Professor Andrew J. Millar (FRS FRSE)

Chair of Systems Biology; Business Owner of University Research Data Service

Biography

Background

1985-1988 University of Cambridge, BA Hons (I) in Genetics
1988-1994 The Rockefeller University, New York, Ph.D.
1994-1995 University of Virginia, NSF Centre for Biological Timing, LSRF post-doc Fellowship.
1996-2004 University of Warwick, Dept. of Biological Sciences, lecturer, reader, professor.
2002-2007 BBSRC Research Development Fellow
2003-2004 Programme Manager of the Interdisciplinary Programme for Cellular Regulation, with Dr. Nigel Burroughs (Maths).
2004-present University of Edinburgh, School of Biological Sciences - Professor of Systems Biology.
2007-2011 Director, Centre for Systems Biology at Edinburgh (CSBE)
2012-present Associate Director, SynthSys
2011-2013 Elected to EMBO Membership, Fellowship of the Royal Society and Royal Society of Edinburgh.
2016-2018 Non-Executive Director, James Hutton Institute.

 

Responsibilities & affiliations

2016 - 2021 Vice-Chair of FAIRDOM e.V.

2018-2024 Member of BBSRC Council

2018-2021 Chief Scientific Advisor on Environment, Natural Resources and Agriculture, for Scottish Government

Undergraduate teaching

  • Quantitative Skills for Biologists in the first year
  • Plant Physiology and on Time, Light and Stress in final year Bachelor's course

Postgraduate teaching

  • Systems and Synthetic Biology Masters course
  • (some years) Ecological Economics M.Sc. guest lecture

Open to PhD supervision enquiries?

Yes

Areas of interest for supervision

I am very happy to hear from students interested in Open Research, research data management, or science policy/science diplomacy.

I am no longer offering to supervise experimental or modelling projects.

Research summary

Please see below for a summary of my first 30 years of research using experiments and mathematical modelling in systems biology. Those activities have ended. We now work in three areas, see 'Current Research Interests' below.

The Bio_RDM team that I supervise supports and advocates Open Research methods, especially in biology: see the team's Research Data Management wiki. This work builds on our decades of linking specialised data analysis software for circadian biology with data management, and with mathematical modelling in systems biology and plant science, linking up to crop science and ecology. For example, the BioDare2 (and older BioDare) integrate rhythmic timeseries analysis with a data-sharing repository. We have also contributed software development for multiple, external repositories: OMERO for microscopy imaging, SynBioHub for synthetic biology designs, and formerly our PlaSMo model repository of curated, crop science, ecology and systems biology models (now hosted on FAIRDOMHub). The Bio_RDM team has worked for many years with EPCC, Edinburgh's advanced computing centre, the University's Digital Research Services group, and external groups including the FAIRDOMHub repository, ETHZ IT, and OMERO.

Our experience in managing interdisciplinary research led us to two, related areas, science policy and research community organisation, which overlaps with current work on Research Cultures. The first area overlaps with Research on Research and the Science of Network Science. Here, we work with colleagues in the University's Business School and in Science, Technology and Innovation Studies, including the Innogen Institute. The interest in community organisation arose from Andrew's involvement in the UK organisation for Arabidopsis researchers, GARNet, from about 2000. The challenge of providing global Food Security in the coming decades sparked Andrew's interest in economics and monetary reform, particularly how the macroeconomic environment affects research systems.

Current research interests

*Science policy*, in a Scottish, UK and international context; related to my previous work as Chief Scientific Adviser. ____ *Research community organisation and the social infrastructure of science*. My group seeks to understand and implement the practical aspects of mission-oriented research management. We are currently working with the UK Chronobiology community, on BioClocks UK: see https://bioclocks.uk ____ *Open Research*, particularly research data management and e-infrastructure (data storage, computing power) that increases research productivity and also promotes FAIR and Open practices. The Bio_RDM team creates, integrates and manages online systems for this area. This is related to previous roles in BBSRC, FAIRDOM e.V. and with the University's Research Data Services (2017-22). ____ We often collaborate.

