Category Archives: Cell Biology

Five Salk professors named among most highly cited researchers in the world – Salk Professors Joanne Chory Joseph Ecker Rusty Gage Reuben Shaw and Kay…

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Salk ProfessorsJoanne Chory,Joseph Ecker,Rusty Gage,Reuben ShawandKay Tyehave been named to theHighly Cited Researchers listby Clarivate. The list identifies researchers who demonstrate significant influence in their chosen field or fieldsthrough the publication of multiple highly cited papers.Professors Chory, Ecker and Gage have been named to this list every year since 2014, when the regular annual rankings began. This is Professor Tyes fourth consecutive time and Professor Shaws second consecutive time receiving the designation. Joseph Nery, a research assistant II in the Ecker lab, was also included on the list.

In the race for knowledge, it is human capital that isfundamentaland this list identifies and celebrates exceptional individual researchers who are having a great impact on the research community as measured by the rate at which their work is being cited by others,says David Pendlebury, senior citation analyst at the Institute for Scientific Information, part of the Web of Science group at Clarivate.

Such consistent production of highly cited reports indicates that the work of these researchers has been repeatedly judged by their peers to be of notable significance and utility, as based on data from the Web of Science, the worlds largest publisher-neutral citation index, with almost 1.9 billion cited references going back to 1900. This years list, which includes 6,167 researchers from more than 60 countries, recognizes researchers across multiple fields whose citation records position them in the highest ranks of influence.

Joanne Chory

Chory is a professor in, and director of, Salks Plant Molecular and Cellular Biology Laboratory, a Howard Hughes Medical Institute investigator, and holder of the Howard H. and Maryam R. Newman Chair in Plant Biology. Chory has won numerous prestigious awards for her work including the Gruber Genetics Prize and the Breakthrough Prize. She also co-directs SalksHarnessing Plants Initiativea bold approach to address climate changeby optimizing a plants natural ability to capture and store carbon and adapt to diverse climate conditions.

Joseph Ecker

Ecker is a professor in Salks Plant Molecular and Cellular Biology Laboratory, director of the Genomic Analysis Laboratory and a Howard Hughes Medical Institute investigator. He is also the Salk International Council Chair in Genetics. He was the first to show that the epigenome is highly dynamic in brain cells during the transition from birth to adulthood. Ecker is the recipient of multiple recentNational Institutes of Health BRAIN Initiative grants, and he is charting the epigenetic differences between brain cell types to better understand disorders such as schizophrenia and Alzheimers disease.

Rusty Gage

Gage, a professor in the Laboratory of Genetics and holder of the Vi and John Adler Chair for Research on Age-Related Neurodegenerative Disease, is the president of the Salk Institute. He discovered that the adult brain continues to produce new neurons throughout the life span in a process known as neurogenesis. Most recently, he, and a team of Salk researchers were awarded anAmerican Heart Association-Allen Initiative in Brain Healthgrant to pursue a new collaborative approach to understanding, detecting and potentially treating Alzheimers disease and age-related cognitive decline.

Reuben Shaw

Shaw, a professor in the Molecular and Cell Biology Laboratory and holder of the William R. Brody Chair, is the director of the Salk Cancer Center, a recipient of the National Cancer Institute Outstanding Investigator Award, and leads SalksConquering Cancer Initiative. He discovered direct connections between cancer and metabolism and continues to work on how nutrient deprivation and cellular energy levels control cancer and other diseases.

Kay Tye

Tye is a professor in Salks Systems Neurobiology Laboratory and holder of the Wylie Vale Chair. Sheseeks to understand the neural-circuit basis of emotion that leads to motivated behaviors such as social interaction, reward-seeking and avoidance. Last year, she published a seminal paper describing herdiscovery of a brain circuit that controls alcohol drinkingbehavior in mice and can be used as a biomarker for predicting the development of compulsive drinking later on. This year, she published work furthering her investigation on the neural circuits associated with the experience of loneliness.

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Five Salk professors named among most highly cited researchers in the world - Salk Professors Joanne Chory Joseph Ecker Rusty Gage Reuben Shaw and Kay...

UCD Post-doctoral Research Fellow Level 1, School of Medicine job with UNIVERSITY COLLEGE DUBLIN (UCD) | 234707 – Times Higher Education (THE)

Applications are invited for a temporary post of a UCD Post-doctoral Research Fellow Level 1 within the UCD School of Medicine

University College Dublin is seeking applications for a Postdoctoral Research Fellow position to work on a collaborative research project in Precision Oncology Ireland (a Science Foundation Ireland Strategic Partnership Programme). The project is in collaboration with AstraZeneca.

Immune Checkpoint inhibitors (ICIs) have opened a new avenue for cancer therapy. However, responses are variable between different patients and cancer types. For instance, clinical studies of ICIs in epithelial ovarian cancer have yielded low response rates despite being an extremely antigenic tumour, with a very dynamic tumour microenvironment, The objective of this study is to work with AstraZeneca, systems biologists and clinician scientists to improve the efficacy of ICIs in low response cancers such as epithelial ovarian cancer - the most lethal form of female cancer.

The main aims of the project are to:

This project is carried out by an interdisciplinary team at Systems Biology Ireland (SBI) at UCD and AstraZeneca (Cambridge, UK). This post will be located at SBI and jointly supervised by Prof Walter Kolch (systems biology, proteomics) and Prof Donal Brennan (clinician, single cell sequencing). The position may also include opportunities to visit AstraZeneca. The ideal candidate will have a strong background in (onco)immunology and cell biology, a solid working knowledge of signal transduction and omics methods to analyse signalling processes.

This is a research focused role, where you will conduct a specified programme of research supported by research training and development under the supervision and direction of a Principal Investigator.

The primary purpose of the role is to further develop your research skills and competences, including the processes of publication in peer-reviewed academic publications, the development of funding proposals, the mentorship of graduate students along with the opportunity to develop your skills in research led teaching.

Precision Oncology Ireland (POI; http://www.precisiononcology.ie) is a consortium of 5 Irish Universities, 6 Irish Charities, and 8 companies aiming to develop new diagnostics and therapeutics for the personalised treatment of cancer. The consortium is part-funded by Science Foundation Ireland under their Strategic Partnership Programme. The shared vision is to combine cutting-edge genomics, proteomics, metabolomics and imaging technologies integrated through computational analysis and modelling to generate molecular profiles that allow us to better understand cancer pathogenesis, progression and response to therapies. Bringing together experimental and computational advances combined through data integration is a key competitive advantage of the POI consortium. The results will be better diagnostics, personalised cancer therapies, and acceleration of cancer drug discovery and development.

Systems Biology Ireland (SBI, http://www.ucd.ie/sbi), established in 2009, has successfully developed an integrated mathematical modelling and experimental research programme focusing on the design of new diagnostic and therapeutic approaches to diseases, primarily cancer, based on a systems level, mechanistic understanding of cellular signal transduction networks. To accomplish these goals, SBI uses mathematical and computational modelling approaches in combination with cutting edge experimental technologies in genomics, transcriptomics, proteomics, advanced microscopy and flow cytometry as well as cell biology and molecular biology methods. SBI's expertise, particularly in the area of modelling in systems pharmacology and therapeutics, strategically position it at the crossroads between biology and medicine. The purpose-built SBI facility sits in the space between the UCD Conway Institute and the Health Sciences Centre (UCD Charles Institute of Dermatology and School of Medicine). It is physically linked to both buildings, providing access to existing technology platforms, educational and conference facilities and ideally placed to train allied healthcare professionals. The facility houses a multidisciplinary team of some 50 researchers including bioinformaticians

Salary: 38,631 - 41,025 per annumAppointment on the above range will be dependent on qualifications and experience

Closing date: 17:00hrs (local Irish time) on 27th November 2021

Applications must be submitted by the closing date and time specified. Any applications which are still in progress at the closing time of 17:00hrs (Local Irish Time) on the specified closing date will be cancelled automatically by the system. UCD are unable to accept late applications.

UCD do not require assistance from Recruitment Agencies. Any CV's submitted by Recruitment Agencies will be returned.

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UCD Post-doctoral Research Fellow Level 1, School of Medicine job with UNIVERSITY COLLEGE DUBLIN (UCD) | 234707 - Times Higher Education (THE)

A New Future Market Insights Study Analyses Growth of Incubator Analyzer Market in Light of the Global Corona Virus Outbreak – The Courier

Incubators are used to cultivate and preserve microbiological cells and microbiological cultures in scientific labs. The incubators maintain optimum humidity, temperature, and other states of affairs such as the carbon dioxide and oxygen content of the inside atmosphere. Incubators are essential for various experimental work in molecular biology, cell biology, and microbiology. Incubators are used to culture both eukaryotic cells as well as bacterial cells. Incubators are produced in a variety of sizes, from warm rooms to preserve and cultivate a large number of samples to bench-top models for small experiments.

The Incubator Analyzer is designed to examine and perform anticipatory maintenance on incubators as well as radiant warmers. Incubator analyzer simultaneously measures the airflow, sound, relative humidity, and also varied range of autonomous temperatures. It can be used with open infant warmers, closed as well as forced-convection incubators including air-controlled, transportable, and baby-controlled units.

For more insights into the Market, request a sample of thisreport@ https://www.futuremarketinsights.com/reports/sample/rep-gb-8997

Market Dynamics Global Incubator Analyzer Market

Infant incubation analysis and growth in incubation for hatching eggs are key driving factors

Incubators are most widely utilized in incubation of microbes as well as in the preservation of plant and animal cell lines. The growth in the field of microbiological research is increasing the requirement to maintain the perfect environmental conditions in the incubator, thereby supporting the demand for incubator analyzers, for measuring and managing the optimum incubator environment throughout the experiment.

Incubator analyzer also plays a vital role in the infant incubation for creating a healthy environment for infant care. A surging number of premature births demanding critical infant care will continue to generate the need for infant incubation. The incubator analyzer plays a crucial role here by recording the test results, creating and maintaining the right environment, recording every detail in long term testing and analyzing the complex test records. These are the key driving factors for incubator analyzer market. The increasing use of incubation for various purposes like infant incubation, microbiological incubation, egg-hatching, insects incubation, and others is giving rise to the demand of incubator analyzer in the market.

The advanced research facilities and high focus on inventions in the field of biology, are boosting the growth of incubator analyzers

Increasing research and development activities in the industries like biotechnology, clinical research, pharmaceutical, hospitals, and research laboratories are stimulating the growth of global incubator analyzer market. Furthermore, the presence of numerous incubator analyzers and intensifying application of incubators for hatching eggs, and incubating insects are driving the incubator analyzer market.

Introduction of neonatal incubator analyzer for a newborn infant suffering from particular disease or disorder further triggers the growth of incubator analyzer market. However, the high cost of incubators, as well as incubator analyzers and lack of awareness in developing countries, are some of the restraints for the growth of the incubator analyzer market.

Key Players- Global Incubator Analyzer Market

The market is centralized with a finite number of manufacturers and is mostly dominated by big players like FLUKE. Some of the key players operating in the incubator analyzer market worldwide are-

Regional Outlook- Global Incubator Analyzer Market

North America, the earliest adopter of technological advances, to represent a key market for incubator analyzer

North America has been a prominent consumer region of incubator analyzers, and in the forecast period, it is anticipated to remain dominant. Owing to high expenditure on healthcare sector and substantial investments in the biological and clinical research sector. Europe is also an important market for manufacturers of incubator analyzer as the region has been raising high demand for neonatal incubator analyzers over the recent past. Asia Pacific is estimated to witness significant growth in incubator analyzer market owing to strong presence of majority of manufacturers in the APEJ region along with booming hatching egg industry. The Middle East and Africa is expected to experience moderate growth in the incubator analyzer market in coming years.

The report is a compilation of first-hand information, qualitative and quantitative assessment by industry analysts, and inputs from industry experts and industry participants across the value chain. The report provides in-depth analysis of parent market trends, macro-economic indicators, and governing factors, along with market attractiveness as per segment. The report also maps the qualitative impact of various market factors on market segments and geographies.

The report covers exhaustive analysis on:

Regional analysis includes:

For Information On The Research Approach Used In The Report, Request Methodology@ https://www.futuremarketinsights.com/askus/rep-gb-8997

Segmentation- Global Incubator Analyzer market

Global incubator analyzer market is segmented on the basis of product type, test type, and region.

Global Incubator Market by Modularity

Global Incubator Analyzer Market by Equipment Type

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A New Future Market Insights Study Analyses Growth of Incubator Analyzer Market in Light of the Global Corona Virus Outbreak - The Courier

Emergex Vaccines Raises US$11 million to Advance Pipeline of Synthetic T-Cell Vaccines for Infectious Diseases – GlobeNewswire

PRESS RELEASE

Emergex Vaccines Raises US$11 million to Advance Pipeline of Synthetic T-Cell Vaccines for Infectious Diseases

Abingdon, Oxon, UK, 18 November 2020 Emergex Vaccines Holding Limited (Emergex), a company tackling major global infectious disease threats through the development of synthetic set point vaccines which prime the T-cell immune response, today announces that it has raised $11 million in a funding round supported by new and existing investors. This round follows a successful $11 million Series A completed in January 2020.

The proceeds of this funding round will, among other things, enable Emergex to further advance and execute its vaccine development strategy, producing vaccine candidates for some of the worlds most threatening and virulent diseases such as COVID-19, Dengue Fever and pandemic flu.

Emergexs next generation vaccines have been designed to expand the bodys natural immune response by programing CD8+ T-cells to rapidly recognise and respond to pathogens. This approach is aimed at providing effective prevention of disease while eliminating the allergic, autoimmune or antibody mediated side effects associated with traditional vaccines. Emergexs vaccines are 100% synthetic - requiring no biology for manufacturing - thus having the potential to be rapidly produced and cost-effectively scaled. They are delivered through a microneedle system which allows for convenient administration and require no cold chain in storage or distribution.

Storme Moore-Thornicroft, co-founder and COO of Emergex, commented: The current COVID-19 pandemic, the ongoing threat of pandemic flu and the global challenge of Dengue Fever demonstrate the urgent need to rethink traditional approaches to vaccine development. This new funding round demonstrates our investors confidence in the Company to meet that need and belief that our unique technology can play a critical role, creating safe, effective vaccines that can be rapidly developed and deployed.

We welcome our new investors to the Company and appreciate the continued support of our existing investors and look forward to rapidly advancing the clinical development of our novel vaccine candidates.

- Ends -

For further information, please contact:

Phone: +44 (0) 1235 527589

Email: smt@emergexvaccines.com

Phone: +44 (0)20 3709 5700

Email: Emergex@consilium-comms.com

About Emergex

Emergex, a UK-based biotechnology company headquartered in Abingdon, UK, is pioneering the development of synthetic set point vaccines which prime the T-cell immune response to address some of the worlds most immediate health threats such as COVID-19, Dengue Fever, Zika, Ebola, pandemic flu and serious intra-cellular bacterial infections.

These set-point vaccines modify the initial immune status of recipients in a way that primes their immune systems to recognise subsequent infectious agents much like a natural infection would do, preventing an acute or severe manifestation of the disease.

Emergex combines validated technologies together with the very latest scientific insights to develop its vaccines, including using synthetic peptide codes determined on actual infected cells and using a proprietary gold nanoparticle carrier system for programming.

The Company has a growing pipeline of vaccine candidates. The most advanced development programme is a vaccine for Dengue Fever, which may also be disease modifying for other Flaviviruses such as the Zika and Yellow Fever viruses. Emergex also has programmes in development for a universal Influenza vaccine and a universal Filovirus vaccine (including viruses such as Ebola and Marburg) and discovery programmes for a Yellow Fever Booster vaccine and a Chikungunya vaccine.

Emergex has partnered with the Institute of Molecular and Cell Biology (IMCB) of Singapore to develop a vaccine for the emerging threat of Hand, Foot and Mouth (HFM) disease and has signed a Memorandum of Understanding (MoU) with Brazil-based Oswaldo Cruz Foundation Fiocruz for the development of viral vaccines. This initially covers the development of a vaccine that universally targets diseases within the flavivirus family such as Dengue Fever, Zika and Yellow Fever but could be expanded to include the development of vaccines to target other viral families that are endemic to the region.

