Allelic correlation is a marker of tradeoffs between barriers to transmission of expression variability and signal responsiveness in genetic networks

Accurately functioning genetic networks should be responsive to signals but prevent transmission of stochastic bursts of expression. Existing data in mammalian cells suggests that such transcriptional "noise" is transmitted by some genes and not others, suggesting that noise transmission is tunable, perhaps at the expense of other signal processing capabilities. However, systematic claims about noise transmission in genetic networks have been limited by the inability to directly measure noise transmission. Here we build a mathematical framework capable of modeling allelic correlation and noise transmission. We find that allelic correlation and noise transmission correspond across a broad range of model parameters and network architectures. We further find that limiting noise transmission comes with the trade-off of being unresponsive to signals, and that within the parameter regimes that are responsive to signals, there is a further trade-off between response time and basal noise transmission. Using a published allele specific single cell RNA-sequencing dataset, we found that genes with high allelic odds ratios are enriched for cell-type specific functions, and that within multiple signaling pathways, factors which are upstream in the pathway have higher allelic odds ratios than downstream factors. Overall, our findings suggest that some degree of noise transmission is required to be responsive to signals, but that minimization of noise transmission can be accomplished by trading-off for a slower response time.

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Mammalian gene expression variability is explained by underlying cell state

10.1101/626424 ◽

2019 ◽

Cited By ~ 5

Author(s):

Robert Foreman ◽

Roy Wollman

Keyword(s):

Gene Expression ◽

Mammalian Cells ◽

Qualitative Difference ◽

Live Cells ◽

Expression Variability ◽

Cell State ◽

Relative Contribution ◽

Allele Specific ◽

Poisson Limit ◽

Transcriptional Bursting

AbstractGene expression variability in mammalian systems plays an important role in physiological and pathophysiological conditions. This variability can come from differential regulation related to cell state (extrinsic) and allele-specific transcriptional bursting (intrinsic). Yet, the relative contribution of these two distinct sources is unknown. Here we exploit the qualitative difference in the patterns of covariance between these two sources to quantify their relative contributions to expression variance in mammalian cells. Using multiplexed error robust RNA fluorescent in situ hybridization (MERFISH) we measured the multivariate gene expression distribution of 150 genes related to Ca2+ signaling coupled with the dynamic Ca2+ response of live cells to ATP. We show that after controlling for cellular phenotypic states such as size, cell cycle stage, and Ca2+ response to ATP, the remaining variability is effectively at the Poisson limit for most genes. These findings demonstrate that the majority of expression variability results from cell state differences and that the contribution of transcriptional bursting is relatively minimal.

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Adjoint tomography with full envelope for scattering and intrinsic attenuation - resolution and trade-off

10.5194/egusphere-egu21-8296 ◽

2021 ◽

Author(s):

Tuo Zhang ◽

Christoph Sens-Schönfelder

Keyword(s):

Spatial Distribution ◽

High Frequency ◽

Small Scale ◽

Model Parameters ◽

Mathematical Framework ◽

Forward Simulation ◽

Trade Off ◽

Intrinsic Attenuation ◽

Scale Heterogeneity ◽

Rigorous Framework

A rigorous framework exists for deterministic imaging the subsurface seismic velocity structure. Full-waveform inversion (FWI) that combines the forward simulation of waveforms with an adjoint (backward) simulation of the data misfit provides the gradient of the model misfit with respect to the changes in the model parameters. This gradient is used for iterative improvements of the model to minimize the data misfit. To investigate the small scale heterogeneity of the medium below the resolution limits the waveform tomography the envelopes of high-frequency seismic waves have been used to derive a statistical description of the small scale structure. Such studies employed a variety of misfit measures or empirical parameters and various assumptions about the spatial sensitivity of the measurements to derive some information about the spatial distribution of the high-frequency attenuation and scattering properties. A rigorous framework for the inversion of seismogram envelopes for the spatial imaging of heterogeneity and attenuation has been missing so far. Here we present a mathematical framework for the full envelope inversion that is based on a forward simulation of seismogram envelopes and an adjoint (backward) simulation of the envelope misfit, in full analogy to FWI.&#160;Different from FWI that works with the wave equation, our approach is based on the Radiative Transfer Equation. In this study, the forward problem is solved by modelling the 2-D multiple nonisotropic scattering in a random elastic medium with spatially variable heterogeneity and attenuation using the Monte-Carlo method. The fluctuation strength &#949; and intrinsic quality factors QP-1 and QS-1 in the random medium are used to describe the spatial variability of attenuation and scattering. The misfit function is defined as the differences between the full observed and modelled envelopes.We derived the sensitivity kernels corresponding to this misfit function that is minimized during the iterative adjoint inversion with the L-BFGS method. We have applied this algorithm in some numerical tests in the acoustic approximation. We show that it is possible in a rigorous way to image the spatial distribution of small scale heterogeneity and attenuation separately using seismogram envelopes. The resolution and the trade-off between scattering and intrinsic attenuation are discussed. Our analysis shows that relative importance of scattering and attenuation anomalies need to be considered when the model resolution is assessed. The inversions confirm, that the early coda is important for imaging the distribution of heterogeneity while later coda waves are more sensitive to intrinsic attenuation.

