Compressed representation of brain genetic transcription

The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space. The task is especially challenging with high-dimensional data, such as gene expression, where the joint complexity of anatomical and transcriptional patterns demands maximum compression. Established practice is to use standard principal component analysis (PCA), whose computational felicity is offset by limited expressivity, especially at great compression ratios. Employing whole-brain, voxel-wise Allen Brain Atlas transcription data, here we systematically compare compressed representations based on the most widely supported linear and non-linear methods-PCA, kernel PCA, non-negative matrix factorization (NMF), t-stochastic neighbour embedding (t-SNE), uniform manifold approximation and projection (UMAP), and deep auto-encoding-quantifying reconstruction fidelity, anatomical coherence, and predictive utility with respect to signalling, microstructural, and metabolic targets. We show that deep auto-encoders yield superior representations across all metrics of performance and target domains, supporting their use as the reference standard for representing transcription patterns in the human brain.Comment: 21 pages, 5 main figures, 1 supplementary figur

A synthetic mammalian electro-genetic transcription circuit

Electric signal processing has evolved to manage rapid information transfer in neuronal networks and muscular contraction in multicellular organisms and controls the most sophisticated man-built devices. Using a synthetic biology approach to assemble electronic parts with genetic control units engineered into mammalian cells, we designed an electric power-adjustable transcription control circuit able to integrate the intensity of a direct current over time, to translate the amplitude or frequency of an alternating current into an adjustable genetic readout or to modulate the beating frequency of primary heart cells. Successful miniaturization of the electro-genetic devices may pave the way for the design of novel hybrid electro-genetic implants assembled from electronic and genetic part

A synthetic mammalian electro-genetic transcription circuit

Electric signal processing has evolved to manage rapid information transfer in neuronal networks and muscular contraction in multicellular organisms and controls the most sophisticated man-built devices. Using a synthetic biology approach to assemble electronic parts with genetic control units engineered into mammalian cells, we designed an electric power-adjustable transcription control circuit able to integrate the intensity of a direct current over time, to translate the amplitude or frequency of an alternating current into an adjustable genetic readout or to modulate the beating frequency of primary heart cells. Successful miniaturization of the electro-genetic devices may pave the way for the design of novel hybrid electro-genetic implants assembled from electronic and genetic parts

Topology of biological networks and reliability of information processing

Biological systems rely on robust internal information processing: Survival depends on highly reproducible dynamics of regulatory processes. Biological information processing elements, however, are intrinsically noisy (genetic switches, neurons, etc.). Such noise poses severe stability problems to system behavior as it tends to desynchronize system dynamics (e.g. via fluctuating response or transmission time of the elements). Synchronicity in parallel information processing is not readily sustained in the absence of a central clock. Here we analyze the influence of topology on synchronicity in networks of autonomous noisy elements. In numerical and analytical studies we find a clear distinction between non-reliable and reliable dynamical attractors, depending on the topology of the circuit. In the reliable cases, synchronicity is sustained, while in the unreliable scenario, fluctuating responses of single elements can gradually desynchronize the system, leading to non-reproducible behavior. We find that the fraction of reliable dynamical attractors strongly correlates with the underlying circuitry. Our model suggests that the observed motif structure of biological signaling networks is shaped by the biological requirement for reproducibility of attractors.Comment: 7 pages, 7 figure

Class of {varphi}X174 Mutants Relatively Deficient in Synthesis of Viral RNA

Nonpermissive cells infected with {varphi}X174 gene D amber mutants synthesized some sixfold less viral RNA than permissive cells. The decrease was unaffected by increasing the multiplicity of infection and was a consequence of an overall decrease in all viral RNA species. It is suggested that the gene D product may function in replicative form DNA unwinding to expose the template for transcription

Matrix Metalloproteinase-9 and Inflammation in Different Types of Multiple Sclerosis

Different clinical courses of multiple sclerosis, heterogeneity of its clinical implications, different effect of immunomodulatory therapy for the same clinical forms implies various pathogenetic mechanisms of central nervous system damage at this disease. Applicability of immunological and biochemical markers for the estimation of immunocorrecting and anti-inflammatory therapy efficacy is important. This research aims at improvement of pathological process stages diagnostics at multiple sclerosis and further therapy optimization depending on the activity of the inflammatory process. In the article matrix metalloproteinase-9 rate was assessed in 135 patients with multiple sclerosis of different course types and at different activity stages of the pathological process. The highest matrix metalloproteinase-9 rates were in patients with relapsing-remitting type at the stage of exacerbation, with the lowest rate being in patients with primary-progressive multiple sclerosis. Determination of matrix metalloproteinase-9 rate allows to assess the degree of inflammatory process expression and to monitor the efficacy of multiple sclerosis treatment

Protein-mediated DNA Loop Formation and Breakdown in a Fluctuating Environment

Living cells provide a fluctuating, out-of-equilibrium environment in which genes must coordinate cellular function. DNA looping, which is a common means of regulating transcription, is very much a stochastic process; the loops arise from the thermal motion of the DNA and other fluctuations of the cellular environment. We present single-molecule measurements of DNA loop formation and breakdown when an artificial fluctuating force, applied to mimic a fluctuating cellular environment, is imposed on the DNA. We show that loop formation is greatly enhanced in the presence of noise of only a fraction of $k_B T$, yet find that hypothetical regulatory schemes that employ mechanical tension in the DNA--as a sensitive switch to control transcription--can be surprisingly robust due to a fortuitous cancellation of noise effects