Towards interpretable speech biomarkers: exploring MFCCs

While speech biomarkers of disease have attracted increased interest in recent years, a challenge is that features derived from signal processing or machine learning approaches may lack clinical interpretability. As an example, Mel frequency cepstral coefficients (MFCCs) have been identified in several studies as a useful marker of disease, but are regarded as uninterpretable. Here we explore correlations between MFCC coefficients and more interpretable speech biomarkers. In particular we quantify the MFCC2 endpoint, which can be interpreted as a weighted ratio of low- to high-frequency energy, a concept which has been previously linked to disease-induced voice changes. By exploring MFCC2 in several datasets, we show how its sensitivity to disease can be increased by adjusting computation parameters.

Distributional data analysis via quantile functions and its application to modeling digital biomarkers of gait in Alzheimer's Disease

With the advent of continuous health monitoring with wearable devices, users now generate their unique streams of continuous data such as minute-level step counts or heartbeats. Summarizing these streams via scalar summaries often ignores the distributional nature of wearable data and almost unavoidably leads to the loss of critical information. We propose to capture the distributional nature of wearable data via user-specific quantile functions (QF) and use these QFs as predictors in scalar-on-quantile-function-regression (SOQFR). As an alternative approach, we also propose to represent QFs via user-specific L-moments, robust rank-based analogs of traditional moments, and use L-moments as predictors in SOQFR (SOQFR-L). These two approaches provide two mutually consistent interpretations: in terms of quantile levels by SOQFR and in terms of L-moments by SOQFR-L. We also demonstrate how to deal with multi-modal distributional data via Joint and Individual Variation Explained using L-moments. The proposed methods are illustrated in a study of association of digital gait biomarkers with cognitive function in Alzheimers disease. Our analysis shows that the proposed methods demonstrate higher predictive performance and attain much stronger associations with clinical cognitive scales compared to simple distributional summaries.

Using a smartwatch and smartphone to assess early Parkinson's disease in the WATCH-PD study

Digital health technologies can provide continuous monitoring and objective, real-world measures of Parkinson's disease (PD), but have primarily been evaluated in small, single-site studies. In this 12-month, multicenter observational study, we evaluated whether a smartwatch and smartphone application could measure features of early PD. 82 individuals with early, untreated PD and 50 age-matched controls wore research-grade sensors, a smartwatch, and a smartphone while performing standardized assessments in the clinic. At home, participants wore the smartwatch for seven days after each clinic visit and completed motor, speech and cognitive tasks on the smartphone every other week. Features derived from the devices, particularly arm swing, the proportion of time with tremor, and finger tapping, differed significantly between individuals with early PD and age-matched controls and had variable correlation with traditional assessments. Longitudinal assessments will inform the value of these digital measures for use in future clinical trials.

Scalar on time-by-distribution regression and its application for modelling associations between daily-living physical activity and cognitive functions in Alzheimer's Disease

Wearable data is a rich source of information that can provide a deeper understanding of links between human behaviors and human health. Existing modelling approaches use wearable data summarized at subject level via scalar summaries in regression, temporal (time-of-day) curves in functional data analysis (FDA), and distributions in distributional data analysis (DDA). We propose to capture temporally local distributional information in wearable data using subject-specific time-by-distribution (TD) data objects. Specifically, we develop scalar on time-by-distribution regression (SOTDR) to model associations between scalar response of interest such as health outcomes or disease status and TD predictors. Additionally, we show that TD data objects can be parsimoniously represented via a collection of time-varying L-moments that capture distributional changes over the time-of-day. The proposed method is applied to the accelerometry study of mild Alzheimer’s disease (AD). We found that mild AD is significantly associated with reduced upper quantile levels of physical activity, particularly during morning hours. In-sample cross validation demonstrated that TD predictors attain much stronger associations with clinical cognitive scales of attention, verbal memory, and executive function when compared to predictors summarized via scalar total activity counts, temporal functional curves, and quantile functions. Taken together, the present results suggest that SOTDR analysis provides novel insights into cognitive function and AD.

Voice Biomarkers of Recovery from Acute Respiratory Illness

Voice analysis is an emerging technology which has the potential to provide low-cost, at-home monitoring of symptoms associated with a variety of health conditions. While voice has received significant attention for monitoring neurological disease, few studies have focused on voice changes related to flu-like symptoms. Herein, we investigate the relationship between changes in acoustic features of voice and self-reported symptoms during recovery from a flu-like illness in a cohort of 29 subjects. Acoustic features were automatically extracted from sick and well visit data collected in the laboratory setting, and feature down-selection was used to identify those that change significantly between visits. The selected acoustic features were extracted from at-home data and used to construct a combined distance metric that correlated with self-reported symptoms (0.63 rank correlation). Changes in self-reported symptoms corresponding to 10% of the ordinal scale used in the study were detected with an area under the curve of 0.72. The results show that acoustic features derived from voice recordings may provide an objective measure for diagnosing and monitoring symptoms of respiratory illnesses.

