Monitoring the Evolution of Relative Product Populations at Early Times during a Photochemical Reaction

Identifying multiple rival reaction products and transient species formed during ultrafast photochemical reactions and determining their time-evolving relative populations are key steps toward understanding and predicting photochemical outcomes. Yet, most contemporary ultrafast studies struggle with clearly identifying and quantifying competing molecular structures/species among the emerging reaction products. Here, we show that mega-electronvolt ultrafast electron diffraction in combination with ab initio molecular dynamics calculations offer a powerful route to determining time-resolved populations of the various isomeric products formed after UV (266 nm) excitation of the five-membered heterocyclic molecule 2(5H)-thiophenone. This strategy provides experimental validation of the predicted high (∼50%) yield of an episulfide isomer containing a strained three-membered ring within ∼1 ps of photoexcitation and highlights the rapidity of interconversion between the rival highly vibrationally excited photoproducts in their ground electronic state.

Evolution of biological cooperation: an algorithmic approach

This manuscript presents an algorithmic approach to cooperation in biological systems, drawing on fundamental ideas from statistical mechanics and probability theory. Fisher's geometric model of adaptation suggests that the evolution of organisms well adapted to multiple constraints comes at a significant complexity cost. By utilizing combinatorial models of fitness, we demonstrate that the probability of adapting to all constraints decreases exponentially with the number of constraints, thereby generalizing Fisher's result. Our main focus is understanding how cooperation can overcome this adaptivity barrier. Through these combinatorial models, we demonstrate that when an organism needs to adapt to a multitude of environmental variables, division of labor emerges as the only viable evolutionary strategy.

The roles of a novel CDKB/KRP/FB3 cell cycle core complex in rice gametes and initiation of embryogenesis

The cell cycle controls division and proliferation of all eukaryotic cells and is tightly regulated at multiple checkpoints by complexes of core cell cycle proteins. Due to the difficulty in accessing female gametes and zygotes of flowering plants, little is known about the molecular mechanisms underlying embryogenesis initiation despite the crucial importance of this process for seed crops. In this study, we reveal three levels of factors involved in rice zygotic cell cycle control and characterize their functions and regulation. Protein-protein interaction studies, including within zygote cells, and in vitro biochemical analyses delineate a model of the zygotic cell cycle core complex for rice. In this model, CDKB1, a major regulator of plant mitosis, is a cyclin (CYCD5)-dependent kinase; its activity is coordinately inhibited by two cell cycle inhibitors, KRP4 and KRP5; and both KRPs are regulated via F-box protein 3 (FB3)-mediated proteolysis. Supporting their critical roles in controlling the rice zygotic cell cycle, mutations in KRP4, KRP5 and FB3 result in the compromised function of sperm cells and abnormal organization of female germ units, embryo and endosperm, thus significantly reducing seed-set rate. This work helps reveal regulatory mechanisms controlling the zygotic cell cycle toward seed formation in angiosperms.

Long-Range Conductivity in Proteins Mediated by Aromatic Residues

Single-molecule measurements show that many proteins, lacking any redox cofactors, nonetheless exhibit electrical conductance on the order of a nanosiemen over 10 nm distances, implying that electrons can transit an entire protein in less than a nanosecond when subject to a potential difference of less than 1 V. This is puzzling because, for fast transport (i.e., a free energy barrier of zero), the hopping rate is determined by the reorganization energy of approximately 0.8 eV, and this sets the time scale of a single hop to at least 1 μs. Furthermore, the Fermi energies of typical metal electrodes are far removed from the energies required for sequential oxidation and reduction of the aromatic residues of the protein, which should further reduce the hopping current. Here, we combine all-atom molecular dynamics (MD) simulations of non-redox-active proteins (consensus tetratricopeptide repeats) with an electron transfer theory to demonstrate a molecular mechanism that can account for the unexpectedly fast electron transport. According to our MD simulations, the reorganization energy produced by the energy shift on charging (the Stokes shift) is close to the conventional value of 0.8 eV. However, the non-ergodic sampling of molecular configurations by the protein results in reaction-reorganization energies, extracted directly from the distribution of the electrostatic energy fluctuations, that are only ∼0.2 eV, which is small enough to enable long-range conductivity, without invoking quantum coherent transport. Using the MD values of the reorganization energies, we calculate a current decay with distance that is in agreement with experiment.

