Tag Archive | systems biology

Interdisciplinary conference on neural engineering at the PRBB this September

Next 21-23 September 2015 an International Conference on System Level Approaches to Neural Engineering (ICSLANE) will take place at the PRBB. Organised by the Neural Engineering Transformative Technologies (NETT) Consortium, the conference presents an outstanding list of invited speakers.

Maciek Jedynak and Alessandro Barardi, both research fellows at the Jordi Garcia-Ojalvo group (CEXS-UPF), and local organisers of this exciting conference, tell us a bit more about it.

brain

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Neural Engineering is an inherently new discipline that brings together engineering, physics, neuroscience and mathematics to design and develop brain-computer interface systems, cognitive computers and neural prosthetics. Neural Engineering Transformative Technologies (NETT) is a Europe-wide consortium of 18 universities, research institutes and private companies. NETT consortium announces registration for this event is now open, and introduces a remarkable list of prominent Invited Speakers with Keynote Lecturers:

  • Eugene Izhikevich– a Co-Founder, Chairman and CEO of the cutting edge technology company Brain Corporation, located in San Diego, USA. The company’s mission is to design, produce and bring to everyday life intelligent machines equipped with the first-in-the-world operating system based on learning: BrainOS. He is also a former scientist well known for his rich contributions to the mathematical theory of dynamics of spiking neurons.
  • Nikos Logothetis– a pioneer in engaging fMRI measurements to neuronal activity studies, director of the department of Physiology of Cognitive Processes at the Max Planck Institute for Biological Cybernetics in Germany. His current research is focused on neural mechanisms of perception and object recognition. It involves a wide variety of brain imaging techniques, which allow to gather and consolidate data from different domains of neuronal activity.

The aim of this conference is to bring together theoretical and experimental neuroscientists and roboticists to discuss the state of the art in the field of Neural Engineering. This three-day long event will also provide young researchers with the opportunity to present their work.

The full list of confirmed speakers, divided into five different theme panels is:

Day 1

Brain-on-chip – engineering of neuronal circuits in-vitro with emphasis on microfluidics
Albert Folch – Department of Bioengineering, University of Washington, Seattle, WA, USA
Thibault Honegger – Laboratoire des Technologies de la Microelectronique, CNRS-CEA, Grenoble, France
Yoonkey Nam – Department for Bio and Brain Engineering, KAIST, South Korea

Optical neurotechnology Methodology – imaging and engineering techniques that allow recording of neuronal activity
Amanda Foust – Neural Coding Laboratory, Imperial College London, London, UK
Fritjof Helmchen – Brain Research Institute, University of Zürich, Zürich, Switzerland
Adam Packer – Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
Eftychios Pnevmatikakis – Department of Statistics and Center for Theoretical Neuroscience, Columbia University, New York, NY, USA

Day 2

Neural Dynamics – mathematical description of neuronal activity
Viktor Jirsa – Institut de Neurosciences des Systèmes, Marseille, France
David Liley – Swinburne University of Technology, Melbourne, Australia
Benjamin Lindner – Bernstein Center for Computational Neuroscience, Berlin, Germany
John Terry – College of Engineering, Mathematics and Physical Sciences, University of Exeter, UK

Neural learning and control – motion planning, controlling and learning neuro-inspired techniques for robotics
Dario Farina – Bernstein Center for Computational Neuroscience, Göttingen, Germany
Sami Haddadin – Institute of Automatic Control, Hannover, Germany
Alexandre Pouget – CMU, Geneva, Switzerland
Gregor Schöner – Institut für Neuroinformatik, Ruhr-Universität Bochum, Germany
Reza Shadmehr – John Hopkins University, Baltimore, MD, USA
Patrick van der Smagt – BRML labs, TUM, Germany