The Effects of a Novel Non-catechol Dopamine Partial Agonist on Working Memory in the Aged Rhesus Monkey

Aged-related declines in cognition, especially working memory and executive function, begin in middle-age and these abilities are known to be mediated by the prefrontal cortex (PFC) and more specifically the dopamine (DA) system within the PFC. In both humans and monkeys, there is significant evidence that the PFC is the first cortical region to change with age and the PFC appears to be particularly vulnerable to age-related loss of dopamine (DA). Therefore, the DA system is a strong candidate for therapeutic intervention to slow or reverse age related declines in cognition. In the present study, we administered a novel selective, potent, non-catechol DA D1 R agonist PF-6294 (Pfizer, Inc.) to aged female rhesus monkeys and assessed their performance on two benchmark tasks of working memory - the Delayed Non-match to Sample Task (DNMS) and Delayed Recognition Span Task (DRST). The DNMS task was administered first with the standard 10 s delay and then with 5 min delays, with and without distractors. The DRST was administered each day with four trials with unique sequences and one trial of a repeated sequence to assess evidence learning and retention. Overall, there was no significant effect of drug on performance on any aspect of the DNMS task. In contrast, we demonstrated that a middle range dose of PF-6294 significantly increased memory span on the DRST on the first and last days of testing and by the last day of testing the increased memory span was driven by the performance on the repeated trials.

Safety, pharmacokinetics and quantitative EEG modulation of TAK-071, a novel muscarinic M1 receptor positive allosteric modulator, in healthy subjects

Aims

TAK‐071 is a muscarinic M₁ receptor positive allosteric modulator designed to have low cooperativity with acetylcholine. This was a first‐in‐human study to evaluate the safety, pharmacokinetics, and pharmacodynamics of TAK‐071.

Methods

TAK‐071 was administered as single and multiple doses in a randomized, double‐blind, placebo‐controlled, parallel‐group design in healthy volunteers alone and in combination with donepezil. Laboratory, electrocardiogram (ECG) and electroencephalogram (EEG) evaluations were performed. Cerebrospinal fluid and blood samples were taken to evaluate the pharmacokinetics (PK), relative bioavailability and food effect.

Results

TAK‐071 was safe and well tolerated, and no deaths or serious adverse events occurred. TAK‐071 demonstrated a long mean (% coefficient of variation) half‐life of 46.3 (25.2%) to 60.5 (51.5%) hours and excellent brain penetration following oral dosing. Coadministration with donepezil had no impact on the PK of either drug. There was no food effect on systemic exposure. Quantitative EEG analysis revealed that TAK‐071 40‐80 mg increased power in the 7‐9 Hz range in the posterior electrode group with eyes open and 120‐160 mg doses increased power in the 16‐18 Hz range and reduced power in the 2‐4 Hz range in central‐posterior areas with eyes open and eyes closed. Functional connectivity was significantly reduced after TAK‐071 at high doses and was enhanced with coadministration of donepezil under the eyes‐closed condition.

Conclusions

PK and safety profiles of TAK‐071 were favorable, including those exceeding expected pharmacologically active doses based on preclinical data. When administered without donepezil TAK‐071 was largely free of cholinergic adverse effects. Further clinical evaluation of TAK‐071 is warranted.

Continuous gait monitoring discriminates community-dwelling mild Alzheimer's disease from cognitively normal controls

INTRODUCTION

Few studies have explored whether gait measured continuously within a community setting can identify individuals with Alzheimer's disease (AD). This study tests the feasibility of this method to identify individuals at the earliest stage of AD.

METHODS

Mild AD (n = 38) and cognitively normal control (CNC; n = 48) participants from the University of Kansas Alzheimer's Disease Center Registry wore a GT3x+ accelerometer continuously for 7 days to assess gait. Penalized logistic regression with repeated five-fold cross-validation followed by adjusted logistic regression was used to identify gait metrics with the highest predictive performance in discriminating mild AD from CNC.

RESULTS

Variability in step velocity and cadence had the highest predictive utility in identifying individuals with mild AD. Metrics were also associated with cognitive domains impacted in early AD.

DISCUSSION

Continuous gait monitoring may be a scalable method to identify individuals at-risk for developing dementia within large, population-based studies.