Time-Resolved X-ray Photoelectron Spectroscopy: Ultrafast Dynamics in CS2 Probed at the S 2p Edge

Recent developments in X-ray free-electron lasers have enabled a novel site-selective probe of coupled nuclear and electronic dynamics in photoexcited molecules, time-resolved X-ray photoelectron spectroscopy (TRXPS). We present results from a joint experimental and theoretical TRXPS study of the well-characterized ultraviolet photodissociation of CS2, a prototypical system for understanding non-adiabatic dynamics. These results demonstrate that the sulfur 2p binding energy is sensitive to changes in the nuclear structure following photoexcitation, which ultimately leads to dissociation into CS and S photoproducts. We are able to assign the main X-ray spectroscopic features to the CS and S products via comparison to a first-principles determination of the TRXPS based on ab initio multiple-spawning simulations. Our results demonstrate the use of TRXPS as a local probe of complex ultrafast photodissociation dynamics involving multimodal vibrational coupling, nonradiative transitions between electronic states, and multiple final product channels.

Model chromatin flows: numerical analysis of linear and nonlinear hydrodynamics inside a sphere

We solve a hydrodynamic model of active chromatin dynamics, within a confined geometry simulating the cell nucleus. Using both analytical and numerical methods, we describe the behavior of the chromatin polymer driven by the activity of motors having polar symmetry, both in the linear response regime as well as in the long-term, fully nonlinear regime of the flows. The introduction of a boundary induces a particular geometry in the flows of chromatin, which we describe using vector spherical harmonics, a tool which greatly simplifies both our analytical and numerical approaches. We find that the long-term behavior of this model in confinement is dominated by steady, transverse flows of chromatin which circulate around the spherical domain. These circulating flows are found to be robust to perturbations, and their characteristic size is set by the size of the domain. This gives us further insight into active chromatin dynamics in the cell nucleus, and provides a foundation for development of further, more complex models of active chromatin dynamics.

Dynamic switching of transcriptional regulators between two distinct low-mobility chromatin states

How chromatin dynamics relate to transcriptional activity remains poorly understood. Using single-molecule tracking, coupled with machine learning, we show that histone H2B and multiple chromatin-bound transcriptional regulators display two distinct low-mobility states. Ligand activation results in a marked increase in the propensity of steroid receptors to bind in the lowest-mobility state. Mutational analysis revealed that interactions with chromatin in the lowest-mobility state require an intact DNA binding domain and oligomerization domains. These states are not spatially separated as previously believed, but individual H2B and bound-TF molecules can dynamically switch between them on time scales of seconds. Single bound-TF molecules with different mobilities exhibit different dwell time distributions, suggesting that the mobility of TFs is intimately coupled with their binding dynamics. Together, our results identify two unique and distinct low-mobility states that appear to represent common pathways for transcription activation in mammalian cells.

Multi-neuronal recording in unrestrained animals with all acousto-optic random-access line-scanning two-photon microscopy

To understand how neural activity encodes and coordinates behavior, it is desirable to record multi-neuronal activity in freely behaving animals. Imaging in unrestrained animals is challenging, especially for those, like larval Drosophila melanogaster, whose brains are deformed by body motion. A previously demonstrated two-photon tracking microscope recorded from individual neurons in freely crawling Drosophila larvae but faced limits in multi-neuronal recording. Here we demonstrate a new tracking microscope using acousto-optic deflectors (AODs) and an acoustic GRIN lens (TAG lens) to achieve axially resonant 2D random access scanning, sampling along arbitrarily located axial lines at a line rate of 70 kHz. With a tracking latency of 0.1 ms, this microscope recorded activities of various neurons in moving larval Drosophila CNS and VNC including premotor neurons, bilateral visual interneurons, and descending command neurons. This technique can be applied to the existing two-photon microscope to allow for fast 3D tracking and scanning.

