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<p class="MsoNormal"><span lang="EN"><img width="1" height="1" style="width:.0104in;height:.0104in" id="_x0000_i1040" src="https://images.e2ma.net/images/spacer.gif" alt="Modelling Host-Microbiome Interactions in Physiological and Pathological"></span><span lang="EN"><o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:9.0pt;font-family:"Arial",sans-serif">Sydney Precision Data Science Centre<o:p></o:p></span></p>
<p class="MsoNormal"><strong><span style="font-size:18.0pt;font-family:"Arial",sans-serif">Judith and David Coffey Seminar</span></strong><span style="font-size:9.0pt;font-family:"Arial",sans-serif"><o:p></o:p></span></p>
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<h1 style="margin-bottom:13.5pt">Modelling Host-Microbiome Interactions in Physiological and Pathological Processes<o:p></o:p></h1>
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<p class="MsoNormal"><strong><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:#333333">Speaker:
</span></strong><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:#333333">Professor Elaine Holmes (Health Futures Institute, Murdoch University)<br>
<strong><span style="font-family:"Arial",sans-serif">Date:</span></strong> 9 October 2023<br>
<strong><span style="font-family:"Arial",sans-serif">Time:</span></strong> 1 pm<br>
<b>Venue</b>: Mackenzie Room, Level 6, CPC<br>
<strong><span style="font-family:"Arial",sans-serif">Zoom link</span></strong>: <a href="https://protect-au.mimecast.com/s/uh96CWLVXkU6rnYWpsKjtCa?domain=t.e2ma.net"><span style="color:#3F7AB9">https://uni-sydney.zoom.us/j/84087321707</span></a><br>
<b>Format:</b> The format of the talk is approximately 40 minutes and 5 minutes of questions.<br>
<br>
<b>Abstract:</b> The use of metabolic profiling to define metabolic phenotypes associated with a wide range of pathologies is expanding and demand for sensitive, high quality disease diagnostics has facilitated the development of new technological and statistical
methods for extracting biomarkers from spectroscopic data obtained from biofluids such as urine, serum and stool extracts. These metabolite signatures can subsequently modelled with other ‘-omic’ data, including next generation sequencing data in order to
establish connections between the gut bacteria and human (patho)physiology. Examples of urinary or faecal metabolites that are products of the microbiota, or microbiota-host interactions include phenols, indoles, bile acids, short chain fatty acids and choline
derivatives, all of which can be quantitatively profiled using spectroscopic technology. Thus the metabolic phenotype can provide a window onto dynamic biochemical responses to physiological and pathological stimuli and also contains information relating to
the metabolic activity and function of the gut microbiome.<br>
<br>
In order to optimise information recovery from the spectra, analytical strategies for spectral alignment, scaling, curve resolution and quantification, statistical correlation and annotation are necessary. Some exemplar analytical pipelines are presented here
with particular focus on a series of methods for enhancing biomarker detection via a family of statistical correlation algorithms. Cross-correlation of multiplatform data allows further characterisation and extraction of improved molecular descriptors of metabolites
identified as candidate biomarkers, which in turn, can provide new insights into perturbed pathways and aetiopathogenetic mechanisms through correlation hierarchies of related metabolites. This systems analysis framework extends to encompass other datatypes
such as metagenomic or metatranscriptomic data and can identify new correlates between datasets and establish biological coherence across metabolic pathways and networks.<br>
<br>
<b>About the speaker:</b> Elaine Holmes is an ARC Laureate Fellow at Murdoch University, where she runs the Centre for Computational and Systems Medicine in the Health Futures Institute. She was elected as a Fellow of the Academy of Medical Sciences in 2018
and the Australian Academy of Science in 2022. Holmes is one of the pioneers in the development and implementation of metabolic phenotyping in translational clinical paradigms. The analytical framework conceptualised for metabolic phenotyping and biomarker
discovery has been applied across several disease areas. She also co-developed the Metabolome-Wide Association Study concept and has shown that the microbial component of the metabolic profile is associated with a wide range of conditions including obesity,
inflammatory bowel disease, allergies, and certain cancers. Her current focus is around computational modelling of metabolic and metagenomic data to understand the role of the gut microbiome in healthy aging with specific interest in the influence of nutrition
on the microbiome. <o:p></o:p></span></p>
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<h1 align="center" style="text-align:center">Add event to calendar<o:p></o:p></h1>
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<h2 style="margin-bottom:13.5pt">About the Statisical Bioinformatics Seminars<o:p></o:p></h2>
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<p class="MsoNormal"><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:#333333">The Statistical Bioinformatics Seminar is hosted jointly by the
<a href="https://protect-au.mimecast.com/s/wmOkC3QNPBiXDrME8SO2Jwr?domain=t.e2ma.net">
<span style="color:#3F7AB9">Sydney Precision Data Science Centre</span></a>, and the Integrative Systems and Modelling Theme and Judith and David Coffey Life Lab in the Charles Perkins Centre. The aim of this series is to provide a forum for people working
within the broad area of computation and statistics and their application to various aspects of biology to present their work and showcase their ongoing projects. It is intended to foster the exchange of ideas and build potential collaborations across multiple
disciplines.<o:p></o:p></span></p>
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