondrial DNA Deletions in Substantia Nigra Neurons. Journal of the American Statistical Association 2009, 104(485), 76-87.
- Miwa S, Lawless C, von Zglinicki T. Mitochondrial turnover in liver is fast in vivo and is accelerated by dietary restriction: application of a simple dynamic model. Aging Cell 2008, 7(6), 920-923.
- Lawless C, Semenov MA, Jamieson PD. Quantifying the effect of uncertainty in soil moisture characteristics on plant growth using a crop simulation model. Field Crops Research 2008, 106(2), 138-147.
- Lawless C, Semenov MA, Jamieson PD. A wheat canopy model linking leaf area and phenology. European Journal of Agronomy 2005, 22(1), 19-32.
- Lawless C, Semenov MA. Assessing lead-time for predicting wheat growth using a crop simulation model. Agricultural and Forest Meteorology 2005, 135(1-4), 302-313.