Toward the Complete Functional Characterization of a Minimal Bacterial Proteome

Recently, we presented a whole-cell kinetic model of the genetically minimal bacterium JCVI-syn3A that described the coupled metabolic and genetic information processes and predicted behaviors emerging from the interactions among these networks. JCVI-syn3A is a genetically reduced bacterial cell that has the fewest number and smallest fraction of genes of unclear function, with approximately 90 of its 452 protein-coding genes (that is less than 20%) unannotated. Further characterization of unclear JCVI-syn3A genes strengthens the robustness and predictive power of cell modeling efforts and can lead to a deeper understanding of biophysical processes and pathways at the cell scale. Here, we apply computational analyses to elucidate the functions of the products of several essential but previously uncharacterized genes involved in integral cellular processes, particularly those directly affecting cell growth, division, and morphology. We also suggest directed wet-lab experiments informed by our analyses to further understand these "missing puzzle pieces" that are an essential part of the mosaic of biological interactions present in JCVI-syn3A. Our workflow leverages evolutionary sequence analysis, protein structure prediction, interactomics, and genome architecture to determine upgraded annotations. Additionally, we apply the structure prediction analysis component of our work to all 452 protein coding genes in JCVI-syn3A to expedite future functional annotation studies as well as the inverse mapping of the cell state to more physical models requiring all-atom or coarse-grained representations for all JCVI-syn3A proteins.

Pseudomonas aeruginosa clinical blood isolates display significant phenotypic variability

Pseudomonas aeruginosa is a significant threat in healthcare settings where it deploys a wide host of virulence factors to cause disease. Many virulence-related phenotypes such as pyocyanin production, biofilm formation, and twitching motility have been implicated in causing disease in a number of hosts. In this study, we investigate these three virulence factors in a collection of 22 clinical strains isolated from blood stream infections. Despite the fact that all 22 strains caused disease and came from the same body site of different patients, they show significant variability in assays for each of the three specific phenotypes examined. There was no significant correlation between the strength of the three phenotypes across our collection, suggesting that they can be independently modulated. Furthermore, strains deficient in each of the virulence-associated phenotypes examined could be identified. To understand the genetic basis of this variability we sequenced the genomes of the 22 strains. We found that the majority of genes responsible for pyocyanin production, biofilm formation, and twitching motility were highly conserved among the strains despite their phenotypic variability, suggesting that the phenotypic variability is likely due to regulatory changes. Our findings thus demonstrate that no one lab-assayed phenotype of pyocyanin production, biofilm production, and twitching motility is necessary for a P. aeruginosa strain to cause blood stream infection and that additional factors may be needed to fully predict what strains will lead to specific human diseases.

α -event characterization and rejection in point-contact HPGe detectors

P-type point contact (PPC) HPGe detectors are a leading technology for rare event searches due to their excellent energy resolution, low thresholds, and multi-site event rejection capabilities. We have characterized a PPC detector's response to α particles incident on the sensitive passivated and p + surfaces, a previously poorly-understood source of background. The detector studied is identical to those in the Majorana Demonstrator experiment, a search for neutrinoless double-beta decay ( 0 ν β β ) in 76 Ge. α decays on most of the passivated surface exhibit significant energy loss due to charge trapping, with waveforms exhibiting a delayed charge recovery (DCR) signature caused by the slow collection of a fraction of the trapped charge. The DCR is found to be complementary to existing methods of α identification, reliably identifying α background events on the passivated surface of the detector. We demonstrate effective rejection of all surface α events (to within statistical uncertainty) with a loss of only 0.2% of bulk events by combining the DCR discriminator with previously-used methods. The DCR discriminator has been used to reduce the background rate in the 0 ν β β region of interest window by an order of magnitude in the Majorana Demonstrator  and will be used in the upcoming LEGEND-200 experiment.

