arXiv subjects

Naama Brenner

Publications and source records attributed to Naama Brenner.

15 recordsLinked to original sources

arXiv2023

Identifying Dynamic Regulation with Adversarial Surrogates

Homeostasis, the ability to maintain a stable internal environment in the face of perturbations, is essential for the functioning of living systems. Given observations of a system, or even a detailed model of one, it is both valuable and extremely challenging to extract the control objectives of the homeostatic mechanisms. Lacking a clear separation between plant and controller, frameworks such as inverse optimal control and inverse reinforcement learning are unable to identify the homeostatic mechanisms. A recently developed data-driven algorithm, Identifying Regulation with Adversarial Surrogates (IRAS), detects highly regulated or conserved quantities as the solution of a min-max optimization scheme that automates classical surrogate data methods. Yet, the definition of homeostasis as regulation within narrow limits is too strict for biological systems which show sustained oscillations such as circadian rhythms. In this work, we introduce Identifying Dynamic Regulation with Adversarial Surrogates (IDRAS), a generalization of the IRAS algorithm, capable of identifying control objectives that are regulated with respect to a dynamical reference value. We test the algorithm on simulation data from realistic biological models and benchmark physical systems, demonstrating excellent empirical results.

eess.SY↗

arXiv2021

Cancer Progression as a Learning Process

Drug resistance and metastasis - the major complications in cancer - both entail adaptation of cancer cells to stress, whether a drug or a lethal new environment. Intriguingly, these adaptive processes share similar features that cannot be explained by a pure Darwinian scheme, including dormancy, increased heterogeneity, and stress-induced plasticity. Here, we propose that learning theory offers a framework to explain these features and may shed light on these two intricate processes. In this framework, learning is performed at the single cell level, by stress-driven exploratory trial-and-error. Such a process is not contingent on pre-existing pathways but on a random search for a state that diminishes the stress. We review underlying mechanisms that may support this search, and show by using a learning model that such exploratory adaptation is feasible in a high dimensional system as the cell. At the population level, we view the tissue as a network of exploring agents that communicate and restrain cancer formation in health. In this view, disease results from the breakdown of homeostasis between cellular exploratory drive and tissue homeostasis.

q-bio.MN↗

arXiv2020

Quality of internal representation shapes learning performance in feedback neural networks

A fundamental feature of complex biological systems is the ability to form feedback interactions with their environment. A prominent model for studying such interactions is reservoir computing, where learning acts on low-dimensional bottlenecks. Despite the simplicity of this learning scheme, the factors contributing to or hindering the success of training in reservoir networks are in general not well understood. In this work, we study non-linear feedback networks trained to generate a sinusoidal signal, and analyze how learning performance is shaped by the interplay between internal network dynamics and target properties. By performing exact mathematical analysis of linearized networks, we predict that learning performance is maximized when the target is characterized by an optimal, intermediate frequency which monotonically decreases with the strength of the internal reservoir connectivity. At the optimal frequency, the reservoir representation of the target signal is high-dimensional, de-synchronized, and thus maximally robust to noise. We show that our predictions successfully capture the qualitative behaviour of performance in non-linear networks. Moreover, we find that the relationship between internal representations and performance can be further exploited in trained non-linear networks to explain behaviours which do not have a linear counterpart. Our results indicate that a major determinant of learning success is the quality of the internal representation of the target, which in turn is shaped by an interplay between parameters controlling the internal network and those defining the task.

q-bio.NC↗

arXiv2019

Stable memory with unstable synapses

What is the physiological basis of long-term memory? The prevailing view in neuroscience attributes changes in synaptic efficacy to memory acquisition. This view implies that stable memories correspond to stable connectivity patterns. However, an increasing body of experimental evidence points to significant, activity-independent dynamics in synaptic strengths. Motivated by these observations, we explore the possibility of memory storage within a global component of network connectivity, while individual connections fluctuate. We find a simple and general principle, stemming from stability arguments, that links eigenvalues in the complex plane to memories. Specifically, imaginary-coded memories are more resilient to noise and homeostatic plasticity than their real-coded counterparts. Memory representations are stored as time-varying attractors in neural state-space and support associative retrieval of learned information. Our results suggest a link between the properties of learning rules and those of network-level memory representations, and point at measurable signatures to be sought in experimental data.

q-bio.NC↗

arXiv2018

Statistical properties and dynamics of phenotype components in individual bacteria