Characterization of PF-6142, a Novel, Non-Catecholamine Dopamine Receptor D1 Agonist, in Murine and Nonhuman Primate Models of Dopaminergic Activation

Selective activation of dopamine D1 receptors remains a promising pro-cognitive therapeutic strategy awaiting robust clinical investigation. PF-6142 is a key example from a recently disclosed novel series of non-catechol agonists and partial agonists of the dopamine D1/5 receptors (D1R) that exhibit pharmacokinetic (PK) properties suitable for oral delivery. Given their reported potential for functionally biased signaling compared to known catechol-based selective agonists, and the promising rodent PK profile of PF-6142, we utilized relevant in vivo assays in male rodents and male and female non-human primates (NHP) to evaluate the pharmacology of this new series. Studies in rodents showed that PF-6142 increased locomotor activity and prefrontal cortex acetylcholine release, increased time spent in wakefulness, and desynchronized the EEG, like known D1R agonists. D1R selectivity of PF-6142 was supported by lack of effect in D1R knock-out mice and blocked response in the presence of the D1R antagonist SCH-23390. Further, PF-6142 improved performance in rodent models of NMDA receptor antagonist-induced cognitive dysfunction, such as MK-801-disrupted paired-pulse facilitation, and ketamine-disrupted working memory performance in the radial arm maze. Similarly, PF-6142 reversed ketamine-induced deficits in NHP performing the spatial delayed recognition task. Of importance, PF-6142 did not alter the efficacy of risperidone in assays predictive of antipsychotic-like effect in rodents including pre-pulse inhibition and conditioned avoidance responding. These data support the continued development of non-catechol based D1R agonists for the treatment of cognitive impairment associated with brain disorders including schizophrenia.

Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device

Objective assessment of Parkinson's disease symptoms during daily life can help improve disease management and accelerate the development of new therapies. However, many current approaches require the use of multiple devices, or performance of prescribed motor activities, which makes them ill-suited for free-living conditions. Furthermore, there is a lack of open methods that have demonstrated both criterion and discriminative validity for continuous objective assessment of motor symptoms in this population. Hence, there is a need for systems that can reduce patient burden by using a minimal sensor setup while continuously capturing clinically meaningful measures of motor symptom severity under free-living conditions. We propose a method that sequentially processes epochs of raw sensor data from a single wrist-worn accelerometer by using heuristic and machine learning models in a hierarchical framework to provide continuous monitoring of tremor and bradykinesia. Results show that sensor derived continuous measures of resting tremor and bradykinesia achieve good to strong agreement with clinical assessment of symptom severity and are able to discriminate between treatment-related changes in motor states.

Continuous Monitoring Using a Wearable Device Detects Activity-Induced Heart Rate Changes After Administration of Amphetamine

Wearable digital devices offer potential advantages over traditional methods for the collection of health-related information, including continuous collection of dense data while study subjects are ambulatory or in remote settings. We assessed the utility of collecting continuous actigraphy and cardiac monitoring by deploying two US Food and Drug Administration (FDA) 510(k)-cleared devices in a phase I clinical trial of a novel compound, which included the use of an amphetamine challenge. The Phillips Actiwatch Spectrum Pro (Actiwatch) was used to assess mobility and sleep. The Preventice BodyGuardian (BodyGuardian) was used for monitoring heart rate (HR) and respiratory rate (RR), via single-lead electrocardiogram (ECG) recordings, together with physical activity. We measured data collection rates, compared device readouts with conventional measures, and monitored changes in HR measures during the amphetamine challenge. Completeness of data collection was good for the Actiwatch (96%) and lower for the BodyGuardian (80%). A good correlation was observed between device and in-clinic measures for HR (r = 0.99; P < 0.001), but was poor for RR (r = 0.39; P = 0.004). Manual reviews of selected ECG strips corresponding to HR measures below, within, and above the normal range were consistent with BodyGuardian measurements. The BodyGuardian device detected clear HR responses after amphetamine administration while subjects were physically active, whereas conventional measures collected at predefined timepoints while subjects were resting and supine did not. Wearable digital technology shows promise for monitoring human subjects for physiologic changes and pharmacologic responses, although fit-for-purpose evaluation and validation continues to be important prior to the wider deployment of these devices.