Past research interests

The first half of my career linked plant science, systems biology and chronobiology - see the legacy website at http://millar.bio.ed.ac.uk/. We aimed to understand how the 24-hour biological clock in plants is constructed and adjusted, why it (and other clocks) evolved in the way that we now find it, and how it affects plant life from the cell to the ecosystem. Most of our research focused on /Arabidopsis thaliana/, which is a small plant with a big following. For an even simpler version of the plant clock mechanism, we studied the smallest free-living eukaryote of all, the tiny alga /Ostreococcus tauri/ (summarised in Noordally & Millar, Biochemistry, 2015). ******** Molecular genetics and transgenic plants revealed rhythms that are usually invisible: for example, my Ph.D. work starting in 1988 applied a reporter gene called luciferase send us "video footage", showing when clock genes were active. This work required new methods in timeseries data analysis; we provided the BRASS interface and then the online BioDare/BioDare2 resources to make these user-friendly. From about 2000, mathematical modelling helped us to understand dynamic, quantitative regulation, to abstract the principles behind the molecular detail, and to cross length and time scales from the cell to the whole organism. The modelling led to new mathematical theory and resources such as the Systems Biology Software Infrastructure (SBSI), which made high-performance computers easily accessible for SBML model optimisation. The modelling was particularly important in the BBSRC Centre for Systems Biology at Edinburgh, and in the BBSRC ROBuST and EU FP7 TiMet projects. TiMet aimed to understand the links from the clock to plant metabolism. As a result, we developed the first models that linked the clock genotype of a plant, quantitatively, to all the canonical, whole-plant phenotypes of clock mutants. We then connected the clock model down to genome sequence (unpublished as of 2018), and up to the whole plant life-cycle in a particular environment. ******** In its last years, this research programme sought to unify biology across scales of analysis. If we claim to understand how genomes in cells build organisms, and how populations of organisms in their environment select genomes, then we ought to understand (explain and predict) the whole of this causal cycle for a single biological system. There is a real prospect that science will soon do so, linking biochemistry, molecular biology, genetics, physiology, ecology and evolution, for example for the plant clock. Our practical steps towards this are summarised in Millar, Annual Review Plant Biology, 2016 and Millar et al., J. Exp. Bot, 2019. ******** In parallel, we re-discovered a completely different clock mechanism that operates without rhythmic gene activity, and might be shared among all organisms (outlined in the 'nutshell' video and in Van Ooijen et al., Trends Biochem. Sci. 2012). Measuring dynamic biochemistry was also a key challenge for our gene circuit models. In both areas, we applied biophysical methods extensively with our colleagues in SynthSys, including label-free (phospho)proteomics. ******** For more research details, please see the legacy lab web page http://millar.bio.ed.ac.uk, which includes older material on our past work.

Knowledge exchange

We research university attitudes to and understanding of knowledge exchange, and practical indicators that reflect interdisciplinarity and cross-sectoral research activity.

The role of science evidence in public deliberation (mini-publics, e.g. citizen juries) is a current interest.

Affiliated research centres

Research activities

View all 137 activities on Research Explorer

Project activity

My projects and funding are listed in the Elsevier/RELX PURE system contracted by the University of Edinburgh, which is public on the Edinburgh Research Explorer website, www.research.ed.ac.uk. You can see my current list of funding awards (here called "projects") on this link:

https://www.research.ed.ac.uk/portal/en/persons/andrew-millar(86879389-dd37-4d9d-886e-f549ed23f6ba)/projects.html

For a different view of research projects, please see Freeman and Millar (2017) "Valuing the Project" in Public Money & Management.

Current project grants

Please use the link to Edinburgh Research Explorer - https://www.research.ed.ac.uk/portal/en/persons/andrew-millar(86879389-dd37-4d9d-886e-f549ed23f6ba)/projects.html

Past project grants

Please use the link to Edinburgh Research Explorer - https://www.research.ed.ac.uk/portal/en/persons/andrew-millar(86879389-dd37-4d9d-886e-f549ed23f6ba)/projects.html

View all 206 publications on Research Explorer

Conference details

Recent conferences are listed in the Activities feed at this link https://www.research.ed.ac.uk/portal/en/persons/andrew-millar(86879389-dd37-4d9d-886e-f549ed23f6ba)/activities.html

This is the most up to date list, drawn automatically from the RELX/Elsevier PURE system contracted by the University of Edinburgh.

