287 research outputs found
Environmental statistics and optimal regulation
Any organism is embedded in an environment that changes over time. The
timescale for and statistics of environmental change, the precision with which
the organism can detect its environment, and the costs and benefits of
particular protein expression levels all will affect the suitability of
different strategies-such as constitutive expression or graded response-for
regulating protein levels in response to environmental inputs. We propose a
general framework-here specifically applied to the enzymatic regulation of
metabolism in response to changing concentrations of a basic nutrient-to
predict the optimal regulatory strategy given the statistics of fluctuations in
the environment and measurement apparatus, respectively, and the costs
associated with enzyme production. We use this framework to address three
fundamental questions: (i) when a cell should prefer thresholding to a graded
response; (ii) when there is a fitness advantage to implementing a Bayesian
decision rule; and (iii) when retaining memory of the past provides a selective
advantage. We specifically find that: (i) relative convexity of enzyme
expression cost and benefit influences the fitness of thresholding or graded
responses; (ii) intermediate levels of measurement uncertainty call for a
sophisticated Bayesian decision rule; and (iii) in dynamic contexts,
intermediate levels of uncertainty call for retaining memory of the past.
Statistical properties of the environment, such as variability and correlation
times, set optimal biochemical parameters, such as thresholds and decay rates
in signaling pathways. Our framework provides a theoretical basis for
interpreting molecular signal processing algorithms and a classification scheme
that organizes known regulatory strategies and may help conceptualize
heretofore unknown ones.Comment: 21 pages, 7 figure
Wooh!
A vision of the early history of Wabunsee County capturing an interaction with a band of Pottawatomie Native Americans
Neural networks grown and self-organized by noise
Living neural networks emerge through a process of growth and self-organization that begins with a single cell and results in a brain, an organized and functional computational device. Artificial neural networks, however, rely on human-designed, hand-programmed architectures for their remarkable performance. Can we develop artificial computational devices that can grow and self-organize without human intervention? In this paper, we propose a biologically inspired developmental algorithm that can ‘grow’ a functional, layered neural network from a single initial cell. The algorithm organizes inter-layer connections to construct retinotopic pooling layers. Our approach is inspired by the mechanisms employed by the early visual system to wire the retina to the lateral geniculate nucleus (LGN), days before animals open their eyes. The key ingredients for robust self-organization are an emergent spontaneous spatiotemporal activity wave in the first layer and a local learning rule in the second layer that ‘learns’ the underlying activity pattern in the first layer. The algorithm is adaptable to a wide-range of input-layer geometries, robust to malfunctioning units in the first layer, and so can be used to successfully grow and self-organize pooling architectures of different pool-sizes and shapes. The algorithm provides a primitive procedure for constructing layered neural networks through growth and self-organization. We also demonstrate that networks grown from a single unit perform as well as hand-crafted networks on MNIST. Broadly, our work shows that biologically inspired developmental algorithms can be applied to autonomously grow functional `brains' in-silico
Neural networks grown and self-organized by noise
Living neural networks emerge through a process of growth and
self-organization that begins with a single cell and results in a brain, an
organized and functional computational device. Artificial neural networks,
however, rely on human-designed, hand-programmed architectures for their
remarkable performance. Can we develop artificial computational devices that
can grow and self-organize without human intervention? In this paper, we
propose a biologically inspired developmental algorithm that can 'grow' a
functional, layered neural network from a single initial cell. The algorithm
organizes inter-layer connections to construct a convolutional pooling layer, a
key constituent of convolutional neural networks (CNN's). Our approach is
inspired by the mechanisms employed by the early visual system to wire the
retina to the lateral geniculate nucleus (LGN), days before animals open their
eyes. The key ingredients for robust self-organization are an emergent
spontaneous spatiotemporal activity wave in the first layer and a local
learning rule in the second layer that 'learns' the underlying activity pattern
in the first layer. The algorithm is adaptable to a wide-range of input-layer
geometries, robust to malfunctioning units in the first layer, and so can be
used to successfully grow and self-organize pooling architectures of different
pool-sizes and shapes. The algorithm provides a primitive procedure for
constructing layered neural networks through growth and self-organization.
