"Kernel Panic” error After upgrading Parrot OS

MAPPING BIOLOGICAL DATA TO DIGITAL DATA

The emergence of molecular biology has produced a vast literature on the cellular function of individual genes and their protein products. It has also generated massive amounts of molecular interaction data derived from high-throughput methods as well as more classical low-throughput methods, such as immunoprecipitation, immunoblotting, and yeast two-hybrid systems. From this accumulation of interaction data, researchers can now attempt to reconstruct and analyses the highly complex molecular networks involved in cellular function.

Intracellular molecular networks are known to be highly deregulated in a number of diseases, most notably in cancer, and targeted molecular inhibitors have emerged as a leading anti-cancer strategy. Despite promising pre-clinical studies, many targeted inhibitors are beset by harmful off-target effects and/or lower than expected efficacy in the clinic. The large number of off-target effects associated with molecular inhibitors was recently termed the “whack a mole problem”¹because inhibiting one molecular target often results in the activation of another non-targeted molecule. It is increasingly clear that the inability of many targeted therapies to keep a disease in check is related to the complex interactions and emergent, non-linear behaviors found in intracellular networks. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level.

Understanding the networks associated with neoplastic diseases offers especially difficult challenges. Fundamental problems in understanding the transition from the normal to near normal to dysplastic to neoplastic to metastatic states of cancer progression can theoretically be modelled by longitudinal comparisons of networks in which, as progression occurs, certain molecular interactions are rendered stronger (for instance through gene amplification) or lost (through mutation, deletion, down-regulation, or methylation). Logic models provide a framework in which these types of network comparisons are possible. Multi-state logic models can simulate signal amplification and random order asynchronous logic models can simulate the heterogeneous response in a population of cells to diverse stimuli. Logic-models are also well suited for performing in silicon molecular perturbations, which could be used to predict a population level response to a targeted therapy or a combination of therapies. In this review, we provide a tutorial on the use of logic-based methods as well as a discussion of their limitations, using biologically motivated examples.
download link for the above article