This initiative intends to make science closer to young people through smartphone apps related to several scientific areas.
By means of this multi game app, user becomes comfortable with science in four different topics:
- Cell Spotting: Real images from a cell culture obtained from an inverse fluorescence microscope at BIFI facilities are analyzed. Namely, cell death is analyzed through two parameters.
- Mind Paths: The goal in this game is to determine the distance between words according to conceptual analysis carried out by the people. Given a couple of words (beginning and target) and a network linked to them, user must choose among one of the words linked to the first one keeping in mind which one of them will lead him/her to reach the target word.
- Sun4All: Users must analyze sun spot obtained by Astronomical Observatory of the University of Coimbra (Portugal) aiming to help to understand solar activity and cycle..
-Mobilab: An app to get measurements from the sensors that your smartphone owns to be used in different situations.
This project is framed within the European project Socientize, taking advantage of the development team at the Institute for Biocomputation and Physics of Complex Systems (BIFI) of the University of Zaragoza in collaboration with the Ibercivis Foundation.
This is a Citizen Science project funded by the Spanish Foundation for Science and Technology (FECYT).
The aim is to fund, thanks to crowdfunding, a web server where researchers can predict the stability and dynamics of a protein they are interested in, through its structure and other data. The goal is to make the work of biomedical researchers easier through rational and optimized design of new experiments, significantly saving time, money and resources. In order to achieve this, in this first phase of our scientific project we wish to import the application of this project’s promoter, Pierpaolo Bruscolini, into the Ibercivis BOINC IT platform.
This initiative aims to capture the growing interest of the average citizen to participate in science, and not to be a mere spectator. This citizen-financing of projects has had excellent results in other areas and will also be supported by the public, and therefore Ibercivis Foundation and iLoveScience are backing it.
To be able to proceed with the research, an online portal has been created via iLoveScience, released today October 1, 2014, to coincide with the World Day of Foundations and their Donors. Its goal is to raise 3500 euros generously donated by collaborators who in return receive information, training and recognition. The campaign will last 60 days, and contributions can range from 10 to 2000 euros.
Through this link you can collaborate, as well as finding more information about the project
The head researcher and promoter of the project is Pierpaolo Bruscolini. Doctor in Physics from the University of Turin (Italy), he is presently a researcher at the University of Zaragoza, especially interested in topics of theoretical biophysics: protein folding, sequencing and design. His lines of research deal with the formulation and analysis of mathematical models to describe protein folding and protein design, the study of Markov processes, the formulation of models of water and of the hydration of non-polar molecules and polymers, the application of statistical physics to the interpretation of MS/MS spectra in proteomics applications.
The following people also collaborate on this project:
Adrian Velázquez Doctor in Physics. ARAID researcher in the Institute For Biocomputation and Physics of Complex Systems. Specialist in experimental biophysics of proteins.
Sergio Pérez Gaviro Doctor in Physics. ARAID researcher in the Institute for Biocomputation and Physics of Complex Systems. Expert in Computational Physics and disordered systems.
Francisco Sanz García Masters in Computational Mechanics, Graduated in Mathematics. Researcher at the University of Zaragoza in the area of Distributed Computation and Citizen Science.
This project is being carried out in collaboration with the Ibercivis Foundation. This non-profit organisation guarantees calculation capacity to research projects that contribute to the common good and improve society. Ibercivis presently has a relevant role in citizen science, both on a national and European level. We create a connecting point of contact between the research community and the public, making it possible for the scientific community to receive help from the public in research projects they carry out, while providing the general public with the means to get acquainted with Science.
Surface screening results for PDB:1QCF. From up left to down right; a) beads represent protein spots and the color of each bead is related with the value of the scoring function, so colors from red to blue indicate lower values for the scoring function, b) histogram with the distribution of scoring function values, c) red and blue molecules represent crystallographic and predicted pose for the ligand, RMSD is lower than 1 Angstrom, and d) depiction of the hydrogen bonds established by the ligand with the closest residues.
What are prime numbers?
In the figure: The Sieve of Eratosthenes was created by Eratosthenes of Cyrene, a greek mathematician from the 3rd century B. C. It is a simple algorithm to find all prime numbers up to a specified integer.
In the figure: Riemann zeta function ζ(s) in the complex plane. The color of a point s encodes the value of ζ(s): dark colors denote values close to zero and hue encodes the value's argument. The white spot at s = 1 is the pole of the zeta function; the black spots on the negative real axis and on the critical line Re(s) = 1/2 are its zeros.
