Final Report Summary - RAUMN (Reconstruction and understanding of molecular networks)
A major challenge for current biology is to understand how bio-molecules interact and cooperate to achieve their joint cellular functions. Understanding a pathway requires that we first reconstruct the network - identify what molecules participate in a given pathway and how they interact, and then characterize its functional modes. Following the explosion of genome wide assays, network reconstruction became a central task in molecular biology, spanning signaling, metabolic, and regulatory pathways. However, despite intensive research, many fundamental cellular pathways are only partially known, and not well understood. Specifically, pathways constituting synapses in the mammalian brain are only very partially understood: their molecular interactions are partially charted and the way they change throughout life is little understood.
Our research focused on developing computational and statistical methods for completing partially known biological pathways and use these methods to reconstruct and understand signal transduction pathways., with a special focus on mammalian synapses. We developed ways to learn how diverse functional data maps onto known networks, identify recurring patterns of this mapping, and use them to predict unknown network components. We have applied this approach to multiple systems, including reconstruction of glutamatergic synapses in the mouse and characterization of changes in the serotonin system in human adolescents.
The objective of the reintegration period was to build a computational neurobiology lab that becomes a knowledge center for analysis and modeling in molecular neuroscience. This objective is part of the overarching goal of the Gonda multidisciplinary center, combining different disciplines of neuroscience under the same roof, to foster collaborations between diverse fields of neuroscience. This goal was very successfully achieved, in terms of researchers, funding and publications.
* People: Dr Chechik founded and manages the computational neurobiology lab. The lab currently includes 1 PostDoctoral research, 5 PhD students who submitted their research proposals, two Phd Candidate working on their research proposal, and 3 MSc students. One PhD student has graduated earlier hits year, and submitted her PhD Dissertation for examination, and one M.Sc. student has graduated in 2012.
* Funding and Equipment: The lab has secured funding in addition to the Marie-curie integration grant, including three ISF grants: one for equipment purchase and two for funding students fellowships. One student of the lab has one the Google EMA PhD scholarship in machine learning, which funded his PhD research. Using these resources, the lab has acquired a high power computing cluster located in Bar Ilan and shared with collaborating laboratories.
The work published so far at the lab follows two thrusts: Developing computational analysis methods of complex data, and applying these methods to understanding and reconstructing of brain-related pathways. From the methods side, publications focused on large scale learning of relations between samples, which has applications both for molecular network reconstruction [Mol. Cell. Neuro, 2013] and for machine vision [J. Mach Learning Research, 2010, 2011, 2013]. From the molecular neurobiology side, recent publication of large scale expression datasets in humans and other mammals, allowed to study brain-related pathways in new ways. Specifically, we reconstructed glutamate receptor pathways [Mol Cell, Neuro 2013A], predicted new genes localized to the cerebellum [PLoS Comp Bio 2013], analyzed developmental timeline of mouse brain expression [PLoS Comp Bio 2013B] and analyzed the human serotonin pathways [Eur. Neuro Pshcyo Pharmacology 2014].
In summary, the reintegration grant has been a major funding source for the lab, and served a critical role in it success. With the support of this grant we have secured funding for the next three years of research in the lab, and have established its role in developing new approaches for analysis and computational modeling of neuroscience in Bar Ilan and in Israel.
Our research focused on developing computational and statistical methods for completing partially known biological pathways and use these methods to reconstruct and understand signal transduction pathways., with a special focus on mammalian synapses. We developed ways to learn how diverse functional data maps onto known networks, identify recurring patterns of this mapping, and use them to predict unknown network components. We have applied this approach to multiple systems, including reconstruction of glutamatergic synapses in the mouse and characterization of changes in the serotonin system in human adolescents.
The objective of the reintegration period was to build a computational neurobiology lab that becomes a knowledge center for analysis and modeling in molecular neuroscience. This objective is part of the overarching goal of the Gonda multidisciplinary center, combining different disciplines of neuroscience under the same roof, to foster collaborations between diverse fields of neuroscience. This goal was very successfully achieved, in terms of researchers, funding and publications.
* People: Dr Chechik founded and manages the computational neurobiology lab. The lab currently includes 1 PostDoctoral research, 5 PhD students who submitted their research proposals, two Phd Candidate working on their research proposal, and 3 MSc students. One PhD student has graduated earlier hits year, and submitted her PhD Dissertation for examination, and one M.Sc. student has graduated in 2012.
* Funding and Equipment: The lab has secured funding in addition to the Marie-curie integration grant, including three ISF grants: one for equipment purchase and two for funding students fellowships. One student of the lab has one the Google EMA PhD scholarship in machine learning, which funded his PhD research. Using these resources, the lab has acquired a high power computing cluster located in Bar Ilan and shared with collaborating laboratories.
The work published so far at the lab follows two thrusts: Developing computational analysis methods of complex data, and applying these methods to understanding and reconstructing of brain-related pathways. From the methods side, publications focused on large scale learning of relations between samples, which has applications both for molecular network reconstruction [Mol. Cell. Neuro, 2013] and for machine vision [J. Mach Learning Research, 2010, 2011, 2013]. From the molecular neurobiology side, recent publication of large scale expression datasets in humans and other mammals, allowed to study brain-related pathways in new ways. Specifically, we reconstructed glutamate receptor pathways [Mol Cell, Neuro 2013A], predicted new genes localized to the cerebellum [PLoS Comp Bio 2013], analyzed developmental timeline of mouse brain expression [PLoS Comp Bio 2013B] and analyzed the human serotonin pathways [Eur. Neuro Pshcyo Pharmacology 2014].
In summary, the reintegration grant has been a major funding source for the lab, and served a critical role in it success. With the support of this grant we have secured funding for the next three years of research in the lab, and have established its role in developing new approaches for analysis and computational modeling of neuroscience in Bar Ilan and in Israel.