University of Dundee

Professor Timothy Newman

Theoretical and computational analysis of fluctuations in biological systems.
Position: 
Professor of Biophysics & SULSA Research Professor of Systems Biology
Affiliation: 
Address: 
College of Life Sciences, University of Dundee, Dundee
Full Telephone: 
+44 (0) 1382 386313, int ext 86313
Email: 

Research

I was trained as a theoretical physicist, but for the past 10 years or so have performed research on living systems. My interests span all of biology, from enzymes to cells to embryos to populations, and from basic research questions to translational applications in biomedicine. The tools I bring to biology are 1. theoretical formulation and analysis of fluctuations, and 2. computer simulation of interacting agents.

 Figure 1. An ScEM simulation of a single cell undergoing slow mechanical stretc 1. Fluctuations are intrinsic to biology at every scale. Without fluctuations life would be impossible. Yet, it is not straightforward to quantify the role of fluctuations, especially in the presence of non-linearity and feedback, both of which are, of course, prevalent in biological systems. I use many tools from the broad field of stochastic process theory to tackle fluctuation problems, such as calculating i) the probability of successful establishment of a metastatic cell, ii) the effect of small molecule concentrations on gene regulation, iii) the statistics of spatial diffusion in crowded environments. Over the years, I have found that fluctuation phenomena discovered at one scale in biology have immediate implications for other scales. For instance, in 2005 Alan McKane (University of Manchester) and I discovered that intrinsic noise associated with birth/death events can cause moderately sized predator-prey populations to spontaneously oscillate over time. We realized the underlying effect was directly relevant to bio molecular feedback systems, and similar noise-induced oscillations were predicted for intracellular biochemical networks.

Figure 2. Multicellular ScEM simulations of primitive streak formation in the ch 2. Sometimes systems are too complicated for theoretical analysis, and one has to turn to computers for help. An example is embryo development. Robust large-scale dynamics are observed, but no one understands how it works. Development is a hard multi scale problem with biochemical and biomechanical interactions having important roles. In 2005 I formulated a computer algorithm, called the subcellular Element Model (ScEM), which provides a biomechanical basis for simulating embryonic systems. My former student Sebastian Sandersius (now in the Stathopolous group at Caltech) and I worked together to calibrate the ScEM using single cell biomechanical measurements, and to add active cell behaviors. My group is applying the ScEM to a range of problems in development and cancer dynamics, and we continue to add sophistication, the next level being incorporation of primitive signalling pathways within and between cells. Some images depicting ScEM simulations of single cells and multicellular tissues are shown here for illustration.

Physicists have been important contributors to biology ever since Max Delbruck formed the Phage Group back in the 1940s. Physics can offer cutting edge instrumentation for visualization and quantitative measurement, and sophisticated theoretical and computational tools for formulating and analyzing complex systems. I believe physics will be most powerful in biology if the practitioners use biology as their inspiration and biologists as their guides. I bring this philosophy of openness and collaboration across fields to my role as Editor-in-Chief of the journal Physical Biology, published by the Institute of Physics.

Publications

1. Albergante, L., Blow, J. J. and Newman, T. J. (2014) Buffered Qualitative Stability explains the robustness and evolvability of transcriptional networks. Elife. 3, e02863
DOI: 10.7554/eLife.02863
PMCID: 4151086
PMID: 25182846

2. Cisneros, L. H. and Newman, T. J. (2014) Quantifying metastatic inefficiency: rare genotypes versus rare dynamics. Physical biology. 11, 046003
DOI: 10.1088/1478-3975/11/4/046003
PMID: 25033031

3. Newman, T. J., Mamun, M. A., Nieduszynski, C. A. and Blow, J. J. (2013) Replisome stall events have shaped the distribution of replication origins in the genomes of yeasts. Nucleic acids research. 41, 9705-9718
DOI: 10.1093/nar/gkt728
PMCID: 3834809
PMID: 23963700

4. Grima, R., Schmidt, D. R. and Newman, T. J. (2012) Steady-state fluctuations of a genetic feedback loop: an exact solution. The Journal of chemical physics. 137, 035104
DOI: 10.1063/1.4736721
PMID: 22830733

5. Sandersius, S. A., Chuai, M., Weijer, C. J. and Newman, T. J. (2011) A 'chemotactic dipole' mechanism for large-scale vortex motion during primitive streak formation in the chick embryo. Physical biology. 8, 045008
DOI: 10.1088/1478-3975/8/4/045008
PMID: 21750368

6. Sandersius, S. A., Weijer, C. J. and Newman, T. J. (2011) Emergent cell and tissue dynamics from subcellular modeling of active biomechanical processes. Physical biology. 8, 045007
DOI: 10.1088/1478-3975/8/4/045007
PMID: 21750367

7. McKane, A. J., Nagy, J. D., Newman, T. J. and Stefanini, M. O. (2007) Amplified Biochemical Oscillations in Cellular Systems. Journal of Statistical Physics. 128, 165-191
DOI: 10.1007/s10955-006-9221-9

8. Newman, T. J. (2005) Modeling multicellular systems using subcellular elements. Mathematical biosciences and engineering : MBE. 2, 613-624
PMID: 20369943

9. McKane, A. J. and Newman, T. J. (2005) Predator-prey cycles from resonant amplification of demographic stochasticity. Physical review letters. 94, 218102
DOI: 10.1103/PhysRevLett.94.218102
PMID: 16090353

10. Newman, T. J. and Grima, R. (2004) Many-body theory of chemotactic cell-cell interactions. Physical review. E, Statistical, nonlinear, and soft matter physics. 70, 051916
DOI: 10.1103/PhysRevE.70.051916
PMID: 15600665