Thibaud Taillefumier

Molecular Combing of Single DNA Molecules on the 10 Megabase Scale.

DNA combing allows the investigation of DNA replication on genomic single DNA molecules, but the lengths that can be analysed have been restricted to molecules of 200–500 kb. We have improved the DNA combing procedure so that DNA molecules can be analysed up to the length of entire chromosomes in fission yeast and up to 12 Mb fragments in human cells. Combing multi-Mb-scale DNA molecules revealed previously undetected origin clusters in fission yeast and shows that in human cells replication origins fire stochastically forming clusters of fired origins with an average size of 370 kb. We estimate that a single human cell forms around 3200 clusters at mid S-phase and fires approximately 100,000 origins to complete genome duplication. The procedure presented here will be adaptable to other organisms and experimental conditions.

A phase transition in the first passage of a Brownian process through a fluctuating boundary with implications for neural coding

Finding the first time a fluctuating quantity reaches a given boundary is a deceptively simple-looking problem of vast practical importance in physics, biology, chemistry, neuroscience, economics, and industrial engineering. Problems in which the bound to be traversed is itself a fluctuating function of time include widely studied problems in neural coding, such as neuronal integrators with irregular inputs and internal noise. We show that the probability p(t) that a Gauss–Markov process will first exceed the boundary at time t suffers a phase transition as a function of the roughness of the boundary, as measured by its Hölder exponent H. The critical value occurs when the roughness of the boundary equals the roughness of the process, so for diffusive processes the critical value is Hc = 1/2. For smoother boundaries, H > 1/2, the probability density is a continuous function of time. For rougher boundaries, H < 1/2, the probability is concentrated on a Cantor-like set of zero measure: the probability density becomes divergent, almost everywhere either zero or infinity. The critical point Hc = 1/2 corresponds to a widely studied case in the theory of neural coding, in which the external input integrated by a model neuron is a white-noise process, as in the case of uncorrelated but precisely balanced excitatory and inhibitory inputs. We argue that this transition corresponds to a sharp boundary between rate codes, in which the neural firing probability varies smoothly, and temporal codes, in which the neuron fires at sharply defined times regardless of the intensity of internal noise.

Metabolic Trade-Offs Promote Diversity in a Model Ecosystem

In nature, a large number of species can coexist on a small number of shared resources; however, resource-competition models predict that the number of species in steady coexistence cannot exceed the number of resources. Motivated by recent studies of phytoplankton, we introduce trade-offs into a resource-competition model and find that an unlimited number of species can coexist. Our model spontaneously reproduces several notable features of natural ecosystems, including keystone species and population dynamics and abundances characteristic of neutral theory, despite an underlying non-neutral competition for resources.

Stable encoding of sounds over a broad range of statistical parameters in the auditory cortex

Natural auditory scenes possess highly structured statistical regularities, which are dictated by the physics of sound production in nature, such as scale‐invariance. We recently identified that natural water sounds exhibit a particular type of scale invariance, in which the temporal modulation within spectral bands scales with the centre frequency of the band. Here, we tested how neurons in the mammalian primary auditory cortex encode sounds that exhibit this property, but differ in their statistical parameters. The stimuli varied in spectro‐temporal density and cyclo‐temporal statistics over several orders of magnitude, corresponding to a range of water‐like percepts, from pattering of rain to a slow stream. We recorded neuronal activity in the primary auditory cortex of awake rats presented with these stimuli. The responses of the majority of individual neurons were selective for a subset of stimuli with specific statistics. However, as a neuronal population, the responses were remarkably stable over large changes in stimulus statistics, exhibiting a similar range in firing rate, response strength, variability and information rate, and only minor variation in receptive field parameters. This pattern of neuronal responses suggests a potentially general principle for cortical encoding of complex acoustic scenes: while individual cortical neurons exhibit selectivity for specific statistical features, a neuronal population preserves a constant response structure across a broad range of statistical parameters.

Microbial consortia at steady supply

Metagenomics has revealed hundreds of species in almost all microbiota. In a few well-studied cases, microbial communities have been observed to coordinate their metabolic fluxes. In principle, microbes can divide tasks to reap the benefits of specialization, as in human economies. However, the benefits and stability of an economy of microbial specialists are far from obvious. Here, we physically model the population dynamics of microbes that compete for steadily supplied resources. Importantly, we explicitly model the metabolic fluxes yielding cellular biomass production under the constraint of a limited enzyme budget. We find that population dynamics generally leads to the coexistence of different metabolic types. We establish that these microbial consortia act as cartels, whereby population dynamics pins down resource concentrations at values for which no other strategy can invade. Finally, we propose that at steady supply, cartels of competing strategies automatically yield maximum biomass, thereby achieving a collective optimum. DOI: http://dx.doi.org/10.7554/eLife.22644.001

Comprehensive analysis reveals how single nucleotides contribute to noncoding RNA function in bacterial quorum sensing

