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To continue closing the gap between the predictive modeling and its real-world application, we report a new data-to-prediction pipeline that advanced the state-of-the-art predictive performance of body mass index (BMI) classifications by integrating siloed claims databases via a common data model.This study adapted the ensemble-based methodology of the baseline prediction model and focused on removing the silos in the claims databases. We applied the Super Learner machine learning algorithm (SLA) to learn a combined dataset consisting of 50% data from the Optum Date of Death database and 50% data from the IBM MarketScan Commercial Claims and Encounters (CCAE), and omitted the commonly used one-hot-encoding step and used multi-categorical variables directly in the feature engineering process. These developments were then optimized via a standard cross-validation scheme and the performance was evaluated on a holdout test set.Sociodemographic and clinical characteristics were used with (denoted as SLAThis study demonstrates dramatic improvements in the prediction of BMI across classifications using integrated databases in a common data model for the generation of real-world evidence.

The unprecedented pandemic of coronavirus disease 2019 (COVID-19) demands effective treatment for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The infection of SARS-CoV-2 critically depends on diverse viral or host proteases, which mediate viral entry, viral protein maturation, as well as the pathogenesis of the viral infection. Endogenous and exogenous agents targeting for proteases have been proved to be effective toward a variety of viral infections ranging from HIV to influenza virus, suggesting protease inhibitors as a promising antiviral treatment for COVID-19. In this Review, we discuss how host and viral proteases participated in the pathogenesis of COVID-19 as well as the prospects and ongoing clinical trials of protease inhibitors as treatments.

Hedgehog (Hh) signaling patterns embryonic tissues and contributes to homeostasis in adults. InDrosophila, Hh transport and signaling are thought to occur along a specialized class of actin-rich filopodia, termed cytonemes. Here, we report that Interference hedgehog (Ihog) not only forms a Hh receptor complex with Patched to mediate intracellular signaling, but Ihog also engages intrans-homophilic binding leading to cytoneme stabilization in a manner independent of its role as the Hh receptor. Both functions of Ihog (trans-homophilic binding for cytoneme stabilization and Hh binding for ligand sensing) involve a region of the first fibronectin repeat of the extracellular domain. Thus, the Ihog-Ihog interaction and the Hh-Ihog interaction cannot occur simultaneously for a single Ihog molecule. By combining experimental data and mathematical modeling, we determined that Hh-Ihog heterophilic interaction dominates and Hh can disrupt and displace Ihog molecules involved intrans-homophilic binding. Consequently, we proposed that the weaker Ihog-Ihogtransinteraction promotes and stabilizes direct membrane contacts along cytonemes and that, as the cytoneme encounters secreted Hh ligands, the ligands trigger release of Ihog fromtransIhog-Ihog complex enabling transport or internalization of the Hh ligand-Ihog-Patched -receptor complex. Thus, the seemingly incompatible functions of Ihog in homophilic adhesion and ligand binding cooperate to assist Hh transport and reception along the cytonemes.One-sentence summaryApparently incompatible functions of Ihog in homophilic adhesion and ligand binding cooperate in Hh transport and reception.

In drug development, protocols define how clinical trials are conducted, and are therefore of paramount importance. They contain key patient-, investigator-, medication-, and study-related information, often elaborated in different sections in the protocol texts. Granular-level parsing on large quantity of existing protocols can accelerate clinical trial design and provide actionable insights into trial optimization. Here, we report our progresses in using deep learning NLP algorithms to enable automated protocol analytics. In particular, we combined a pre-trained BERT transformer model with joint-learning strategies to simultaneously identify clinically relevant entities (i.e. Named Entity Recognition) and extract the syntactic relations between these entities (i.e. Relation Extraction) from the eligibility criteria section in protocol texts. When comparing to standalone NER and RE models, our joint-learning strategy can effectively improve the performance of RE task while retaining similarly high NER performance, likely due to the synergy of optimizing toward both tasks’ objectives via shared parameters. The derived NLP model provides an end-to-end solution to convert unstructured protocol texts into structured data source, which will be embedded into a comprehensive clinical analytics workflow for downstream trial design missions such like patient population extraction, patient enrollment rate estimation, and protocol amendment prediction.