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Faculty Opinions recommendation of Allele-specific analysis of DNA replication origins in mammalian cells.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.725503495.793508003 ◽

2015 ◽

Author(s):

Susan Gerbi ◽

John Urban

Keyword(s):

Dna Replication ◽

Mammalian Cells ◽

Replication Origins ◽

Specific Analysis ◽

Allele Specific ◽

Dna Replication Origins

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Multi-Domain Communication Systems and Networks: A Tensor-Based Approach

Network ◽

10.3390/network1020005 ◽

2021 ◽

Vol 1 (2) ◽

pp. 50-74

Author(s):

Divyanshu Pandey ◽

Adithya Venugopal ◽

Harry Leib

Keyword(s):

Communication Systems ◽

Physical Layer ◽

Mathematical Framework ◽

Trade Off ◽

Information Theoretic ◽

Time Frequency ◽

Tensor Formulation ◽

Processing Techniques ◽

Multiple Domains ◽

Signal Processing Techniques

Most modern communication systems, such as those intended for deployment in IoT applications or 5G and beyond networks, utilize multiple domains for transmission and reception at the physical layer. Depending on the application, these domains can include space, time, frequency, users, code sequences, and transmission media, to name a few. As such, the design criteria of future communication systems must be cognizant of the opportunities and the challenges that exist in exploiting the multi-domain nature of the signals and systems involved for information transmission. Focussing on the Physical Layer, this paper presents a novel mathematical framework using tensors, to represent, design, and analyze multi-domain systems. Various domains can be integrated into the transceiver design scheme using tensors. Tools from multi-linear algebra can be used to develop simultaneous signal processing techniques across all the domains. In particular, we present tensor partial response signaling (TPRS) which allows the introduction of controlled interference within elements of a domain and also across domains. We develop the TPRS system using the tensor contracted convolution to generate a multi-domain signal with desired spectral and cross-spectral properties across domains. In addition, by studying the information theoretic properties of the multi-domain tensor channel, we present the trade-off between different domains that can be harnessed using this framework. Numerical examples for capacity and mean square error are presented to highlight the domain trade-off revealed by the tensor formulation. Furthermore, an application of the tensor framework to MIMO Generalized Frequency Division Multiplexing (GFDM) is also presented.

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A Cybernetic Approach to Modeling Lipid Metabolism in Mammalian Cells

Processes ◽

10.3390/pr6080126 ◽

2018 ◽

Vol 6 (8) ◽

pp. 126 ◽

Cited By ~ 2

Author(s):

Lina Aboulmouna ◽

Shakti Gupta ◽

Mano Maurya ◽

Frank DeVilbiss ◽

Shankar Subramaniam ◽

...

Keyword(s):

Mammalian Cells ◽

Metabolic Regulation ◽

Metabolic Flux ◽

Optimal Parameter ◽

Gene Knockout ◽

Model Parameters ◽

Mouse Bone Marrow ◽

Optimization Approach ◽

Substrate Uptake ◽

Data Set

The goal-oriented control policies of cybernetic models have been used to predict metabolic phenomena such as the behavior of gene knockout strains, complex substrate uptake patterns, and dynamic metabolic flux distributions. Cybernetic theory builds on the principle that metabolic regulation is driven towards attaining goals that correspond to an organism’s survival or displaying a specific phenotype in response to a stimulus. Here, we have modeled the prostaglandin (PG) metabolism in mouse bone marrow derived macrophage (BMDM) cells stimulated by Kdo2-Lipid A (KLA) and adenosine triphosphate (ATP), using cybernetic control variables. Prostaglandins are a well characterized set of inflammatory lipids derived from arachidonic acid. The transcriptomic and lipidomic data for prostaglandin biosynthesis and conversion were obtained from the LIPID MAPS database. The model parameters were estimated using a two-step hybrid optimization approach. A genetic algorithm was used to determine the population of near optimal parameter values, and a generalized constrained non-linear optimization employing a gradient search method was used to further refine the parameters. We validated our model by predicting an independent data set, the prostaglandin response of KLA primed ATP stimulated BMDM cells. We show that the cybernetic model captures the complex regulation of PG metabolism and provides a reliable description of PG formation.

