Dr Mark Read

School of Life and Environmental Sciences

F07 - Carslaw Building
The University of Sydney


Website Personal website

Biographical details

Mark joined the Charles Perkins Centre as a research fellow in 2013, enticed by its cross-disciplinary emphasis on understanding and tackling disease. Here he will employ computational modelling and simulation techniques to help understand how bacterial ecosystems in the gut influence nutrient uptake, and how this in turn can influence health.

Mark’s background lies in computational immunology; he created the world’s first agent-based simulation of experimental autoimmune encephalomyelitis, a murine model of T-cell mediated multiple sclerosis. His research has explored how computational modelling and simulation can complement traditional wet-lab research, and the complex issue of ensuring that models and simulations adequately capture biological systems that are typically not well understood. He is well versed in both the benefits and challenges of conducting cross-disciplinary research, having worked closely with Biologists, Engineers, Mathematicians, Statisticians and Computer Scientists throughout his research career. He is a versatile researcher, and has in recent years applied his skills in developing and exploring underwater swarm robotics algorithms: algorithms that allow systems of underwater robots to collectively act in a decentralised and self-organised manner to solve complex tasks.

Mark fully embraces interdisciplinary research; he enjoys the challenges and the unique success that comes from tackling new problems as part of a team of experts from a wide variety of fields.

Research interests

Mark’s research interests lie on the interdisciplinary boundary between computer science, engineering and the life sciences. This includes both the furthering of natural science research through the application of computational techniques, and taking inspiration from natural systems in providing novel and effective solutions to engineering problems. Through his work on computationally modelling disease, Mark explores the fields of biology; modelling; simulation; calibration; statistics and sensitivity analysis; and the design, conductance and interpretation of simulation-based experimentation. His research places a particular emphasis on instilling trust and confidence that simulation results are representative of the biology, and on performing novel experimentation using simulation.

Current projects

"Integrative simulation techniques to disentangle host-diet-gut microbiome interactions." With A/Prof Andrew Holmes (USyd), Prof Stephen Simpson (Usyd), Prof David Raubenheimer (Usyd). The microbiome is increasingly implicated in our health status, but the factors underlying the composition of this complex cross-feeding community remain elusive. We use novel simulation techniques to determine intervention strategies to manipulate the microbiome to our benefit.

"Determining optimal search strategies through theory, functional analysis and simulation." With Dr Maté Biro (UNSW), Dr Greg Rice (Waterloo, Canada). We are developing technologies to determine the best way for motile agents to find their targets, be they mates, food or shelter. This has applications in immunology, ecology, zoology and in swarm robotic systems.

"Unravelling the dynamics of haematopoietic stem cells." With Dr. Ben Roediger, Dr. Chris Jolly (Centenary Inst). These stem cells ultimately give rise to all blood and immune system cells, their function is critical to our health. We are using simulation to reconcile seeming contradictions around their dynamics, and determining how their behaviour can lead to either homeostasis or cancer.

"Factors underlying neutrophil swarming elucidated through simulation." With Dr Tatyana Chtanova (Garvan), Prof Jon Timmis (University of York, UK). Neutrophils, key immune system cells, exhibit a striking swarming motility pattern in response to pathogens and injury. We are using simulation techniques to recreate these dynamics and thereby understand their emergence.

"Methdological advancements in simulation-based science." With Prof Jon Timmis, Dr. Kieran Alden (both University of York, UK). Simulation is increasingly employed in biological science, it facilitates interventions and a degree of experimental precision not possible in the real world.We are investigatingmachine learning and optimisation techniques to advance simulation-based science, yielding greater insights and ensuring simulations are representative of the biological systems we are investigating.

"Mapping dynamic immunity: next-generation computational approaches to track the evolution of immune responses." With A/Prof Irena Koprinska, A/Prof Uwe Roehm, Dr. Thomas Asshurt, Prof Nick King (all Usyd). Cytometry technology, whereby individual cells can be characterised in up to 45 dimensions, offers a powerful means of analysing the immune response. We are developing novel machine learning techniques (clustering) that can accommodate the challenge of 45 dimensional temporal data, and map out how our immune systems respond to different challenges.