Pharmacological evaluation of clinically relevant concentrations of (2R,6R)-hydroxynorketamine

Ketamine is a rapid-onset antidepressant whose efficacy long outlasts its pharmacokinetics. Multiple studies suggest ketamine's antidepressant effects require increased α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR)-dependent currents, which have recently been exclusively attributed to its N-methyl-d-aspartate receptor-inactive metabolite (2R,6R)-hydroxynorketamine ((2R,6R)-HNK). To investigate this AMPAR-activation claim further, we estimated and evaluated preclinically and clinically relevant unbound brain HNK concentrations (C_b,u). (2S,6S)-HNK and (2R,6R)-HNK were novelly synthesized, and their neuropharmacokinetic profiles were determined to project relevant C_b,u. Using concentrations (0.01-10 μM) bracketing the pertinent cross-species C_b,u, both compounds' AMPAR modulation was assessed in vitro by electrophysiological recordings and GluA1 surface expression. Neither (2S,6S)-HNK nor (2R,6R)-HNK bound orthosterically to or directly functionally activated AMPARs. (2R,6R)-HNK failed to evoke AMPAR-centric changes in any electrophysiological endpoint from adult rodent hippocampal slices. Conversely, time- and concentration-dependent increases in GluA1 expression occurred only with (2R,6R)-HNK (≥0.1 μM at ≥90 min). The (2R,6R)-HNK concentrations that increased GluA1 expression are consistent with its maximal C_b,u (0.92-4.84 μM) at reportedly efficacious doses of ketamine or (2R,6R)-HNK in mouse depression models, but ≥3-fold above its projected maximal human C_b,u (≤37.8 ± 14.3 nM) following ketamine's clinically antidepressant infusion. These findings provide insight into the observed AMPAR-affecting (2R,6R)-HNK concentrations versus its exposures attained clinically at an antidepressant ketamine dose. To optimize any clinical study with (2R,6R)-HNK to fully assess its translational pharmacology, future preclinical work should test (2R,6R)-HNK concentrations and/or C_b,u of 0.01-0.1 μM to parallel its projected human C_b,u at a clinically antidepressant ketamine dose.

Impaired β-arrestin recruitment and reduced desensitization by non-catechol agonists of the D1 dopamine receptor

Selective activation of dopamine D1 receptors (D1Rs) has been pursued for 40 years as a therapeutic strategy for neurologic and psychiatric diseases due to the fundamental role of D1Rs in motor function, reward processing, and cognition. All known D1R-selective agonists are catechols, which are rapidly metabolized and desensitize the D1R after prolonged exposure, reducing agonist response. As such, drug-like selective D1R agonists have remained elusive. Here we report a novel series of selective, potent non-catechol D1R agonists with promising in vivo pharmacokinetic properties. These ligands stimulate adenylyl cyclase signaling and are efficacious in a rodent model of Parkinson's disease after oral administration. They exhibit distinct binding to the D1R orthosteric site and a novel functional profile including minimal receptor desensitization, reduced recruitment of β-arrestin, and sustained in vivo efficacy. These results reveal a novel class of D1 agonists with favorable drug-like properties, and define the molecular basis for catechol-specific recruitment of β-arrestin to D1Rs.

Passive immunotherapy targeting amyloid-β reduces cerebral amyloid angiopathy and improves vascular reactivity

Prominent cerebral amyloid angiopathy is often observed in the brains of elderly individuals and is almost universally found in patients with Alzheimer’s disease. Cerebral amyloid angiopathy is characterized by accumulation of the shorter amyloid-β isoform(s) (predominantly amyloid-β40) in the walls of leptomeningeal and cortical arterioles and is likely a contributory factor to vascular dysfunction leading to stroke and dementia in the elderly. We used transgenic mice with prominent cerebral amyloid angiopathy to investigate the ability of ponezumab, an anti-amyloid-β40 selective antibody, to attenuate amyloid-β accrual in cerebral vessels and to acutely restore vascular reactivity. Chronic administration of ponezumab to transgenic mice led to a significant reduction in amyloid and amyloid-β accumulation both in leptomeningeal and brain vessels when measured by intravital multiphoton imaging and immunohistochemistry. By enriching for cerebral vascular elements, we also measured a significant reduction in the levels of soluble amyloid-β biochemically. We hypothesized that the reduction in vascular amyloid-β40 after ponezumab administration may reflect the ability of ponezumab to mobilize an interstitial fluid pool of amyloid-β40 in brain. Acutely, ponezumab triggered a significant and transient increase in interstitial fluid amyloid-β40 levels in old plaque-bearing transgenic mice but not in young animals. We also measured a beneficial effect on vascular reactivity following acute administration of ponezumab, even in vessels where there was a severe cerebral amyloid angiopathy burden. Taken together, the beneficial effects ponezumab administration has on reducing the rate of cerebral amyloid angiopathy deposition and restoring cerebral vascular health favours a mechanism that involves rapid removal and/or neutralization of amyloid-β species that may otherwise be detrimental to normal vessel function.

