Aug
12
Mon
2013
Plenary Talk: Realistic modeling-new insight into the functions of the cerebellar network @ Amriteshwari Hall
Aug 12 @ 1:37 pm – 2:24 pm

egidioEgidio D’Angelo, MD, Ph.D.
Full Professor of Physiology & Director, Brain Connectivity Center, University of Pavia, Italy


Realistic modeling: new insight into the functions of the cerebellar network

Realistic modeling is an approach based on the careful reconstruction of neurons synapses starting from biological details at the molecular and cellular level. This technique, combined with the connection topologies derived from histological measurements, allows the reconstruction of precise neuronal networks. Finally, the advent of specific software platforms (PYTHON-NEURON) and of super-computers allows large-scale network simulation to be performed in reasonable time. This approach inverts the logics of older theoretical models, which anticipated an intuition on how the network might work.  In realistic modeling, network properties “emerge” from the numerous biological properties embedded into the model.

This approach is illustrated here through an outstanding application of realistic modeling to the cerebellar cortex network. The neurons (over 105) are reproduced at a high level of detail generating non-linear network effects like population oscillations and resonance, phase-reset, bursting, rebounds, short-term and long-term plasticity, spatiotemporal redistrbution of input patterns. The model is currently being used in the context of he HUMAN BRAIN PROJECT to investigate the cerebellar network function.

Correspondence should be addressed to

Dr. EgidioD’Angelo,
Laboratory of Neurophysiology
Via Forlanini 6, 27100 Pavia, Italy
Phone: 0039 (0) 382 987606
Fax: 0039 (0) 382 987527
dangelo@unipv.it

Acknowledgments

This work was supported by grants from European Union to ED (CEREBNET FP7-ITN238686, REALNET FP7-ICT270434) and by grants from the Italian Ministry of Health to ED (RF-2009-1475845).

Egidio

Aug
13
Tue
2013
Invited Talk: The system of PAS proteins (HIF and AhR) as an interface between environment and skin homeostasis @ Acharya Hall
Aug 13 @ 2:33 pm – 2:50 pm

andreyAndrey Panteleyev, Ph.D.
Vice Chair, Division of Molecular Biology, NBICS Centre-Kurchatov Institute, Moscow, Russia


The system of PAS proteins (HIF and AhR) as an interface between environment and skin homeostasis

Regulation of normal skin functions as well as etiology of many skin diseases are both tightly linked to the environmental impact. Nevertheless, molecular aspects of skin-environment communication and mechanisms coordinating skin response to a plurality of environmental stressors remain poorly understood.

Our studies along with the work of other groups have identified the family of PAS dimeric transcription factors as an essential sensory and regulatory component of communication between skin and the environment. This protein family comprises a number of hypoxia-induced factors (HIF-alpha proteins), aryl hydrocarbon receptor (AhR), AhR nuclear translocator (ARNT), and several proteins implicated in control of rhythmic processes (Clock, Period, and Bmal proteins). Together, various PAS proteins (and first of all ARNT – as the central dimerization partner in the family) control such pivotal aspects of cell physiology as drug/xenobiotic metabolism, hypoxic and UV light response, ROS activity, pathogen defense, overall energy balance and breathing pathways.

In his presentation Dr. Panteleyev will focus on the role of ARNT activity and local hypoxia in control of keratinocyte differentiation and cornification. His recent work revealed that ARNT negatively regulates expression of late differentiation genes through modulation of amphiregulin expression and downstream alterations in activity of EGFR pathway. All these effects are highly dependent on epigenetic mechanisms such as histone deacetylation. Characterisation of hypoxia as a key microenvironmental factor in the skin and the role of HIF pathway in control of dermal vasculature and epidermal functions is another major focus of Dr. Panteleyev’s presentation.

In general, the studies of Dr. Panteleyev’s laboratory provide an insight into the PAS-dependent maintenance of skin homeostasis and point to the potential role of these proteins in pathogenesis of environmentally-modulated skin diseases such as barrier defects, desquamation abnormalities, psoriasis, etc.

 

Invited Talk: Applying Machine learning for Automated Identification of Patient Cohorts @ Sathyam Hall
Aug 13 @ 2:40 pm – 3:05 pm

SriSairamSrisairam Achuthan, Ph.D.
Senior Scientific Programmer, Research Informatics Division, Department of Information Sciences, City of Hope, CA, USA


Applying Machine learning for Automated Identification of Patient Cohorts

Srisairam Achuthan, Mike Chang, Ajay Shah, Joyce Niland

Patient cohorts for a clinical study are typically identified based on specific selection criteria. In most cases considerable time and effort are spent in finding the most relevant criteria that could potentially lead to a successful study. For complex diseases, this process can be more difficult and error prone since relevant features may not be easily identifiable. Additionally, the information captured in clinical notes is in non-coded text format. Our goal is to discover patterns within the coded and non-coded fields and thereby reveal complex relationships between clinical characteristics across different patients that would be difficult to accomplish manually. Towards this, we have applied machine learning techniques such as artificial neural networks and decision trees to determine patients sharing similar characteristics from available medical records. For this proof of concept study, we used coded and non-coded (i.e., clinical notes) patient data from a clinical database. Coded clinical information such as diagnoses, labs, medications and demographics recorded within the database were pooled together with non-coded information from clinical notes including, smoking status, life style (active / inactive) status derived from clinical notes. The non-coded textual information was identified and interpreted using a Natural Language Processing (NLP) tool I2E from Linguamatics.