Online resources, mostly from multi-partner projects:

  1. COVID Wastewater Scotland, 2022. https://covid-ww-scotland.github.io/ (Open outputs of monitoring COVID in wastewater in Scotland). Developed by the Bio_RDM team, U. of E.
  2. BioClocks UK, 2022. https://bioclocks.uk. Volunteer-led project to fund a research community organisation, initiated by AJM.
  3. Biodare2, by Tomasz Zielinski, June 2017, www.biodare2.ed.ac.uk. Online analysis, visualisation and repository of biological rhythm data. YouTube video tutorial channel, https://www.youtube.com/channel/UC1_kTC0PFOkoKueJGo4h7dA,
  4. Plant Systems Modelling (PlaSMo), 2012 repository of XML models, www.plasmo.ed.ac.uk, supported construction of the FMv1 model, Chew et al. PNAS 2014. Migrated to FAIRDOMHub.org in 2019, see Zielinski, T., Hay, J. & Millar, A. J. Wellcome Open Research, 2019.
  5. Biological Data Repository (BioDare), 2009, online data repository with analysis replacing BRASS software, www.biodare.ed.ac.uk.
  6. GARNet online resources supporting the UK Arabidopsis research community. Established 2004, latterly at https://garnetcommunity.org.uk; archived at http://web.archive.org/web/20230000000000*/https://garnetcommunity.org.uk/

 

Software

The Millar group has developed and distributed the following software, which is available from online repositories such as Sourceforge, GitHub:

  1. Biological Rhythms Analysis Software Suite (rhythmic data analysis tools, for biologists; BRASS v3, see ref. 81.), 2002-2008. Used by > 80 external publications, Google Scholar 2014. Replaced by BioDare/BioDare2.
  2. Circadian Watch, 2002 (mathematical analysis of clock models), now part of Rand’s TeSSy suite,
  3. Circadian Modelling, 2003 (simulation of clock models for biologists, see ref. 61), now deprecated because SBML Level 2 simulators can have similar functionality using the ISSF (see Adams et al. 2012) to describe external signals.
  4. Systems Biology Software Infrastructure, 2012, fitting of SBML models to data on high-performance computers, sourceforge.net/projects/sbsi/. Now inactive.
  5. qpMerge, 2016, Merging different peptide isoforms using a motif centric strategy, http://sourceforge.net/projects/ppmerge
  6. Biodare2 software: BioDare2-Backend, https://github.com/SynthSys/BioDare2-BACK;

BioDare2-UI, https://github.com/SynthSys/BioDare2-UI.

  1. SBML Data Tools, by Alastair Hume, released 2016. https://github.com/allyhume/SBMLDataTools. Published in Millar et al. J. Exp. Bot. 2019.
  2. Chromar, modelling environment in Haskell by Argyris Zardilis, released Oct 2017. https://github.com/azardilis/Chromar. Published in Honorato-Zimmer et al. 2016.
  3. OpenNGS, NGS data resource by Tomasz Zielinski; pre-release Jan 2019. Not yet public.
  4. Tools for SEEK power users, published in Hay et al. Synthetic Biology 2019. https://github.com/SynthSys/Seek-Java-RESTClient and https://github.com/SynthSys/Seek-Bulk-Update
  5. pyNMMSO, 2019, a python implementation of the Niching Migratory Multi-Swarm [model] Optimiser of J. Fieldsend et al. 2014, by Alastair Hume and Chris Wood https://github.com/EPCCed/pynmmso
  6. PyOmeroUpload, 2020, by Johnny Hay and Tomasz Zielinski. Published in Hay et al. Wellcome Open Res. 2020. https://github.com/SynthSys/pyOmeroUpload
  7. OmeroTools, 2021, by Johnny Hay. Extends PyOmeroUpload. https://github.com/SynthSys/omero-toolkit
  8. SBOL-toolkit, 2021, by Johnny Hay and Tomasz Zielinski. Published in Zielinski et al. 2022, Synthetic Biology (ref 144). Utilities for enriching SBOL files with metadata. https://github.com/SynthSys/sbol-toolkit
  9. Synbio-toolkit, 2021, by Johnny Hay. Extends SBOL-toolkit, see ref. 144. Tools for interacting with SynBioHub repositories. https://github.com/SynthSys/synbio-toolkit

Major resources are included in the automated data feed from PURE under the Publications section, please click on 'All Publications' at the foot of that page.

