arXiv subjects

Nathalie Q. Balaban

Publications and source records attributed to Nathalie Q. Balaban.

6 recordsLinked to original sources

arXiv2021

Detecting chaos in lineage-trees: A deep learning approach

Many complex phenomena, from weather systems to heartbeat rhythm patterns, are effectively modeled as low-dimensional dynamical systems. Such systems may behave chaotically under certain conditions, and so the ability to detect chaos based on empirical measurement is an important step in characterizing and predicting these processes. Classifying a system as chaotic usually requires estimating its largest Lyapunov exponent, which quantifies the average rate of convergence or divergence of initially close trajectories in state space, and for which a positive value is generally accepted as an operational definition of chaos. Estimating the largest Lyapunov exponent from observations of a process is especially challenging in systems affected by dynamical noise, which is the case for many models of real-world processes, in particular models of biological systems. We describe a novel method for estimating the largest Lyapunov exponent from data, based on training Deep Learning models on synthetically generated trajectories, and demonstrate that this method yields accurate and noise-robust predictions given relatively short inputs and across a range of different dynamical systems. Our method is unique in that it can analyze tree-shaped data, a ubiquitous topology in biological settings, and specifically in dynamics over lineages of cells or organisms. We also characterize the types of input information extracted by our models for their predictions, allowing for a deeper understanding into the different ways by which chaos can be analyzed in different topologies.

cs.LG↗

arXiv2017

Correlations of single-cell division times with and without periodic forcing

Periodic forcing of nonlinear oscillators leads to a large number of dynamic behaviors. The coupling of the cell-cycle to the circadian clock provides a biological realization of such forcing. Using high throughput single-cell microscopy, we have studied the correlations between cell cycle duration in discrete lineages of several different organisms including those with known coupling to a circadian clock and those without known coupling to a circadian clock. Correlations between cell cycles duration in discrete lineages observed in the organisms with a circadian clock cannot be explained by a simple statistical model but are consistent with predictions of a biologically plausible two dimensional nonlinear map. Surprisingly, the nonlinear map is equivalent to a classic nonlinear map called the fattened Arnold map. The model predicts that circadian coupling may increase cell to cell variability in a clonal population of cells. In agreement with this prediction, deletion of the circadian clock reduces variability. Our results show that simple correlations can identify systems under periodic forcing and that studies of nonlinear coupling of biological oscillators provide insight into basic cellular processes of growth.

q-bio.CB↗

arXiv2007

Deterministic and Stochastic Simulations of Simple Genetic Circuits

We analyze three simple genetic circuits which involve transcriptional regulation and feedback: the autorepressor, the switch and the repressilator, that consist of one, two and three genes, respectively. Such systems are commonly simulated using rate equations, that account for the concentrations of the mRNAs and proteins produced by these genes. Rate equations are suitable when the concentrations of the relevant molecules in a cell are large and fluctuations are negligible. However, when some of the proteins in the circuit appear in low copy numbers, fluctuations become important and the rate equations fail. In this case stochastic methods, such as direct numerical integration of the master equation or Monte Carlo simulations are required. Here we present deterministic and stochastic simulations of the autorepressor, the switch and the repressilator. We show that fluctuations give rise to quantitative and qualitative changes in the dynamics of these systems. In particular, we demonstrate a fluctuations-induced bistability in a variant of the genetic switch and and noisy oscillations obtained in the repressilator circuit.

q-bio.MN↗

arXiv2007

Stochastic Simulations of Genetic Switch Systems

Genetic switch systems with mutual repression of two transcription factors are studied using deterministic methods (rate equations) and stochastic methods (the master equation and Monte Carlo simulations). These systems exhibit bistability, namely two stable states such that spontaneous transitions between them are rare. Induced transitions may take place as a result of an external stimulus. We study several variants of the genetic switch and examine the effects of cooperative binding, exclusive binding, protein-protein interactions and degradation of bound repressors. We identify the range of parameters in which bistability takes place, enabling the system to function as a switch. Numerous studies have concluded that cooperative binding is a necessary condition for the emergence of bistability in these systems. We show that a suitable combination of network structure and stochastic effects gives rise to bistability even without cooperative binding. The average time between spontaneous transitions is evaluated as a function of the biological parameters.

q-bio.MN↗

arXiv2006

Genetic Toggle Switch Without Cooperative Binding

Genetic switch systems with mutual repression of two transcription factors are studied using deterministic and stochastic methods. Numerous studies have concluded that cooperative binding is a necessary condition for the emergence of bistability in these systems. Here we show that for a range of biologically relevant conditions, a suitable combination of network structure and stochastic effects gives rise to bistability even without cooperative binding.

q-bio.MN↗

arXiv2005

Modeling of negative autoregulated genetic networks in single cells

We discuss recent developments in the modeling of negative autoregulated genetic networks. In particular, we consider the temporal evolution of the population of mRNA and proteins in simple networks using rate equations. In the limit of low copy numbers, fluctuation effects become significant and more adequate modeling is then achieved using the master equation formalism. The analogy between regulatory gene networks and chemical reaction networks on dust grains in the interstellar medium is discussed. The analysis and simulation of complex reaction networks are also considered.

q-bio.MN↗