Algebraic and Topological Indices of Molecular Pathway Networks in Human Cancers

Protein-protein interaction networks associated with diseases have gained prominence as an area of research. We investigate algebraic and topological indices for protein-protein interaction networks of 11 human cancers derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We find a strong correlation between relative automorphism group sizes and topological network complexities on the one hand and five year survival probabilities on the other hand. Moreover, we identify several protein families (e.g. PIK, ITG, AKT families) that are repeated motifs in many of the cancer pathways. Interestingly, these sources of symmetry are often central rather than peripheral. Our results can aide in identification of promising targets for anti-cancer drugs. Beyond that, we provide a unifying framework to study protein-protein interaction networks of families of related diseases (e.g. neurodegenerative diseases, viral diseases, substance abuse disorders).Comment: 15 pages, 4 figure

Separation of bacterial spores from flowing water in macro-scale cavities by ultrasonic standing waves

The separation of micron-sized bacterial spores (Bacillus cereus) from a steady flow of water through the use of ultrasonic standing waves is demonstrated. An ultrasonic resonator with cross-section of 0.0254 m x 0.0254 m has been designed with a flow inlet and outlet for a water stream that ensures laminar flow conditions into and out of the resonator section of the flow tube. A 0.01905-m diameter PZT-4, nominal 2-MHz transducer is used to generate ultrasonic standing waves in the resonator. The acoustic resonator is 0.0356 m from transducer face to the opposite reflector wall with the acoustic field in a direction orthogonal to the water flow direction. At fixed frequency excitation, spores are concentrated at the stable locations of the acoustic radiation force and trapped in the resonator region. The effect of the transducer voltage and frequency on the efficiency of spore capture in the resonator has been investigated. Successful separation of B. cereus spores from water with typical volume flow rates of 40-250 ml/min has been achieved with 15% efficiency in a single pass at 40 ml/min.Comment: 11 pages, 6 figure

Interactomes, manufacturomes and relational biology: analogies between systems biology and manufacturing systems

<p>Abstract</p> <p>Background</p> <p>We review and extend the work of Rosen and Casti who discuss category theory with regards to systems biology and manufacturing systems, respectively.</p> <p>Results</p> <p>We describe anticipatory systems, or long-range feed-forward chemical reaction chains, and compare them to open-loop manufacturing processes. We then close the loop by discussing metabolism-repair systems and describe the rationality of the self-referential equation <it>f </it>= <it>f </it>(<it>f</it>). This relationship is derived from some boundary conditions that, in molecular systems biology, can be stated as the cardinality of the following molecular sets must be about equal: metabolome, genome, proteome. We show that this conjecture is not likely correct so the problem of self-referential mappings for describing the boundary between living and nonliving systems remains an open question. We calculate a lower and upper bound for the number of edges in the molecular interaction network (the interactome) for two cellular organisms and for two manufacturomes for CMOS integrated circuit manufacturing.</p> <p>Conclusions</p> <p>We show that the relevant mapping relations may not be Abelian, and that these problems cannot yet be resolved because the interactomes and manufacturomes are incomplete.</p

Forward Signal Propagation Learning

We propose a new learning algorithm for propagating a learning signal and updating neural network parameters via a forward pass, as an alternative to backpropagation. In forward signal propagation learning (sigprop), there is only the forward path for learning and inference, so there are no additional structural or computational constraints on learning, such as feedback connectivity, weight transport, or a backward pass, which exist under backpropagation. Sigprop enables global supervised learning with only a forward path. This is ideal for parallel training of layers or modules. In biology, this explains how neurons without feedback connections can still receive a global learning signal. In hardware, this provides an approach for global supervised learning without backward connectivity. Sigprop by design has better compatibility with models of learning in the brain and in hardware than backpropagation and alternative approaches to relaxing learning constraints. We also demonstrate that sigprop is more efficient in time and memory than they are. To further explain the behavior of sigprop, we provide evidence that sigprop provides useful learning signals in context to backpropagation. To further support relevance to biological and hardware learning, we use sigprop to train continuous time neural networks with Hebbian updates and train spiking neural networks without surrogate functions