Measuring the repertoire of age-related behavioral changes in Drosophila melanogaster

Aging affects almost all aspects of an organism—its morphology, its physiology, its behavior. Isolating which biological mechanisms are regulating these changes, however, has proven difficult, potentially due to our inability to characterize the full repertoire of an animal’s behavior across the lifespan. Using data from fruit flies (D. melanogaster) we measure the full repertoire of behaviors as a function of age. We observe a sexually dimorphic pattern of changes in the behavioral repertoire during aging. Although the stereotypy of the behaviors and the complexity of the repertoire overall remains relatively unchanged, we find evidence that the observed alterations in behavior can be explained by changing the fly’s overall energy budget, suggesting potential connections between metabolism, aging, and behavior.

Aging is a ubiquitous biological phenomenon that affects many aspects of an animal’s appearance, physiology, and behavior. Our understanding of how changes in physiology lead to behavioral changes, however, has been partially limited by our ability to robustly quantify how behavior alters over timescales of days and weeks. In this study, we measure a large repertoire of behaviors of fruit flies at various ages, finding how the actions the animals perform shift with age. We observe a difference between the aging dynamics of male and female flies, and we show that many of these changes can be explained with a model of energy consumption, leading us to make predictions as to the role of metabolism in changes in aging behavior.

Unsupervised Learning Methods for Molecular Simulation Data

Unsupervised learning is becoming an essential tool to analyze the increasingly large amounts of data produced by atomistic and molecular simulations, in material science, solid state physics, biophysics, and biochemistry. In this Review, we provide a comprehensive overview of the methods of unsupervised learning that have been most commonly used to investigate simulation data and indicate likely directions for further developments in the field. In particular, we discuss feature representation of molecular systems and present state-of-the-art algorithms of dimensionality reduction, density estimation, and clustering, and kinetic models. We divide our discussion into self-contained sections, each discussing a specific method. In each section, we briefly touch upon the mathematical and algorithmic foundations of the method, highlight its strengths and limitations, and describe the specific ways in which it has been used-or can be used-to analyze molecular simulation data.

Adaptive shifts underlie the divergence in wing morphology in bombycoid moths

The evolution of flapping flight is linked to the prolific success of insects. Across Insecta, wing morphology diversified, strongly impacting aerodynamic performance. In the presence of ecological opportunity, discrete adaptive shifts and early bursts are two processes hypothesized to give rise to exceptional morphological diversification. Here, we use the sister-families Sphingidae and Saturniidae to answer how the evolution of aerodynamically important traits is linked to clade divergence and through what process(es) these traits evolve. Many agile Sphingidae evolved hover feeding behaviours, while adult Saturniidae lack functional mouth parts and rely on a fixed energy budget as adults. We find that Sphingidae underwent an adaptive shift in wing morphology coincident with life history and behaviour divergence, evolving small high aspect ratio wings advantageous for power reduction that can be moved at high frequencies, beneficial for flight control. By contrast, Saturniidae, which do not feed as adults, evolved large wings and morphology which surprisingly does not reduce aerodynamic power, but could contribute to their erratic flight behaviour, aiding in predator avoidance. We suggest that after the evolution of flapping flight, diversification of wing morphology can be potentiated by adaptative shifts, shaping the diversity of wing morphology across insects.

The Manipulation of the Internal Hydrophobicity of FraC Nanopores Augments Peptide Capture and Recognition

The detection of analytes and the sequencing of DNA using biological nanopores have seen major advances over recent years. The analysis of proteins and peptides with nanopores, however, is complicated by the complex physicochemical structure of polypeptides and the lack of understanding of the mechanism of capture and recognition of polypeptides by nanopores. In this work, we show that introducing aromatic amino acids at precise positions within the lumen of α-helical fragaceatoxin C (FraC) nanopores increased the capture frequency of peptides and largely improved the discrimination among peptides of similar size. Molecular dynamics simulations determined the sensing region of the nanopore, elucidated the microscopic mechanism enabling accurate characterization of the peptides via ionic current blockades in FraC, and characterized the effect of the pore modification on peptide discrimination. This work provides insights to improve the recognition and to augment the capture of peptides by nanopores, which is important for developing a real-time and single-molecule size analyzer for peptide recognition and identification.

