Silvina Ponce Dawson

Cells continuously sense their surroundings to detect modifications and generate responses. Very often changes in extracellular concentrations initiate signaling cascades that eventually result in changes in gene expression. Increasing stimulus strengths can be encoded in increasing concentration amplitudes or increasing activation frequencies of intermediaries of the pathway. In this paper we show that the different way in which amplitude and frequency encoding map environmental changes endow cells with qualitatively different information transmission capabilities. While amplitude encoding is optimal for a limited range of stimuli strengths, frequency encoding can transmit information with equal reliability over much broader ranges. The qualitative difference between the two strategies stems from the scale invariant discriminating power of the first transducing step in frequency codification. The apparently redundant combination of both strategies in some cell types may then serve the purpose of expanding the span over which stimulus strengths can be reliably discriminated. In this paper we discuss a possible example of this mechanism in yeast.

Cells detect changes in their environment and generate responses, often involving changes in gene expression. In this paper we use information theory and a simple transcription model to analyze whether the resulting gene expression serves to identify extracellular stimuli and assess their intensity when they are encoded in the amplitude, duration or frequency of pulses of a transcription factor’s nuclear concentration (or activation state). We find, for all cases, that about three ranges of input strengths can be distinguished and that maximum information transmission occurs for fast and high activation threshold promoters. The three input modulation modes differ in the sensitivity to changes in the promoters parameters. Frequency modulation is the most sensitive and duration modulation, the least. This is key for signal identification: there are promoter parameters that yield a relatively high information transmission for duration or amplitude modulation and a much smaller value for frequency modulation. The reverse situation cannot be found with a single promoter transcription model. Thus, pulses of transcription factors can selectively activate the “frequency-tuned” promoter while prolonged nuclear accumulation would activate promoters of all three modes simultaneously. Frequency modulation is therefore highly selective and better suited than the other encoding modes for signal identification without requiring other mediators of the transduction process.

Editorial: Women in science: Physics

Cells continuously interact with their environment, detect its changes and generate responses accordingly. This requires interpreting the variations and, in many occasions, producing changes in gene expression. In this paper we use information theory and a simple transcription model to analyze the extent to which the resulting gene expression is able to identify and assess the intensity of extracellular stimuli when they are encoded in the amplitude, duration or frequency of a transcription factor’s nuclear concentration. We find that the maximal information transmission is, for the three codifications, ~ 1.5 – 1.8 bits, i.e ., approximately 3 ranges of input strengths can be distinguished in all cases. The types of promoters that yield maximum transmission for the three modes are all similarly fast and have a high activation threshold. The three input modulation modes differ, however, in the sensitivity to changes in the parameters that characterize the promoters, with frequency modulation being the most sensitive and duration modulation, the least. This turns out to be key for signal identification. Namely, we show that, because of this sensitivity difference, it is possible to find promoter parameters that yield an information transmission within 90% of its maximum value for duration or amplitude modulation and less than 1 bit for frequency modulation. The reverse situation cannot be found within the framework of a single promoter transcription model. This means that pulses of transcription factors in the nucleus can selectively activate the promoter that is tuned to respond to frequency modulations while prolonged nuclear accumulation would activate several promoters at the same time. Thus, frequency modulation is better suited than the other encoding modes to allow the identification of external stimuli without requiring other mediators of the transduction.

In order to identify the infected individuals of a population, their samples are divided in equally sized groups called pools and a single laboratory test is applied to each pool. Individuals whose samples belong to pools that test negative are declared healthy, while each pool that tests positive is divided into smaller, equally sized pools which are tested in the next stage. In the (k + 1)-th stage all remaining samples are tested. If p < 1 -3-1/3, we minimize the expected number of tests per individual as a function of the number k + 1 of stages, and of the pool sizes in the first k stages. We show that for each p ∈ (0, 1 -3-1/3) the optimal choice is one of four possible schemes, which are explicitly described. We conjecture that for each p, the optimal choice is one of the two sequences of pool sizes (3k or 3k-14, 3k-1,..., 32, 3), with a precise description of the range of p’s where each is optimal. The conjecture is supported by overwhelming numerical evidence for p > 2-51. We also show that the cost of the best among the schemes (3k,..., 3) is of order O(p log(1/p)), comparable to the information theoretical lower bound p log2(1/p) + (1 -p) log2(1/(1 -p)), the entropy of a Bernoulli(p) random variable.

The progress of the SARS-CoV-2 pandemic requires the design of large-scale, cost-effective testing programs. Pooling samples provides a solution if the tests are sensitive enough. In this regard, the use of the gold standard, RT-qPCR, raises some concerns. Recently, droplet digital PCR (ddPCR) was shown to be 10–100 times more sensitive than RT-qPCR, making it more suitable for pooling. Furthermore, ddPCR quantifies the RNA content directly, a feature that, as we show, can be used to identify nonviable samples in pools. Cost-effective strategies require the definition of efficient deconvolution and re-testing procedures. In this paper we analyze the practical implementation of an efficient hierarchical pooling strategy for which we have recently derived the optimal, determining the best ways to proceed when there are impediments for the use of the absolute optimum or when multiple pools are tested simultaneously and there are restrictions on the throughput time. We also show how the ddPCR RNA quantification and the nested nature of the strategy can be combined to perform self-consistency tests for a better identification of infected individuals and nonviable samples. The studies are useful to those considering pool testing for the identification of infected individuals.

Many natural systems, especially biological ones, exhibit complex multivariate nonlinear dynamical behaviors that can be hard to capture by linear autoregressive models. On the other hand, generic nonlinear models such as deep recurrent neural networks often require large amounts of training data, not always available in domains such as brain imaging; also, they often lack interpretability. Domain knowledge about the types of dynamics typically observed in such systems, such as a certain type of dynamical systems models, could complement purely data-driven techniques by providing a good prior. In this work, we consider a class of ordinary differential equation (ODE) models known as van der Pol (VDP) oscil lators and evaluate their ability to capture a low-dimensional representation of neural activity measured by different brain imaging modalities, such as calcium imaging (CaI) and fMRI, in different living organisms: larval zebrafish, rat, and human. We develop a novel and efficient approach to the nontrivial problem of parameters estimation for a network of coupled dynamical systems from multivariate data and demonstrate that the resulting VDP models are both accurate and interpretable, as VDP's coupling matrix reveals anatomically meaningful excitatory and inhibitory interactions across different brain subsystems. VDP outperforms linear autoregressive models (VAR) in terms of both the data fit accuracy and the quality of insight provided by the coupling matrices and often tends to generalize better to unseen data when predicting future brain activity, being comparable to and sometimes better than the recurrent neural networks (LSTMs). Finally, we demonstrate that our (generative) VDP model can also serve as a data-augmentation tool leading to marked improvements in predictive accuracy of recurrent neural networks. Thus, our work contributes to both basic and applied dimensions of neuroimaging: gaining scientific insights and improving brain-based predictive models, an area of potentially high practical importance in clinical diagnosis and neurotechnology.