The Drosophila Hedgehog receptor functions to regulate the essential downstream pathway component, Smoothened, and to limit the range of signaling by sequestering Hedgehog protein signal within imaginal disc epithelium. Hedgehog receptor function requires both Patched and Ihog activity, the latter interchangeably encoded by interference hedgehog (ihog) or brother of ihog (boi). Here we show that Patched and Ihog activity are mutually required for receptor endocytosis and degradation, triggered by Hedgehog protein binding, and causing reduced levels of Ihog/Boi proteins in a stripe of cells at the anterior/posterior compartment boundary of the wing imaginal disc. This Ihog spatial discontinuity may contribute to classically defined cell segregation and lineage restriction at the anterior/posterior wing disc compartment boundary, as suggested by our observations that Ihog activity mediates aggregation of otherwise non-adherent cultured cells and that loss of Ihog activity disrupts wing disc cell segregation, even with downstream genetic rescue of Hedgehog signal response., The range of Hedgehog (Hh) signalling in the Drosophila imaginal disc is limited by Hh/receptor degradation at the anterior/posterior (A/P) compartment boundary. Here, the authors show that degradation is mediated by co-receptor proteins Ihog/Boi, whose cell adhesion functions also contribute to A/P cell segregation.

Oscillatory morphodynamics provides necessary mechanical cues for many multicellular processes. Owing to their collective nature, these processes require robustly coordinated dynamics of individual cells, which are often separated too distantly to communicate with each other through biomaterial transportation. Although it is known that the mechanical balance generally plays a significant role in the systems' morphologies, it remains elusive whether and how the mechanical components may contribute to the systems' collective morphodynamics. Here, we study the collective oscillations in the Drosophila amnioserosa tissue to elucidate the regulatory roles of the mechanical components. We identify that the tensile stress is the key activator that switches the collective oscillations on and off. This regulatory role is shown analytically using the Hopf bifurcation theory. We find that the physical properties of the tissue boundary are directly responsible for synchronizing the oscillatory intensity and polarity of all inner cells and for orchestrating the spatial oscillation patterns inthe tissue.

In the epithelium of Drosophila during tissue elongation, contractile forces in follicle cells can oscillate. These oscillations correlate with increasing tension in the epithelium from egg chamber growth. A mathematical model is proposed to explain the observed oscillations, together with a mechanism of active regulation of cellular contractile forces., During tissue elongation from stage 9 to stage 10 in Drosophila oogenesis, the egg chamber increases in length by ∼1.7-fold while increasing in volume by eightfold. During these stages, spontaneous oscillations in the contraction of cell basal surfaces develop in a subset of follicle cells. This patterned activity is required for elongation of the egg chamber; however, the mechanisms generating the spatiotemporal pattern have been unclear. Here we use a combination of quantitative modeling and experimental perturbation to show that mechanochemical interactions are sufficient to generate oscillations of myosin contractile activity in the observed spatiotemporal pattern. We propose that follicle cells in the epithelial layer contract against pressure in the expanding egg chamber. As tension in the epithelial layer increases, Rho kinase signaling activates myosin assembly and contraction. The activation process is cooperative, leading to a limit cycle in the myosin dynamics. Our model produces asynchronous oscillations in follicle cell area and myosin content, consistent with experimental observations. In addition, we test the prediction that removal of the basal lamina will increase the average oscillation period. The model demonstrates that in principle, mechanochemical interactions are sufficient to drive patterning and morphogenesis, independent of patterned gene expression.

The rotation of a bacterial flagellar motor (BFM) is driven by multiple stators tethered to the cell wall. Here, we extend a recently proposed power-stroke model to study the BFM dynamics under different biophysical conditions. Our model explains several key experimental observations and reveals their underlying mechanisms. 1), The observed independence of the speed at low load on the number of stators is explained by a force-dependent stepping mechanism that is independent of the strength of the stator tethering spring. Conversely, without force-dependent stepping, an unrealistically weak stator spring is required. 2), Our model with back-stepping naturally explains the observed absence of a barrier to backward rotation. Using the same set of parameters, it also explains BFM behaviors in the high-speed negative-torque regime. 3), From the measured temperature dependence of the maximum speed, our model shows that stator-stepping is a thermally activated process with an energy barrier. 4), The recently observed asymmetry in the torque-speed curve between counterclockwise- and clockwise-rotating BFMs can be quantitatively explained by the asymmetry in the stator-rotor interaction potentials, i.e., a quasilinear form for the counterclockwise motor and a quadratic form for the clockwise motor.

