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Patients with polycythemia vera (PV) are at significant risk of thromboembolic events (TE). The PV-AIM study used the Optum® de-identified Electronic Health Record dataset and machine learning to identify markers of TE in a real-world population. Data for 82,960 patients with PV were extracted: 3852 patients were treated with hydroxyurea (HU) only, while 130 patients were treated with HU and then changed to ruxolitinib (HU-ruxolitinib). For HU-alone patients, the annualized incidence rates (IR; per 100 patients) decreased from 8.7 (before HU) to 5.6 (during HU) but increased markedly to 10.5 (continuing HU). Whereas for HU-ruxolitinib patients, the IR decreased from 10.8 (before HU) to 8.4 (during HU) and was maintained at 8.3 (after switching to ruxolitinib). To better understand markers associated with TE risk, we built a machine-learning model for HU-alone patients and validated it using an independent dataset. The model identified lymphocyte percentage (LYP), neutrophil percentage (NEP), and red cell distribution width (RDW) as key markers of TE risk, and optimal thresholds for these markers were established, from which a decision tree was derived. Using these widely used laboratory markers, the decision tree could be used to identify patients at high risk for TE, facilitate treatment decisions, and optimize patient management.

Introduction Exposure to traumatic brain injury is a core risk factor that predisposes an individual to sporadic neurodegenerative diseases. We provide evidence that mechanical stress increases brain levels of hallmark proteins associated with neurodegeneration. Methods Wild-type mice were exposed to multiple regimens of repetitive mild traumatic brain injury, generating a range of combinations of impact energies, frequencies, and durations of exposure. Brain concentrations of amyloid β 1–42 (Aβ 1–42 ), total tau, and α-synuclein were measured by sandwich enzyme-linked immunosorbent assay. Results There was a highly significant main effect of impact energy, frequency, and duration of exposure on Aβ 1–42 , tau, and α-synuclein levels ( P 1–42 and tau ( P Discussion Dose-dependent and cumulative influence of repetitive mild traumatic brain injury–induced mechanical stress may trigger and/or accelerate neurodegeneration by pushing protein concentration over the disease threshold.

International audience; One proposed scenario for the emergence of biochemical oscillations is that they may have provided the basic mechanism behind cellular self-replication by growth and division. However, alternative scenarios not requiring any chemical oscillation have also been proposed. Each of the various protocell models proposed to support one or another scenario comes with its own set of specific assumptions, which makes it difficult to ascertain whether chemical oscillations are required or not for cellular self-replication. This article compares these two cases within a single whole-cell model framework. This model relies upon a membrane embedding a chemical reaction network (CRN) synthesizing all the cellular constituents, including the membrane, by feeding from an external nutrient. Assuming the osmolarity is kept constant, the system dynamics are governed by a set of nonlinear differential equations coupling the chemical concentrations and the surface-area-to-volume ratio. The resulting asymptotic trajectories are used to determine the cellular shape by minimizing the membrane bending energy (within an approximate predefined family of shapes). While the stationary case can be handled quite generally, the oscillatory one is investigated using a simple oscillating CRN example, which is used to identify features that are expected to hold for any network. It is found that cellular self-replication can be reached with or without chemical oscillations, and that a requirement common to both stationary and oscillatory cases is that a minimum spontaneous curvature of the membrane is required for the cell to divide once its area and volume are both doubled. The oscillatory case can result in a greater variety of cellular shape trajectories but raises additional constraints for cellular division and self-replication: (i) the ratio of doubling time to oscillation period should be an integer, and (ii) if the oscillation amplitude is sufficiently high, then the spontaneous curvature must be below a maximum value to avoid early division before the end of the cycle. Because of these additional stringent constraints, it is likely that early protocells did not rely upon chemical oscillations. Biochemical oscillations typical of modern evolved cells may have emerged later through evolution for other reasons (e.g., metabolic advantage) and must have required additional feedback mechanisms for such a self-replicating system to be robust against even slight environmental variations (e.g., temperature fluctuations).