Programming active cohesive granular matter with mechanically induced phase changes

At the macroscale, controlling robotic swarms typically uses substantial memory, processing power, and coordination unavailable at the microscale, e.g., for colloidal robots, which could be useful for fighting disease, fabricating intelligent textiles, and designing nanocomputers. To develop principles that can leverage physical interactions and thus be used across scales, we take a two-pronged approach: a theoretical abstraction of self-organizing particle systems and an experimental robot system of active cohesive granular matter that intentionally lacks digital electronic computation and communication, using minimal (or no) sensing and control. As predicted by theory, as interparticle attraction increases, the collective transitions from dispersed to a compact phase. When aggregated, the collective can transport non-robot "impurities," thus performing an emergent task driven by the physics underlying the transition. These results reveal a fruitful interplay between algorithm design and active matter robophysics that can result in principles for programming collectives without the need for complex algorithms or capabilities.

Probing the ecological and evolutionary history of a thermophilic cyanobacterial population via statistical properties of its microdiversity

Despite extensive DNA sequencing data derived from natural microbial communities, it remains a major challenge to identify the key evolutionary and ecological forces that shape microbial populations. We have focused on the extensive microdiversity of the cyanobacterium Synechococcus sp., which is a dominant member of the dense phototrophic biofilms in the hot springs of Yellowstone National Park. From deep amplicon sequencing of many loci and statistical analyses of these data, we showed previously that the population has undergone an unexpectedly high degree of homologous recombination, unlinking synonymous SNP-pair correlations even on intragenic length scales. Here, we analyze the genic amino acid diversity, which provides new evidence of selection and insights into the evolutionary history of the population. Surprisingly, some features of the data, including the spectrum of distances between genic-alleles, appear consistent with primarily asexual neutral drift. Yet the non-synonymous site frequency spectrum has too large an excess of low-frequency polymorphisms to result from negative selection on deleterious mutations given the distribution of coalescent times that we infer. And our previous analyses showed that the population is not asexual. Taken together, these apparently contradictory data suggest that selection, epistasis, and hitchhiking all play essential roles in generating and stabilizing the diversity. We discuss these as well as potential roles of ecological niches at genomic and genic levels. From quantitative properties of the diversity and comparative genomic data, we infer aspects of the history and inter-spring dispersal of the meta-population since it was established in the Yellowstone Caldera. Our investigations illustrate the need for combining multiple types of sequencing data and quantitative statistical analyses to develop an understanding of microdiversity in natural microbial populations.

Comparing two classes of biological distribution systems using network analysis

Distribution networks-from vasculature to urban transportation pathways-are spatially embedded networks that must route resources efficiently in the face of pressures induced by the costs of building and maintaining network infrastructure. Such requirements are thought to constrain the topological and spatial organization of these systems, but at the same time, different kinds of distribution networks may exhibit variable architectural features within those general constraints. In this study, we use methods from network science to compare and contrast two classes of biological transport networks: mycelial fungi and vasculature from the surface of rodent brains. These systems differ in terms of their growth and transport mechanisms, as well as the environments in which they typically exist. Though both types of networks have been studied independently, the goal of this study is to quantify similarities and differences in their network designs. We begin by characterizing the structural backbone of these systems with a collection of measures that assess various kinds of network organization across topological and spatial scales, ranging from measures of loop density, to those that quantify connected pathways between different network regions, and hierarchical organization. Most importantly, we next carry out a network analysis that directly considers the spatial embedding and properties especially relevant to the function of distribution systems. We find that although both the vasculature and mycelia are highly constrained planar networks, there are clear distinctions in how they balance tradeoffs in network measures of wiring length, efficiency, and robustness. While the vasculature appears well organized for low cost, but relatively high efficiency, the mycelia tend to form more expensive but in turn more robust networks. As a whole, this work demonstrates the utility of network-based methods to identify both common features and variations in the network structure of different classes of biological transport systems.