Summary Background Cytokinesis in bacteria is mediated by a cytokinetic ring, termed the Z ring, which forms a scaffold for recruitment of other cell-division proteins. The Z ring is composed of FtsZ filaments, but their organization in the Z ring is poorly understood. In Escherichia coli , the Min system contributes to the spatial regulation of cytokinesis by preventing the assembly of the Z ring away from midcell. The effector of the Min system, MinC, inhibits Z ring assembly by a mechanism that is not clear. Results Here, we report that MinC controls the scaffolding function of FtsZ by antagonizing the mechanical integrity of FtsZ structures. Specifically, MinC antagonizes the ability of FtsZ filaments to be in a solid-like gel state. MinC is a modular protein whose two domains (MinC C and MinC N ) synergize to inhibit FtsZ function. MinC C interacts directly with FtsZ polymers to target MinC to Z rings. MinC C also prevents lateral interactions between FtsZ filaments, an activity that seems to be unique among cytoskeletal proteins. Because MinC C is inhibitory in vivo, it suggests that lateral interactions between FtsZ filaments are important for the structural integrity of the Z ring. MinC N contributes to MinC activity by weakening the longitudinal bonds between FtsZ molecules in a filament leading to a loss of polymer rigidity and consequent polymer shortening. On the basis of our results, we develop the first computational model of the Z ring and study the effects of MinC. Conclusions Control over the scaffolding activity of FtsZ probably represents a universal regulatory mechanism of bacterial cytokinesis.

The life cycle of bacterial cells consists of repeated elongation, septum formation, and division. Before septum formation, a division ring called the Z-ring, which is made of a filamentous tubulin analog, FtsZ, is seen at the mid cell. Together with several other proteins, FtsZ is essential for cell division. Visualization of strains with GFP-labeled FtsZ shows that the Z-ring contracts before septum formation and pinches the cell into two equal halves. Thus, the Z-ring has been postulated to act as a force generator, although the magnitude of the contraction force is unknown. In this article, we develop a mathematical model to describe the process of growth and Z-ring contraction in rod-like bacteria. The elasticity and growth of the cell wall is incorporated in the model to predict the contraction speed, the cell shape, and the contraction force. With reasonable parameters, the model shows that a small force from the Z-ring (8 pN in Escherichia coli ) is sufficient to accomplish division.

We present here the analytical relation between the gain of eukaryotic gradient sensing network and the associated thermodynamic cost. By analyzing a general incoherent type-1 feed-forward loop, we derive the gain function (G) through the reaction network and explicitly show that G depends on the nonequilibrium factor (0≤γ≤1 with γ=0 and 1 representing irreversible and equilibrium reaction systems, respectively), the Michaelis constant (K_{M}), and the turnover ratio (r_{cat}) of the participating enzymes. We further find the maximum possible gain is intrinsically determined by K_{M}/G_{max}=(1/K_{M}+2)/4. Our model also indicates that the dissipated energy (measured by -lnγ), from the intracellular energy-bearing bioparticles (e.g., ATP), is used to generate a force field F_{γ}∝(1-sqrt[γ]) that reshapes and disables the effective potential around the zero gain region, which leads to the ultrasensitive response to external chemical gradients.

Cell migration is essential in biology, and it is closely related to biological functions such as wound healing, immune responses and cancer cell metastases. Without chemical or physical gradients, cells migrate randomly. Recently, the Wirtz lab discovered the large scale periodic cell migration of Zyxin-depleted human fibrosarcoma cells with period longer than 2 hours. These cells exhibit distinct regular oscillatory migration patterns in three-dimensional ECM and along one-dimensional chambers. Here, we present a reaction-transportation model based on a coarse-grained molecular picture of the process. Migrating cells have well-defined polarity and microtubules are known to play important roles. By explicitly incorporating k, we successfully reproduced the experimentally observed periodic migrating patterns. Our results suggest that, although diffusion and motor-based active material transportation (convection) both exist in cell, the periodic switching of cell's polarity is mainly due to the motor-based convection. Surprisingly, we discovered two distinct oscillatory phases: in the first phase, the polarization factors undergo simple and fast end-to-end oscillation, which would not lead to the observed large scale periodic migration; whereas in the second phase, the polarization factors not only oscillate between two cell ends but also generate vortex-like local patterns at either ends. These vortex-like patterns greatly elongate the period of the oscillation, which effectively stabilizes the migration in either direction, leading to the large scale oscillatory migration. Based on our model, the cell length dependences of various oscillatory characteristics have been predicted for future experimental verification. The identified two oscillatory phases may provide useful insights to the general picture of how cells alter direction during rather persistent migration, and the developed reaction-transportation model provides a general framework for studying the long-range cytoplasmic translation dynamics of any molecules.