Aims: We previously reported results of the first machine learning study to identify the novel biomarkers associated with crude incidence of TEs in PV patients (pts) treated with hydroxyurea (HU) (Verstovsek et al, Blood 2019). In the current study, we have expanded the database to perform an in-depth machine learning analysis of biomarkers predicting the annual standardized incidence rate (IR) of TEs in PV pts treated with HU. In addition, we examined the IR of TEs in HU-treated pts and pts treated first with HU, then switched to ruxolitinib (HU-RUX). Methods: The study is part of PV-AIM (polycythemia vera advanced integrated models for prevention of TEs). The US OPTUM database includes the electronic medical records from 105 million pts (2007-2020), including 82 960 PV pts with a median record length of 8.4 years. TEs were assessed (A) before the treatment (Tx) initiation in both groups; (B) while on HU (median: 29 months) and (C) while continuing HU or switching to RUX (median, 17 months). A total of 3852 HU-alone pts and 130 HU-RUX pts passed inclusion/exclusion criteria. To avoid selection bias, only HU-alone pts treated prior to RUX market launch were included (n=704). Cohorts were then matched by selecting the nearest cases from the HU-alone groups based on the propensity scores calculated from the total Tx period, gender, race, age at index, and geographical division (n=130 pts in both HU-alone and HU-RUX). TEs were identified from a restrictive list of ICD-CM diagnosis codes defined in the RESPONSE study (Kiladjian et al, Lancet Haematol 2020). Annualized IR of TEs were then calculated per 100 pts for each of the above Tx periods for each cohort. A random survival forest (RSF) model was then constructed for HU-alone pts, with at least 6 months of HU Tx and 18 months of follow-up, to predict the risk of TEs 6 to 18 months after the first HU Tx. Pts with at least 1 lab test and 1 observation obtained at 3 to 6 months post index were included in the model (n=1012). The features also included demographics, history of TEs, phlebotomy and anti-coagulant use. The performance was assessed on a 70:30 train:test split using Receiver Operating Characteristic-Area Under the Curve (ROC-AUC). RSF variable importance was then used to identify the variables with the largest impact on prediction of TEs. The feature interactions for the top 10 features were mapped and assessed in terms of risk of TEs via log-rank using a brute-force combinatorial approach. Risk was assessed in the context of total population and in pts with/without a history of TEs. Optimal interactions were presented as interpretable 4-group Kaplan-Meier plots. Lastly, the feature interactions were scored for synergy in the context of risk of TEs based on the difference in expected and observed pairwise probabilities. Results: The RSF model achieved an accuracy of > 0.80 AUC for prediction of TEs 6 to12 months after the first HU Tx. RSF variable importance revealed that the history of TEs had the largest effect on prediction (>2-fold) relative to other features. The top observational and lab variables in terms of feature importance were body mass index and neutrophil percentage (NEP), respectively, followed by the previously observed lymphocyte percentage (LYP) and RDW. The largest synergistic pairwise interactions (Table 1) were NEP + RDW (Figure 1a), followed by LYP + RDW (Figure 1b), which occurred in pts without history of TEs. The pre-Tx baseline incidence of TEs in the HU-alone and HU-RUX groups were 8.7 and 10.8, respectively. The rates of TEs decreased by 36% and 22% to 5.6 and 8.4, respectively during the HU Tx. Following the switch to RUX, the rate of TEs was maintained in the HU-RUX group at 8.3 (−1%) but increased to 10.53 (+89%) in the HU-alone group during the same period of 17 months (Figure 2). Conclusion: An in-depth machine learning analysis to identify the markers that predict TEs in HU-treated PV pts revealed interaction of key hematological parameters with RDW. This finding could help recognize the pts at high risk of TE and possibly prevent a major cause of morbidity and mortality. The results reconfirm the role of RDW, as well as NEP/LYM ratios in predicting TE occurrence, routine lab parameters in the clinic, but not usually taken into account. Finally, we observed a renewed increase in TEs in HU-treated pts after an initial decrease, which was not the case in pts switching to RUX in the same period. We aim to present similar biomarker analyses on RUX pts in the final presentation. Disclosures Verstovsek: Promedior: Research Funding; NS Pharma: Research Funding; PharmaEssentia: Research Funding; Novartis: Consultancy, Research Funding; Sierra Oncology: Consultancy, Research Funding; AstraZeneca: Research Funding; Blueprint Medicines Corp: Research Funding; Genentech: Research Funding; CTI Biopharma Corp: Research Funding; ItalPharma: Research Funding; Incyte Corporation: Consultancy, Research Funding; Protagonist Therapeutics: Research Funding; Roche: Research Funding; Gilead: Research Funding; Celgene: Consultancy, Research Funding. De Stefano:Janssen Cilag: Other: Non-financial support; Celgene: Other: Non-financial support, personal fee; Bayer: Other: Non-financial support; Amgen: Other: Personal fee; Novartis: Other: Personal fee, Research Funding. Heidel:Novartis: Consultancy, Honoraria, Research Funding. Zuurman:Novartis Pharma A.G.: Current Employment. Zaiac:Novartis: Current Employment, Current equity holder in private company. Bryan:Novartis: Current Employment. Buckley:Novartis: Current Employment. Mathur:Novartis: Current Employment. Morelli:Novartis: Current Employment. Bigan:Novartis: Consultancy. Ruhl:Boston Consulting Group: Current Employment. Meier:Novartis: Consultancy.

