PHILIP PEARCE 
  • HOME
  • GROUP
  • RESEARCH
  • PUBLICATIONS
  • JOIN
  • CONTACT

Selected Research Projects

Pattern formation in spatio-temporally varying morphogens

In biological systems, chemical signals termed morphogens self-organise into patterns that are vital for many physiological processes. As observed by Turing in 1952, these patterns are in a state of continual development, and are usually transitioning from one pattern into another. How do cells decode these spatio-temporal patterns into signals in the presence of confounding effects caused by unpredictable or heterogeneous environments? In this paper, we answer this question by developing a general theory of pattern formation in spatio-temporal morphogen variations. Through mathematical analysis, we identify universal dynamical regimes that apply to wide classes of biological systems. We apply our theory to two paradigmatic pattern-forming systems, and predict that they are robust with respect to non-physiological morphogen variations.

  • Universal dynamics of biological pattern formation in spatio-temporal morphogen variations
    M.P. Dalwadi* and P. Pearce*. bioRxiv (2022) (DOI)

Bacterial quorum sensing in complex environments

Bacteria use intercellular signaling, or quorum sensing (QS), to share information and respond collectively to aspects of their surroundings. The autoinducers that carry this information are exposed to the external environment; consequently, they are affected by factors such as removal through fluid flow, a ubiquitous feature of bacterial habitats ranging from the gut and lungs to lakes and oceans. To understand how QS genetic architectures in cells promote appropriate population-level phenotypes throughout the bacterial life cycle requires knowledge of how these architectures determine the QS response in realistic spatiotemporally varying flow conditions. In this project, we develop and apply a general theory that identifies and quantifies the conditions required for QS activation in fluid flow by systematically linking cell- and population-level genetic and physical processes. Our theory is readily extendable, and provides a framework for assessing the functional roles of diverse QS network architectures in realistic flow conditions. Joint work with Mohit Dalwadi.

  • Emergent robustness of bacterial quorum sensing in fluid flow
    MP Dalwadi*, P Pearce*. PNAS 118.10 (2021): e2022312118. (DOI)

Physical determinants of bacterial biofilm architectures

In many situations bacteria aggregate to form biofilms: dense, surface-associated, three-dimensional structures populated by cells embedded in matrix. Biofilm architectures are sculpted by mechanical processes including cell growth, cell-cell interactions and external forces. In this project, using single-cell live imaging in combination with simulations, we characterize the cell-cell interactions that generate Vibrio cholerae biofilm morphologies. Fluid shear is shown to affect biofilm shape through the growth rate and orientation of cells, despite spatial differences in shear stress being balanced by cell-cell adhesion. Our results demonstrate the importance of cell dynamics mediated by adhesion proteins and matrix generation in determining the global architecture of biofilm structures. In collaboration with the groups of Jörn Dunkel and Knut Drescher.

  • Flow-induced symmetry breaking in growing bacterial biofilms
    P Pearce, B Song, DJ Skinner, R Mok, R Hartmann, PK Singh, H Jeckel, K Drescher and J Dunkel. Physical Review Letters 123.25 (2019): 258101. (DOI, bioRxiv)
  • Emergence of three-dimensional order and structure in growing biofilms
    R Hartmann, PK Singh*, P Pearce*, R Mok*, B Song, F Diaz-Pascual, J Dunkel and K Drescher. Nature Physics 15.3 (2019): 251-256. (DOI, PubMed)
    [see also Nature Microbiology Community blog]

Learning dynamical information from static protein and sequencing data

Protein folding and microbial evolution belong to the large class of physical, chemical and biological processes that can be described as diffusive exploration of an effective high-dimensional energy landscape. Recent advances in electronic and optical data acquisition technologies have been accompanied by substantial progress in the development of mathematical dimensionality reduction techniques for complex systems. By contrast, the reliable reconstruction of relevant dynamical information from static ensemble data, as provided by modern sequencing protocols and similar instantaneous sampling methods, still poses major challenges. In this project, we introduce a generic computational framework to reconstruct low-dimensional dynamical transition networks from high-dimensional static samples. We demonstrate the broad applicability of the underlying concepts by successfully predicting protein folding transitions and HIV evolution pathways. In collaboration with the groups of Jörn Dunkel and Halim Kusumaatmaja.

  • Learning dynamical information from static protein and sequencing data
    P Pearce, F Woodhouse, A Forrow, A Kelly, H Kusumaatmaja and J Dunkel. Nature Communications 10 (2019): 5368. (DOI)
    [my talk on this work at the European Conference on Computational Biology (2019) in Basel is available on YouTube, starting at around 1:45]

Blood flow and solute transport in the placenta

Throughout the mammalian species, solute exchange takes place in complex microvascular networks. In recent years, multi-scale models have proved successful in investigating the structure-function relationship of such networks in specific contexts. However, general methods for incorporating experimental data on complex, heterogeneous capillary networks into whole-organ multi-scale models remain under-developed. In this project we introduce a theoretical framework, tested against image-based computations, for quantifying the transport capacity of feto-placental capillary networks using experimental data. We find that solute transfer can be described using a near-universal physical scaling based on two non-dimensional parameters (the diffusive capacity and a Damköhler number), which can be extracted from microscopy images via standard computational and image-analysis tools. In collaboration with Oliver Jensen, Igor Chernyavsky and others.

  • Physical and geometric determinants of transport in feto-placental microvascular networks
    A Erlich*, P Pearce*, R Plitman Mayo, OE Jensen and IL Chernyavsky. Science Advances 5.4 (2019): eaav6326 (DOI)
    [featured in University of Manchester In Abstract]
  • Image-based modeling of blood flow and oxygen transfer in feto-placental capillaries
    P Pearce, P Brownbill, J Janacek, M Jirkovska, L Kubinova, IL Chernyavsky and OE Jensen. PLoS ONE 11.10 (2016): e0165369 (DOI)