Skeletal muscle contraction is a canonical example of motor-driven force generation. Despite the long history of research in this topic, a mechanistic explanation of the collective myosin force generation is lacking. We present a theoretical model of muscle contraction based on the conformational movements of individual myosins and experimentally measured chemical rate constants. Detailed mechanics of the myosin motor and the geometry of the sarcomere are taken into account. Two possible scenarios of force generation are examined. We find only one of the scenarios can give rise to a plausible contraction mechanism. We propose that the synchrony in muscle contraction is due to a force-dependent ADP release step. Computational results of a half sarcomere with 150 myosin heads can explain the experimentally measured force-velocity relationship and efficiency data. We predict that the number of working myosin motors increases as the load force is increased, thus showing synchrony among myosin motors during muscle contraction. We also find that titin molecules anchoring the thick filament are passive force generators in assisting muscle contraction.

Myosin-V is an actin-associated processive molecular motor. Single molecule experiments revealed that myosin-V walks in a stepwise fashion with occasional backward steps. By combining the mechanical structure of the motor with the ATP hydrolysis kinetics, we construct a dynamical model that accounts for the stepwise processivity. The molecular properties of the protein chains connecting the myosin heads are important. A simple elastic model demonstrates that the stress transmitted from the leading head to the trailing head leads to net forward motion. The step-sizes are non-uniform. We also predict there are several substeps. The translational speed and step-size distributions are computed for several different conditions. The computed force-versus-velocity curve shows that under an external load, myosin-V slows down. However, the sizes of the steps remain the same.

Cytokinesis is a critical step in cell reproduction. In prokaryotic cells, such as Escherichia coli, this process is mediated by a cytoskeletal structure (i.e., Z ring), which is assembled from FtsZ protofilaments that are “anchored” to the cell membrane through ZipA/FtsA molecules, and serves as the scaffold for a dozen or so downstream proteins. Fluorescence recovery after photobleaching (FRAP) experiments have shown that the Z ring is highly dynamic, with recovery half time of 8∼30 seconds, yet has a rather persistent overall structure. But the mechanism of how a single narrow dynamic Z ring emerges from a big pool of cytoplasmic FtsZ molecules remains elusive. In this work, we developed a rule-based molecular model with FtsZ and ZipA/FtsA molecules, by explicitly taking into account the elementary assembling events of these two types of molecules and their diffusion on membrane. Our model can not only efficiently reproduce the Z ring with statistical properties in accordance with experimental observation, but provide a convenient way to combine biochemical dynamic and physical assembling processes within the same spatio-temporal modeling framework. Our results indicate that the FtsZ and ZipA can spontaneously self-assemble into a ring structure. And as observed in experiments, our model also shows that either high or low FtsZ to ZipA/FtsA ratios would result in multiple Z rings or aggregated bundles. Our in silico FRAP experiment further yields a recovery half time comparable to experimental results. Our systematic simulation unveils that it is the rapid turnover dynamics that prevents the FtsZ molecules from being sequestered by small FtsZ bundles dispersed over the membrane, therefore allowing one single Z ring to emerge and mature. This compete-and-survive mechanism provides cells a simple way for spatial regulation.

Adaptation is one of the key functions for living organisms to properly operate in complex internal and external signalling environments. A series of recent studies demonstrated that robust adaptation is achievable through 3-node regulatory networks, such as Negative-Feedback-Loops (NFL) or Incoherent-Feed-Forward-Loops (IFFL), that are intrinsically dissipative and must consume energy to operate (e.g. to adapt). And the underlying thermodynamics follows a trade-off relation that adaptation accuracy can be improved exponentially by dissipating more energy within certain adaptation time. In this work, however, we constructed a tree-like biochemical network that operates at dynamic equilibrium. We showed that this network is also capable to provide robust and accurate adaptation. The tree-like network shares certain degrees of similarity with the NFL reaction network found in E. coli. chemotaxis, yet with some distinct topological characteristics. We systematically analysed this tree-like network's static (e.g. adaptation accuracy and operating range) and dynamic (e.g. response magnitude and responding/adapting speed) performances in the model parameter space. Our analyses indicate that such network can maintain its function in a broad range of backgrounds and perturbations. And because it is essentially comprised of the operator itself, this network is more resistant to other intracellular defects (e.g. abnormal expression levels or ratios of participating enzymes). Therefore, it could act as a safety net for living organisms to carry out vital physiological functions, which is in line with the modal gating regulation of Ryanodine receptors (RyRs) in Cardiac muscle cells that is implemented through this type of adaptive network.