"Predicting Asthma and other respiratory diseases through machine learning." With A/Prof Irena Koprinska (USyd), Dr. Cindy Thamrin (Woolcock Inst). We are building more powerful predictors of patient disease, and when they will experience clinical exacerbations, such that they can better manage their health.

"Predicting potential weightloss from faecal samples through machine learning." With A/Prof Irena Koprinska (USyd), Dr. Nick Fuller (Boden Inst), A/Prof Andrew Holmes (USyd). People respond very differently to a given diet, with only some experiencing success. Here we use classification and regression of high-dimensional microbiome sequencing data to predict the best dietary intervention for a given patient.

"Determiningasdfasdf optimal search strategies through theory, functional analysis and simulation." With Dr Maté Biro (UNSW), Dr Greg Rice (Waterloo, Canada). We are developing technologies to determine the best way for motile agents to find their targets, by they mates, food or shelter. This has applications in immunology, ecology, zoology and in swarm robotic systems.
"Unravelling the dynamics of haematopoietic stem cells." With Dr. Ben Roediger, Dr. Chris Jolly (Centenary Inst). These stem cells ultimately give rise to all blood and immune system cells, their function is critical to our health. We are using simulation to reconcile seeming contradictions around their dynamics, and determining how their behaviour can lead to either homeostasis or cancer.
"Factors underlying neutrophil swarming elucidated through simulation." With Dr Tatyana Chtanova (Garvan), Prof Jon Timmis (University of York, UK). Neutrophils, key immune system cells, exhibit a striking swarming motility pattern in response to pathogens and injury. We are using simulation techniques to recreate these dynamics and thereby understand their emergence.
"Methdological advancements in simulation-based science." With Prof Jon Timmis, Dr. Kieran Alden (both University of York, UK). Simulation is increasingly employed in biological science, it facilitates interventions and a degree of experimental precision not possible in the real world.We are investigatingmachine learning and optimisation techniques to advance simulation-based science, yielding greater insights and ensuring simulations are representative of the biological systems we are investigating.
"Mapping dynamic immunity: next-generation computational approaches to track the evolution of immune responses." With A/Prof Irena Koprinska, A/Prof Uwe Roehm, Dr. Thomas Asshurt, Prof Nick King (all Usyd). Cytometry technology, whereby individual cells can be characterised in up to 45 dimensions, offers a powerful means of analysing the immune response. We are developing novel machine learning techniques (clustering) that can accommodate the challenge of 45 dimensional temporal data, and map out how our immune systems respond to different challenges.
"Predicting Asthma and other respiratory diseases through machine learning." With A/Prof Irena Koprinska (USyd), Dr. Cindy Thamrin (Woolcock Inst). We are building more powerful predictors of patient disease, and when they will experience clinical exacerbations, such that they can better manage their health.
"Predicting potential weightloss from faecal samples through machine learning." With A/Prof Irena Koprinska (USyd), Dr. Nick Fuller (Boden Inst), A/Prof Andrew Holmes (USyd). People respond very differently to a given diet, with only some experiencing success. Here we use classification and regression of high-dimensional microbiome sequencing data to predict the best dietary intervention for a given patient.