STEP levels are unchanged in pre-frontal cortex and associative striatum in post-mortem human brain samples from subjects with schizophrenia, bipolar disorder and major depressive disorder

Increased protein levels of striatal-enriched tyrosine phosphatase (STEP) have recently been reported in postmortem schizophrenic cortex. The present study sought to replicate this finding in a separate cohort of postmortem samples and to extend observations to striatum, including subjects with bipolar disorder and major depressive disorder in the analysis. No statistically significant changes between disease and control subjects were found in STEP mRNA or protein levels in dorsolateral prefrontal cortex or associative striatum. Although samples were matched for several covariates, postmortem interval correlated negatively with STEP protein levels, emphasizing the importance of including these analyses in postmortem studies.

Evaluation of TrkB and BDNF transcripts in prefrontal cortex, hippocampus, and striatum from subjects with schizophrenia, bipolar disorder, and major depressive disorder

Brain-derived neurotrophic factor (BDNF) signaling is integral to a range of neural functions, including synaptic plasticity and exhibits activity-dependent regulation of expression. As altered BDNF signaling has been implicated in multiple psychiatric diseases, here we report a quantitative reverse transcription polymerase chain reaction (RT-PCR) analysis of mRNAs encoding TrkB, total BDNF, and the four most abundant BDNF transcripts (I, IIc, IV, and VI) in postmortem tissue from matched tetrads of subjects with schizophrenia, bipolar disorder, or major depressive disorder (MDD) and healthy comparison subjects. In all three regions examined, dorsolateral prefrontal cortex (DLPFC), associative striatum and hippocampus, total BDNF mRNA levels did not differ in any disease state. In DLPFC, BDNF IIc was significantly lower in schizophrenia relative to healthy comparison subjects. In hippocampus, BDNF I, IIc, and VI were lower in subjects with both schizophrenia and bipolar disorder relative to comparison subjects. In striatum, TrkB mRNA was lower in bipolar disorder and MDD, while BDNF IIc was elevated in MDD, relative to comparison subjects. These data highlight potential alterations in BDNF signaling in the corticohippocampal circuit in schizophrenia, and within the striatum in mood disorders. Novel therapies aimed at improving BDNF-TrkB signaling may therefore have potential to impact on a range of psychiatric disorders.

Axonal Charcot-Marie-Tooth disease patient-derived motor neurons demonstrate disease-specific phenotypes including abnormal electrophysiological properties

OBJECTIVE

Charcot-Marie-Tooth (CMT) disease is a group of inherited peripheral neuropathies associated with mutations or copy number variations in over 70 genes encoding proteins with fundamental roles in the development and function of Schwann cells and peripheral axons. Here, we used iPSC-derived cells to identify common pathophysiological mechanisms in axonal CMT.

METHODS

iPSC lines from patients with two distinct forms of axonal CMT (CMT2A and CMT2E) were differentiated into spinal cord motor neurons and used to study axonal structure and function and electrophysiological properties in vitro.

RESULTS

iPSC-derived motor neurons exhibited gene and protein expression, ultrastructural and electrophysiological features of mature primary spinal cord motor neurons. Cytoskeletal abnormalities were found in neurons from a CMT2E (NEFL) patient and corroborated by a mouse model of the same NEFL point mutation. Abnormalities in mitochondrial trafficking were found in neurons derived from this patient, but were only mildly present in neurons from a CMT2A (MFN2) patient. Novel electrophysiological abnormalities, including reduced action potential threshold and abnormal channel current properties were observed in motor neurons derived from both of these patients.

INTERPRETATION

Human iPSC-derived motor neurons from axonal CMT patients replicated key pathophysiological features observed in other models of MFN2 and NEFL mutations, including abnormal cytoskeletal and mitochondrial dynamics. Electrophysiological abnormalities found in axonal CMT iPSC-derived human motor neurons suggest that these cells are hyperexcitable and have altered sodium and calcium channel kinetics. These findings may provide a new therapeutic target for this group of heterogeneous inherited neuropathies.

Computationally efficient permutation-based confidence interval estimation for tail-area FDR

Challenges of satisfying parametric assumptions in genomic settings with thousands or millions of tests have led investigators to combine powerful False Discovery Rate (FDR) approaches with computationally expensive but exact permutation testing. We describe a computationally efficient permutation-based approach that includes a tractable estimator of the proportion of true null hypotheses, the variance of the log of tail-area FDR, and a confidence interval (CI) estimator, which accounts for the number of permutations conducted and dependencies between tests. The CI estimator applies a binomial distribution and an overdispersion parameter to counts of positive tests. The approach is general with regards to the distribution of the test statistic, it performs favorably in comparison to other approaches, and reliable FDR estimates are demonstrated with as few as 10 permutations. An application of this approach to relate sleep patterns to gene expression patterns in mouse hypothalamus yielded a set of 11 transcripts associated with 24 h REM sleep [FDR = 0.15 (0.08, 0.26)]. Two of the corresponding genes, Sfrp1 and Sfrp4, are involved in wnt signaling and several others, Irf7, Ifit1, Iigp2, and Ifih1, have links to interferon signaling. These genes would have been overlooked had a typical a priori FDR threshold such as 0.05 or 0.1 been applied. The CI provides the flexibility for choosing a significance threshold based on tolerance for false discoveries and precision of the FDR estimate. That is, it frees the investigator to use a more data-driven approach to define significance, such as the minimum estimated FDR, an option that is especially useful for weak effects, often observed in studies of complex diseases.