D-Amino acids are largely excluded from protein synthesis, yet they are of great interest in biotechnology. Unnatural amino acids have been introduced into proteins using engineered aminoacyl-tRNA synthetases (aaRSs), and this strategy might be applicable to D-amino acids. Several aaRSs can aminoacylate their tRNA with a D-amino acid; of these, tyrosyl-tRNA synthetase (TyrRS) has the weakest stereospecificity. We use computational protein design to suggest active site mutations in Escherichia coli TyrRS that could increase its D-Tyr binding further, relative to L-Tyr. The mutations selected all modify one or more sidechain charges in the Tyr binding pocket. We test their effect by probing the aminoacyl-adenylation reaction through pyrophosphate exchange experiments. We also perform extensive alchemical free energy simulations to obtain L-Tyr/D-Tyr binding free energy differences. Agreement with experiment is good, validating the structural models and detailed thermodynamic predictions the simulations provide. The TyrRS stereospecificity proves hard to engineer through charge-altering mutations in the first and second coordination shells of the Tyr ammonium group. Of six mutants tested, two are active towards D-Tyr; one of these has an inverted stereospecificity, with a large preference for D-Tyr. However, its activity is low. Evidently, the TyrRS stereospecificity is robust towards charge rearrangements near the ligand. Future design may have to consider more distant and/or electrically neutral target mutations, and possibly design for binding of the transition state, whose structure however can only be modeled.

Gene deregulation has been associated with several neurodegenerative diseases (NDs) and modeling genome-wide data such as transcriptomic data has provided holistic models of ND progression on a gene expression level. However, the consequences of gene deregulation on the temporal dynamics of gene-gene interaction systems (genetic cooperativity) is poorly understood in NDs. Using a multi-layer network approach, we cross-integrated three families of networks describing RNA-seq time series data in Huntington’s disease (HD) knock-in mice for reconstructing the dynamics of genetic cooperativity in the brain of these mice based on the consistency and complementarity of edge information in source networks. The resulting model suggests that the HD process may develop as two critical phases of genetic cooperativity, pre-symptomatically in the cortex, involving a neurotransmission response, and symptomatically in the striatum, involving cell survival responses intertwined with cellular senescence and DNA damage responses. These data highlight a 2-step logic for injury/adaptation of the HD mouse brain in which a cortical response for modulating neurotransmission may precede a striatal response for regulating cellular homeostasis synchronous to symptoms.