People respond very differently to a given diet, with only some experiencing success. Here we use classification and regression of high-dimensional microbiome sequencing data to predict the best dietary intervention for a given patient.Integrative simulation techniques to disentangle host-diet-gut microbiome interactions."
With A/Prof Andrew Holmes (USyd), Prof Stephen Simpson (Usyd), Prof David Raubenheimer (Usyd).
The microbiome is increasingly implicated in our health status, but the factors underlying the composition of this complex cross-feeding community remain elusive. We use novel simulation techniques to determine intervention strategies to manipulate the microbiome to our benefit.
"Determining optimal search strategies through theory, functional analysis and simulation."
WithDr Maté Biro (UNSW), Dr Greg Rice (Waterloo, Canada).
We are developing technologies to determine the best way for motile agents to find their targets, by they mates, food or shelter. This has applications in immunology, ecology, zoology and in swarm robotic systems.
"Unravelling the dynamics of haematopoietic stem cells."
WithDr. Ben Roediger, Dr. Chris Jolly (Centenary Inst)
These stem cells ultimately give rise to all blood and immune system cells, their function is critical to our health. We are using simulation to reconcile seeming contradictions around their dynamics, and determining how their behaviour can lead to either homeostasis or cancer.
"Factors underlying neutrophil swarming elucidated through simulation."
WithDr Tatyana Chtanova (Garvan), Prof Jon Timmis (University of York, UK).
Neutrophils, key immune system cells, exhibit a striking swarming motility pattern in response to pathogens and injury. We are using simulation techniques to recreate these dynamics and thereby understand their emergence.
"Methdological advancements in simulation-based science"
With Prof Jon Timmis, Dr. Kieran Alden (both University of York, UK)
Simulation is increasingly employed in biological science, it facilitates interventions and a degree of experimental precision not possible in the real world.We are investigatingmachine learning and optimisation techniques to advance simulation-based science, yielding greater insights and ensuring simulations are representative of the biological systems we are investigating.
"Mapping dynamic immunity: next-generation computational approaches to track the evolution of immune responses."
WithA/Prof Irena Koprinska, A/Prof Uwe Roehm, Dr. Thomas Asshurt, Prof Nick King (all Usyd).
Cytometry technology, whereby individual cells can be characterised in up to 45 dimensions, offers a powerful means of analysing the immune response. We are developing novel machine learning techniques (clustering) that can accommodate the challenge of 45 dimensional temporal data, and map out how our immune systems respond to different challenges.
"Predicting Asthma and other respiratory diseases through machine learning."
WithA/Prof Irena Koprinska (USyd), Dr. Cindy Thamrin (Woolcock Inst).
We are building more powerful predictors of patient disease, and when they will experience clinical exacerbations, such that they can better manage their health.
"Predicting potential weightloss from faecal samples through machine learning."
WithA/Prof Irena Koprinska (USyd), Dr. Nick Fuller (Boden Inst), A/Prof Andrew Holmes (USyd).
People respond very differently to a given diet, with only some experiencing success. Here we use classification and regression of high-dimensional microbiome sequencing data to predict the best dietary intervention for a given patient.

