Soheil Feizi

Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay

Learning to read and write the transcriptional regulatory code is of central importance to progress in genetic analysis and engineering. Here we describe a massively parallel reporter assay (MPRA) that facilitates the systematic dissection of transcriptional regulatory elements. In MPRA, microarray-synthesized DNA regulatory elements and unique sequence tags are cloned into plasmids to generate a library of reporter constructs. These constructs are transfected into cells and tag expression is assayed by high-throughput sequencing. We apply MPRA to compare >27,000 variants of two inducible enhancers in human cells: a synthetic cAMP-regulated enhancer and the virus-inducible interferon-β enhancer. We first show that the resulting data define accurate maps of functional transcription factor binding sites in both enhancers at single-nucleotide resolution. We then use the data to train quantitative sequence-activity models (QSAMs) of the two enhancers. We show that QSAMs from two cellular states can be combined to design enhancer variants that optimize potentially conflicting objectives, such as maximizing induced activity while minimizing basal activity.

Network deconvolution as a general method to distinguish direct dependencies in networks

Recognizing direct relationships between variables connected in a network is a pervasive problem in biological, social and information sciences as correlation-based networks contain numerous indirect relationships. Here we present a general method for inferring direct effects from an observed correlation matrix containing both direct and indirect effects. We formulate the problem as the inverse of network convolution, and introduce an algorithm that removes the combined effect of all indirect paths of arbitrary length in a closed-form solution by exploiting eigen-decomposition and infinite-series sums. We demonstrate the effectiveness of our approach in several network applications: distinguishing direct targets in gene expression regulatory networks; recognizing directly-interacting amino-acid residues for protein structure prediction from sequence alignments; and distinguishing strong collaborations in co-authorship social networks using connectivity information alone.

A power efficient sensing/communication scheme: Joint source-channel-network coding by using compressive sensing

We propose a joint source-channel-network coding scheme, based on compressive sensing principles, for wireless networks with AWGN channels (that may include multiple access and broadcast), with sources exhibiting temporal and spatial dependencies. Our goal is to provide a reconstruction of sources within an allowed distortion level at each receiver. We perform joint source-channel coding at each source by randomly projecting source values to a lower dimensional space. We consider sources that satisfy the restricted eigenvalue (RE) condition as well as more general sources for which the randomness of the network allows a mapping to lower dimensional spaces. Our approach relies on using analog random linear network coding. The receiver uses compressive sensing decoders to reconstruct sources. Our key insight is the fact that, compressive sensing and analog network coding both preserve the source characteristics required for compressive sensing decoding.

Robust Audio Data Hiding Using Correlated Quantization With Histogram-Based Detector

In this paper, two blind audio watermarking methods using correlated quantization for data embedding with histogram-based detector have been proposed. First, a novel mapping called the point-to-point graph (PPG) is introduced. In this mapping, the value of samples is important as well as the correlation among them. As this mapping increases the dimension of the signal, the data embedding procedure (quantization) will be diversified more securely than that of the 1-D domains such as the time or frequency domains. Hence, two watermarking techniques coined as hard and soft quantization methods based on the quantization of the PPG point radii are suggested. The performance of both techniques is analyzed by obtaining the radii distribution of PPG points after watermarking. Experimental results against AWGN attack confirm the validity of theoretical analysis. Moreover, the robustness of the proposed methods against other common attacks such as echo, low pass, resampling, and MP3 are investigated through extensive simulations.

Energy-efficient Time-Stampless Adaptive Nonuniform Sampling

Nowadays, since more and more battery-operated devices are involved in applications with continuous sensing, development of an efficient sampling mechanisms is an important issue for these applications. In this paper, we investigate power efficiency aspects of a recently proposed adaptive nonuniform sampling. This sampling scheme minimizes the energy consumption of the sampling process, which is approximately proportional to sampling rate. The main characteristics of our method are that, first, sampling times do not need to be transmitted, since the receiver can compute them by using a function of previously taken samples, and second, only innovative samples are taken from the signal of interest, reducing the sampling rate and therefore the energy consumption. We call this scheme Time-Stampless Adaptive Nonuniform Sampling (TANS). TANS can be used in several scenarios, showing promising results in terms of energy savings, and can potentially enable the development of new applications that require continuous signals sensing, such as applications related to health monitoring, location tracking and entertainment.

