Award details

A Linear Syst0ems Toolkit for Biology

Reference	BB/M00113X/1
Principal Investigator / Supervisor	Professor Alex Webb
Co-Investigators / Co-Supervisors	Dr Jorge Goncalves
Institution	University of Cambridge
Department	Plant Sciences
Funding type	Research
Value (£)	297,684
Status	Completed
Type	Research Grant
Start date	23/03/2015
End date	22/03/2018
Duration	36 months

Abstract

We have developed a tool set from Engineering to identify the causal connections in biological networks. We use linear time invariant (LTI) models to describe the dynamic relationships in biological systems based on analysis of time series datasets. The LTI models describe causal relationships in the network and have predictive power concerning the dynamical system. We have used LTI modelling successfully to describe the circadian clock of Arabidopsis. We wish to build on this advance by developing a new approach based on the Nu gap metric, which identifies the causal changes in a network in response to stimulation or perturbation, (e.g. pharmacological agents or genetic mutation). The Nu gap identifies those connections that are altered by measuring the degree of change in the LTI models that describe those connections. Identification of those network connections that have changed in response to treatment allows follow up studies to be focused exclusively on the affected nodes that represent primary candidate gene targets. Our preliminary studies demonstrate the utility of our approach. LTI modelling and Nu gap analyses of circadian transcriptomes of Arabidopsis has identified the target for the metabolite nicotinamide in the circadian clock and we have confirmed the mathematical prediction through experimentation. We will develop LTI modelling with Nu gap analysis both theoretically and practically. We will apply Nu gap analysis to circadian datasets obtained both from model systems that are well understood and from crop plants in which circadian networks are not fully understood. We will use LTI modelling coupled with Nu gap analysis to investigate transcriptional responses to pharmacological and genetic. We will extend the utility of the LTI modelling with Nu gap analysis by incorporating non-transcriptional data. Theoretical developments will attempt to extend the power of Nu gap analysis to non-linear models.

Summary

Biology is complex; cells are made up of 1000s of proteins, a similar number of metabolites and tens of thousands of genes. A goal of biological research is to understand how this complexity brings about the functions of life. One way to achieve this goal is to understanding the connections between the 1000s of components that make up cells. Measuring the connections between all the components is challenging, particularly because cells are dynamical systems that are constantly changing. Accurate descriptions of the dynamical network interactions that take place in a cell are required to make the advances required for improved crops for food security and new medicines. We have adapted a new tool set from Engineering to describe biological networks in a mathematical form. We make models of each of the connections which are used to predict how the system will change over time, which is very useful in discovering how cells respond to signals such as changes in temperature, hormones or drugs. Our new mathematical tool set allows researchers to identify and quantify the changes in a biological network, which can lead to the discovery of the gene(s) or pathways that are involved in responses to stresses or drugs and might underlie disease. Our new mathematical tool set will have wide utility in understanding a wide range of cellular systems, from the effects of drugs in humans to the response of a crop plant to environmental changes or attack by pests. Our development of a tool that measures how biological networks change is important for understanding biology, curing disease and improving crop plants to provide enhanced food security. We propose to develop this so called Nu gap analysis as a practical tool for biologists. In our implementation, we identify and describe connections in biological systems using simple liner models. The Nu gap measures the difference between the mathematical descriptions of the connections obtained in different conditions, such as followinga response to a drug, or an environmental stress. To develop the Nu gap as a practical tool we will undertake a research programme that increases with complexity over time. This will permit rigorous testing, development and deployment of Nu gap analyses. First, we will perform theoretical analyses of the Nu gap on models derived from fabricated datasets designed specifically to assess the strengths and limitations of the Nu gap. This will inform as to where application of the toolset would be best, and conversely the situations where the Nu gap might be less informative. Having developed good theoretical understanding of the system, we will apply the Nu gap to real world data obtained by our laboratories. We will begin using data describing the circadian regulation of gene expression in the model plant Arabidopsis. A major goal will be to investigate the effect of a pharmacological and a genetic perturbation to the circadian system. Both profoundly affect the functioning of the circadian clock, but the mechanisms by which these affect the circadian clock is uncertain. We will move from investigating the fundamental properties of the circadian clock in the model plant Arabidopsis to using linear modelling and Nu gap analyses to describe the circadian clock in a major crop, barley. The circadian clock regulates many important agronomic traits such as flowering time, seed set and cold tolerance. Our studies have the potential to inform breeders of useful gene targets. Recognising that biological systems are more than a series of interactions between genetic components we will extend our analysis to incorporate the physiology of the cell, such as changes in the concentration of calcium in the cytosol, which act as key regulators of signalling in stressful conditions.