Day 3

Neural Coding – investigation of neuronal strategies for encoding information
Andre Bastos – The Picower Institute for Learning & Memory at MIT, Boston, MA, USA
Romain Brette – Institut de la Vision, Paris, France
Sophie Deneve – Laboratoire de Neurosciences Cognitives, LNC, Paris, France
Kenneth Harris – Institute of Neurology and the Department of Physiology, Pharmacology and Neuroscience, UCL, London, UK
Stefano Panzeri – Neural Computation Lab, IIT, Rovereto, Italy
Jan Schnupp – Auditory Neuroscience Group, Oxford, UK

 

REGISTRATION

We invite you to submit poster abstracts and apply for contributed talks. We introduced a one-day participation option: now you can attend one day of the conference for 80 Euros. The cost of participation in the whole event is 200 Euros (plus 50 Euros for optional conference dinner).

There is a 50% fee reduction for students who will present posters. Registration is available on the event’s on the registration form and all necessary information is on the event’s website. The registration deadline is on June 20th, so hurry up!!

 

Science at PRBB: Jordi Garcia Ojalvo’s Dynamical Systems Biology lab (CEXS-UPF)

In this sort video the physicist Jordi Garcia-Ojalvo, at the Department of Health and Experimental Sciences of the University Pompeu Fabra – located at the PRBB in Barcelona – talks about his group’s studies on circadian rhythms and other biochemical oscillations at a systems biology level.

Video produced by the Barcelona Biomedical Research Park www.prbb.org

 

Comparative Analysis of Developmental Systems – video of Johannes Jaeger’s research

 

Where do we come from? Why are we the way we are? Why aren’t there any 6-legged mammals? In this short video, Yogi Jaeger, head of the Comparative Analysis of Developmental Systems lab at the Centre for Genomic Regulation (CRG) talks about his research into these and other questions.

“We’re evolving towards systems pharmacology”

 

Jordi Mestres in the lab

A theoretical chemist by training, Jordi Mestres started up the chemogenomics lab of the IMIM, currently part of the GRIB, in 2003. The structure of the group, made up of graduates and doctors in chemistry, biology, biotechnology and computer science, perfectly reflects its three main lines of research: molecules, proteins and programming to predict the interaction between them.

“We apply our predictions to both drug discovery and chemical biology”, summarises Mestres. This last discipline consists of using small molecules to sound out biology, for example inhibiting a protein to understand its function. According to the scientist from Girona the optimisation of these chemical probes is just as important as that for drugs. “They have been used for years as if they were selective for a single target protein, but now we are beginning to understand that they are not.”

In fact, drugs do not owe their effectiveness to the fact that they are very selective for a single target, rather to their affinity for a whole group of proteins. “We are evolving towards systems pharmacology, where the drug is placed in the context of all of the proteins with which it can potentially interact, the organs it can reach, the polymorphisms of the person that takes the drug, and so on”, explains the head of the group.

 

OLYMPUS DIGITAL CAMERA

 A multitude of projects

The laboratory is involved in several European projects, including Open PHACTS, where they have developed an interactive tool to show ligand-protein interactions via the web (www.pharmatrek.org), and eTOX coordinated by Ferran Sanz (GRIB), where they design new methods to predict drug safety profiles. “Drug safety profiles are not really known until they are on sale and the drug is exposed to millions of users. If we were able to anticipate any adverse effects before entering the market and we understood the mechanisms, we could modify the structure of the drug in advance”, reasons Mestres.

They also look at ethnopharmacology, and try to explain how medicinal plants work. “We have made predictions for 109 plants and we are trying to rationalise their use for cardiovascular disease.”

In collaboration with Pilar Navarro (IMIM) they have found molecules inhibiting the formation of b-amyloid plaques that work as well or better than memantine, an Alzheimer’s drug. The research was funded by a pharmaceutical company and has generated two patents. In total, the group has four patents in collaboration with companies and one with the CSIC.

