From Bacterial Chemotaxis to the Prediction of Complex Networks: The Oxford Integrative Systems Biology Centre
Lead Research Organisation:
University of Oxford
Department Name: Biochemistry
Abstract
Life arises not just from individual molecules, but from their dynamic interactions. A great deal of time and money has been spent globally on sequencing genomes, followed by high-throughput methods for transcriptomic, proteomic and metabolomic measurement. Cells, however, are complex systems of networks and pathways. A long term goal of molecular biology must be to take a sequenced genome and be able to model the intracellular activities of a cell, and ultimately a complex organism. The availability of accurate and robust models has the potential to enable major advances in a range of activities ranging through pharmaceuticals and health care to food production and bioremediation. Approaches can be top down, bottom up or through accurate definition of limited inputs and outputs whilst ignoring the intervening detail. The goal is an accurate model of the dynamic activities of a cell under a specific set of growth conditions, when many proteins may be present but not active, only interacting with target proteins or DNA when activated to do so (the so-called 'sensome' or 'interactome'). None of the techniques available to date can reliably say which proteins are active when or which interact with which. Even recent developments in, for example, fast through-put guided yeast two hybrid systems tell you what can interact, not what does interact in vivo and when. We believe the best way forward is to initially focus on a limited number of tractable and important biological systems, for example, the bacterial histidine protein kinase (HPK) dependent chemosensory pathway or a defined section of the yeast cell cycle pathway, and to develop a reliable, predictive model for those systems. With, for example the bacterial chemosensory pathway, models can be tested through to species with complex pathways and these then extended to the total complement of HPK pathways in selected bacterial species. The long term goal is to carry these approaches to more complex pathways in higher organisms, and eventually to extend the models to sensory systems in general, with each model tested biologically as the programme develops. These studies will lead not only to an understanding of the complex interactive networks of signalling pathways, but also to the identification of signature motifs, possibly providing new targets for antimicrobials.
Technical Summary
Theoretical and computational methods will be applied to a 'core' of related but different biological problems namely to bacterial 2 component pathways, the histidine kinase network of bacteria and mitochondria, the serine/threonine kinase pathway of yeast, the Trypanosome flagellar proteome. The overall challenge is to develop both molecular and network based models of these pathways, and to integrate these two levels of description. At the molecular level, we will proceed via establishing a database of molecular dynamics and related simulations of the component structures and models, which in turn will be used to parameterise coarse grained models based upon e.g. Gaussian network models and electrostatics/diffusion and docking calculations. We will extend the coarse-grained approach beyond 'one amino acid = one particle' models to 'domain-based' models. Preliminary results suggest these will allow an adequate description of larger scale molecular motion and conformational changes. At the level of modelling networks we will establish an optimal level of granularity to: (i) match currently available data; (ii) match future data (and help to drive its acquisition); and (iii) enable link-up with molecular and coarse-grained models. We will develop methods to allow vertical integration of molecular, coarse-grained and network models, allowing information to be passed both upwards and downwards between adjacent levels. The modelling will at all time be verified by experimental measurements to develop robust predictive models of complex networks.
Publications
Agarwal S
(2010)
Revisiting date and party hubs: novel approaches to role assignment in protein interaction networks.
in PLoS computational biology
Beyer JA
(2019)
Mechanism of Signalling and Adaptation through the Rhodobacter sphaeroides Cytoplasmic Chemoreceptor Cluster.
in International journal of molecular sciences
Chang YC
(2013)
A single phosphatase can convert a robust step response into a graded, tunable or adaptive response.
in Microbiology (Reading, England)
Chen PY
(2008)
Predicting and validating protein interactions using network structure.
in PLoS computational biology
Collingridge PW
(2012)
MergeAlign: improving multiple sequence alignment performance by dynamic reconstruction of consensus multiple sequence alignments.
in BMC bioinformatics
Corrias A
(2012)
Modelling tissue electrophysiology with multiple cell types: applications of the extended bidomain framework.
in Integrative biology : quantitative biosciences from nano to macro
Fletcher AG
(2012)
Mathematical modeling of monoclonal conversion in the colonic crypt.
in Journal of theoretical biology
Hall BA
(2012)
Mechanism of bacterial signal transduction revealed by molecular dynamics of Tsr dimers and trimers of dimers in lipid vesicles.
in PLoS computational biology
Hamadeh A
(2011)
Feedback control architecture and the bacterial chemotaxis network.
in PLoS computational biology
He E
(2011)
System-level feedbacks make the anaphase switch irreversible.
in Proceedings of the National Academy of Sciences of the United States of America
Kelly S
(2013)
DendroBLAST: approximate phylogenetic trees in the absence of multiple sequence alignments.
in PloS one
Little M
(2011)
Steps and Bumps: Precision Extraction of Discrete States of Molecular Machines
in Biophysical Journal
Manna P
(2013)
Adaptin evolution in kinetoplastids and emergence of the variant surface glycoprotein coat in African trypanosomatids
in Molecular Phylogenetics and Evolution
Mirams GR
(2013)
Chaste: an open source C++ library for computational physiology and biology.
in PLoS computational biology
Mirams GR
(2012)
A theoretical investigation of the effect of proliferation and adhesion on monoclonal conversion in the colonic crypt.
in Journal of theoretical biology
Murray PJ
(2009)
From a discrete to a continuum model of cell dynamics in one dimension.
in Physical review. E, Statistical, nonlinear, and soft matter physics
O'Dea RD
(2013)
The interplay between tissue growth and scaffold degradation in engineered tissue constructs.
in Journal of mathematical biology
Osborne JM
(2010)
The influence of bioreactor geometry and the mechanical environment on engineered tissues.
in Journal of biomechanical engineering
Osborne JM
(2017)
Comparing individual-based approaches to modelling the self-organization of multicellular tissues.
in PLoS computational biology
Pitt-Francis J
(2008)
Chaste: using agile programming techniques to develop computational biology software
in Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Rosser G
(2014)
Modelling and analysis of bacterial tracks suggest an active reorientation mechanism in Rhodobacter sphaeroides.
in Journal of the Royal Society, Interface
Schwarzländer M
(2012)
Pulsing of membrane potential in individual mitochondria: a stress-induced mechanism to regulate respiratory bioenergetics in Arabidopsis.
in The Plant cell
Slaymaker M
(2012)
On an infrastructure to support sharing and aggregating pre- and post-publication systems biology research data.
in Systems and synthetic biology
| Description | This developed a major continuing netwrok of physical and biomedical researchers, who now work together to understand the mechanisms underpinning cellular activities |
| Exploitation Route | Many individual grants and a BBSRC LoLa have been funded and software is in general use |
| Sectors | Education,Environment,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology |