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RESEARCH GROUP

BIOTECHNOLOGY AREA

COMPUTATIONAL BIOPHYSICS AND BIOLOGICAL DATA ANALYSIS RESEARCH GROUP

PERSONNEL

LEAD RESEARCHER:

Dr. Diego Herráez Aguilar  diego.herraez@ufv.es

RESEARCHERS:

Dr. Ramiro Perezzan Rodríguez
Dr. Sandra Montalvo Quirós

EXTERNAL COLLABORATIONS:

Stable collaborations with the following groups:

1- Cell Migration and Epigenetics Group. Department of Immunology. Faculty of Medicine. Universidad Complutense de Madrid (IP: Dr. Javier Redondo-Muñoz)

2- BIOPHYS-Hub. Department de Physical Chemistry, Faculty of Chemical Sciences. Universidad Complutense de Madrid and the Translational Biophysics Unit, Biomedical Research Institute of the Hospital 12 Octubre, Madrid (IP: Dr. Francisco Monroy Muñoz)

3- Dynamics of Chemical Reactions Group. Department of Physical Chemistry, Faculty of Chemical Sciences, Universidad Complutense de Madrid.(IP: Francisco Javier Aoiz)

4- Neurorehabilitation and Brain Damage Group. Institute of Life Sciences. Universidad Francisco de Vitoria. (IP: Juan Pablo Romero)

LINE OF RESEARCH

Development of computational methods for the identification and analysis of non-equilibrium dynamics in time series of stochastic systems at the mesoscopic scale.

OUTLINE OF RESEARCH

One of the scientific paradigms of living organisms is that they necessarily stay away from the thermodynamic equilibrium. In other words, living structures and functions require a constant consumption of energy in order to maintain relatively low entropic levels. But, how far are living systems from thermodynamic equilibrium? The answer to this question has become one of the principal challenges of Physical Biology in the last five years.

Recent studies have shown how living systems perform most of their functions in a spatio-temporal scale dominated by an extremely dissipative, non-inertial stochastic regime (nondeterministic). Thus, it is difficult to think that living systems have the capacity to depart greatly from thermodynamic equilibrium and behave as our determinist macroscopic experience would have us believe. Rather, systems appear to take advantage of the stochastic dynamics to perform their functions, so that only slight alterations can be observed.

The Computational Biophysics and Biological Data Analysis group is engaged in the study of non-equilibrium stochastic dynamics from different perspectives.

1- Development of algorithms (Multiple Particle Auto-Tracking and Optical Flow) for high-resolution spatio-temporal microscopy for the detection and analysis of stochastic dynamics on the mesoscopic scale. Implementation of image segmentation procedures using traditional methods and machine learning.

 

2- Analysis of the modifications and alterations of the mechanical, viscoelastic and dissipative properties of biological materials. Application of microrheological techniques and generalised Stokes-Einstein equations. 

 

3- Development of computational methodologies for the identification of unbalanced fluids between energy states in subcellular systems, leading to the breaking of the Detailed Balance. Analysis of reversibility of temporal series and hidden states in Markov chains.

 

4 - Development of algorithms for the estimation of the physical entropy and information entropy in unbalanced stochastic systems.

PUBLICATIONS in the last 5 years.

  • Time series analysis applied to EEG shows increased global connectivity during motor activation detected in PD patients compared to controls. R. Perezzan, A. Mitín, D. Herráez-Aguilar, J. Ignacio Serrano, M. Dolores Castillo, A. Arroyo, J. Andreo, J.P. Romero Muñoz. Article submitted to Applied Sciences

  • Multiple particle tracking analysis in isolated nuclei reveals the mechanical phenotype of leukaemia cells. D. Herráez-Aguilar, E. Madrazo, H. Lopéz-Menéndez, F. Monroy, J. Redondo-Munoz. Article submitted for publication to Scientific Reports.

  • Experimental and theoretical studies of the Xe–OH (A/X) quenching system. j Kłos, G McCrudden, M Brouard, T Perkins, SA Seamons, D. Herráez-Aguilra, F. J. Aoiz . The Journal of Chemical Physics 149 (18), 184301, 2018.

  • Quantum interference between H + D2 quasiclassical reaction mechanisms. PG Jambrina, D Herráez-Aguilar, FJ Aoiz, M Sneha, J Jankunas, RN Zare. Nature Chemistry 7 (8), 661, 2015

PROJECTS

Collaborating Researchers in the project: SYNERGY CAM 2018 NUCLEUX MADRID Y2018/BIO 5207

CONTACT

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    icon marker Computational Biophysics and Biological Data Analysis RESEARCH GROUP Estudiar en Universidad Privada Madrid

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