Living systems have to constantly sense their external environment and adjust their internal state in order to survive and reproduce. Biological systems, from as complex as the brain to a single E. coli cell, have to process these data in order to make appropriate decisions. How do biological systems sense external signals? How do they process the information? How do they respond to signals? Through years of intense study by biologists, many key molecular players and their interactions have been identified in different biological machineries that carry out these signaling functions. However, an integrated, quantitative understanding of the whole system is still lacking for most cellular signaling pathways, not to say the more complicated neural circuits. To study signaling processes in biology, the key thing to measure is the input-output relationship. The input is the signal itself, such as chemical concentration, external temperature, light (intensity and frequency), and more complex signals such as the face of a cat. The output can be protein conformational changes and covalent modifications (phosphorylation, methylation, etc), gene expression, cell growth and motility, as well as more complex output such as neuron firing patterns and behaviors of higher animals. Due to the inherent noise in biological systems, the measured input-output dependence is often noisy. These noisy data can be analysed by using powerful tools and concepts from information theory such as mutual information, channel capacity, and the maximum entropy hypothesis. This information theory approach has been successfully used to reveal the underlying correlations between key components of biological networks, to set bounds for network performance, and to understand possible network architecture in generating observed correlations. Although the information theory approach provides a general tool in analysing noisy biological data and may be used to suggest possible network architectures in preserving information, it does not reveal the underlying mechanism that leads to the observed input-output relationship, nor does it tell us much about which information is important for the organism and how biological systems use information to carry out specific functions. To do that, we need to develop models of the biological machineries, e.g. biochemical networks and neural networks, to understand the dynamics of biological information processes. This is a much more difficult task. It requires deep knowledge of the underlying biological network-the main players (nodes) and their interactions (links)-in sufficient detail to build a model with predictive power, as well as quantitative input-output measurements of the system under different perturbations (both genetic variations and different external conditions) to test the model predictions to guide further development of the model. Due to the recent growth of biological knowledge thanks in part to high throughput methods (sequencing, gene expression microarray, etc) and development of quantitative in vivo techniques such as various florescence technology, these requirements are starting to be realized in different biological systems. The possible close interaction between quantitative experimentation and theoretical modeling has made systems biology an attractive field for physicists interested in quantitative biology. In this review, we describe some of the recent work in developing a quantitative predictive model of bacterial chemotaxis, which can be considered as the hydrogen atom of systems biology. Using statistical physics approaches, such as the Ising model and Langevin equation, we study how bacteria, such as E. coli, sense and amplify external signals, how they keep a working memory of the stimuli, and how they use these data to compute the chemical gradient. In particular, we will describe how E. coli cells avoid cross-talk in a heterogeneous receptor cluster to keep a ligand-specific memory. We will also study the thermodynamic costs of adaptation for cells to maintain an accurate memory. The statistical physics based approach described here should be useful in understanding design principles for cellular biochemical circuits in general.

Adaptation is the essential process by which an organism becomes better suited to its environment. The benefits of adaptation are well documented, but the cost it incurs remains poorly understood. Here, by analysing a stochastic model of a minimum feedback network underlying many sensory adaptation systems, we show that adaptive processes are necessarily dissipative, and continuous energy consumption is required to stabilize the adapted state. Our study reveals a general relation among energy dissipation rate, adaptation speed and the maximum adaptation accuracy. This energy-speed-accuracy relation is tested in the Escherichia coli chemosensory system, which exhibits near-perfect chemoreceptor adaptation. We identify key requirements for the underlying biochemical network to achieve accurate adaptation with a given energy budget. Moreover, direct measurements confirm the prediction that adaptation slows down as cells gradually de-energize in a nutrient-poor medium without compromising adaptation accuracy. Our work provides a general framework to study cost-performance tradeoffs for cellular regulatory functions and information processing.

In chemotactic bacteria, transmembrane chemoreceptors, CheA and CheW form the core signalling complex of the chemotaxis sensory apparatus. These complexes are organized in extended arrays in the cytoplasmic membrane that allow bacteria to respond to changes in concentration of extracellular ligands via a cooperative, allosteric response that leads to substantial amplification of the signal induced by ligand binding. Here, we have combined cryo-electron tomographic studies of the 3D spatial architecture of chemoreceptor arrays in intact E. coli cells with computational modelling to develop a predictive model for the cooperativity and sensitivity of the chemotaxis response. The predictions were tested experimentally using fluorescence resonance energy transfer (FRET) microscopy. Our results demonstrate that changes in lateral packing densities of the partially ordered, spatially extended chemoreceptor arrays can modulate the bacterial chemotaxis response, and that information about the molecular organization of the arrays derived by cryo-electron tomography of intact cells can be translated into testable, predictive computational models of the chemotaxis response.