Introduction: Thromboembolic events (TEs) are one of the most prevalent complications in patients (pts) with polycythemia vera (PV). This real-world evidence study of the US OPTUM database evaluated the incidence of TEs in hydroxyurea (HU)-treated PV pts who either switched to ruxolitinib (RUX) after initial treatment (Tx) with HU (HU-RUX group) or continued HU Tx without switching (HU-alone group). Machine learning was then used to build a precise and scientifically robust model to predict the occurrence of TEs in PV pts with/without a history of TEs and HU failure (defined by either European LeukemiaNet [ELN] hematologic criteria or TEs). Methods: The OPTUM database comprises claims data and electronic medical records from 90 million pts (2007-2017, median stay in the database=7 years), including 69,464 PV pts. To avoid any selection bias during comparison, only pts treated prior to the RUX market launch were included in the HU-alone group (HU-RUX, n=81; HU-alone, n=195). Due to unavailability of Tx duration, time difference between the first and the last prescription was used as a proxy, and overall Tx duration was matched in both groups. TEs were assessed before Tx initiation in both groups. For HU-RUX pts, it was also assessed while on HU (median duration 27 months) and on RUX (median duration 14 months). For HU-alone pts, it was assessed during the first 27 months of Tx (any pt included in the analysis was treated for longer than this due to duration matching) and during remaining period of Tx (median duration 14 months). TEs were identified by either a restrictive definition (a list of ICD codes containing keywords from the RESPONSE study was automatically generated and manually curated) or a less restrictive one (list of ICD codes was manually expanded to include any TEs matching those from the GEMFIN study). PV pts who were exclusively treated with HU for ≥6 months were selected (n=2057) for modeling. Outcomes to be predicted were TEs in the 12 months following the end of the 6-month HU Tx period, and HU failure within 3 months of Tx. A logistic regression model was used for prediction. The baseline features extracted from the database included median lab parameters (3-6 months after HU initiation), history of thrombosis prior to primary diagnosis of PV, sociological features (age, gender), comorbidities, and concomitant medications (from inpatient/outpatient tables). Performance assessment methods included Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) in early stages and confusion matrix in later stages; the findings were converted to clinically interpretable decision-tree classification algorithms. Results: Based on the extensive definition, the annual incidence of TEs in the HU-RUX and HU-alone groups, respectively, was 9% and 7% before HU initiation, which increased to 17% and 13% on HU Tx. The small difference in baseline incidence may reflect residual differences between the two groups. After a median duration of 14 months, the incidence of TEs decreased to 15% in pts who switched to RUX vs an increase to 20% in pts who continued HU Tx. A similar trend was observed using less restrictive definition (Figure 1). This definition resulted in a substantially increased incidence of TEs and a decreased predictive power of the machine-learning model. Using modeling, decision trees were developed to predict the occurrence of TEs in PV pts with/without a history of TEs. Lymphocyte percentage (13%) and platelet count (>393x103/µL) were predictors in pts with a history of TEs (Figure 2). Based on the decision tree developed to predict HU failure, phlebotomy-dependent pts with >15% RDW had a higher risk of HU failure within 3 months of Tx (Figure 3). Conclusions: A reduction in the incidence of TEs was observed in pts switching to RUX vs those who continued HU Tx. Based on the findings from this machine-learning model in PV pts, phlebotomy dependency and RDW were indicated as predictors of HU Tx failure within 3 months, whereas lymphocyte percentage+platelet count and lymphocyte percentage+RDW were predictors of incidence of TEs in pts with and without a history of TEs, respectively. Non-adjustment of the results for antiplatelet/anticoagulant Tx was a study limitation. Further validation of this machine-learning model is planned in other European databases. Disclosures Verstovsek: Celgene: Consultancy, Research Funding; Gilead: Research Funding; Promedior: Research Funding; CTI BioPharma Corp: Research Funding; Genetech: Research Funding; Protaganist Therapeutics: Research Funding; Constellation: Consultancy; Pragmatist: Consultancy; Incyte: Research Funding; Roche: Research Funding; NS Pharma: Research Funding; Blueprint Medicines Corp: Research Funding; Novartis: Consultancy, Research Funding; Sierra Oncology: Research Funding; Pharma Essentia: Research Funding; Astrazeneca: Research Funding; Ital Pharma: Research Funding. De Stefano:Celgene: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Novartis: Consultancy, Honoraria, Research Funding, Speakers Bureau; Alexion: Consultancy, Honoraria, Speakers Bureau. Heidel:Novartis: Consultancy, Honoraria, Research Funding; Celgene: Consultancy; CTI: Consultancy. Zuurman:Novartis Pharma B.V.: Employment. Zaiac:Novartis: Employment, Equity Ownership. Bigan:Novartis: Consultancy. Ruhl:Novartis: Consultancy. Meier:Novartis: Consultancy. Kiladjian:Celgene: Consultancy; Novartis: Honoraria, Research Funding; AOP Orphan: Honoraria, Research Funding.