Selected grants

2017

  • Mapping dynamic immunity: next generation computational approaches to track the evolution of immune responses in West Nile virus & Zika virus encephalitis; Ashhurst T, Read M, King N, Roehm U, Koprinska I, Scalzo R; Marie Bashir Institute for Infectious Diseases and Biosecurity/Seed Funding Grants.

2015

  • Large dairy herds: Creating value from data; Clark C, Garcia S, Read M; Dairy Australia/Research and Development Grants.

Selected publications

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Journals

  • Holmes, A., Chew, Y., Colakoglu, F., Cliff, J., Klaassens, E., Read, M., Solon-Biet, S., McMahon, A., Cogger, V., Ruohonen, K., Raubenheimer, D., Le Couteur, D., Simpson, S. (2017). Diet-Microbiome Interactions in Health Are Controlled by Intestinal Nitrogen Source Constraints. Cell Metabolism, 25(1), 140-151. [More Information]
  • Read, M., Holmes, A. (2017). Towards an integrative understanding of diet-host-gut microbiome interactions. Frontiers in Immunology, 8(MAY), 1-9. [More Information]
  • Read, M., Alden, K., Rose, L., Timmis, J. (2016). Automated multi-objective calibration of biological agent-based simulations. Journal of the Royal Society Interface, 13(122), 1-17. [More Information]
  • Read, M., Bailey, J., Timmis, J., Chtanova, T. (2016). Leukocyte Motility Models Assessed through Simulation and Multi-objective Optimization-Based Model Selection. PLoS Computational Biology, 12(9), 1-34. [More Information]
  • Hywood, J., Read, M., Rice, G. (2016). Statistical analysis of spatially homogeneous dynamic agent-based processes using functional time series analysis. Spatial Statistics, 17, 199-219. [More Information]
  • Cosgrove, J., Butler, J., Alden, K., Read, M., Kumar, V., Cucurull-Sanchez, L., Timmis, J., Coles, M. (2015). Agent-Based Modeling in Systems Pharmacology. CPT: Pharmacometrics and Systems Pharmacology, 4, 615-629. [More Information]
  • Alden, K., Read, M., Andrews, P., Timmis, J., Coles, M. (2014). Applying spartan to Understand Parameter Uncertainty in Simulations. The R Journal, 6(2), 63-80.
  • Read, M., Andrews, P., Timmis, J., Kumar, V. (2014). Modelling biological behaviours using the unified modelling language: an immunological case study and critique. Journal of the Royal Society Interface, 11(99), 1-16. [More Information]
  • Read, M., Andrews, P., Timmis, J., Williams, R., Greaves, R., Sheng, H., Coles, M., Kumar, V. (2013). Determining disease intervention strategies using spatially resolved simulations. PloS One, 8(11), 1-14. [More Information]
  • Williams, R., Greaves, R., Read, M., Timmis, J., Andrews, P., Kumar, V. (2013). In silico investigation into dendritic cell regulation of CD8Treg mediated killing of Th1 cells in murine experimental autoimmune encephalomyelitis. BMC Bioinformatics, 14(Suppl 6), 1-9. [More Information]
  • Alden, K., Read, M. (2013). Scientific software needs quality control. Nature, 502(7472), 448-448. [More Information]
  • Alden, K., Read, M., Timmis, J., Andrews, P., Veiga-Fernandes, H., Coles, M. (2013). Spartan: A Comprehensive Tool for Understanding Uncertainty in Simulations of Biological Systems. PLoS Computational Biology, 9(2), 1-9. [More Information]
  • Read, M., Andrews, P., Timmis, J., Kumar, V. (2012). Techniques for Grounding Agent-Based Simulations in the Real Domain: a case study in Experimental Autoimmune Encephalomyelitis. Mathematical and Computer Modelling of Dynamical Systems, 18(1), 67-86. [More Information]

Conferences

  • Read, M., Holmes, A., Hartill-Law, M., Solon-Biet, S., Raubenheimer, D., Simpson, S. (2015). Simulating the Influence of Diet on the Intestinal Microbiome Composition. The 13th European Conference on Artificial Life (ECAL 2015), York, UK: The MIT Press.
  • Read, M., Timmis, J., Chtanova, T. (2015). Simulation-Based Analysis of in Situ Cellular Motility. The 13th European Conference on Artificial Life (ECAL 2015), York, UK: The MIT Press. [More Information]
  • Naylor, B., Read, M., Timmis, J., Tyrrell, A. (2014). The Relay Chain: A Scalable Dynamic Communication link between an Exploratory Underwater Shoal and a Surface Vehicle. The 14th International Conference on the Synthesis and Simulation of Living Systems, Cambridge, Massachusetts: MIT Press. [More Information]
  • Williams, R., Read, M., Timmis, J., Andrews, P., Kumar, V. (2011). In silico investigation into CD8Treg mediated recovery in murine experimental autoimmune encephalomyelitis. 10th International Conference on Artificial Immune Systems (ICARIS 2011), Heidelberg: Springer. [More Information]
  • Read, M., Timmis, J., Andrews, P., Kumar, V. (2009). A Domain Model of Experimental Autoimmune Encephalomyelitis. 2009 Workshop on Complex Systems Modelling and Simulation. Luniver Press.

2017

  • Holmes, A., Chew, Y., Colakoglu, F., Cliff, J., Klaassens, E., Read, M., Solon-Biet, S., McMahon, A., Cogger, V., Ruohonen, K., Raubenheimer, D., Le Couteur, D., Simpson, S. (2017). Diet-Microbiome Interactions in Health Are Controlled by Intestinal Nitrogen Source Constraints. Cell Metabolism, 25(1), 140-151. [More Information]
  • Read, M., Holmes, A. (2017). Towards an integrative understanding of diet-host-gut microbiome interactions. Frontiers in Immunology, 8(MAY), 1-9. [More Information]