Integrated data, models and/or software bundles, often related to publications:

  1. Schaum, Elisa; Rost, Björn; Millar, Andrew J; Collins, Sinéad (2013): Seawater carbonate chemistry and net photosynthesis, C/N ratio, growth rate, size of Ostreococcus tauri in a laboratory experiment. PANGAEA, https://doi.org/10.1594/PANGAEA.823378,  Supplement to: Schaum, E et al. (2012) Nature Climate Change.
  2. Chew Y.H. et al. Arabidopsis Framework Model v1, predicting rosette growth with a multi-model ensemble. Live repository: https://fairdomhub.org/investigations/222. Supplement to Chew et al. PNAS 2014, Multiscale digital Arabidopsis predicts individual organ and whole-organism growth.
  3. Data for Hindle et al. BMC Genomics 2014, The reduced kinome of Ostreococcus tauri: core eukaryotic signalling components in a tractable model species. PURE ID 17865462. Edinburgh Datashare, http://dx.doi.org/10.7488/ds/72
  4. Data, Code and Models for Flis et al. RS Open Biology 2015, PURE ID 21502727, https://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=12820611467827 and 30 further records, including 6 SBML models (also in BioModels).
  5. Data and scripts for Seaton et al. 2018, Photoperiodic control of the Arabidopsis proteome reveals a translational coincidence mechanism. PURE ID 54485938. Live repository: https://fairdomhub.org/investigations/163 Snapshot, as in publication: https://doi.org/10.15490/fairdomhub.1.investigation.163.2
  6. Kinmonth-Schultz et al., 2019. Temperature effects on Arabidopsis floral induction at the leaf-specific level. FAIRDOMHub.org (Live) https://fairdomhub.org/investigations/289, supplement to Kinmonth-Schultz et al. in Silico Plants, 2019.
  7. Urquiza, U. and Millar A.J., 2021. Absolute units in Arabidopsis clock models up to U2020.3, FAIRDOMHub.org, (Live) https://fairdomhub.org/investigations/170 and 10.15490/fairdomhub.1.investigation.170.3 (Snapshot) Supplement to Urquiza and Millar, in Silico Plants, 2021.
  8. Chew, Y.H. et al. 2022. Prediction and analysis of phenotypes in the Arabidopsis clock mutant prr7prr9 using the Framework Model v2 (FMv2). FAIRDOMHub.org, https://fairdomhub.org/investigations/123 (live) and 10.15490/fairdomhub.1.investigation.123.1 (Snapshot). Supplement to Chew et al. 2022 in Silico Plants.
  9. Scorza, Livia; Baby, Sumy V; Zieliński, Tomasz. 2022. SARS-CoV-2 RNA levels in Scotland's wastewater. https://doi.org/10.5281/zenodo.6339631 and https://github.com/BioRDM/COVID-Wastewater-Scotland. Data, scripts and protocols supporting Scorza et al. Scientific Data 2022.

 

 Standalone data sets

In addition to hundreds of datasets on BioDare.ed.ac.uk and BioDare2.ed.ac.uk, the following data sets are available from public repositories:

  1. Proteomic data in ProteomeXchange repositories:
    1. PRIDE data PXD001134, from "Sample preparation for phosphoproteomic analysis of circadian time series in Arabidopsis thaliana" DOI: 10.7488/ba4de74b-7d98-48c8-b587-c43f44dc37c9, http://www.ebi.ac.uk/pride/archive/projects/PXD001134
    2. MassIVE data from GIGANTEA IP timeseries in Arabidopsis under LD cycles, PRIDE PXD006859, supplement to Krahmer et al. FEBS Lett. 2019, http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD006859-1 .
    3. MassIVE data from Arabidopsis LL proteomic and phosphoproteomic timeseries experiments, PRIDE PXD009230, supplement to Krahmer et al. Mol Cell Prot. 2022, http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD009230
    4. PRIDE data from Ostreococcus LD and DA and DD proteomic and phosphoproteomic timeseries experiments, IDs PXD001735, PXD001734 and PXD002909 are supplements to Noordally et al. bioRxiv 2018, 2022. PXD002874 and PXD002909 are to be published.
    5.  
  2. Timeseries data in the BioDare/BioDare2 repositories, including RNA timeseries:
    1. Close-up images and timeseries data from "Spontaneous spatiotemporal waves of gene expression from biological clocks in the leaf", Wenden, Toner et al. PNAS 2012, 109: 6757-6762. PURE ID 15155940 http://www.biodare.ed.ac.uk/robust/ShowExperiment.action?experimentId=13394437504756 and 10 more links.
    2. Numerical timeseries data from >100 experiments of in vivo imaging from LUCIFERASE reporter fusions in Arabidopsis plants, 2006-9, in series numbered ATnnnn.
    3. ‘TiMet’ reference timeseries RNA data from Flis et al. Open Biology 2015, BioDare ID’s to follow.
    4. RNA data from Edwards et al. Mol Syst Biol 2010 for seven clock genes in Arabidopsis thaliana; now in BioDare, ID to follow.
    5. Curated data from the literature for clock and photoperiod genes in Arabidopsis
    6. RNA data from Edwards et al. Plant Cell 2006 for five clock genes in Arabidopsis