The creation of a spin-off

In some cases, they are asked by companies or other groups to prioritise which molecules to use at the beginning of a research project or to predict the proteins of active molecules in phenotypic trials. This was the origin of Chemotargets, in 2006, where currently three people work. “The students who were doing this could not publish anything, so we created this spin-off service”, explains the head of the group.

Chemotargets is still going and has quite a lot of work. They are currently designing the screening collection for the Karolinska Institute in Stockholm, with more than 10,000 molecules. They did something similar for the CRG, creating a list of small molecules that interact with proteins of interest to the researchers. Lately, they have also been contracted by the Swiss foundation ‘Medicines for Malaria Venture’ (MMV) to investigate the action of 400 antimalarials identified in phenotypic tests. “Chemotargets predicts targets for each molecule. Afterwards it is necessary to confirm the predictions experimentally, and this work is usually outsourced”.

It is, according to Mestres, the future of drug design. “Everything will be done from an office in a skyscraper in Manhattan or London, outsourcing molecule design to companies like Chemotargets, synthesis to a chemical company in China, and the trial to a pharmacology firm in India”, he predicts. “In fact it is already happening with the big pharmaceutical companies -they close their research centres, but do not abandon projects: they subcontract them out”.

 

Horses with spots and giraffes with stripes

Shigeru Kondo (Institute of Frontier Biosciences, Osaka University, Japan) gave one of the last talks at the “Computational approaches to networks, cells and tissues” meeting that took place this week  at the PRBB Auditorium.

Co-organised by James Sharpe (CRG) and Hernán López-Schier (HZM), the meeting was supported by QuanTissue, a collaborative European network to bridge the gap between the traditional developmental cell biology, biophysics and systems biology. And so it did!

Most of the nearly 200 participants were physicysts or mathematicians, as one could tell from their presentations and posters full of complicated mathematical formulae. But the subjects they studied were all related to the development of tissues and organs within organisms.

Kondo, for example, talked about the pigmentation pattern of zebrafish and how the Turing model could explain it.

Although his lab found there is no actual diffusion of any molecules, they showed that the interaction between the two types of pigment cells that define the skin patterns in the fish can still be explained by the Turing reaction-diffusion model. Melanophores, one of the cell types, elongate long projections towards xanthophores, the other cell type, and the effect of this is mathematically equivalent to the classical Turing model. Interestingly, he showed how, changing one single gene his lab was able to generate fish with skin patterns resembling most of those present in nature, from leopards and jaguars to zebras. Hence, the title of this posts, with which he finished his talk: “If you want horses with spots or giraffes with stripes, I can make it!”.

The meeting is still going on – another two hours of good science if you rush!

A report by Maruxa Martinez, Scientific Editor at the PRBB

“The same mutation can have a different outcome in different individuals”

The English researcher Ben Lehner started as a junior group leader at the CRG in December 2006 and has been an ICREA Professor since 2009. His lab, Genetics Systems, consists of five postdoctoral fellows, four PhD students, and a technician who hail from Italy, the UK, Germany, the Netherlands, Poland, Chile, Peru, Canada and Switzerland. About half of the group members are computational biologists and the rest work primarily in the ‘wet’ lab. They all have the same aim – to understand basic questions in genetics – but they use diverse approaches and model systems.

From individual genome sequences to individual phenotypes 

“In humans many mutations in genes are associated with an increased risk of particular diseases such as cancer”, the scientist explains. “But human geneticists are terrible at predicting disease risk. Most people with disease mutations never get the disease”. One aim of Lehner’s group is to better understand how the thousands of mutations in the genome of a particular individual interact to influence phenotypes such as disease risk. “What causes the same mutation to have a different outcome in different individuals? That’s one of our favourite questions”, states the researcher. This means understanding how genetics, the environment and ‘chance’ influence the outcome of particular genetic variants.
Recent research has focused on finding ways to predict the ‘normal’ outcome when genes are inhibited. In collaboration with labs from Korea, Toronto and Texas the group has created prediction models using generalisations such as the shared function of genes. “If two proteins interact physically, one can assume that they are involved in similar processes”, Lehner explains. The consequence of this hypothesis is that a mutation in either of the genes is likely to result in a similar phenotype, according to their shared function. By assuming this, one can generalise and expand the findings by using all available information on physical data, genetic interaction and co-evolution of every single gene analysed.