Osmotic pressure arising from a higher total chemical concentration inside proto-cells is thought to have played a role in the emergence and selection of self-replicating proto-cells. We present two chemical schemes through which different equilibrium compositions can coexist on each side of a semi-permeable membrane. The first scheme relies upon the concept of moieties and associated number of degrees of freedom. The second scheme relies upon the concept of siphons and of pass reaction capable of transferring matter from outside a siphon into it. Using simple example reaction networks, we show that both schemes are compatible with stationary proto-cell growth with up-concentration, but suffer from shortcomings. To alleviate these we propose a third scheme derived from the second one by having the pass reaction catalyzed by the membrane surface instead of occurring in bulk solution. This may have proven an intermediate step before having the pass reaction occurring only when the nutrient crosses the membrane. This suggests an evolutionary path for the emergence of active transport.

International audience; We show that self-replication of a chemical system encapsulated within a membrane growing from within is possible without any explicit feature such as autocatalysis or metabolic closure, and without the need for their emergence through complexity. We use a protocell model relying upon random conservative chemical reaction networks with arbitrary stoichiometry, and we investigate the protocell's capability for self-replication, for various numbers of reactions in the network. We elucidate the underlying mechanisms in terms of simple minimal conditions pertaining only to the topology of the embedded chemical reaction network. A necessary condition is that each moiety must be fed, and a sufficient condition is that each siphon is fed. Although these minimal conditions are purely topological, by further endowing conservative chemical reaction networks with thermodynamically consistent kinetics, we show that the growth rate tends to increase on increasing the Gibbs energy per unit molecular weight of the nutrient and on decreasing that of the membrane precursor.

International audience; We consider a generic protocell model consisting of any conservative chemical reaction network embedded within a membrane. The membrane results from the self-assembly of a membrane precursor and is semi-permeable to some nutrients. Nutrients are metabolized into all other species including the membrane precursor, and the membrane grows in area and the protocell in volume. Faithful replication through cell growth and division requires a doubling of both cell volume and surface area every division time (thus leading to a periodic surface area-to-volume ratio) and also requires periodic concentrations of the cell constituents. Building upon these basic considerations, we prove necessary and sufficient conditions pertaining to the chemical reaction network for such a regime to be met. A simple necessary condition is that every moiety must be fed. A stronger necessary condition implies that every siphon must be either fed, or connected to species outside the siphon through a pass reaction capable of transferring net positive mass into the siphon. And in the case of nutrient uptake through passive diffusion and of constant surface area-to-volume ratio, a sufficient condition for the existence of a fixed point is that every siphon be fed. These necessary and sufficient conditions hold for any chemical reaction kinetics, membrane parameters or nutrient flux diffusion constants.