Locally Adaptive Sampling

In this paper, we introduce a class of Locally Adaptive Sampling schemes. In this sampling family, time intervals between samples can be computed by using a function of previously taken samples, called a sampling function. Hence, though it is a non-uniform sampling scheme, we do not need to keep sampling times. The aim of LAS is to have the average sampling rate and the reconstruction error satisfy some requirements. We propose four different schemes of LAS. The first two are designed for deterministic signals. First, we derive a Taylor Series Expansion (TSE) sampling function, which only assumes the third derivative of the signal is bounded, but requires no other specific knowledge of the signal. Then, a Discrete Time-Valued (DTV) sampling function is proposed, where the sampling time intervals are chosen from a lattice. Next, we consider stochastic signals. We propose two sampling methods based on linear prediction filters: a Generalized Linear Prediction (GLP) sampling function, and a Linear Prediction sampling function with Side Information (LPSI). In GLP method, we only assume the signal is locally stationary. However, LPSI is specifically designed for a known signal model.

Compressive sensing over networks

In this paper, we demonstrate some applications of compressive sensing over networks. We make a connection between compressive sensing and traditional information theoretic techniques in source coding and channel coding. Our results provide an explicit trade-off between the rate and the decoding complexity. The key difference of compressive sensing and traditional information theoretic approaches is at their decoding side. Although optimal decoders to recover the original signal, compressed by source coding have high complexity, the compressive sensing decoder is a linear or convex optimization. First, we investigate applications of compressive sensing on distributed compression of correlated sources. Here, by using compressive sensing, we propose a compression scheme for a family of correlated sources with a modularized decoder, providing a trade-off between the compression rate and the decoding complexity. We call this scheme Sparse Distributed Compression. We use this compression scheme for a general multicast network with correlated sources. Here, we first decode some of the sources by a network decoding technique and then, we use a compressive sensing decoder to obtain the whole sources. Then, we investigate applications of compressive sensing on channel coding. We propose a coding scheme that combines compressive sensing and random channel coding for a high-SNR point-to-point Gaussian channel. We call this scheme Sparse Channel Coding. We propose a modularized decoder providing a trade-off between the capacity loss and the decoding complexity. At the receiver side, first, we use a compressive sensing decoder on a noisy signal to obtain a noisy estimate of the original signal and then, we apply a traditional channel coding decoder to find the original signal.

Tunable sparse network coding for multicast networks

This paper shows the potential and key enabling mechanisms for tunable sparse network coding, a scheme in which the density of network coded packets varies during a transmission session. At the beginning of a transmission session, sparsely coded packets are transmitted, which benefits decoding complexity. As the transmission continues and the receivers have accumulated coded packets, the coding density is increased. We propose a family of tunable sparse network codes (TSNCs) for multicast erasure networks with a controllable trade-off between completion time performance to decoding complexity. Coding density tuning can be performed by designing time-dependent coding matrices. In multicast networks, this tuning can be performed within the network by designing time-dependent pre-coding and network coding matrices with mild conditions on the network structure for specific densities. We present a mechanism to perform efficient Gaussian elimination over sparse matrices going beyond belief propagation but maintaining low decoding complexity. Supporting implementation results are provided showing the trade-off between decoding complexity and completion time.

Impulsive Noise Cancellation Based on Soft Decision and Recursion

In this paper, we propose a new method to recover band-limited signals corrupted by impulsive noise. The proposed method successively uses adaptive thresholding and soft decisioning to find the locations and amplitudes of the impulses. In our proposed method, after estimating the positions and amplitudes of the additive impulsive noise, an adaptive algorithm, followed by soft decision, is employed to detect and attenuate the impulses. In the next step, by using an iterative method, an approximation of the signal is obtained. This signal approximation is successively used to improve the noise estimate. The algorithm is analyzed and verified by computer simulations. Simulation results confirm the robustness of the proposed algorithm, even if the impulsive noise exceeds the theoretical reconstruction capacity.

Microfluidic neurite guidance to study structure-function relationships in topologically-complex population-based neural networks.

The central nervous system is a dense, layered, 3D interconnected network of populations of neurons, and thus recapitulating that complexity for in vitro CNS models requires methods that can create defined topologically-complex neuronal networks. Several three-dimensional patterning approaches have been developed but none have demonstrated the ability to control the connections between populations of neurons. Here we report a method using AC electrokinetic forces that can guide, accelerate, slow down and push up neurites in un-modified collagen scaffolds. We present a means to create in vitro neural networks of arbitrary complexity by using such forces to create 3D intersections of primary neuronal populations that are plated in a 2D plane. We report for the first time in vitro basic brain motifs that have been previously observed in vivo and show that their functional network is highly decorrelated to their structure. This platform can provide building blocks to reproduce in vitro the complexity of neural circuits and provide a minimalistic environment to study the structure-function relationship of the brain circuitry.