A cellular model for sporadic ALS using patient-derived induced pluripotent stem cells

Development of therapeutics for genetically complex neurodegenerative diseases such as sporadic amyotrophic lateral sclerosis (ALS) has largely been hampered by lack of relevant disease models. Reprogramming of sporadic ALS patients' fibroblasts into induced pluripotent stem cells (iPSC) and differentiation into affected neurons that show a disease phenotype could provide a cellular model for disease mechanism studies and drug discovery. Here we report the reprogramming to pluripotency of fibroblasts from a large cohort of healthy controls and ALS patients and their differentiation into motor neurons. We demonstrate that motor neurons derived from three sALS patients show de novo TDP-43 aggregation and that the aggregates recapitulate pathology in postmortem tissue from one of the same patients from which the iPSC were derived. We configured a high-content chemical screen using the TDP-43 aggregate endpoint both in lower motor neurons and upper motor neuron like cells and identified FDA-approved small molecule modulators including Digoxin demonstrating the feasibility of patient-derived iPSC-based disease modeling for drug screening.

In-silico patterning of vascular mesenchymal cells in three dimensions

Cells organize in complex three-dimensional patterns by interacting with proteins along with the surrounding extracellular matrix. This organization provides the mechanical and chemical cues that ultimately influence a cell's differentiation and function. Here, we computationally investigate the pattern formation process of vascular mesenchymal cells arising from their interaction with Bone Morphogenic Protein-2 (BMP-2) and its inhibitor, Matrix Gla Protein (MGP). Using a first-principles approach, we derive a reaction-diffusion model based on the biochemical interactions of BMP-2, MGP and cells. Simulations of the model exhibit a wide variety of three-dimensional patterns not observed in a two-dimensional analysis. We demonstrate the emergence of three types of patterns: spheres, tubes, and sheets, and show that the patterns can be tuned by modifying parameters in the model such as the degradation rates of proteins and chemotactic coefficient of cells. Our model may be useful for improved engineering of three-dimensional tissue structures as well as for understanding three dimensional microenvironments in developmental processes.

Effect of noise on solid-to-liquid transition in small granular systems under shear

The effect of noise on the solid-to-liquid transition of a dense granular assembly under planar shear is studied numerically using soft-particle molecular dynamics simulations in two dimensions. We focus on small systems in a thin planar Couette cell, examining the bistable region while increasing shear, with varying amounts of random noise, and determine statistics of the shear required for fluidization. In the absence of noise, the threshold value of the shear stress depends on the preparation of the system and has a broad distribution. However, adding force fluctuations both lowers the mean threshold value of the shear stress and decreases its variability. This behavior is interpreted as thermoactivated escape through a fluctuating barrier.

Swarming and swirling in self-propelled polar granular rods

Using experiments with anisotropic vibrated rods and quasi-2D numerical simulations, we show that shape plays an important role in the collective dynamics of self-propelled (SP) particles. We demonstrate that SP rods exhibit local ordering, aggregation at the side walls, and clustering absent in round SP particles. Furthermore, we find that at sufficiently strong excitation SP rods engage in a persistent swirling motion in which the velocity is strongly correlated with particle orientation.

Swirling motion in a system of vibrated elongated particles

Large-scale collective motion emerging in a monolayer of vertically vibrated elongated particles is studied. The motion is characterized by recurring swirls, with the characteristic scale exceeding several times the size of an individual particle. Our experiments identified a small horizontal component of the oscillatory acceleration of the vibrating plate in combination with orientation-dependent bottom friction (with respect to horizontal acceleration) as a source for the swirl formation. We developed a continuum model operating with the velocity field and local alignment tensor, which is in qualitative agreement with the experiment.