The first non-enzymatic self-replicating systems, as proposed by von Kiedrowski (Angew Chem Int Ed Engl 25(10):932–935, 1986) and Orgel (Nature 327(6120):346–347, 1987), gave rise to the analytical background still used today to describe artificial replicators. What separates a self-replicating from an autocatalytic system is the ability to pass on structural information (Orgel, Nature 358(6383):203–209, 1992). Utilising molecular information, nucleic acids were the first choice as prototypical examples. But early self-replicators showed parabolic over exponential growth due to the strongly bound template duplex after template-directed ligation of substrates. We propose a self-replicating scheme with a weakly bound template duplex, using an informational leaving group. Such a scheme is inspired by the role of tRNA as leaving group and information carrier during protein synthesis, and is based on our previous experience with nucleotide chemistry. We analyse theoretically this scheme and compare it to the classical minimal replicator model. We show that for an example hexanucleotide template mirroring that is used by von Kiedrowski (Bioorganic chemistry frontiers, 1993) for the analysis of the classical minimal replicator, the proposed scheme is expected to result in higher template self-replication rate. The proposed self-replicating scheme based on an informational leaving group is expected to outperform the classical minimal replicator because of a weaker template duplex bonding, resulting in reduced template inhibition.

D-Amino acids are largely excluded from protein synthesis, yet they are of great interest in biotechnology. Unnatural amino acids have been introduced into proteins using engineered aminoacyl-tRNA synthetases (aaRSs), and this strategy might be applicable to D-amino acids. Several aaRSs can aminoacylate their tRNA with a D-amino acid; of these, tyrosyl-tRNA synthetase (TyrRS) has the weakest stereospecificity. We use computational protein design to suggest active site mutations in Escherichia coli TyrRS that could increase its D-Tyr binding further, relative to L-Tyr. The mutations selected all modify one or more sidechain charges in the Tyr binding pocket. We test their effect by probing the aminoacyl-adenylation reaction through pyrophosphate exchange experiments. We also perform extensive alchemical free energy simulations to obtain L-Tyr/D-Tyr binding free energy differences. Agreement with experiment is good, validating the structural models and detailed thermodynamic predictions the simulations provide. The TyrRS stereospecificity proves hard to engineer through charge-altering mutations in the first and second coordination shells of the Tyr ammonium group. Of six mutants tested, two are active towards D-Tyr; one of these has an inverted stereospecificity, with a large preference for D-Tyr. However, its activity is low. Evidently, the TyrRS stereospecificity is robust towards charge rearrangements near the ligand. Future design may have to consider more distant and/or electrically neutral target mutations, and possibly design for binding of the transition state, whose structure however can only be modeled.

International audience; The different phases of the eukaryotic cell cycle are exceptionally well-preserved phenomena. DNA decompaction, RNA and protein synthesis (in late G1 phase) followed by DNA replication (in S phase) and lipid synthesis (in G2 phase) occur after resting cells (in G0) are committed to proliferate. The G1 phase of the cell cycle is characterized by an increase in the glycolytic metabolism, sustained by high NAD+/NADH ratio. A transient cytosolic acidification occurs, probably due to lactic acid synthesis or ATP hydrolysis, followed by cytosolic alkalinization. A hyperpolarized transmembrane potential is also observed, as result of sodium/potassium pump (NaK-ATPase) activity. During progression of the cell cycle, the Pentose Phosphate Pathway (PPP) is activated by increased NADP+/NADPH ratio, converting glucose 6-phosphate to nucleotide precursors. Then, nucleic acid synthesis and DNA replication occur in S phase. Along with S phase, unpublished results show a cytosolic acidification, probably the result of glutaminolysis occurring during this phase. In G2 phase there is a decrease in NADPH concentration (used for membrane lipid synthesis) and a cytoplasmic alkalinization occurs. Mitochondria hyperfusion matches the cytosolic acidification at late G1/S transition and then triggers ATP synthesis by oxidative phosphorylation. We hypothesize here that the cytosolic pH may coordinate mitochondrial activity and thus the different redox cycles, which in turn control the cell metabolism.

