Subject: Society for Mathematical Biology Digest

SMB Digest      May 11, 2016   Volume 16  Issue 19
ISSN 1086-6566

Editor: Alex Fletcher digest.alex(at)gmail(dot)com

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Issue's Topics:
    Combined CHARME-EMBNet & NETTAB 2016 Workshop..., Oct 25-26, Italy
    Research Scientist: Computational Bioinformatician, BHSAI, USA
    PhD: Energy variability & genetic decision making..., Birmingham, UK
    SMBnet Reminders


From: Paolo Romano <paolo.romano@hsanmartino.it>
Date: Thu, May 5, 2016 at 10:51 AM
Subject: Combined CHARME-EMBNet & NETTAB 2016 Workshop..., Oct 25-26, Italy

Combined CHARME ? EMBnet and NETTAB 2016 Workshop
Reproducibility, standards and SOP in bioinformatics
Oct 25-26, National Research Council, Piazzale Aldo Moro 7, Rome, Italy

Next deadline: Submission of abstracts for oral communications: May 30, 2016


The Workshop 'Reproducibility, standards and SOP in bioinformatics' is 
co-organised by the COST European Action CHARME (CA15110), EMBnet (The 
Global Bioinformatics Network) and NETTAB (International Workshop Series on 
Network Tools and Applications for Biology). It is hosted by the ELIXIR-ITA 
Node and will be held at the Italian CNR (National Research Council) head 
quarter, in Rome.

The workshop will be preceded by a GOBLET/ELIXIR-ITA Tutorial and a ELIXIR 
Hacktahon on Monday 24th and followed by the EMBNet Annual General Meeting 
on Thursday 27th.

Keynote Speakers (confirmed only):
 - Jacques van Helden, Université d'Aix-Marseille (AMU), Marseille, France
 - Barend Mons, Leiden University Medical Center (LUMC), Leiden, Netherlands
 - Further Keynote speakers will be announced soon.

For further details, see: http://www.igst.it/nettab/2016/


From: Jena Peters <jpeters@hjf.org>
Date: Tue, May 10, 2016 at 3:52 PM
Subject: Research Scientist: Computational Bioinformatician, BHSAI, USA

The Henry M. Jackson Foundation for the Advancement of Military Medicine, 
Inc. (HJF) is seeking a Computational Bioinformatician to support the U.S. 
Army Medical Research and Materiel Command's Biotechnology High Performance 
Computing Software Applications Institute (BHSAI) [www.BHSAI.org]. HJF 
provides scientific, technical, and programmatic support services to the 
BHSAI. This opening is for dynamic bioinformatics research scientists 
interested in working in multiple cross-disciplinary research projects.


The Bioinformatics Research Scientist is responsible for advancing 
scientific knowledge by developing and applying their knowledge to original 
research problems in Military Medicine. The Research Scientist will apply 
their experiences in the analysis, collection, mining, and integration of 
multiple high-throughput datasets using computational bioinformatics and 
systems biology methods to understand chemical and biological interactions 
at the cellular and organism level.

Foreign nationals are welcome to apply. U.S. citizenship or permanent 
resident status is not required. This position is located in Frederick, 

The candidate is expected to simultaneously work on multiple projects, 
involving a diverse and interdisciplinary team of scientists across multiple 

For further details, see: 


From: Iain Johnston <i.johnston.1@bham.ac.uk>
Date: Tue, May 10, 2016 at 2:46 PM
Subject: PhD: Energy variability & genetic decision making..., Birmingham, UK

Title: Energy variability and genetic decision making in stochastic cells
Supervisors: (Dr Iain Johnston) and Prof George Bassel 

FindAPhD entry:


Why do some cancer cells die immediately in response to chemotherapy, while 
others remain in a growing tumour? How do stem cells decide which tissue to 
form, and when to do so? Understanding cellular decision making is crucial 
in our attempts to predict how diseases will progress and to understand how 
cell-to-cell differences arise in biology. These decisions are often made by 
networks of interacting genes acting as biological "processors", with genes 
modulating each others' expression and governing cell behaviour [1]. A vital 
feature of these networks, often neglected in their analysis, is that they 
are embedded in a world subject to physical constraints: the processes 
involved in gene interactions require a source of energy, and take place in 
the chaotic and noisy environment that is the biological cell. 

Energy and noise are of central importance in cellular decisions: research 
from IGJ and others has shown that variability in mitochondria (fundamental 
energy sources in the cell) modulates stem cell fate choices, and that 
random influences (including collisions between molecules, and partitioning 
at cell divisions) have profound effects on cellular decisions [2]. 
Descriptions of gene regulation often omit these important dependences and 
so are only of limited use in predictive biomedical modelling, particularly 
in personalised medicine where biological heterogeneity is a key focus. Our 
ability to unify and harness biological "big data", particularly large-scale 
omics measurements of noisy transcript and protein populations, also suffers 
from this lack of a physical framework to quantify uncertainty and 
distinguish functional and spurious connections between genes. Clearly, 
appropriate physical models for the energy dependence and stochastic 
dynamics of gene regulation are required. 

This transformative project will develop, in concert, new modelling 
approaches to provide a bottom-up physical description of these important 
features, and new statistical tools forging a connection between these 
models and a wealth of currently available, large-scale, heterogeneous data. 
The researcher will use tools from stochastic processes [2, 3], dynamical 
systems [2, 4], and statistical inference (including likelihood-free 
approaches) [3, 5] to develop a unified descriptive and predictive theory 
linking regulation of energy-dependent and stochastic gene expression with 
noisy data. Some experience with these fields, and/or with biophysics/systems 
biology, will be helpful, but prior experience with biological topics is not 
essential. Through this research we aim to understand physical features 
governing variability in cellular decision making, to improve the reliability 
of in silico predictions of the behaviour of regulatory motifs and biological 
pathways (including stem cell fate decisions and cell death triggers), and 
to increase the statistical and interpretative power of omics data for 
understanding biology and disease.

Funding Notes: Funding available for three years from the EPSRC, for UK or 
EU students only.

[1] Karleback & Shamir, Nat Rev Mol Cell Biol 9 770 (2008) 
[2] Johnston et al., PLoS Comput Biol 8 e1002416 (2012) 
[3] Johnston et al., eLife 4 e07464 (2015) 
[4] Wang et al., Biophys J 99 29 (2010) 
[5] Johnston, Stat App Genet Mol Biol 13 379 (2014)


Subject: SMBnet Reminders

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