 

  1. Microarrays from NASC or GEO:
    1. NASCarrays 430 / E-GEOD-19263, Identifying Y candidate genes, 2009.
    2. NASCarrays 108 / E-GEOD-5612, Transcription profiling of Arabidopsis in continuous white light - circadian experiment, 2008.
    3. NASCarrays 334 / E-GEOD-5528, Transcription profiling of Arabidopsis FLC genotype seedlings at 27°C, 2008.
    4. NASCarrays 196, Circadian gene expression under different light treatments (cFR and T cycles), 2008.
    5. NASCarrays 2, Circadian Clock in dark-grown seedlings, 2006.

Models

  1. SBML models from the public Biomodels repository, PlaSMo database or FAIRDOMHub:
    1. 4 versions of U2019, U2020 models from Urquiza and Millar in Silico Plants 2021, FAIRDOMHub.org https://fairdomhub.org/investigations/170.
  1. 6 versions of P2011 model from Flis et al, Open Biology 2015, identifiers to follow.
  1. 6 models from Dixon et al, New Phytologist 2014, identifiers to follow.
  1. BIOMD0000000445, is the Arabidopsis clock model with ABA and TOC1 functions, from Pokhilko et al. BMC Syst. Biol. 2013.
  2. BIOMD0000000412, is the Arabidopsis repressilator clock model, from Pokhilko et al. Mol Syst. Biol. 2012.
  3. BIOMD0000000273, is the Arabidopsis multiloop clock model, from Pokhilko et al. Mol Syst. Biol. 2010.
  4. BIOMD0000000350, single-loop clock model for Ostreococcus, from Troein et al. Plant Journal 2011.
  5. MODEL1005050000, photoperiod pathway model 3 for flowering time in Salazar et al. Cell 2009.  
  6. MODEL0911120000 is the phytochrome-based, synthetic light switch in yeast of Sorokina et al. J. Biol. Eng. 2009.
  7. BIOMD0000000214 is the temperature-compensated Neurospora clock model (model2) of Akman et al. Mol. Syst. Biol. 2008. Model 1 of that paper is MODEL8306248909 in the uncurated branch.
  8. BIOMD0000000089 is the Arabidopsis 3-loop clock model, from Locke et al. Mol Syst Biol 2006. 
  9. BIOMD0000000055 is the Arabidopsis interlocking loop clock model, from Locke et al. Mol Syst Biol 2005. 
  1. 10 published crop and plant biology models refactored into the SimileXML format in the PlaSMo database at SynthSys (www.plasmo.ed.ac.uk), now hosted at FAIRDOMHub.
  2. The following non-SBML models from our papers (PLM_ are PlaSMo identifiers):
  1. PLM_47                                   arabidopsis_clock_biopepa, from Guerriero et al. Interface 2011.
  2. PLM_50           DomijanTS_AtClock2011, from Gould et al. Mol Syst Biol 2013.
  3. PLM_51                                   Neurospora Circadian Clock 3-variable model, from Adams et al. JBR 2012.
  4. PLM_52           Neurospora Circadian Clock 3-variable model - sinusoidal light oscillations
  5. PLM_66           Modified Locke Arabadopsis 3 loop Circadian Clock
  6. PLM_70           Arabidopsis clock model with ABA and TOC1 effects, from Pokhilko et al. BMC Syst Biol. 2013.
  7. 10 versions of Troein et al. 2011 model, from Dixon et al. New Phytol. 2014
  8.  PLM_75 Refactored subset of Arabidopsis Greenlab model
  9.  PLM_76 Arabidopsis Framework Model, FMv1, from Chew et al. PNAS 2014
  10.  PLM_1010 Linked clock outputs model from Seaton et al. Mol Syst Biol 2015.
  11. Arabidopsis Framework Model FMv2, from Chew et al. BioRxiv 2017; in Silico Plants 2022, https://github.com/danielseaton/frameworkmodel;   https://fairdomhub.org/models/248
  12. Arabidopsis Framework model (simplified; FM-lite) and Whole-Lifecycle (FM-life), from Zardilis et al. J. Exp. Bot. 2019. https://github.com/azardilis/ChromarFM
  13. Arabidopsis Framework Model FMv1.5, from Kinmonth-Schultz et al. in silico Plants 2019. https://fairdomhub.org/assays/1011
This article was published on