International audience; To better understand the energetic status of proliferating cells, we have measured the intracellular pH (pHi) and concentrations of key metabolites, such as adenosine triphosphate (ATP), nicotinamide adenine dinucleotide (NAD), and nicotinamide adenine dinucleotide phosphate (NADP) in normal and cancer cells, extracted from fresh human colon tissues. Cells were sorted by elutriation and segregated in different phases of the cell cycle (G0/G1/S/G2/M) in order to study their redox (NAD, NADP) and bioenergetic (ATP, pHi) status. Our results show that the average ATP concentration over the cell cycle is higher and the pHi is globally more acidic in normal proliferating cells. The NAD + /NADH and NADP + /NADPH redox ratios are, respectively, five times and ten times higher in cancer cells compared to the normal cell population. These energetic differences in normal and cancer cells may explain the well-described mechanisms behind the Warburg effect. Oscillations in ATP concentration, pHi, NAD + /NADH, and NADP + /NADPH ratios over one cell cycle are reported and the hypothesis addressed. We also investigated the mitochondrial membrane potential (MMP) of human and mice normal and cancer cell lines. A drastic decrease of the MMP is reported in cancer cell lines compared to their normal counterparts. Altogether, these results strongly support the high throughput aerobic glycolysis, or Warburg effect, observed in cancer cells.

Network methods are useful for reducing data complexity and modelling biological and pathogenic processes on a system level, thus having strong potential for prioritising testable hypotheses in Huntington’s disease (HD) research. Several types of mathematical formalisms are being used for network analysis of signal variability in HD datasets, each of them shedding particular light on transcriptional codes and network dynamics in HD. We tested for HD sub-graphs that may be consistently highlighted by several of these mathematical formalisms and whether this may be linked to specific features of the HD process and translate into a more selective level of gene prioritisation. We will present data based on integrating two types of HD networks and discuss how network comparison methods may add value to systems modelling in HD.

Osmotic pressure influences cellular shape. In a growing cell, chemical reactions and dilution induce changes in osmolarity, which in turn influence the cellular shape. Using a protocell model relying upon random conservative chemical reaction networks with arbitrary stoichiometry, we find that when the membrane is so flexible that its shape adjusts itself quasi-instantaneously to balance the osmotic pressure, the protocell either grows filamentous or fails to grow. This behavior is consistent with a mathematical proof. This suggests that filamentation may be a primitive growth mode resulting from the simple physical property of balanced osmotic pressure. We also find that growth is favored if some chemical species are only present inside the protocell, but not in the outside growth medium. Such an insulation requires specific chemical schemes. Modern evolved cells such as E. coli meet these requirements through active transport mechanisms such as the phosphotransferase system.

The authors present optical waveguide modulation results obtained using Wannier-Stark localization in InGaAs-InAlAs superlattices grown by molecular beam epitaxy on InP substrates. It is shown that the results are in good agreement with a previously reported wavefunction model. The authors experimentally investigate the modulation behavior as a function of the electroabsorptive superlattice thickness. For this purpose the authors introduce the extinction ratio per unit waveguide length and per unit electric field as a relevant figure of merit that is broadly applicable to any electroabsorptive superlattice modulator. It is shown that inhomogeneous broadening imposes an optimum thickness for the electroabsorptive superlattice. >

We investigate the electroabsorption properties of an InGaAs‐InAlAs superlattice optical waveguide. When reverse biased, the structure exhibits large extinction ratios over short waveguide lengths with very low drive voltages by using low‐energy oblique transitions below the superlattice band gap. Although the structure has been optimized for modulation, laser emission is observed under forward bias. The peak emission wavelength stands in the ‘‘blue‐shift’’ region which opens a way to straightforward laser‐modulator monolithic integration.

We report the first room‐temperature observation of Wannier–Stark localization under waveguide configuration in a short‐period InGaAs‐InAlAs superlattice. Using the ‘‘oblique’’ transition connecting a hole localized in a well with an electron localized in the adjacent well we have achieved a modulator having a 20 dB extinction ratio and a 3 dB attenuation with a drive voltage as low as 0.8 V. Our device is a 560‐μm‐long waveguide operating at 1.55 μm under TE polarization mode.