Stochastic methods have been a staple for understanding complex systems in chemistry and physics. In the biological context, they are useful for understanding phenomena ranging from molecular-level fluctuations to cellular movement. We review the basic formalism behind stochastic methods and outline how they can be implemented for quantifying gene expression, movement of molecular motors, and the dynamics of cytoplasmic components. We show that stochastic methods are quantitative checks for proposed molecular mechanisms and can pose new questions for experiments. Structural information of cellular components can be incorporated into stochastic models to reveal new biological insights.

Forces are important in biological systems for accomplishing key cell functions, such as motility, organelle transport, and cell division. Currently, known force generation mechanisms typically involve motor proteins. In bacterial cells, no known motor proteins are involved in cell division. Instead, a division ring (Z-ring) consists of mostly FtsZ, FtsA, and ZipA is used to exerting a contractile force. The mechanism of force generation in bacterial cell division is unknown. Using computational modeling, we show that Z-ring formation results from the colocalization of FtsZ and FtsA mediated by the favorable alignment of FtsZ polymers. The model predicts that the Z-ring undergoes a condensation transition from a low-density state to a high-density state and generates a sufficient contractile force to achieve division. FtsZ GTP hydrolysis facilitates monomer turnover during the condensation transition, but does not directly generate forces. In vivo fluorescence measurements show that FtsZ density increases during division, in accord with model results. The mechanism is akin to van der Waals picture of gas-liquid condensation, and shows that organisms can exploit microphase transitions to generate mechanical forces.

Stochastic methods have been a staple for understanding complex systems in chemistry and physics. In the biological context, they are useful for understanding phenomena ranging from molecular-level fluctuations to cellular movement. We review the basic formalism behind stochastic methods and outline how they can be implemented for quantifying gene expression, movement of molecular motors, and the dynamics of cytoplasmic components. We show that stochastic methods are quantitative checks for proposed molecular mechanisms and can pose new questions for experiments. Structural information of cellular components can be incorporated into stochastic models to reveal new biological insights.

Prokaryotic cells often utilize a MinCDE oscillatory system to locate the mid-cell location for symmetric cell division. Several diffusion-reaction based models have been developed to explain the occurrence of the sustainable oscillation of Min proteins from pole to pole, and extensive efforts have been devoted to understanding the patterns of oscillation and the precision of the designated mid-cell location. However, how this highly dissipative yet vital biological oscillation is driven by energy-bearing molecules is left uninvestigated. We address this fundamental question by studying the MinCDE oscillator in Escherichia coli. We assess the oscillator's performance of spatially differentiating mid-cell region from the rest of cell body, and further relate this quantified performance to the amount of dissipated energy as well as the stage of cell growth. Unlike the two adaptive reaction networks (Negative-Feedback-Loop and Feedfoward-Loop) whose performances get monotonically improved upon larger energy input, the MinCDE oscillator shows nonmonotonic performance-to-cost relation that depends on the reaction rates and the cell length. Our analysis further indicated that this oscillator operates optimally at cell length around 4 micro-meters and to achieve the best performance, energy is dissipated unevenly through the reaction pathway with the largest dissipation at immobilizing MinD and hydrolyzing ATP. These results present a novel mode of converting biochemical energy into spatiotemporal information in living systems and suggest that the MinCDE oscillator in prokaryotic cells are highly optimized both functionally and energetically to ensure high fitness under natural selection.

The incoherent type-1 feed-forward loop (I1-FFL) is ubiquitous in biological regulatory circuits. Although much is known about the functions of the I1-FFL motif, the energy cost incurred in the network and how it affects the performance of the network have not been investigated. Here, we study a generic I1-FFL enzymatic reaction network modelled after the GEF–GAP–Ras pathway responsible for chemosensory adaptation in eukaryotic cells. Our analysis shows that the I1-FFL network always operates out of equilibrium. Continuous energy dissipation is necessary to drive an internal phosphorylation–dephosphorylation cycle that is crucial in achieving strong short-time response and accurate long-time adaptation. In particular, we show quantitatively that the energy dissipated in the I1-FFL network is used (i) to increase the system's initial response to the input signals; (ii) to enhance the adaptation accuracy at steady state; and (iii) to expand the range of such accurate adaptation. Moreover, we find that the energy dissipation rate, the catalytic speed and the maximum adaptation accuracy in the I1-FFL network satisfy the same energy–speed–accuracy relationship as in the negative-feedback-loop (NFL) networks. Because the I1-FFL and NFL are the only two basic network motifs that enable accurate adaptation, our results suggest that a universal cost–performance trade-off principle may underlie all cellular adaptation processes independent of the detailed biochemical circuit architecture.