History of the prime numbers
GRIPENET: end of the first season
For a specific chemical compound to be absorbed by human cells, it needs to cross the cell membrane. This is valid for compounds with effects that are potentially beneficial (such as pharmaceuticals) or harmful (like toxins and pollutants). The process of absorption implies the interaction of the compound with aqueous environments which vary from more hydrophilic (with affinity to water) to more hydrophobic (which repels water) regions.
Image: Typical cell membrane schema. The extracellular fluid and the cytoplasm are typically hydrophilic whereas the interior of the membrane is highly hydrophobic.
Consequently, the ability of human cells to absorb and intake drugs and toxins is strongly related with the solubility differences of these compounds in aqueous and organic environments. For example, to develop a new drug hundreds of compounds are tested in the laboratory, which drastically increases the cost and time needed for their development. The great majority of these compounds are then discarded because they do not possess the solubility properties required for their practical use. If it were possible to predict in advance the solubility of a new compound just based on its molecular structure, it would be possible to develop more effective drugs in less time and consuming fewer resources. Likewise, the a priori calculation of the solubility of a compound would allow us to estimate its degree of toxicity, which in turn would have a significant impact on environmental studies.
The solubility of a compound is directly related to its free energy of solvation. The free energy of solvation is one of the most important thermodynamic properties, allowing for the estimation of not only the solubility but also the degree of partition of a molecule between environments with different characteristics. The free energy of solvation can be precisely calculated using molecular simulation methods coupled to a thermodynamic integration algorithm. Initially, the simulation starts with a compound in a box surrounded by a solvent. In the next step, we make the compound gradually "disappear", monitoring the variation of the energy of the system during the process. At the end, we obtain the compound free energy of solvation in this solvent by integrating the energy curve.
The calculation of energies of solvation using molecular simulations is a highly precise approach, although very demanding from the computational point of view. For example, the calculation of the energy of solvation of a single simple compound in water requires about 500 hours of computation in a personal computer. Taking into account the high number of compounds which need to be tested and the large variety of solvents used, it is necessary to make use of more advanced computing platforms, such as Ibercivis.
The main objective of this project is to predict computationally the free energies of solvation of a large number of compounds in a large variety of solvents. The type of solutes studied will go from simple molecules (e.g., ethanol) to more complicated ones (e.g., drugs and multifunctional toxins), and the solvents will span the entire polarity range, from water to hydrocarbons. This large variety of systems will allow us to develop and test molecular models, which may then be used as generally applicable predictive tools. The ability to predict the solubility of a new compound just from its molecular structure will have a major impact on the discovery of new drugs and on the toxicity evaluation of compounds to which human beings may be exposed.
Image: Example of a pharmaceutical compound (large spheres) dissolved in water (blue and white molecules).
Project Solúvel is conducted by the Molecular Simulation group at the Laboratory of Separation and Reaction Engineering (LSRE) of the Faculty of Engineering of the University of Porto. The objective of the LSRE is the development of new methodologies, concepts, ideas and experimental techniques to understand, design, operate and optimise Separation and Reaction Processes involved in Process and Product Engineering.
The Molecular Simulation Group of LSRE studies physical phenomena at the molecular scale, promoting the use of this knowledge to develop new products and processes through a molecular based Engineer. Its research covers areas as diverse as adsorption, synthesis and characterization of nanomaterials, thermodynamics and phase equilibrium, interfacial physics and solubility.
Soluvel ( 2011, Spanish )
Lonizing radiation is used routinely in the medical field, mainly in diagnostic by images (radiology, using X-ray beams, and nuclear medicine using radioisotope) and in the treatment of various diseases through the use of external beam of photons or electrons (external radiation therapy), encapsulated sources (brachytherapy) or non-encapsulated radioisotope (nuclear medicine).
The numerical simulation techniques for the transport of particles (photons, electrons and positrons) using Monte Carlo techniques in complex media have been used for over 50 years in a wide range of situations (complement experimental measurements, characterization equipment, patient dosimetry , etc). In recent years this methodology has experienced a great growth in its utilization and applications, where its use is being limited only by the CPU times required for obtaining results in complex models.
In the image: Model of a patient and a virtual radiographic generation using monte-carlo simulation techniques.
The description of particle transport using Monte Carlo techniques is based on the characterization of the transport of particles by probability distributions that describe on the one hand, the length that the particle has to travel between two interactions in each material, and on the other hand,the scattering angles and energy balance that it suffers while interact. In this way, and by using a random number generator it's produced the simulation of transport of particles within the material taken sequentially and for each simulated particle, decisions on a) the initial conditions of the same (energy, direction, etc), b) the length until the next interaction, c) the physical process by which it interacts (Compton, photoelectric, etc.) and d) the outcome of the interaction (scattering angle, producing secondary particles, etc.). The Monte Carlo simulation techniques are necessarily statistical by nature and require monitoring a large number of particles to achieve the required accuracy, which involves considerable computing time.
As advantages associated with this methodology there are the ability to obtain results with the space and temporal accuracy required, and the search results for multiple physical parameters simultaneously. All of them makes this technique a powerful tool that makes it is often considered as the standard for results in many biomedical applications.
In the image: Temporal evolution of the energy deposited by a narrow beam of photons in a dummy semi-infinite dimensions.
In Ibercivis, each job of Sanidad sent to the computers of the people runs the simulation of particle transport with certain environmental conditions (material with which it interacts, geometries of them, ...).
The detailed simulation under these conditions, complemented with experimental measurements, allows us to improve diagnostic techniques, the quality of treatment and protection of patients and professionals in view of ionizing radiation.
Thanks to these simulations we hope to achieve results in conditions that would be prohibitive without your support in times of achievement. The analysis of these results will allow us to obtain valuable information.
In the image: View of a prostate cancer treatment using 112 radioactive seeds of Iodine-125.
PENELOPE, an acronym for "Penetration and Energy Loss of Positrons and Electrons" is a general purpose Monte Carlo simulation for describing the transport of photons, electrons and positrons in any material for the energy range between 50 eV and 1GeV. PENELOPE is developed at the University of Barcelona and it is distributed through the Nuclear Energy Agency (http://www.nea.fr).
Image: The broccoli is an example of a natural fractal with approximate self-similarity.
Image: The Sierpinski triangle is one of the well-known mathematical fractal by a clear example of exact self-similarity.
Image: Example of an internal state for the three-dimensional Anderson model in the metal-insulator transition. In the critical point, the states are fractals but, unlike natural fractals (with approximate self-similarity) or the mathematical fractals (with exact self-similarity), in critical disordered systems the internal states show statistical self-similarity. Note that the critical state is extended in the interior of the cube, which defines the system's volume, but it does not occupy all the permitted volume. For this reason, we say critical states have an effective dimension (fractal dimension) which is smaller than the real dimension of the system, which in this case is 3. Image from L. J. Vasquez, A. Rodriguez, and R. A. Roemer, Phys. Rev. B 78 195106 (2008).
Image: Three-dimensional arrangement of points connected to their first neighbours. Also known as three-dimensional Anderson model. In the example, the system linear size is L=3, however the total volume is L^3=27. Each point represents an atom or molecule within a finite crystal network. In this model, the disorder is represented by a random potential assigned to each of the points. Note that the left face of the cube is connected to 9 terminals or ports from where the electrons are beamed. The beamed electrons interact with the points in the network before being dispersed or expelled to the outside of the crystal lattice through its terminals.
In the image: Transistors y semiconductors
In the image : Quantic interference between two sources.
In the image : Nanometric semiconductor device
These pictures show the differences between both processes.
Image 1: Crystal structure of the zeolite ZSM-11 with "hollows" where gas molecules pass through. Image 2: Other view of the crystal structure of the zeolite ZSM-11 with cylindrical galleries inside the material. Image 3: Channels of the zeolite getting full of gas molecules represented with blue spheres. Image 4: 3D Channel network of a zeolite.
Image: 2D representation of a pillars (blue circles) configuration tessellated with Dealuny triangles. In red we can see regions where methane molecules can not go trough.
Image: Visualization of the various routes through a portion of the Internet. From 'The Opte Project'.
Image: Example of a network with community structure. Nodes represent email users from the Universitat Rovira i Virgili de Tarragona, and links means communication among them. Different colors stand for the departments of the university. Obviously, individuals inside a department communicate much more often. A. Arenas, L. Danon, A. Díaz-Guilera, P. Gleiser and R. Guimerà, European Physics Journal B, 38(2), 373-380 (2004).
Image: Gen regulation network for the Mycobacterium tuberulosis. Every node represent a gen, and the links stand for the regulation relationship between a transcription factor and the correspondent regulated gen. Different colors mean different character of the gens, as far as regulation dynamics is concern.
Image: from left to right, Pablo Piedrahita, Julia Poncela, Yamir Moreno, Carlos Gracia, Joaquín Sanz, Mario Floría
Proteins are fundamental for living organisms. Almost every biological process depends on the presence or activity of this kind of molecules, whose function in an organism is determined by its molecular structure.
In the image: CPK representation of the molecular structure of a protein in the cellular membrane of the central nervous system neurons. It is made of 374 amino acids and 6700 atoms. Please note the complexity of the structure. Right: protein representation. Please note the helices, they are the secondary α-hélices called.
The different substructures are coupled to each other and form the tertiary structure of the protein, ultimately responsible for its biological function. Sometimes, several tertiary structures are combined in a certain way and are then told that they form a quaternary structure.
In the image: Box for simulating molecular dynamics. The structure of the amino acid is represented in the form of spheres, as the so-called Van der Waals representation. The set of 'rods' represents the surrounding water molecules. The simulation is performed modelling this box in three dimensions in such a way that corresponds to a real dissolution of the amino acid.
In the image: Map showing the energy landscape of an amino acid. It represents the energy of the system depending on two geometric parameters, in particular, the torsion angles that form the main chain atoms. Different valleys can be seen where the energy is lower. This would correspond to the most stable conformations of the amino acid and therefore to the more likely structures.
In these graphs one can observe a series of valleys separated by hills. The depth of the valley is related to the stability of this structure in particular. The deeper a valley, the most stable conformation is concerned. However, there are numerous valleys, corresponding to more or less stable structures, separated by barriers of varying height, that indicate how easy or difficult it is to move from one stable to another.
Image: from left to right, Sara Sanmartín, Nuria Robledo, Dr. Juan Francisco Vega, Prof. Javier Martínez-Salazar, Dr. Javier Ramos, Jon Otegui and Dr. Victor Cruz.
Image: Above: System with a 10% dilution in which there is no coexistence of phases. However, there are magnetized regions of all sizes; it is the equivalent of a hailstorm. Bottom: Pure models near the transition which shows a bubble and a magnetized band. The black region is the equivalent of an iceberg floating in the sea.
Image: Material in a macroscopic magnetisation.
Glass and disordered systems
Image: simulation of magnetic materials by computer
In the figure: ttr-estabilizadores
Understanding the binding or docking process between human proteins and small molecules is crucial for the development of drugs improving the treatment of diseases like cancer.
These small molecules, usually known as ligands, may modify the properties of the proteins with therapeutic effects. With Ibercivis, scientist from the Centro de Biología Molecular Severo Ochoa (CSIC-UAM) simulate the docking of certain proteins with a collection of ligands in order to identify the more promising ones for further experimental analysis.
MGMT, a protein making certain cancer cells resistant to some drugs used in chemotherapy, is the first protein they have worked with. The goal is to find a ligand inhibiting MGMT.
Image: Protein-ligand docking.
Identifying a ligand able to bind to a protein is a complex task. The available potential ligands constitute a chemical library, a collection of millions of molecules. Trying all of them in a laboratory would be unviable, in terms of time and resources. Therefore, a virtual screening is needed.
To perform a screening, first, the information about the protein and the ligands must be converted to computing data. In the case of the protein, this should be restricted to its active site, that is, the region where the protein interacts with the ligands. And in the case of the ligands, we must consider all possible spatial configurations.
Second, the docking of each ligand onto the protein must be simulated, and its interaction energy must be evaluated. Different conformations of the ligand are translated and rotated throughout the active site, and the interaction energy in the protein-ligand complex is calculated. The lower this energy, the better the interaction, and, therefore, the more stable the complex.
After the screening, the docking results are introduced in a database and ordered according to the interaction energy. For a more reliable ranking, the best candidates undergo a more precise computational analysis.
Finally, the outcome of the entire process is the selection of 20-30 ligands to be assayed experimentally in the laboratory.
Image: Characterization of the active site for carbon atoms interactions. The box shows the region where docking studies will be preformed. The yellow colour indicates the areas more favorable to the binding.
Image: Characterization of the active site for carbon atoms interactions. The box shows the region where docking studies will be preformed. The yellow colour indicates the areas more favorable to the binding.
A single docking (a protein with one ligand) takes five minutes approximately. Processing all the ligands in a library will take 40 years in a single machine. Fortunately, thanks to the Ibercivis computers this process will be performed in a more reasonable shorter time. Two complete virtual screenings have been accomplished since the launching of the project. If any of the selected ligands is experimentally successful, it will become the active principle of a new drug in the future.
Image: Example of ligand, exhibiting in vitro and in vivo activity, on the protein surface.
Docking: Searching new drugs against cancer using the computer
All drugs include a chemical substance called active principle into their composition. This molecule, generically named ligand, is responsible for the drug activity. The other constituents, named excipients, are inactive substances guaranteeing the active principle reaches the appropriate place. These places are usually localized either on the surface or inside certain macromolecular receptor structures, such as proteins or nucleic acids. At the end of the journey, a ligand must find its corresponding active site and fit into it.
This process, called docking, is quite complex, and a series of chemical events governed by physical laws, among them those related to the net energy balance in the process, take place.
It is evident that if the ligand binding to the active site is energetically favourable, the complex constituted by both must be more stable than each one separately; this is the principle of minimum energy. In other words, an association will be profitable if the partners earn more working together than working separately. Knowing how this interaction occurs, as well as the characterization and quantification of the several events taking place in the whole process, is the focus of a scientific area under continuous development. This knowledge will provide the necessary elements allowing, in theory, designing molecules with optimal structure for not only higher potency, but also less interferences with other undesired active sites (related to the side-effects).
Currently we have sophisticated experimental techniques for extracting three-dimensional information in proteins and ligands, that is, how their atoms are located in space and which is their relative geometric configuration in the binding. Besides, in many cases there are pharmacological studies of these systems available where the interaction energy is measured experimentally. Therefore, we know how these molecules bind and how much energy is involved. If we were able to reproduce the experimental results from the three-dimensional structures and well-known physico-chemical laws, we could predict the binding, and the energy involved, for any other ligand, before carrying out costly pharmacological assays.
At the Unidad de Bioinformática del Centro de Biología Molecular "Severo Ochoa" (CBMSO, CSIC-UAM) we have been working for several years in the development of a computing platform that allows an automated search for new ligands with adequate pharmacological profiles, in order to improve the current treatment of some of the more socially relevant diseases, in particular, cancer. We use libraries comprising millions of chemical compounds to look for those compounds better suited to the active site of the receptor using our docking algorithms. This process is called virtual screening. As a result of this search, thousands of candidates are obtained and must be classified in order to select a reasonable small number of potential ligands for experimental assays (e.g. between 20 and 30). The ranking is performed using scoring functions assigning a score to each molecule based on the calculated interaction energy with the active site. This is the key of the complexity of the protocol: finding scoring functions quick (to evaluate millions of compounds) and precise (fair agreement with experimental results) enough. For the calculations it is necessary to employ either potent computers, or a large number of computers.
In simple words, a docking program tries to find the best configuration for each ligand, in terms of structure, geometry, and energy) in the active site. All possible configurations of the ligand (there may be millions of them) must be explored and ranked accordingly. This gives an idea of the astronomical amount of calculations required. The collaboration of people involved and participating in Ibercivis (unused CPU time from thousands of computer loaned for these calculations) is invaluable for accelerating the search of new and more efficient drugs against diseases like cancer.
Researchers from the Bioinformatics unity of the Centro de Biología Molecular Severo Ochoa focus on the computational development that'll use the genome information to discover new drugs. They have three different researching areas: Computational genomic, Structural Bioinformatics and Drug design based in the receptor structure.
More information about the group on : http://ub.cbm.uam.es
Proceso de Docking ( 2009, Spanish )
Docking Benzamidina betaTripsina ( 2009, Spanish )
Docking Sildenafil Fosfodiesterasa ( 2009, Spanish )
Figure (left to right and up down): Cut of the vacuum chamber, where the plasma shall be confined. It's approximate measures are 3.5 x 8 m / 3 superconducting coils crating the main magnetic field, some 100,000 times the terrestrial field / ITER, within the cryostat keeping the coils in the superconducting state and within the shielding isolating it from the exterior.
In the figures: Part of a trajectory and behavior of the plasma as an ensemble. A home computer can take between 15 and 30 minutes in calculating a complete trajectory. The calculation of many trajectories allows us to get an idea about the aspect and properties of plasma in the reactor. Sooner or later, the nuclei in ITER eventually escape. Ibercivis helps us to learn the escape regions (in this case, the upper region).
Fusion and the energy panorama
Figure: Tokamak is a Russian invention, its names standing for Toroidnalnaya Kamera v Magnetnikh Katushkakh (literal English translation: Toroidal Chamber in Magnetic Coil) .
Fusion Reactors: Tokamaks and Stellarators
Figure: Tokamak. The engineer from the Lawrence Livermore Laboratory of California (right) and a visiting Japanese engineer examine the superconducting material for tests.
Fusion and the Science of Materials
Fusion reactors: ITER