Broadly, our work shows that biologically inspired developmental algorithms can
be applied to autonomously grow functional 'brains' in-silico.Comment: 21 pages (including 11 pages of appendix
Phenomenological model of motility by spatiotemporal modulation of active interactions
Transport at microscopic length scales is essential in biological systems and
various technologies, including microfluidics. Recent experiments achieved
self-organized transport phenomena in microtubule active matter using light to
modulate motor-protein activity in time and space. Here, we introduce a novel
phenomenological model to explain such experiments. Our model, based on
spatially modulated particle interactions, reveals a possible mechanism for
emergent transport phenomena in light-controlled active matter, including
motility and contraction. In particular, the model's analytic treatment
elucidates the conservation of the center of mass of activated particles as a
fundamental mechanism of material transport and demonstrates the necessity of
memory for sustained motility. Furthermore, we generalize the model to explain
other phenomena, like microtubule aster-aster interactions induced by more
complicated activation geometries. Our results demonstrate that the model
provides a possible foundation for the phenomenological understanding of
light-controlled active matter, and it will enable the design and optimization
of transport protocols for active matter devices
Designing signaling environments to steer transcriptional diversity in neural progenitor cell populations
Stem cell populations within developing embryos are diverse, composed of many different subpopulations of cells with varying developmental potential. The structure of stem cell populations in cell culture remains poorly understood and presents a barrier to differentiating stem cells for therapeutic applications. In this paper we develop a framework for controlling the architecture of stem cell populations in cell culture using high-throughput single cell mRNA-seq and computational analysis. We find that the transcriptional diversity of neural stem cell populations collapses in cell culture. Cell populations are depleted of committed neuron progenitor cells and become dominated by a single pre-astrocytic cell population. By analyzing the response of neural stem cell populations to forty distinct signaling conditions, we demonstrate that signaling environments can restructure cell populations by modulating the relative abundance of pre-astrocyte and pre-neuron subpopulations according to a simple linear code. One specific combination of BMP4, EGF, and FGF2 ligands switches the default population balance such that 70% of cells correspond to the committed neurons. Our work demonstrates that single-cell RNA-seq can be applied to modulate the diversity of in vitro stem cell populations providing a new strategy for population-level stem cell control
An Empirical Assessment of House Price Adjustments on Aggregate Consumption
This study measures the effect of changes in net housing and financial wealth on household consumption. The link between consumption, income and net wealth is measured within a Dynamic Ordinary Least Squares and a Dynamic Generalised Least Squares framework for the period Q2:1988-Q1:2003. It is found a permanent one dollar rise in housing wealth leads to a six cent increase in consumption, three times the effect of a one dollar rise in net financial wealth. A policy experiment is conducted to quantify the effect of a fall in house prices on aggregate consumption. The house price fall is defined as the price movement required to realign house prices to a steady state valuation using a price-to-rental yield indicator. If the required house price realignment were to occur over one year, it is estimated that household consumption would fall by 4.1 to 10.6 per cent
Generating counterfactual explanations of tumor spatial proteomes to discover effective, combinatorial therapies that enhance cancer immunotherapy
Recent advances in spatial omics methods enable the molecular composition of
human tumors to be imaged at micron-scale resolution across hundreds of
patients and ten to thousands of molecular imaging channels. Large-scale
molecular imaging datasets offer a new opportunity to understand how the
spatial organization of proteins and cell types within a tumor modulate the
response of a patient to different therapeutic strategies and offer potential
insights into the design of novel therapies to increase patient response.
However, spatial omics datasets require computational analysis methods that can
scale to incorporate hundreds to thousands of imaging channels (ie colors)
while enabling the extraction of molecular patterns that correlate with
treatment responses across large number of patients with potentially
heterogeneous tumors presentations. Here, we have develop a machine learning
strategy for the identification and design of signaling molecule combinations
that predict the degree of immune system engagement with a specific patient
tumors. We specifically train a classifier to predict T cell distribution in
patient tumors using the images from 30-40 molecular imaging channels. Second,
we apply a gradient descent based counterfactual reasoning strategy to the
classifier and discover combinations of signaling molecules predicted to
increase T cell infiltration. Applied to spatial proteomics data of melanoma
tumor, our model predicts that increasing the level of CXCL9, CXCL10, CXCL12,
CCL19 and decreasing the level of CCL8 in melanoma tumor will increase T cell
infiltration by 10-fold across a cohort of 69 patients. The model predicts that
the combination is many fold more effective than single target perturbations.
Our work provides a paradigm for machine learning based prediction and design
of cancer therapeutics based on classification of immune system activity in
spatial omics data
- …