Facultad de Ciencias, Universidad de Valparaíso
Doctor en Ciencias mención Biología Molecular, Celular y Neurociencias. Universidad de Chile (2004)
Director of Biophysics and Computational Biology PhD Program at University of Valparaíso
E-Mail: patricio.orio at uv.cl
Teléfono: (56-32) 299 5538
Fax: (56-32) 250 8027
Dirección: Centro Interdisciplinario de Neurociencia de Valparaiso,
Pasaje Harrington 287. Valparaíso, Chile.
Mathematical Modelling of Neuron Behavior
Neurons and neural networks show a complex behavior due to the high non-linearity of their responses to different stimuli. Much of what we currently understand about this behavior has come from the mathematical modeling of the different processes taking place in the cell membrane. The analysis of dynamical systems that arise from the physical and chemical principles underlying the transmission of electrical signals in living organisms, has been a great aid in the understanding of experimental data, and is also a whole research field on its own.
My current research interests include:
- How neural dynamics and network topology can shape the dynamics of a neural network. The dynamics of networks in the brain show unique features, like the ability to engage in different types and degrees of synchrony and the jumping between several stable attractrors -known as multistability. How these properties arise from the different properties of a network? I am interested in two particular features:
- Chaotic behavior of neural oscillators. Individual networks and neural circuits, like many other high-dimensional systems, show a behavior known as chaos. This consists in a high sensitivity to initial conditions, where two systems starting at very close initial conditions diverge exponentially in time. Is this property of the individual nodes relevant to the network behavior?
- Network topology. The topology of neural networks show distinct features, such as being scale-free and showing small-world properties. At the same time, they are different from other typical small-world networks. Which features of the network topology determine or contribute to the multistability of a network?
- How sensory systems detect complex features of the environment.
- Understanding the dynamic response of cold-sensitive nerve terminals. Cold receptors in our skin and peripheral tissues display a exquisite sensitivity to changes in temperature. At the same time, they have a spontaneous, rhythmic activity of action potential generation that appears to follow an ongoing sub-threshold oscillation of the membrane potential. Based on previous models that were focused on the oscillatory pattern generation, we developed a model that reproduces both aspects of the response of cold-sensitive nerve endings, by taking into account recent experimental findings. Currently we are using this model to understand the abnormal behavior of these terminals in pathological conditions such as cold allodynia and hypersensitivity to cold. This is in collaboration with Dr. Rodolfo Madrid (USACH)
- Modeling of the neural networks in the retina, in collaboration with Dr. Adrián Palacios. My interest in this initiative is to develop and analyze conductance-based models of the Starburst Amacrine Cells network in the retina and their role in the direction selectivity of some ganglion cells.
- Channel noise in neurons: Through mathematical modeling and analysis, we want to understand the diverse effects that ion channel stochasticity has on neural excitability.
- Xu K., Maidana JP, Caviedes M, Quero D, Aguirre P and Orio P. (2017). Hyperpolarization-activated current induces period-doubling cascades and chaos in a cold thermoreceptor model. Front. Comput. Neurosci. 11:12.
- González A., Ugarte G., Restrepo C., Herrera G., Piña R., Gómez-Sánchez JA., Pertusa M., Orio P. and Madrid R. (2017). Role of the excitability brake potassium current IKD in cold allodynia induced by chronic peripheral nerve injury. J Neurosci 8 February 2017, 3553-16; doi:10.1523/JNEUROSCI.3553-16.2017
- Olivares E, Salgado S, Maidana JP, Herrera G, Campos M, Madrid R and Orio P (2015). TRPM8-dependent dynamic response in a mathematical model of cold thermoreceptor. PLOS One 588(Pt 17):3141-8 doi: 10.1371/journal.pone.0139314
- Pezo D, Soudry D and Orio P (2014) Diffusion approximation-based simulation of stochastic ion channels: which method to use? Front. Comput. Neurosci. 8:139. doi:10.3389/fncom.2014.00139
- Escobar MJ, Pezo D, Orio P. (2013) Mathematical Analysis and Modeling of Motion Direction Selectivity in the Retina. J Physiol Paris 107(5):349-359. doi:10.1016/j.jphysparis.2013.08.003
- Orio P., Parra A., Madrid R., González O., Belmonte C., Viana F. (2012) Role of Ih in the Firing Pattern of Mammalian Cold Thermoreceptors. J Neurophysiol 108:3009-3023 doi:10.1152/jn.01033.2011
- Orio P. and Soudry D. (2012) Simple and Fast Implementation of the Diffusion Approximation Algorithm for Stochastic Ion Channels with Multiple States. PLoS ONE 7(5): e36670.
- Brauchi S., Orio P. (2011) Voltage Sensing in ThermoTRP Channels. Adv. Exp. Med. Biol. 704:517-530.
- Orio P., Madrid R., de la Peña E., Parra A., Meseguer V., Bayliss D.A., Belmonte C., Viana F. (2009) Characteristics and physiological role of hyperpolarization-activated current Ih in mouse cold thermoreceptors. J Physiol 587:1961-1976.
- Orio P., Torres Y., Rojas P., Carvacho I., Garcia M.L., Toro L., Valverde M.A., Latorre R. (2006). Structural Determinants for Functional Coupling Between the β and α Subunits in the Ca2+-activated K+ (BK) Channel. J. Gen. Physio
- Orio, P., Latorre, R. (2005) Differential effect of β1 and β2 subunits on BK Channel Activity. J. Gen. Physiol. 125:395-411.
- Brauchi, S., Orio, P., Latorre, R. (2004) Clues to understanding cold sensation. Thermodynamics and electrophysiological analysis of the cold receptor TRPM8. Proc Natl Acad Sci USA. 101:15494-15499
- Orio, P., Rojas, P., Ferreira, G. and Latorre, R. (2002) New Disguises for an Old Channel: MaxiK Channel β subunits. News Physiol. Sci. 17:156-161.
- Valverde, M.A.; Rojas, P.; Amigo, J.; Cosmelli, D.; Orio, P.; Bahamonde, M.I.; Mann, G.E.; Vergara, C. and Latorre, R. (1999). Acute Activation of Maxi-K Channels (hSlo) by Estradiol Binding to the β Subunit. Science 285:1929-1931.