Egidio 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).
Michelle Hermiston, MD, Ph.D.
Assistant Professor, Department of Pediatrics University of California San Francisco, USA
Interrogating Signaling Networks at the Single Cell Level In Primary Human Patient Samples
Multiparameter phosphoflow cytometry is a highly sensitive proteomic approach that enables monitoring of biochemical perturbations at the single cell level. By combining antisera to cell surface markers and key intracellular proteins, perturbations in signaling networks, cell survival and apoptosis mediators, cell cycle regulators, and/or modulators of other cellular processes can be analyzed in a highly reproducible and sensitive manner in the basal state and in response to stimulation or drug treatment. Advantages of this approach include the ability to identify the biochemical consequences of genetic and/or epigenetic changes in small numbers of cells, to map potential interplay between various signaling networks simultaneously in a single cell, and to interrogate potential mechanisms of drug resistance or response in a primary patient sample. Application of this technology to patients with acute lymphoblastic leukemia or the autoimmune disease systemic lupus erythematosus (SLE) will be discussed.
Sharmila Mande, Ph.D.
Principal Scientist and Head, Bio Sciences R&D, TCS Innovation Labs, Pune
Gut microbiome and health: Moving towards the new era of translational medicine
The microbes inhabiting our body outnumber our own cells by a factor of 10. The genomes of these microbes, called the ‘second genome’ are therefore expected to have great influence on our health and well being. The emerging field of metagenomics is rapidly becoming the method of choice for studying the microbial community (called microbiomes) present in various parts of the human body. Recent studies have implicated the role of gut microbiomes in several diseases and disorders. Studies have indicated gut microbiome’s role in nutrient absorption, immuno-modulation motor-response, and other key physiological processes. However, our understanding of the role of gut microbiota in malnutrition is currently incomplete. Exploration of these aspects are likely to help in understanding the microbial basis for several physiological disorders associated with malnutrition (eg, increased susceptibility to diarrhoeal pathogens) and may finally aid in devising appropriate probiotic strategies addressing this menace. A metagenomic approach was employed for analysing the differences between gut microbial communities obtained from malnourished and healthy children. Results of the analysis using TCS’ ‘Metagenomic Analysis Platform’ were discussed in detail during my talk.
Srisairam 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.
Bodo Eickhoff, Ph.D.
Senior Vice-President, Head of Sales and Marketing for Roche Applied Science, Germany
New paths for treatment of complex diseases: target combinatorial drug therapy
Several types of diseases show a complex pathogenesis and require targeted as well as combinatorial drug treatment. A classical example, Tuberculosis, was thought for decades to be managable by triple therapy, however now requiring new therapeutic approaches due to multi drug resistant strains. HIV and AIDS can only be kept under control by combinations of specific, virus-protein targeted drugs, requiring constant monitoring of resistance patterns and modulation of drug combinations during life-long therapy. As a third example, Cancer in all its different variations, requires detailled molecular understanding to enable targeted therapy. New technologies provide more and in depths molecular insights into pathomechanisms and resulting treatment options. However, is there an alternative way to approach complex diseases by holistic models? Can restoring of apoptosis-capabilities of transformed cells be an example of such an alternative path? How do we in future adress major unresolved topics like increasing drug resistance in bacterial infections, lack of anti-viral drugs, treatment of parasite diseases like Malaria, and newly emerging infectious diseases in research and fast translation of these results into diagnosis and treatment?