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Analytical and Numerical Connections between Fractional Fickian and Intravoxel Incoherent Motion Models of Diffusion MRI

Mathematics ◽

10.3390/math9161963 ◽

2021 ◽

Vol 9 (16) ◽

pp. 1963

Author(s):

Jingting Yao ◽

Muhammad Ali Raza Anjum ◽

Anshuman Swain ◽

David A. Reiter

Keyword(s):

Skeletal Muscle ◽

Goodness Of Fit ◽

Numerical Models ◽

Tissue Perfusion ◽

Parametric Models ◽

Intravoxel Incoherent Motion ◽

Fickian Diffusion ◽

Model Parameters ◽

Mathematical Framework ◽

Stretched Exponential

Impaired tissue perfusion underlies many chronic disease states and aging. Diffusion-weighted imaging (DWI) is a noninvasive MRI technique that has been widely used to characterize tissue perfusion. Parametric models based on DWI measurements can characterize microvascular perfusion modulated by functional and microstructural alterations in the skeletal muscle. The intravoxel incoherent motion (IVIM) model uses a biexponential form to quantify the incoherent motion of water molecules in the microvasculature at low b-values of DWI measurements. The fractional Fickian diffusion (FFD) model is a parsimonious representation of anomalous superdiffusion that uses the stretched exponential form and can be used to quantify the microvascular volume of skeletal muscle. Both models are established measures of perfusion based on DWI, and the prognostic value of model parameters for identifying pathophysiological processes has been studied. Although the mathematical properties of individual models have been previously reported, quantitative connections between IVIM and FFD models have not been examined. This work provides a mathematical framework for obtaining a direct, one-way transformation of the parameters of the stretched exponential model to those of the biexponential model. Numerical simulations are implemented, and the results corroborate analytical results. Additionally, analysis of in vivo DWI measurements in skeletal muscle using both biexponential and stretched exponential models is shown and compared with analytical and numerical models. These results demonstrate the difficulty of model selection based on goodness of fit to experimental data. This analysis provides a framework for better interpreting and harmonizing perfusion parameters from experimental results using these two different models.

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Particle motion on burned and vegetated hillslopes

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1922495117 ◽

2020 ◽

Vol 117 (41) ◽

pp. 25335-25343

Author(s):

Danica L. Roth ◽

Tyler H. Doane ◽

Joshua J. Roering ◽

David J. Furbish ◽

Aaron Zettler-Mann

Keyword(s):

Sediment Transport ◽

Particle Motion ◽

Debris Flows ◽

Field Experiments ◽

Transport Theory ◽

Surface Characteristics ◽

Model Parameters ◽

Mathematical Framework ◽

Long Distance ◽

Heavy Tailed

Climate change is causing increasingly widespread, frequent, and intense wildfires across the western United States. Many geomorphic effects of wildfire are relatively well studied, yet sediment transport models remain unable to account for the rapid transport of sediment released from behind incinerated vegetation, which can fuel catastrophic debris flows. This oversight reflects the fundamental inability of local, continuum-based models to capture the long-distance particle motions characteristic of steeplands. Probabilistic, particle-based nonlocal models may address this deficiency, but empirical data are needed to constrain their representation of particle motion in real landscapes. Here we present data from field experiments validating a generalized Lomax model for particle travel distance distributions. The model parameters provide a physically intuitive mathematical framework for describing the transition from light- to heavy-tailed distributions along a continuum of behavior as particle size increases and slopes get steeper and/or smoother. We show that burned slopes are measurably smoother than vegetated slopes, leading to 1) lower rates of experimental particle disentrainment and 2) runaway motion that produces the heavy-tailed travel distances often associated with nonlocal transport. Our results reveal that surface roughness is a key control on steepland sediment transport, particularly after wildfire when smoother surfaces may result in the preferential delivery of coarse material to channel networks that initiate debris flows. By providing a first-order framework relating the statistics of particle motion to measurable surface characteristics, the Lomax model both advances the development of nonlocal sediment transport theory and reveals insights on hillslope transport mechanics.

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