In bacterial chemotaxis, several types of receptors form mixed clusters. Receptor adaptation is shown to depend on the receptor's own conformational state rather than on the cluster's global activity, enabling cells to differentiate stimuli in complex environments., We develop here a model for mixed chemoreceptor clusters in which receptors interact directly with their nearest neighbors. A local adaptation scheme is used to describe the methylation kinetics of individual receptors. Predictions made by this model were tested by direct measurements of the receptor methylation dynamics for both Tar and Tsr in response to ligands sensed by either receptor. We show that the local adaptation mechanism tunes each receptor in the mixed cluster to its most responsive state, to maintain the cell's high sensitivity in complex environments with multiple cues. This mechanism also prevents the saturation of the whole receptor cluster with exposure to environments with extreme level of one type of stimulus., In environments with multiple cues, organisms need to sense different signals and to respond accordingly to enhance their chances of survival (Adler and Tso, 1974). In bacterial chemotaxis systems (see Hazelbauer et al (2008) for a recent review), different chemical stimuli are sensed by different types of chemoreceptors. In Escherichia coli cells, different types of chemoreceptors, e.g., the aspartate-sensing Tar receptor and the serine-sensing Tsr receptor, form mixed receptor cluster (Maddock and Shapiro, 1993; Ames et al, 2002), within which different types of chemoreceptors interact with each other cooperatively to amplify external signals (Bray et al, 1998; Sourjik and Berg, 2002; Mello and Tu, 2003b). The bacterial chemosensory system also adapts to prolonged stimuli by covalently modifying the chemoreceptors (methylation and demethylation). However, despite strong interactions between different receptors in the mixed cluster, the adaptive covalent modifications of individual receptors are observed to be insulated from each other. At moderate stimulation, only the receptors that bind the respective ligand adjust their methylation levels significantly in the adapted state (Silverman and Simon, 1977; Sanders and Koshland, 1988; Antommattei et al, 2004). This observed ligand-specific receptor methylation pattern challenges all-or-none allosteric models, such as the Monod–Wyman–Changdeux model (Monod et al, 1965; Sourjik and Berg, 2004; Mello and Tu, 2005; Keymer et al, 2006), that are commonly used to describe behavior of chemoreceptor clusters. It prompts the fundamental questions of how a highly cooperative mixed chemoreceptor complex adapts to multiple stimuli and whether it can distinguish different signals. In this paper, we combine theoretical and experimental methods to understand the adaptation mechanism of mixed chemoreceptor clusters. We propose a local adaptation mechanism (model) for the mixed receptor cluster (Figure 1). In our model, receptors interact with their neighboring receptors in the mixed cluster (Figure 1D) and thus act collectively to generate strong response to small external signals. However, the adaptation of an individual receptor in the mixed cluster depends predominantly on its own local conformational state (Figure 1B), rather than the activity of the entire cluster (Figure 1A and C). Much to our surprise, despite strong interaction between different receptors, our model predicts that only the receptor which binds with the external ligand changes its methylation level in steady state when the system adapts, while other types of receptors only change their methylation levels transiently during adaptation. Our model also predicts that permanent (steady state) methylation crosstalk occurs only when the system fails to adapt accurately, and there exists a direct connection between the adaptation error and the degree of permanent methylation crosstalk. Both predictions are verified by direct quantitative measurements of the dynamics of the Tar and Tsr methylation levels in response to MeAsp and serine (Figure 5). These experimental results cannot be explained by the existing models, such as the MWC-type model and the recently proposed independent receptor model by Goldman et al (2009). The predicted transient adaptation dynamics for a mixed receptor cluster also provides a mechanistic explanation for the previously observed overshoot of activity when E. coli cells adapt to a large step stimulus (Berg and Tedesco, 1975). After establishing the validity of the local adaptation mechanism in E. coli, we next explore the possible advantages of this adaptation mechanism for bacterial chemotaxis. We show that while the previously proposed global adaptation mechanisms compress different external environmental information, e.g., concentrations of different types of attractant ligands into one quantity (the overall activity of the cluster), the local adaptation mechanism preserves environmental information. The concentrations of different chemoeffectors are encoded (‘remembered') by the specific receptor methylation levels in the local adaptation model. These ligand-specific information can then be used by bacterial cells to precisely tune each type of receptor in the mixed cluster to its most responsive state, therefore maintaining high sensitivity and responsiveness in complex environments with multiple stimuli. The local adaptation mechanism, by effectively preventing methylation crosstalk, also prevents the poisoning effect by methylation contamination when bacterial cells are exposed to environments with extreme level of one type of stimulus. In summary, we have developed a modeling framework for understanding how a mixed chemoreceptor cluster adapts to complex environments with multiple cues. The local adaptation and global activation mechanism of the mixed receptor cluster proposed in this paper resolves the seemingly conflicting observations between strong receptor–receptor interactions and the absence of permanent receptor methylation crosstalk within a unified and predictive model. Direct quantitative measurements of the receptor methylation dynamics have confirmed the model predictions. The proposed model also predicts several characteristic consequences of the local adaptation mechanism, e.g., elimination of sensory poisoning, which may be tested in future experiments. Bacterial chemosensory machinery is a paradigm for studying adaptive sensory systems for detecting and adapting to environmental changes and signals, and we expect that the strategy of ‘adapting locally (individually) and acting globally (collectively)' may be used by other sensory systems that utilize multiple receptors to respond complex environmental changes., In bacterial chemotaxis, several types of ligand-specific receptors form mixed clusters, wherein receptor–receptor interactions lead to signal amplification and integration. However, it remains unclear how a mixed receptor cluster adapts to individual stimuli and whether it can differentiate between different types of ligands. Here, we combine theoretical modeling with experiments to reveal the adaptation dynamics of the mixed chemoreceptor cluster in Escherichia coli. We show that adaptation occurs locally and is ligand-specific: only the receptor that binds the external ligand changes its methylation level when the system adapts, whereas other types of receptors change methylation levels transiently. Permanent methylation crosstalk occurs when the system fails to adapt accurately. This local adaptation mechanism enables cells to differentiate individual stimuli by encoding them into the methylation levels of corresponding types of chemoreceptors. It tunes each receptor to its most responsive state to maintain high sensitivity in complex environments and prevents saturation of the cluster by one signal.