Transient dynamics of genetic regulatory networks

We present an approximation scheme for deriving reaction rate equations of genetic regulatory networks. This scheme predicts the timescales of transient dynamics of such networks more accurately than does standard quasi-steady state analysis by introducing prefactors to the ODEs that govern the dynamics of the protein concentrations. These prefactors render the ODE systems slower than their quasi-steady state approximation counterparts. We introduce the method by examining a positive feedback gene regulatory network, and show how the transient dynamics of this network are more accurately modeled when the prefactor is included. Next, we examine the repressilator, a genetic oscillator, and show that the period, amplitude, and bifurcation diagram defining the onset of the oscillations are better estimated by the prefactor method. Finally, we examine the consequences of the method to the dynamics of reduced models of the phage lambda switch, and show that the switching times between the two states is slowed by the presence of the prefactor that arises from protein multimerization and DNA binding.

Thermal collapse of a granular gas under gravity

Free cooling of a gas of inelastically colliding hard spheres represents a central paradigm of kinetic theory of granular gases. At zero gravity the temperature of a freely cooling homogeneous granular gas follows a power law in time. How does gravity, which brings inhomogeneity, affect the cooling? We combine molecular dynamics simulations, a numerical solution of hydrodynamic equations and an analytic theory to show that a granular gas cooling under gravity undergoes thermal collapse: it cools down to zero temperature and condenses on the bottom of the container in a finite time.

Stochastically driven genetic circuits

Transcriptional regulation in small genetic circuits exhibits large stochastic fluctuations. Recent experiments have shown that a significant fraction of these fluctuations is caused by extrinsic factors. In this paper we review several theoretical and computational approaches to modeling of small genetic circuits driven by extrinsic stochastic processes. We propose a simplified approach to this problem, which can be used in the case when extrinsic fluctuations dominate the stochastic dynamics of the circuit (as appears to be the case in eukaryots). This approach is applied to a model of a single nonregulated gene that is driven by a certain gating process that affects the rate of transcription, and to a simplified version of the galactose utilization circuit in yeast.

Origins of extrinsic variability in eukaryotic gene expression

Variable gene expression within a clonal population of cells has been implicated in a number of important processes including mutation and evolution, determination of cell fates and the development of genetic disease. Recent studies have demonstrated that a significant component of expression variability arises from extrinsic factors thought to influence multiple genes simultaneously, yet the biological origins of this extrinsic variability have received little attention. Here we combine computational modelling with fluorescence data generated from multiple promoter-gene inserts in Saccharomyces cerevisiae to identify two major sources of extrinsic variability. One unavoidable source arising from the coupling of gene expression with population dynamics leads to a ubiquitous lower limit for expression variability. A second source, which is modelled as originating from a common upstream transcription factor, exemplifies how regulatory networks can convert noise in upstream regulator expression into extrinsic noise at the output of a target gene. Our results highlight the importance of the interplay of gene regulatory networks with population heterogeneity for understanding the origins of cellular diversity.

Monitoring dynamics of single-cell gene expression over multiple cell cycles

Recent progress in reconstructing gene regulatory networks has established a framework for a quantitative description of the dynamics of many important cellular processes. Such a description will require novel experimental techniques that enable the generation of time-series data for the governing regulatory proteins in a large number of individual living cells. Here, we utilize microfabrication to construct a Tesla microchemostat that permits single-cell fluorescence imaging of gene expression over many cellular generations. The device is used to capture and constrain asymmetrically dividing or motile cells within a trapping region and to deliver nutrients and regulate the cellular population within this region. We illustrate the operation of the microchemostat with Saccharomyces cerevisiae and explore the evolution of single-cell gene expression and cycle time as a function of generation. Our findings highlight the importance of novel assays for quantifying the dynamics of gene expression and cellular growth, and establish a methodology for exploring the effects of gene expression on long-term processes such as cellular aging.

Delay-induced stochastic oscillations in gene regulation

The small number of reactant molecules involved in gene regulation can lead to significant fluctuations in intracellular mRNA and protein concentrations, and there have been numerous recent studies devoted to the consequences of such noise at the regulatory level. Theoretical and computational work on stochastic gene expression has tended to focus on instantaneous transcriptional and translational events, whereas the role of realistic delay times in these stochastic processes has received little attention. Here, we explore the combined effects of time delay and intrinsic noise on gene regulation. Beginning with a set of biochemical reactions, some of which are delayed, we deduce a truncated master equation for the reactive system and derive an analytical expression for the correlation function and power spectrum. We develop a generalized Gillespie algorithm that accounts for the non-Markovian properties of random biochemical events with delay and compare our analytical findings with simulations. We show how time delay in gene expression can cause a system to be oscillatory even when its deterministic counterpart exhibits no oscillations. We demonstrate how such delay-induced instabilities can compromise the ability of a negative feedback loop to reduce the deleterious effects of noise. Given the prevalence of negative feedback in gene regulation, our findings may lead to new insights related to expression variability at the whole-genome scale.