Significance Five noncoding small RNAs (sRNAs) called the Qrr1-5 sRNAs act at the heart of the Vibrio harveyi quorum-sensing cascade. The Qrr sRNAs posttranscriptionally regulate 20 mRNA targets. Here, we use a method we call RSort-Seq that is based on unbiased high-throughput screening to define the critical bases in Qrr4 that specify its function. The power of our study comes from using the screening results to pinpoint particular nucleotides for follow-up biological analyses that define function. Using this approach, we discover how Qrr4 differentially regulates two of its targets, luxO and luxR. We also show how this strategy can be used to identify intramolecular suppressor mutations. This approach can be applied to any sRNA and any mRNA target. Five homologous noncoding small RNAs (sRNAs), called the Qrr1-5 sRNAs, function in the Vibrio harveyi quorum-sensing cascade to drive its operation. Qrr1-5 use four different regulatory mechanisms to control the expression of ∼20 mRNA targets. Little is known about the roles individual nucleotides play in mRNA target selection, in determining regulatory mechanism, or in defining Qrr potency and dynamics of target regulation. To identify the nucleotides vital for Qrr function, we developed a method we call RSort-Seq that combines saturating mutagenesis, fluorescence-activated cell sorting, high-throughput sequencing, and mutual information theory to explore the role that every nucleotide in Qrr4 plays in regulation of two mRNA targets, luxR and luxO. Companion biochemical assays allowed us to assign specific regulatory functions/underlying molecular mechanisms to each important base. This strategy yielded a regional map of nucleotides in Qrr4 vital for stability, Hfq interaction, stem-loop formation, and base pairing to both luxR and luxO, to luxR only, and to luxO only. In terms of nucleotides critical for sRNA function, the RSort-Seq analysis provided strikingly different results from those predicted by commonly used regulatory RNA-folding algorithms. This approach is applicable to any RNA–RNA interaction, including sRNAs in other bacteria and regulatory RNAs in higher organisms.

Optimal census by quorum sensing

Quorum sensing is the regulation of gene expression in response to changes in cell density. To measure their cell density, bacterial populations produce and detect diffusible molecules called autoinducers. Individual bacteria internally represent the external concentration of autoinducers via the level of monitor proteins. In turn, these monitor proteins typically regulate both their own production and the production of autoinducers, thereby establishing internal and external feedbacks. Here, we ask whether feedbacks can increase the information available to cells about their local density. We quantify available information as the mutual information between the abundance of a monitor protein and the local cell density for biologically relevant models of quorum sensing. Using variational methods, we demonstrate that feedbacks can increase information transmission, allowing bacteria to resolve up to two additional ranges of cell density when compared with bistable quorum-sensing systems. Our analysis is relevant to multi-agent systems that track an external driver implicitly via an endogenously generated signal.

A Haar-like Construction for the Ornstein Uhlenbeck Process

The classical Haar construction of Brownian motion uses a binary tree of triangular wedge-shaped functions. This basis has compactness properties which make it especially suited for certain classes of numerical algorithms. We present a similar basis for the Ornstein-Uhlenbeck process, in which the basis elements approach asymptotically the Haar functions as the index increases, and preserve the following properties of the Haar basis: all basis elements have compact support on an open interval with dyadic rational endpoints; these intervals are nested and become smaller for larger indices of the basis element, and for any dyadic rational, only a finite number of basis elements is nonzero at that number. Thus the expansion in our basis, when evaluated at a dyadic rational, terminates in a finite number of steps. We prove the covariance formulae for our expansion and discuss its statistical interpretation.

A transition to sharp timing in stochastic leaky integrate-and-fire neurons driven by frozen noisy input

The firing activity of intracellularly stimulated neurons in cortical slices has been demonstrated to be profoundly affected by the temporal structure of the injected current (Mainen & Sejnowski, 1995). This suggests that the timing features of the neural response may be controlled as much by its own biophysical characteristics as by how a neuron is wired within a circuit. Modeling studies have shown that the interplay between internal noise and the fluctuations of the driving input controls the reliability and the precision of neuronal spiking (Cecchi et al., 2000; Tiesinga, 2002; Fellous, Rudolph, Destexhe, & Sejnowski, 2003). In order to investigate this interplay, we focus on the stochastic leaky integrate-and-fire neuron and identify the Hölder exponent H of the integrated input as the key mathematical property dictating the regime of firing of a single-unit neuron. We have recently provided numerical evidence (Taillefumier & Magnasco, 2013) for the existence of a phase transition when becomes less than the statistical Hölder exponent associated with internal gaussian white noise (H=1/2). Here we describe the theoretical and numerical framework devised for the study of a neuron that is periodically driven by frozen noisy inputs with exponent H>0. In doing so, we account for the existence of a transition between two regimes of firing when H=1/2, and we show that spiking times have a continuous density when the Hölder exponent satisfies H>1/2. The transition at H=1/2 formally separates rate codes, for which the neural firing probability varies smoothly, from temporal codes, for which the neuron fires at sharply defined times regardless of the intensity of internal noise.

Exact event-driven implementation for recurrent networks of stochastic perfect integrate-and-fire neurons

In vivo cortical recording reveals that indirectly driven neural assemblies can produce reliable and temporally precise spiking patterns in response to stereotyped stimulation. This suggests that despite being fundamentally noisy, the collective activity of neurons conveys information through temporal coding. Stochastic integrate-and-fire models delineate a natural theoretical framework to study the interplay of intrinsic neural noise and spike timing precision. However, there are inherent difficulties in simulating their networks’ dynamics in silico with standard numerical discretization schemes. Indeed, the well-posedness of the evolution of such networks requires temporally ordering every neuronal interaction, whereas the order of interactions is highly sensitive to the random variability of spiking times. Here, we answer these issues for perfect stochastic integrate-and-fire neurons by designing an exact event-driven algorithm for the simulation of recurrent networks, with delayed Dirac-like interactions. In addition to being exact from the mathematical standpoint, our proposed method is highly efficient numerically. We envision that our algorithm is especially indicated for studying the emergence of polychronized motifs in networks evolving under spike-timing-dependent plasticity with intrinsic noise.