Data based identification and prediction of nonlinear and complex dynamical systems

We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin

Towards a dynamic view of genetic networks: A Kalman filtering framework for recovering temporally-rewiring stable networks from undersampled data

It is widely accepted that cellular requirements and environmental conditions dictate the architecture of genetic regulatory networks. Nonetheless, the status quo in regulatory network modeling and analysis assumes an invariant network topology over time. We refocus on a dynamic perspective of genetic networks, one that can uncover substantial topological changes in network structure during biological processes such as developmental growth and cancer progression. We propose a novel outlook on the inference of time-varying genetic networks, from a limited number of noisy observations, by formulating the networks estimation as a target tracking problem. Assuming linear dynamics, we formulate a constrained Kalman ltering framework, which recursively computes the minimum mean-square, sparse and stable estimate of the network connectivity at each time point. The sparsity constraint is enforced using the weighted l1-norm; and the stability constraint is incorporated using the Lyapounov stability condition. The proposed constrained Kalman lter is formulated to preserve the convex nature of the problem. The algorithm is applied to estimate the time-varying networks during the life cycle of the Drosophila Melanogaster (fruit fly)

Gene regulatory network inference by point-based Gaussian approximation filters incorporating the prior information

Examinar a ampliação do uso de TICs por organizações sociais e governamentais na gestão da cidade é o objetivo do presente estudo. Nossa intenção é entender de que forma as tecnologias da informação e comunicação podem ser uma via alternativa que redefine as relações entre Estado e sociedade, substituindo políticas urbanas tradicionais por formas colaborativas de interação dos atores sociais. Entre os resultados alcançados pela pesquisa, é possível destacar a elaboração de uma metodologia capaz de mapear os princípios de organização, articulação, conexão e interação que constituem a existência de redes tecnossociais. A aplicação da metodologia nas cidades do Rio de Janeiro e de São Paulo demonstrou indicadores, gráficos e práticas políticas. A análise desses dados revela como as redes se constituem por uma arquitetura móvel, fluída, flexível, organizadas em torno de políticas comuns de ação e formadas por uma identidade coletiva que aproxima os atores das redes tecnossociais. Os princípios que mediam esta coesão são de compartilhamento, confiança e solidariedade, que redefinem as formas da organização do poder em direção a alternativas de organização política e desenvolvimento social

Efficient and Robust Algorithms for Statistical Inference in Gene Regulatory Networks

Inferring gene regulatory networks (GRNs) is of profound importance in the field of computational biology and bioinformatics. Understanding the gene-gene and gene- transcription factor (TF) interactions has the potential of providing an insight into the complex biological processes taking place in cells. High-throughput genomic and proteomic technologies have enabled the collection of large amounts of data in order to quantify the gene expressions and mapping DNA-protein interactions. This dissertation investigates the problem of network component analysis (NCA) which estimates the transcription factor activities (TFAs) and gene-TF interactions by making use of gene expression and Chip-chip data. Closed-form solutions are provided for estimation of TF-gene connectivity matrix which yields advantage over the existing state-of-the-art methods in terms of lower computational complexity and higher consistency. We present an iterative reweighted ℓ2 norm based algorithm to infer the network connectivity when the prior knowledge about the connections is incomplete. We present an NCA algorithm which has the ability to counteract the presence of outliers in the gene expression data and is therefore more robust. Closed-form solutions are derived for the estimation of TFAs and TF-gene interactions and the resulting algorithm is comparable to the fastest algorithms proposed so far with the additional advantages of robustness to outliers and higher reliability in the TFA estimation. Finally, we look at the inference of gene regulatory networks which which essentially resumes to the estimation of only the gene-gene interactions. Gene networks are known to be sparse and therefore an inference algorithm is proposed which imposes a sparsity constraint while estimating the connectivity matrix.The online estimation lowers the computational complexity and provides superior performance in terms of accuracy and scalability. This dissertation presents gene regulatory network inference algorithms which provide computationally efficient solutions in some very crucial scenarios and give advantage over the existing algorithms and therefore provide means to give better understanding of underlying cellular network. Hence, it serves as a building block in the accurate estimation of gene regulatory networks which will pave the way for finding cures to genetic diseases