Dynamics of a bouncing dimer

We investigate the dynamics of a dimer bouncing on a vertically oscillated plate. The dimer, composed of two spheres rigidly connected by a light rod, exhibits several modes depending on initial and driving conditions. The first excited mode has a novel horizontal drift in which one end of the dimer stays on the plate during most of the cycle, while the other end bounces in phase with the plate. The speed and direction of the drift depend on the aspect ratio of the dimer. We employ event-driven simulations based on a detailed treatment of frictional interactions between the dimer and the plate in order to elucidate the nature of the transport mechanism in the drift mode.

Anisotropy-driven dynamics in vibrated granular rods

The dynamics of a set of rods bouncing on a vertically vibrated plate is investigated using experiments, simulations, and theoretical analysis. The experiments and simulations are performed within an annulus to impose periodic boundary conditions. Rods tilted with respect to the vertical are observed to spontaneously develop a horizontal velocity depending on the acceleration of the plate. For high plate acceleration, the rods are observed to always move in the direction of tilt. However, the rods are also observed to move opposite to direction of tilt for a small range of plate acceleration and rod tilt. A phase diagram of the observed motion is presented as a function of plate acceleration and the tilt of the rods which is varied by changing the number of rods inside the annulus. Next we introduce a molecular dynamics method to simulate the dynamics of the rods using the dimensions and dissipation parameters from the experiments. We reproduce the observed horizontal rod speeds as a function of rod tilt and plate acceleration in the simulations. By decreasing the friction between the rods and the base plate to zero in the simulation, we identify the friction during the collision as the crucial ingredient for occurrence of the horizontal motion. Guided by the data from the experiments and the simulations, we construct a mechanical model for the dynamics of the rods in the limit of thin rods. The starting point of the analysis is the collision of a single rod with an oscillating plate. Three friction regimes are identified: slide, slip-stick, and slip reversal. A formula is derived for the observed horizontal velocity as a function of tilt angle. Good agreement for the horizontal velocity as a function of rod tilt and plate acceleration is found between experiments, simulations and theory.

Cellular growth and division in the Gillespie algorithm

Recent experimental studies elucidating the importance of noise in gene regulation have ignited widespread interest in Gillespie's stochastic simulation technique for biochemical networks. We formulate modifications to the Gillespie algorithm which are necessary to correctly simulate chemical reactions with time-dependent reaction rates. We concentrate on time dependence of kinetic rates arising from the periodic process of growth and division of the cellular volume, and demonstrate that a careful re-derivation of the Gillespie algorithm is important when all stochastically simulated reactions have rates slower or comparable to the cellular growth rate. For an unregulated single-gene system, we illustrate our findings using recently proposed hybrid simulation techniques, and systematically compare our algorithm with analytic results obtained from the chemical master equation.

Stick-slip dynamics of a granular layer under shear

Stick-slip regime of shear granular flows is studied theoretically and numerically. Numerical experiments are carried out for a thin Couette cell using soft-particle molecular dynamics code in two dimensions. We apply order parameter theory of partially fluidized granular flows and find a good agreement with simulations and experiments by Nasuno et al.

Partially fluidized shear granular flows: continuum theory and molecular dynamics simulations

The continuum theory of partially fluidized shear granular flows is tested and calibrated using two-dimensional soft particle molecular dynamics simulations. The theory is based on the relaxational dynamics of the order parameter that describes the transition between static and flowing regimes of granular material. We define the order parameter as a fraction of static contacts among all contacts between particles. We also propose and verify by direct simulations the constitutive relation based on the splitting of the shear stress tensor into a"fluid part" proportional to the strain rate tensor, and a remaining "solid part." The ratio of these two parts is a function of the order parameter. The rheology of the fluid component agrees well with the kinetic theory of granular fluids even in the dense regime. Based on the hysteretic bifurcation diagram for a thin shear granular layer obtained in simulations, we construct the "free energy" for the order parameter. The theory calibrated using numerical experiments with the thin granular layer is applied to the surface-driven stationary two-dimensional granular flows in a thick granular layer under gravity.

Order parameter description of stationary partially fluidized shear granular flows

We carry out a detailed comparison of soft-particle molecular dynamics simulations with the theory of partially fluidized shear granular flows. We verify by direct simulations a constitutive relation based on the separation of the shear stress tensor into a fluid part proportional to the strain rate tensor, and a remaining solid part. The ratio of these two components is determined by the order parameter. Based on results of the simulations we construct the "free energy" function for the order parameter. We also present the simulations of the stationary deep 2D granular flows driven by an upper wall and compare it with the continuum theory.