2016

  • Read, M., Alden, K., Rose, L., Timmis, J. (2016). Automated multi-objective calibration of biological agent-based simulations. Journal of the Royal Society Interface, 13(122), 1-17. [More Information]
  • Read, M., Bailey, J., Timmis, J., Chtanova, T. (2016). Leukocyte Motility Models Assessed through Simulation and Multi-objective Optimization-Based Model Selection. PLoS Computational Biology, 12(9), 1-34. [More Information]
  • Hywood, J., Read, M., Rice, G. (2016). Statistical analysis of spatially homogeneous dynamic agent-based processes using functional time series analysis. Spatial Statistics, 17, 199-219. [More Information]

2015

  • Cosgrove, J., Butler, J., Alden, K., Read, M., Kumar, V., Cucurull-Sanchez, L., Timmis, J., Coles, M. (2015). Agent-Based Modeling in Systems Pharmacology. CPT: Pharmacometrics and Systems Pharmacology, 4, 615-629. [More Information]
  • Read, M., Holmes, A., Hartill-Law, M., Solon-Biet, S., Raubenheimer, D., Simpson, S. (2015). Simulating the Influence of Diet on the Intestinal Microbiome Composition. The 13th European Conference on Artificial Life (ECAL 2015), York, UK: The MIT Press.
  • Read, M., Timmis, J., Chtanova, T. (2015). Simulation-Based Analysis of in Situ Cellular Motility. The 13th European Conference on Artificial Life (ECAL 2015), York, UK: The MIT Press. [More Information]

2014

  • Alden, K., Read, M., Andrews, P., Timmis, J., Coles, M. (2014). Applying spartan to Understand Parameter Uncertainty in Simulations. The R Journal, 6(2), 63-80.
  • Read, M., Andrews, P., Timmis, J., Kumar, V. (2014). Modelling biological behaviours using the unified modelling language: an immunological case study and critique. Journal of the Royal Society Interface, 11(99), 1-16. [More Information]
  • Naylor, B., Read, M., Timmis, J., Tyrrell, A. (2014). The Relay Chain: A Scalable Dynamic Communication link between an Exploratory Underwater Shoal and a Surface Vehicle. The 14th International Conference on the Synthesis and Simulation of Living Systems, Cambridge, Massachusetts: MIT Press. [More Information]

2013

  • Read, M., Andrews, P., Timmis, J., Williams, R., Greaves, R., Sheng, H., Coles, M., Kumar, V. (2013). Determining disease intervention strategies using spatially resolved simulations. PloS One, 8(11), 1-14. [More Information]
  • Williams, R., Greaves, R., Read, M., Timmis, J., Andrews, P., Kumar, V. (2013). In silico investigation into dendritic cell regulation of CD8Treg mediated killing of Th1 cells in murine experimental autoimmune encephalomyelitis. BMC Bioinformatics, 14(Suppl 6), 1-9. [More Information]
  • Alden, K., Read, M. (2013). Scientific software needs quality control. Nature, 502(7472), 448-448. [More Information]
  • Alden, K., Read, M., Timmis, J., Andrews, P., Veiga-Fernandes, H., Coles, M. (2013). Spartan: A Comprehensive Tool for Understanding Uncertainty in Simulations of Biological Systems. PLoS Computational Biology, 9(2), 1-9. [More Information]

2012

  • Read, M., Andrews, P., Timmis, J., Kumar, V. (2012). Techniques for Grounding Agent-Based Simulations in the Real Domain: a case study in Experimental Autoimmune Encephalomyelitis. Mathematical and Computer Modelling of Dynamical Systems, 18(1), 67-86. [More Information]

2011

  • Williams, R., Read, M., Timmis, J., Andrews, P., Kumar, V. (2011). In silico investigation into CD8Treg mediated recovery in murine experimental autoimmune encephalomyelitis. 10th International Conference on Artificial Immune Systems (ICARIS 2011), Heidelberg: Springer. [More Information]

2009

  • Read, M., Timmis, J., Andrews, P., Kumar, V. (2009). A Domain Model of Experimental Autoimmune Encephalomyelitis. 2009 Workshop on Complex Systems Modelling and Simulation. Luniver Press.

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