Double and triple inclusive gluon production at mid rapidity: quantum interference in p-A scattering

We compute double and triple inclusive gluon production in p-A scattering beyond the so-called "glasma graph" approximation. We consider quantum interference effects and identify in this general setup the terms responsible for the gluon HBT and initial wave function Bose enhancement which lead to correlations in particle production. Both of these terms originate from the factorizable part of the quadrupole and sextupole terms in the production cross section. We also show that the target Bose enhancement in this regime is suppressed at large number of colors.

Designing bacterial signaling interactions with coevolutionary landscapes

Selecting amino acids to design novel protein-protein interactions that facilitate catalysis is a daunting challenge. We propose that a computational coevolutionary landscape based on sequence analysis alone offers a major advantage over expensive, time-consuming brute-force approaches currently employed. Our coevolutionary landscape allows prediction of single amino acid substitutions that produce functional interactions between non-cognate, interspecies signaling partners. In addition, it can also predict mutations that maintain segregation of signaling pathways across species. Specifically, predictions of phosphotransfer activity between the Escherichia coli histidine kinase EnvZ to the non-cognate receiver Spo0F from Bacillus subtilis were compiled. Twelve mutations designed to enhance, suppress, or have a neutral effect on kinase phosphotransfer activity to a non-cognate partner were selected. We experimentally tested the ability of the kinase to relay phosphate to the respective designed Spo0F receiver proteins against the theoretical predictions. Our key finding is that the coevolutionary landscape theory, with limited structural data, can significantly reduce the search-space for successful prediction of single amino acid substitutions that modulate phosphotransfer between the two-component His-Asp relay partners in a predicted fashion. This combined approach offers significant improvements over large-scale mutations studies currently used for protein engineering and design.

Interdependence theory of tissue failure: bulk and boundary effects

The mortality rate of many complex multicellular organisms increases with age, which suggests that net ageing damage is accumulative, despite remodelling processes. But how exactly do these little mishaps in the cellular level accumulate and spread to become a systemic catastrophe? To address this question we present experiments with synthetic tissues, an analytical model consistent with experiments, and a number of implications that follow the analytical model. Our theoretical framework describes how shape, curvature and density influences the propagation of failure in a tissue subjected to oxidative damage. We propose that ageing is an emergent property governed by interaction between cells, and that intercellular processes play a role that is at least as important as intracellular ones.

Numb prevents a complete epithelial-mesenchymal transition by modulating Notch signalling

Epithelial-mesenchymal transition (EMT) plays key roles during embryonic development, wound healing and cancer metastasis. Cells in a partial EMT or hybrid epithelial/mesenchymal (E/M) phenotype exhibit collective cell migration, forming clusters of circulating tumour cells-the primary drivers of metastasis. Activation of cell-cell signalling pathways such as Notch fosters a partial or complete EMT, yet the mechanisms enabling cluster formation remain poorly understood. Using an integrated computational-experimental approach, we examine the role of Numb-an inhibitor of Notch intercellular signalling-in mediating EMT and clusters formation. We show via an mathematical model that Numb inhibits a full EMT by stabilizing a hybrid E/M phenotype. Consistent with this observation, knockdown of Numb in stable hybrid E/M cells H1975 results in a full EMT, thereby showing that Numb acts as a brake for a full EMT and thus behaves as a 'phenotypic stability factor' by modulating Notch-driven EMT. By generalizing the mathematical model to a multi-cell level, Numb is predicted to alter the balance of hybrid E/M versus mesenchymal cells in clusters, potentially resulting in a higher tumour-initiation ability. Finally, Numb correlates with a worse survival in multiple independent lung and ovarian cancer datasets, hence confirming its relationship with increased cancer aggressiveness.

