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Cell phenotypes are controlled by complex interactions between genes, proteins, and other molecules within a cell, along with extracellular signals. Gene regulatory networks (GRNs), which describe these interactions mathematically, are multi-stable dynamical systems, in which attractor states represent cell phenotypes. Transitions between these states are thought to underlie critical cellular processes, including cell fate-decisions, phenotypic plasticity, and carcinogenesis. In principle, a GRN model can produce a map of possible cell phenotypes and phenotype-transitions, potentially informing experimental strategies for controlling cell phenotypes. Such a map could have a profound impact on many medical fields, ranging from stem cell therapies to wound healing. As such, there is increasing interest in the development of theoretical and computational approaches that can shed light on the dynamics of these state-transitions in multi-stable gene networks. In this work, we approach the problem of understanding cellular decision-making on two fronts. First, we develop and extend rare-event stochastic simulation methods, toward efficient characterization of the global dynamics of multi-stable stochastic systems, such as GRNs. When applied to a mutual inhibition network motif and a model of pluripotency in stem cells, our sampling methods demonstrate that spontaneous cell phenotype transitions involve collective behavior of both regulatory proteins and DNA, and that transition dynamics are sensitive to parameters governing transcription factor-DNA binding kinetics. Our approach significantly expands the capability of stochastic simulation to investigate gene regulatory network dynamics. In the second portion of this work, we model the dynamic response of macrophages to complex stimuli by inferring cell-decision networks from data, in the absence of detailed molecular information. Macrophage activation has been described as a continuum, and different stimuli lead to M1, M

This dissertation presents works on two significant application areas of semiconductor nanowire. The first is nanowire photodetectors and their applications for image sensing and retinal prosthesis. The second is nanowire solar cells for renewable energy application and both Si and III-V nanowire devices are extensively discussed. Three structures of Si nanowire photodetectors are presented, including nanowire photoconductor, axial and radial junction nanowire photodiodes. Using the design of a crossbar structure, an individually addressable vertical Si nanowire photoconductor array was presented, followed by discussion on integration to CMOS for image sensing applications. In addition, the Si axial junction nanowire photodiodes were also demonstrated, which showed excellent detectivities in visible spectrum at zero bias, with a peak value of 2.14×1013 Jones at wavelength of 636 nm. Furthermore, the radial junction nanowire photodiodes were discussed as well, which exhibited enhanced photo responsivity due to shorter carrier separation and collection paths. These nanowire photodiode arrays promises potential application for retinal prosthesis by replacing the dysfunctional photo receptors in human retina In addition to photodetectors, I also demonstrated Si nanowire solar cells including both planar Si solar cells with nanowire absorber and Si radial junction nanowire solar cells. A low-cost chemical etching method was introduced to fabricate large-scale Si nanowire array as light absorber to improve the light absorption of planar Si solar cells. The efficiency of this device is over one order of magnitude higher than the one without nanowires. In addition, the Si radial junction nanowire solar cells were studied. A systematic study on design parameters, including nanowire core and shell doping concentrations, band structures and surface passivation methods, were discussed to achieve optimal device performance. With optimal design, a power conversion efficiency of 8