The next step for the group is to understand how the thousands of mutations in an individual’s genome combine to influence their characteristics. “Two humans differ by thousands of mutations, so how do we evaluate the outcome of all of this genetic variation in one go?” Beyond this they are also trying to understand why it can be impossible to predict disease phenotypes from a genome sequence. “Even genetically ‘identical’ twins are not identical when it comes to disease susceptibility. The same is true in simple organisms – if you control the genetics and you control the environment, you still cannot predict what will happen. We are working to understand why this is”, says the head of the lab.

Focus on basic problems 

In their studies the group uses experimentally tractable model organisms like yeast or the transparent roundworm C. elegans, but they also use existing data from many different sources. “Rather than working with a single system or approach, we like to choose the best system to study a particular problem with. And if you look at the history of biology, ‘best’ normally means ‘simplest’”, explains the biologist. “The problem is the important thing – it doesn’t really matter how you choose to solve it. But the problem must be basic and general, and one that you can actually solve!”.

This article was published in the El·lipse publication of the PRBB.

Listening to the language of neurones

Coming from the Rockefeller University in NY, Matthieu Louis leads the Sensory Systems and Behaviour group at the CRG, the only lab in Barcelona, and one of the few in Spain, investigating Drosophila neuroscience. His team comprises eight people with backgrounds in molecular biology, engineering and physics. Their aim is to correlate neural circuit function with behaviour using fruit fly larvae. “The Drosophila larva has a repertoire of complex behaviours and key cognitive functions. Yet its nervous system has 10 million neurones fewer than humans”, explains the physicist.

The group tries to understand how odours are encoded by the olfactory system. Features such as quality, “Does this smell like banana?”, and intensity, “Is this a morsel of banana or a bunch?”, are efficiently represented by only 21 olfactory sensory neurones, so that the larva can distinguish between hundreds of food-related odours. The researcher says that there must be a combinatorial code, yet it does not seem to be as trivial as the activation of different combinations of neurones by distinct odours. “We have evidence that the nature and the intensity of an odour is represented not only by the identity of the sensory neurones it activates, but also the way each one is activated”, he explains.

From information processing to chemotaxis 

Once a smell has been encoded, it has to be processed. To find the higher-order neurones involved in the integration of olfactory information, the group is undertaking a large behavioural screen. They test thousands of fly lines in which subsets of neurones are inhibited or over-activated. They then characterise how these perturbations affect chemotaxis, the orientation behaviour observed in response to an odour gradient. To decide whether to go straight ahead or turn, the larva monitors information about odour concentration changes. When a wild-type animal detects an intensity increase of an attractive odour, it keeps going forwards, but, as the group has recently described, if the odour intensity decreases the larva reorients through an active-sampling mechanism: much like rats and dogs, the larva sweeps its head laterally to check intensity levels on either side.

Drosophila larva

With their screen, the researchers are looking for mutants showing reorientation defects. To that end, they have developed their own computer-vision software. “We needed an algorithm to quantify subtle movements of the head and body posture with a high space-time accuracy. As no tool like this existed, we spent a year developing one”, says the head of the group.

Predicting behaviour 

“If we understand the neural logic of larval chemotaxis well enough, we should be able to synthetically produce predictable behavioural sequences”. Such a model could be useful for robotics. “Currently, dogs are trained to find mines. We could design robots that navigate spatially, searching for the chemical compounds present in explosives”.