Cellular phenotype is characterized by different components such as cell size, protein content and cell cycle time. These are global variables that are the outcome of multiple internal microscopic processes. Accordingly, they display some universal statistical properties and scaling relations, such as distribution collapse and relation between moments. Cell size statistics and its relation to growth and division has been mostly studied separately from proteins and other cellular variables. Here we present experimental and theoretical analyses of these phenotype components in a unified framework that reveals their correlations and interactions inside the cell. We measure these components simultaneously in single cells over dozens of generations, quantify their correlations, and compare population to temporal statistics. We find that cell size and highly expressed proteins have very similar dynamics over growth and division cycles, which result in parallel statistical properties, both universal and individual. In particular, while distribution shapes of fluctuations along time are common to all cells and components, other properties are variable and remain distinct in individual cells for a surprisingly large number of generations. These include temporal averages of cell size and protein content, and the structure of their auto-correlation functions. We explore possible roles of the different components in controlling cell growth and division. We find that in order to stabilize exponential accumulation and division of all components across generations, coupled dynamics among them is required. Finally, we incorporate effective coupling within the cell cycle with a phenomenological mapping across consecutive cycles, and show that this model reproduces the entire array of experimental observations.

q-bio.CB↗

arXiv2018

Individuality and slow dynamics in bacterial growth homeostasis

Microbial growth and division are fundamental processes relevant to many areas of life science. Of particular interest are homeostasis mechanisms, which buffer growth and division from accumulating fluctuations over multiple cycles. These mechanisms operate within single cells, possibly extending over several division cycles. However, all experimental studies to date have relied on measurements pooled from many distinct cells. Here, we disentangle long-term measured traces of individual cells from one another, revealing subtle differences between temporal and pooled statistics. By analyzing correlations along up to hundreds of generations, we find that the parameter describing effective cell-size homeostasis strength varies significantly among cells. At the same time, we find an invariant cell size which acts as an attractor to all individual traces, albeit with different effective attractive forces. Despite the common attractor, each cell maintains a distinct average size over its finite lifetime with suppressed temporal fluctuations around it, and equilibration to the global average size is surprisingly slow (> 150 cell cycles). To demonstrate a possible source of variable homeostasis strength, we construct a mathematical model relying on intracellular interactions, which integrates measured properties of cell size with those of highly expressed proteins. Effective homeostasis strength is then influenced by interactions and by noise levels, and generally varies among cells. A predictable and measurable consequence of variable homeostasis strength appears as distinct oscillatory patterns in cell size and protein content over many generations. We discuss the implications of our results to understanding mechanisms controlling division in single cells and their characteristic timescales

q-bio.CB↗

arXiv2018

Visual detection of time-varying signals: opposing biases and their timescales

Human visual perception is a complex, dynamic and fluctuating process. In addition to the incoming visual stimulus, it is affected by many other factors including temporal context, both external and internal to the observer. In this study we investigate the dynamic properties of psychophysical responses to a continuous stream of visual near-threshold detection tasks. We manipulate the incoming signals to have temporal structures with various characteristic timescales. Responses of human observers to these signals are analyzed using tools that highlight their dynamical features as well. We find that two opposing biases shape perception, and operate over distinct timescales. Positive recency appears over short times, e.g. consecutive trials. Adaptation, entailing an increased probability of changed response, reflects trends over longer times. Analysis of psychometric curves conditioned on various temporal events reveals that the balance between the two biases can shift depending on their interplay with the temporal properties of the input signal. A simple mathematical model reproduces the experimental data in all stimulus regimes. Taken together, our results support the view that visual response fluctuations reflect complex internal dynamics, possibly related to higher cognitive processes.

q-bio.NC↗

arXiv2017

Exploratory Adaptation in Large Random Networks

The capacity of cells and organisms to respond to challenging conditions in a repeatable manner is limited by a finite repertoire of pre-evolved adaptive responses. Beyond this capacity, cells can use exploratory dynamics to cope with a much broader array of conditions. However, the process of adaptation by exploratory dynamics within the lifetime of a cell is not well understood. Here we demonstrate the feasibility of exploratory adaptation in a high-dimensional network model of gene regulation. Exploration is initiated by failure to comply with a constraint and is implemented by random sampling of network configurations. It ceases if and when the network reaches a stable state satisfying the constraint. We find that successful convergence (adaptation) in high dimensions requires outgoing network hubs and is enhanced by their auto-regulation. The ability of these empirically-validated features of gene regulatory networks to support exploratory adaptation without fine-tuning, makes it plausible for biological implementation.

q-bio.MN↗

arXiv2015

Universal Protein Distributions in a Model of Cell Growth and Division

Protein distributions measured under a broad set of conditions in bacteria and yeast were shown to exhibit a common skewed shape, with variances depending quadratically on means. For bacteria these properties were reproduced by temporal measurements of protein content, showing accumulation and division across generations. Here we present a stochastic growth-and-division model with feedback which captures these observed properties. The limiting copy number distribution is calculated exactly, and a single parameter is found to determine the distribution shape and the variance-to-mean relation. Estimating this parameter from bacterial temporal data reproduces the measured distribution shape with high accuracy, and leads to predictions for future experiments.