Impact Summary

WHO WILL BENEFIT? (1) Academic scientists interested in circadian rhythms, crop biology, stress physiology of plants and systems biologists in all organisms (2) Industrial scientists interested in generating crop varieties with enhanced stress tolerance and those using systems tools for gene and drug discovery. (3) Research staff. (4) The general public. HOW WILL THEY BENEFIT? (1) We will ensure wide dissemination and use of the Nu gap in systems biology. (a) We will organise a one day training course in Nu gap techniques at Cambridge for doctoral and pre-doctoral scientists. We have requested funds to support travel and subsistence for up to 20 participants. We will advertise the Nu gap training course through our contact networks, the Cambridge Networks Network, UKPSF, GARNet and Engineering and Systems Biology message boards. Materials associated with the training course will be made available via our websites. (b) We will present our research at the following conferences; IEE Conference on Decision and Control, the International Conference on Systems Biology and other appropriate meetings. These will also be used to advertise the Nu gap training course. (c) We will ensure maximum impact by publishing our research in a timely manner. The applicants have a track record of publishing in high impact journals and widespread dissemination. (2) Industrial scientists will benefit because we will develop a new STEM tool that allows identification of the causal changes in biological systems that occur in response to stimulation. We envisage that this will have utility in both the agricultural and pharmaceutical industries. In agriculture the Nu gap might be implemented to identify candidate genes for breeding programmes. In the pharmaceutical industry Nu gap could be exceptionally powerful in identifying drug targets and may offer considerable advantage compared to correlative tools currently in use. We will use our current relationships with industrial partners at Bayer Crop Science and Microsoft Research to attract industrially-based scientists to the Nu gap training course. (3) The PDRA will gain considerable benefit from being employed on the project. This will include training in circadian systems and linear modelling tools. The training in the specialist control theory approaches will place the PDRA in a good position for a further career in academia or the pharmaceutical, agricultural, financial or engineering industries. PDRAs from the Webb laboratory have had excellent career advancement. All BBSRC-funded PDRAs in the Webb laboratory have obtained publications in Science or Nature and eight former members of the Webb laboratory have obtained Faculty positions. The PDRA will gain considerable experience on helping develop and deliver the Nu gap training course and associated material. (4) The general public will benefit from outreach activities at the Department of Plant Sciences, Cambridge. During Science Week numerous interactive and more formal displays on aspects of plant biology and research are presented and 7,000 visit the Plant Sciences displays on 'Science Saturday' which will include dissemination of findings from this project. We take every opportunity to publicise our findings, Dr Webb has appeared on Radio interviews (e.g. BBC Farming Today) and his recent findings have been summarised in media outlets as diverse as the Financial Times and Comedy Central's Colbert Report. It is hoped in the long term that the public will benefit from food security generated from the novel agricultural products that arise from our findings. Whilst recognising that in any field of study the translation rate from laboratory finding to industrial product is always low, we make every effort with our industrial partners (Bayer Cropscience) to translate our findings for public benefit. We are currently registering IP on one of our discoveries

Committee	Research Committee C (Genes, development and STEM approaches to biology)
Research Topics	Crop Science, Plant Science, Systems Biology, Technology and Methods Development
Research Priority	X – Research Priority information not available
Research Initiative	X - not in an Initiative
Funding Scheme	X – not Funded via a specific Funding Scheme

Associated awards:

BB/M000435/1 A Linear Systems Toolkit for Biology

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