Many questions remain to be answered before this becomes reality. How does the larva integrate a series of stimuli (touch, light, heat, smell) that are received simultaneously before deciding what to do next? And how is sensory input converted into motor output? “There are many challenges ahead. But it is thrilling to witness the genesis of a decision in a minibrain, from elementary spikes in sensory neurones down to the coordinated contraction of dozen of muscles. Flies have much to teach us about the function of our own brain”, concludes the Belgian researcher.

This article was published in the El·lipse publication of the PRBB.

EMBO meeting on yeast gene transcription

The EMBO meeting on “Gene transcription in yeast: from mechanisms to gene regulatory networksaims to catalyze the transformation of the field from classical genetics, biochemistry and structural biology to functional genomics and molecular systems biology of gene transcription and regulation.

Organised by Francesc Posas, the director of the Department of Experimental and Health Sciences of the UPF, the meeting will take place in Girona on June 16-21, 2012. However, registration deadline is already on March 16, so don’t wait too long! You can already check out the programme on the conference website.

Make sure you don’t miss this opportunity!

Postdoc position in Systems Biology

A postdoctoral fellowship position is available within the Department of Systems Biology, in the laboratory of Luis Serrano, at CRG in Barcelona. Application deadline is on December 15, 2011.

Details: Postdoc position in Systems Biology

How does genotype determine phenotype?

Very interesting talk by Edward Marcotte today at the PRBB!

He is an expert in proteomics, but touches all aspects of systems biology, and today he asked the following question: how does genotype determine phenotype? Can we predict the outcome of all the genomic variation we are uncovering with the many genomic projects we are doing nowadays?

Well, his lab is certainly trying to do so, and using three different strategies which I will summarise very briefly:

1.  Using functional gene networks, which are based on data such as mRNA expression, protein-protein interactions (PPI), etc. These networks presumably are formed by genes that are involved in the same biological processes. From here one only needs to follow the “guilt-by-assotiation” principle and assume that, if a gene in that network is involved in a particular phenotype (a disease, for example), the genes around it might also be so. They have tested this in yeast, C.elegans, Arabidopsis, rice and mouse, at least. They have managed to validate predictions for up to 200 genes. And they have come up with a valuable principle: that phenotypes reflect biological modules, rather than single proteins. That is, it is not a specific proteins that is essential, but a specific complex.

protein interaction networks

2. A systematic mapping of stable protein complexes in humans, which they have done in collaboration with labs in Toronto and which includes more than 2000 Mass spec experiments. From here they have inferred more than 600 stable complexes in humans (more than 500 of them with more than 3 components), of which 1/3 are unknown. Now the idea is to use this PPI network as a framework for linking genes to diseases. And they are doing so with one children developmental disease, the Cornelia de Lange syndrome, for which 3 known genes explain only the 50% of cases. They have selected some of the proteins which are around those three in the network and are currently sequencing them in patients.

3. Using model organisms to infer human disease genes. This is by far the one that I was most surprised about. It turns out that looking for what he called phenologs (orthologous phenotypes between organisms, for example, which yeast phenotype is equivalent to breast cancer in humans) one can find surprising disease models. For example, yeast sensitivity to lovastatin is a model for angiogenesis defects in humans! This is found looking for the yeast orthologous  of human genes involved in angiogenesis, and checking which phenotype those yeast genes are involved in. Then one can look at the rest of the yeast genes involved in that phenotype, and check if their human orthologous might be involved in angiogenesis.

And then, in principle, one could even use screening in yeast to find angiogenesis inhibitors. And the surprising thing is that it works! The Marcotte lab is actually about to start a phase I clinical trial on a drug they found this way and which they hope might be useful for glioblastoma. This is just one example, but according to him, this ‘phenologs’ strategy seems to work for more than 50% of the human genetic diseases… One big lesson that stems from this knowledge is that protein modules are conserved through evolution even if the phenotype is not – a concept he called ‘evolutionary repurposing’. Very interesting indeed.

Report by Maruxa Martinez, Scientific Editor at the PRBB

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