physics.bio-ph↗

arXiv2015

Single-cell protein dynamics reproduce universal fluctuations in cell populations

Protein variability in single cells has been studied extensively in populations, but little is known about temporal protein fluctuations in a single cell over extended times. We present here traces of protein copy number measured in individual bacteria over multiple generations and investigate their statistical properties, comparing them to previously measured population snapshots. We find that temporal fluctuations in individual traces exhibit the same universal features as those previously observed in populations. Scaled fluctuations around the mean of each trace exhibit the same universal distribution shape as found in populations measured under a wide range of conditions and in two distinct microorganisms. Additionally, the mean and variance of the traces over time obey the same quadratic relation. Analyzing the temporal features of the protein traces in individual cells, reveals that within a cell cycle protein content increases as an exponential function with a rate that varies from cycle to cycle. This leads to a compact description of the protein trace as a 3-variable stochastic process - the exponential rate, the cell-cycle duration and the value at the cycle start - sampled once each cell cycle. This compact description is sufficient to preserve the universal statistical properties of the protein fluctuations, namely, the protein distribution shape and the quadratic relationship between variance and mean. Our results show that the protein distribution shape is insensitive to sub-cycle intracellular microscopic details and reflects global cellular properties that fluctuate between generations.

physics.bio-ph↗

arXiv2012

Universal protein fluctuations in populations of microorganisms

The copy number of any protein fluctuates among cells in a population; characterizing and understanding these fluctuations is a fundamental problem in biophysics. We show here that protein distributions measured under a broad range of biological realizations collapse to a single non-Gaussian curve under scaling by the first two moments. Moreover in all experiments the variance is found to depend quadratically on the mean, showing that a single degree of freedom determines the entire distribution. Our results imply that protein fluctuations do not reflect any specific molecular or cellular mechanism, and suggest that some buffering process masks these details and induces universality.

physics.bio-ph↗

arXiv2011

Adaptive response and enlargement of dynamic range

Many membrane channels and receptors exhibit adaptive, or desensitized, response to a strong sustained input stimulus, often supported by protein activity-dependent inactivation. Adaptive response is thought to be related to various cellular functions such as homeostasis and enlargement of dynamic range by background compensation. Here we study the quantitative relation between adaptive response and background compensation within a modeling framework. We show that any particular type of adaptive response is neither sufficient nor necessary for adaptive enlargement of dynamic range. In particular a precise adaptive response, where system activity is maintained at a constant level at steady state, does not ensure a large dynamic range neither in input signal nor in system output. A general mechanism for input dynamic range enlargement can come about from the activity-dependent modulation of protein responsiveness by multiple biochemical modification, regardless of the type of adaptive response it induces. Therefore hierarchical biochemical processes such as methylation and phosphorylation are natural candidates to induce this property in signaling systems.

q-bio.MN↗

arXiv2008

From cellular properties to population asymptotics in the Population Balance Equation

Proliferating cell populations at steady state growth often exhibit broad protein distributions with exponential tails. The sources of this variation and its universality are of much theoretical interest. Here we address the problem by asymptotic analysis of the Population Balance Equation. We show that the steady state distribution tail is determined by a combination of protein production and cell division and is insensitive to other model details. Under general conditions this tail is exponential with a dependence on parameters consistent with experiment. We discuss the conditions for this effect to be dominant over other sources of variation and the relation to experiments.

q-bio.PE↗

arXiv2000

Universality and individuality in a neural code

The problem of neural coding is to understand how sequences of action potentials (spikes) are related to sensory stimuli, motor outputs, or (ultimately) thoughts and intentions. One clear question is whether the same coding rules are used by different neurons, or by corresponding neurons in different individuals. We present a quantitative formulation of this problem using ideas from information theory, and apply this approach to the analysis of experiments in the fly visual system. We find significant individual differences in the structure of the code, particularly in the way that temporal patterns of spikes are used to convey information beyond that available from variations in spike rate. On the other hand, all the flies in our ensemble exhibit a high coding efficiency, so that every spike carries the same amount of information in all the individuals. Thus the neural code has a quantifiable mixture of individuality and universality.

physics.bio-ph↗

arXiv1996

Excitation of Small Quantum Systems by High-Frequency Fields

The excitation by a high frequency field of multi--level quantum systems with a slowly varying density of states is investigated. A general approach to study such systems is presented. The Floquet eigenstates are characterized on several energy scales. On a small scale, sharp universal quasi--resonances are found, whose shape is independent of the field parameters and the details of the system. On a larger scale an effective tight--binding equation is constructed for the amplitudes of these quasi--resonances. This equation is non--universal; two classes of examples are discussed in detail.

cond-mat↗