Population FBA predicts metabolic phenotypes in yeast

Using protein counts sampled from single cell proteomics distributions to constrain fluxes through a genome-scale model of metabolism, Population flux balance analysis (Population FBA) successfully described metabolic heterogeneity in a population of independent Escherichia coli cells growing in a defined medium. We extend the methodology to account for correlations in protein expression arising from the co-regulation of genes and apply it to study the growth of independent Saccharomyces cerevisiae cells in two different growth media. We find the partitioning of flux between fermentation and respiration predicted by our model agrees with recent ¹³C fluxomics experiments, and that our model largely recovers the Crabtree effect (the experimentally known bias among certain yeast species toward fermentation with the production of ethanol even in the presence of oxygen), while FBA without proteomics constraints predicts respirative metabolism almost exclusively. The comparisons to the ¹³C study showed improvement upon inclusion of the correlations and motivated a technique to systematically identify inconsistent kinetic parameters in the literature. The minor secretion fluxes for glycerol and acetate are underestimated by our method, which indicate a need for further refinements to the metabolic model. For yeast cells grown in synthetic defined (SD) medium, the calculated broad distribution of growth rates matches experimental observations from single cell studies, and we characterize several metabolic phenotypes within our modeled populations that make use of diverse pathways. Fast growing yeast cells are predicted to perform significant amount of respiration, use serine-glycine cycle and produce ethanol in mitochondria as opposed to slow growing cells. We use a genetic algorithm to determine the proteomics constraints necessary to reproduce the growth rate distributions seen experimentally. We find that a core set of 51 constraints are essential but that additional constraints are still necessary to recover the observed growth rate distribution in SD medium.

No two living cells are exactly the same. Even cells from a clonal population with identical genomes living in the same environment will express proteins in different numbers simply due to the random nature of the chemistry involved in gene expression. The consequences of this stochastic gene expression are complex and not well understood, especially at the level of large reaction networks like metabolism. Here we investigate how variability in the copy numbers of metabolic enzymes affects how individual cells extract nourishment from their environment and grow. We model 100,000 independent yeast cells, each with their own set of enzyme copy numbers sampled from experimental distributions, and use flux balance analysis (FBA) to compute the optimal way that each cell can use its metabolic pathways—an approach we dubbed Population FBA. We find that enzyme variability gives rise to a wide distribution of growth rates, and several metabolic phenotypes—subpopulations relying on diverse metabolic pathways. Most importantly, we compare the predicted fluxes through the different pathways to experimental values; we find that Population FBA is able to correctly predict Crabtree effect, while traditional FBA, which lacks the proteomics constraints our method imposes, differs both qualitatively and quantitatively from experiment.

Mechanisms of fast and stringent search in homologous pairing of double-stranded DNA

Self-organization in the cell relies on the rapid and specific binding of molecules to their cognate targets. Correct bindings must be stable enough to promote the desired function even in the crowded and fluctuating cellular environment. In systems with many nearly matched targets, rapid and stringent formation of stable products is challenging. Mechanisms that overcome this challenge have been previously proposed, including separating the process into multiple stages; however, how particular in vivo systems overcome the challenge remains unclear. Here we consider a kinetic system, inspired by homology dependent pairing between double stranded DNA in bacteria. By considering a simplified tractable model, we identify different homology testing stages that naturally occur in the system. In particular, we first model dsDNA molecules as short rigid rods containing periodically spaced binding sites. The interaction begins when the centers of two rods collide at a random angle. For most collision angles, the interaction energy is weak because only a few binding sites near the collision point contribute significantly to the binding energy. We show that most incorrect pairings are rapidly rejected at this stage. In rare cases, the two rods enter a second stage by rotating into parallel alignment. While rotation increases the stability of matched and nearly matched pairings, subsequent rotational fluctuations reduce kinetic trapping. Finally, in vivo chromosome are much longer than the persistence length of dsDNA, so we extended the model to include multiple parallel collisions between long dsDNA molecules, and find that those additional interactions can greatly accelerate the searching.