Biological sensory systems adapt to prolonged stimuli in order to maintain high sensitivity in different environments. Sensory adaptations are carried out by various molecular feedback mechanisms. Here, we show that all adaptation dynamics are dissipative and feedback control consumes energy to achieve high adaptation accuracy against intrinsic fluctuations in the underlying molecular signaling pathways. A universal relation among energy dissipation rate, adaptation time, and the optimum adaptation accuracy is established in a general continuum model and for the specific case of adaptation in E. coli chemotaxis. Our study finds that sensory adaptations are fueled by high-energy biomolecules (e.g., ATP), which provide the energy necessary in stabilizing the adapted state. For E. coli chemotaxis, hydrolysis of S-adenosylmethionine (SAM) drives the chemo-receptor adaptation, and the high energy content in SAM is crucial in maintaining the near perfect adaptation of the system. Finally, we point out that the energy-accuracy relation found here has deep connections with the energy dissipation required for molecular level error-correction and information processing.View Large Image | View Hi-Res Image | Download PowerPoint Slide

Myosin-VI is a dimeric isoform of unconventional myosins. Single molecule experiments indicate that myosin-VI and myosin-V are processive molecular motors, but travel toward opposite ends of filamentous actin. Structural studies show several differences between myosin-V and VI, including a significant difference in the light-chain domain connecting the motor domains. Combining the measured kinetics of myosin-VI with the elasticity of the light chains, and the helical structure of F-actin, we compare and contrast the motility of myosin-VI with myosin-V. We show that the elastic properties of the light-chain domain control the stepping behavior of these motors. Simple models incorporating the motor elastic energy can quantitatively capture most of the observed data. Implications of our result for other processive motors are discussed.

Periodic processes are an indispensable part of biological phenomena. Circadian rhythms, heart rhythms, neuronal oscillations, cell cycle, and cytoskeletal structures such as the axonemes of cilia are all examples of systems exhibiting oscillatory dynamics. The underlying mechanisms of several such processes can be explained by understanding the origins of these oscillations and characterizing them. One particular example is the follicle cell basal surface area oscillations observed in Drosophila egg chamber during oogenesis. It has been suggested that these oscillations restrict the egg chamber width, and thus help in elongation of the tissue. In this work, we attempt to model these oscillations in follicle cell length using a mechano-chemical model. Our model predicts an increase in oscillation period, upon removal of the basement membrane, which has been observed experimentally upon collagenase treatment of the egg chamber. The model also predicts an inverse relationship of maximum contractile force and oscillation period.