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

Theoretical Limits of Energy Extraction in Active Fluids

Active materials form a class of far-from-equilibrium systems that are driven internally and exhibit self-organization which can be harnessed to perform mechanical work. Inspired by experiments on synthetic active networks we examine limits of work extraction from an active viscoelastic medium by analyzing the transport of a particle. The active viscoelastic material possesses an equilibrium density where the active and passive forces are balanced out. In one dimension, a gliding activation front (AF) that converts a passive to an active medium, provides active energy at a constant rate, which is injected into the system at one end and propagates to the other. We demonstrate that there exists a maximum velocity of the AF, above which the activated region fails to deliver the transport power. We hypothesize, and intuitively argue based on the limit cases, that the feasibility and the velocity of transport can be interpreted in terms of the velocity of an equilibration Domain Wall of the field, which is set by two parameters: a measure of activity, and the viscoelastic timescale. The phase diagram comprises Transport and No-Transport sectors, namely for any pair of the two parameters, there exists a threshold velocity of the AF above which the particle transport becomes impossible. Constructing the phase diagram we find that there are regions of the phase diagram for which the threshold velocity of the AF diverges. Larger viscoelastic timescale makes the transport region more accessible, and increases the transport velocity therein. Also, we find that increasing the velocity of AF results in larger extracted power but smaller transport coefficient; the ratio of the transport velocity and that of the AF. Our model provides a framework for understanding the energetics of transport phenomena in biology, and designing efficient mechanisms of transport in synthetic active materials.Comment: 8+7 pages, 8 figure

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

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

Herd: Using multiple, smaller LLMs to match the performances of proprietary, large LLMs via an intelligent composer

Currently, over a thousand LLMs exist that are multi-purpose and are capable of performing real world tasks, including Q&A, text summarization, content generation, etc. However, accessibility, scale and reliability of free models prevents them from being widely deployed in everyday use cases. To address the first two issues of access and scale, organisations such as HuggingFace have created model repositories where users have uploaded model weights and quantized versions of models trained using different paradigms, as well as model cards describing their training process. While some models report performance on commonly used benchmarks, not all do, and interpreting the real world impact of trading off performance on a benchmark for model deployment cost, is unclear. Here, we show that a herd of open source models can match or exceed the performance of proprietary models via an intelligent router. We show that a Herd of open source models is able to match the accuracy of ChatGPT, despite being composed of models that are effectively 2.5x smaller. We show that in cases where GPT is not able to answer the query, Herd is able to identify a model that can, at least 40% of the time