This has been cross-posted to Tumbld Thoughts.
The cold and emotionless holiday season......
Have a recursive holiday season!
"Cell functional diversity is a significant determinant on how biological processes unfold. Most accounts of diversity involve a search for sequence or expression differences. Perhaps there are more subtle mechanisms at work. Using the metaphor of information processing and decision-making might provide a clearer view of these subtleties. Understanding adaptive and transformative processes (such as cellular reprogramming) as a series of simple decisions allows us to use a technique called cellular signal detection theory (cellular SDT) to detect potential bias in mechanisms that favor one outcome over another. We can apply method of detecting cellular reprogramming bias to cellular reprogramming and other complex molecular processes. To demonstrate the scope of this method, we will critically examine differences between cell phenotypes reprogrammed to muscle fiber and neuron phenotypes. In cases where the signature of phenotypic bias is cryptic, signatures of genomic bias (pre-existing and induced) may provide an alternative. The examination of these alternates will be explored using data from a series of fibroblast cell lines before cellular reprogramming (pre-existing) and differences between fractions of cellular RNA for individual genes after drug treatment (induced). In conclusion, the usefulness and limitations of this method and associated analogies will be discussed."
"A semi-supervised model of peer review is introduced that is intended to overcome the bias and incompleteness of traditional peer review. Traditional approaches are reliant on human biases, while consensus decision-making is constrained by sparse information. Here, the architecture for one potential improvement (a semi-supervised, human-assisted classifier) to the traditional approach will be introduced and evaluated. To evaluate the potential advantages of such a system, hypothetical receiver operating characteristic (ROC) curves for both approaches will be assessed. This will provide more specific indications of how automation would be beneficial in the manuscript evaluation process. In conclusion, the implications for such a system on measurements of scientific impact and improving the quality of open submission repositories will be discussed".
"One way to understand complexity in biological networks is to isolate simple motifs like switches and bi-fans. However, this does not fully capture the outcomes of evolutionary processes. In this talk, I will introduce a class of process model called convolution architectures. These models demonstrate bricolage and ad-hoc formation of new mechanisms atop existing complexity. Unlike simple motifs (e.g. straightforward mechanisms), these models are intended to demonstrate how evolution can produce complex processes that operate in a sub-optimal fashion. The concept of convolution architectures can be extended to complex network topologies. Simple convolution architectures with evolutionary constraints and subject to natural selection can produce step lengths that deviate from optimal expectation. When convolution architectures are represented as components of bidirectional complex network topologies, these circuitous paths should become “spaghetti-fied”, as they are not explicitly constrained by inputs and outputs. This may also allow for itinerant and cyclic self-regulation resembling chaotic dynamics. The use of complex network topologies also allows us to better understand how higher-level constraints (e.g. hub formation, modularity, preferential attachment) affect the evolution of sub-optimality and subtlety. Such embedded convolution architectures are also useful for modeling physiological, economic, and social complexity".