Computational based modeling and engineering of the fitness of synthetic variants of Porcine Circovirus based on the analysis of a large scale genomic data

By Lia Baron

Viral genomes not only determine protein products, but also include silent, overlapping codes which are important to the viral life cycle and affect its evolution. Due to the high density of these codes, their non-modular nature, and the complex intracellular processes they encode, the ability of the current approaches to decipher them is very limited.
My research is a part of the first computational-experimental pipeline for studying the effects of viral silent information on its fitness. The pipeline was implemented to study the Porcine Circovirus (PCV2), which is a major pig pathogen, and includes the following steps: 1) Based on the analyses of over 2,000 viral genomes suspected silent codes were inferred. 2) 500 variants of the PCV were designed to include various smart silent mutations. 3) Using state of the art synthetic biology approaches the genomes of these 500 variants were generated. 4) Competition experiments between the variants were performed in PK15 cell-lines. 5) The variants titers were analyzed based on novel NGS experiments and in comparison to endogenous PCV genomes. 
The analysis enables the detection of various novel silent functional sequences and structural motives in the PCV genome, which can be integrated for predicting the fitness of a PCV variant based on its genome. This predictor can be used both for understating the genome of the PCV and for engineering novel synthetic PCV vaccines.

(The talk will be given in English)

Speaker:     Dr. Amichai Painsky
                   HUJI  &  MIT

Monday, January 7^th, 2019
15:00 - 16:00

Room 011, Kitot Bldg., Faculty of Engineering

Generalized Neyman-Pearson for Multiple Detections
 

Abstract

The classical single hypothesis testing problem considers a set of observations that is drawn from one of two possible distributions, typically denoted as the Null (no signal) and the Alternative (signal). The goal of the test is to maximize the power (correct detection) subject to a prescribed probability of false alarm (false detection). It is well-known that the Neyman-Pearson procedure provides the uniformly most powerful test for the single hypothesis case. However, the problem becomes more complicated when we consider more than two hypotheses. In this work, we formulate the multiple testing problem as an infinite-dimensional optimization problem, seeking the most powerful decision policy under commonly used false detection measures (such as family-wise error rate (FWER) and false discovery rate (FDR)). In this sense, our approach is a generalization of the optimal Neyman-Pearson procedure for testing multiple hypotheses. Using calculus of variations, we show that for exchangeable hypotheses, our problem can be reformulated as infinite linear programs and can be solved for any number of hypotheses, by applying the derived optimality conditions. We demonstrate our results in several setups and show that the power gain over natural competitors is substantial in all the examined settings. Finally, we discuss several engineering applications, from classical communications systems, to more recent sensing devices in autonomous car. 

Short Bio

Amichai Painsky is a Post-Doctoral Fellow, co-affiliated with the Israeli Center for Research Excellence in Algorithms (I-CORE ALGO) at the Hebrew University, and the Signals, Information and Algorithms (SIA) Laboratory at MIT. Amichai received his B.Sc. in Electrical Engineering from Tel Aviv University (2007), his M.Eng. in Electrical Engineering from Princeton University (2009) and his Ph.D. in Statistics from Tel Aviv University (2016). He is a recipient an outstanding Ph.D. students award from the school of Mathematical Sciences, the Weinstein Institute of Signal Processing and the Marejn Foundation. Previously, he received a Brain Return Ph.D. Scholarship from the Israeli Center for Returning Scientists.  

You are invited to attend a department seminar on

Partially Coherent Radar

:By

Komissarov Rony

MSc student under the supervision of Dr. Pavel Ginzburg

Abstract

It is widely believed that range resolution, the ability to distinguish between two closely situated targets, depends inversely on the bandwidth of the transmitted radar signal. In this seminar, I will present a novel type of ranging system with controllable coherence length of radiation, which possesses superior range resolution that is almost completely free of bandwidth limitations. By sweeping over the coherence length of the transmitted signal, the novel partially coherent radar is experimentally shown to resolve targets at distances that are two orders of magnitude below what standard coherent radars could achieve with the same bandwidth. This concept offers solutions to problems whereby high range resolution and accuracy are necessary but available bandwidth is limited, as is the case for the autonomous car industry, optical imaging, and astronomy to name just few.

On Wednesday, December 19, 2018, 11:00

Room 206, Wolfson Building