Protein folding and the binding of sequence dependent proteins to DNA are examples of self-assembling systems in which the binding energy varies continuously throughout the interaction. Previous theoretical work has highlighted the importance of dividing the interaction into separate stages characterized by interaction times and binding energies that vary by orders of magnitude. Insight into how such a division might naturally arise and promote accurate and efficient self-assembly is provided by our study of a simple tractable model inspired by the homology dependent pairing of double stranded DNA molecules in vivo. In the model, the binding energy is controlled by one single continuously tunable variable whose natural evolution creates stages that efficiently and accurately form stable products.

The modular organization of human anatomical brain networks: Accounting for the cost of wiring

Brain networks are expected to be modular. However, existing techniques for estimating a network's modules make it difficult to assess the influence of organizational principles such as wiring cost reduction on the detected modules. Here we present a modification of an existing module detection algorithm that allowed us to focus on connections that are unexpected under a cost-reduction wiring rule and to identify modules from among these connections. We applied this technique to anatomical brain networks and showed that the modules we detected differ from those detected using the standard technique. We demonstrated that these novel modules are spatially distributed, exhibit unique functional fingerprints, and overlap considerably with rich clubs, giving rise to an alternative and complementary interpretation of the functional roles of specific brain regions. Finally, we demonstrated that, using the modified module detection approach, we can detect modules in a developmental dataset that track normative patterns of maturation. Collectively, these findings support the hypothesis that brain networks are composed of modules and provide additional insight into the function of those modules.

A tale of two antennules: the performance of crab odour-capture organs in air and water

Odour capture is an important part of olfaction, where dissolved chemical cues (odours) are brought into contact with chemosensory structures. Antennule flicking by marine crabs is an example of discrete odour capture (sniffing) where an array of chemosensory hairs is waved through the water to create a flow-no flow pattern based on a narrow range of speeds, diameters of and spacings between hairs. Changing the speed of movement and spacing of hairs at this scale to manipulate flow represents a complicated fluid dynamics problem. In this study, we use numerical simulation of the advection and diffusion of a chemical gradient to reveal how morphological differences of the hair arrays affect odour capture. Specifically, we simulate odour capture by a marine crab (Callinectes sapidus) and a terrestrial crab (Coenobita rugosus) in both air and water to compare performance. We find that the antennule morphologies of each species are adaptions to capturing odours in their native habitats. Sniffing is an important part of odour capture for marine crabs in water where the diffusivity of odorant molecules is low and flow through the array is necessary. On the other hand, flow within the hair array diminishes odour-capture performance in air where diffusivities are high. This study highlights some of the adaptations necessary to transition from water to air.

Small-x resummation from HELL

Small-x logarithmic enhancements arising from high-energy gluon emissions affect both the evolution of collinearly-factorized parton densities and partonic coefficient functions. With the higher collider energy reached by the LHC, the prospect of a future high-energy collider, and the recent deep-inelastic scattering (DIS) results at small-x from HERA, providing phenomenological tools for performing small-x resummation has become of great relevance. In this paper we discuss a framework to perform small-x resummation for both parton evolution and partonic coefficient functions and we describe its implementation in a computer code named High-Energy Large Logarithms (HELL). We present resummed and matched results for the DGLAP splitting functions and, as a proof of principle, for the massless structure functions in DIS. Furthermore, we discuss the uncertainty from subleading terms on our results.

Transverse pion structure beyond leading twist in constituent models

The understanding of the pion structure as described in terms of transverse-momentum-dependent parton distribution functions (TMDs) is of importance for the interpretation of currently ongoing Drell-Yan experiments with pion beams. In this work we discuss the description of pion TMDs beyond leading twist in a pion model formulated in the light-front constituent framework. For comparison, we also review and derive new results for pion TMDs in the bag and spectator model.