Find out more online at http://www.emergexvaccines.com.

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Emergex Vaccines Raises US$11 million to Advance Pipeline of Synthetic T-Cell Vaccines for Infectious Diseases - GlobeNewswire

Nereid Therapeutics Launches: ATP $50M Series A NewCo Co-Founded with Brangwynne, Pioneer of Biomolecular Condensates Field – Newswise

Newswise BOSTON, Nov. 16, 2020 /PRNewswire/ --Apple Tree Partners (ATP), a leading life sciences venture firm, today announced the launch of Nereid Therapeutics, a company dedicated to discovering new disease treatments by applying pioneering research and technologies in biomolecular condensates. ATP created Nereid with Clifford P. Brangwynne, Ph.D., professor in the Department of Chemical and Biological Engineering at Princeton University and an investigator at the Howard Hughes Medical Institute. Nereid commences operations with a $50 million Series A funding commitment from ATP.

Brangwynne, a biophysicist, is a pioneer of the field of biomolecular condensateswork for which he has won numerous awards, including a 2018 MacArthur Foundation "Genius" grant. He has discovered and elucidated the biophysical principles underlying how liquid-liquid phase separation drives the organization, material properties, function, and dysfunction of these ubiquitous structures in living cells. The Nereid drug discovery platform builds from a set of proprietary technologies, developed in the Brangwynne Lab at Princeton, that utilize advanced microscopy and computer vision to enable the precise measurement, interrogation, and control of phase separation in living mammalian cells. The platform holds promise to enable completely new approaches to discovering and developing therapeutics across a range of diseases; Nereid's near-term efforts will focus on certain cancers and neurodegenerative disorders in which pathological protein behaviors are governed or influenced by phase transitions.

"We are excited to partner with Cliff, an originator of and luminary in the fast-expanding field of condensate biophysics, to translate the vast potential of this science into new medical treatments to improve patients' lives," said Spiros Liras, Ph.D., a venture partner at ATP who will be Nereid's interim CEO. "Nereid possesses a unique suite of technologies with unmatched capabilities in phase separation, droplet visualization, and machine-learning-enabled quantitative mapping and measurementand together these tools comprise a system well-suited to rapidly identify and study novel therapeutic interventions."

The Nereid Board of Directors chaired by Seth Harrison, M.D., ATP's founder and managing partner, includes ATP venture partner and Chief Scientific Officer Michael Ehlers, M.D., Ph.D.; Liras; and Robert J. Hugin, former Chairman and Chief Executive Officer of Celgene Corporation. Brangwynne will maintain a Board observer seat and will chair Nereid's Scientific Advisory Board.

Clifford Brangwynne received a B.S. (2001) in Materials Science and Engineering from Carnegie Mellon University and a Ph.D. (2007) in Applied Physics from Harvard University. He was a postdoctoral researcher at the Max Planck Institute of Molecular Cell Biology and Genetics and the Max Planck Institute for the Physics of Complex Systems from 2007 to 2010, prior to joining the faculty of Princeton University in 2011, where he is currently a professor in the Department of Chemical and Biological Engineering, and an investigator at the Howard Hughes Medical Institute. His pioneering work on biomolecular condensates has been recognized with numerous awards, including a Macarthur Fellowship (2018), Wiley Prize (2020), Blavatnik Award (2020), and the HFSP Nakasone Award (2021).

About ATP

Founded in 1999, ATP is a leader in life sciences venture capital. ATP creates companies starting with assets at various stages, from working with scientists on pre-IP ideas, to spinning out assets from existing companies. We provide flexible capital, strategic insight, and operational resources to build sustainable, research-driven enterprises that create therapies for unmet medical needs.We invest in our companies from seed stage through IPO and beyond, enabling their success with our world-class team of venture partners and EIRs. For more information, visitwww.appletreepartners.com.

About Nereid Therapeutics

Nereid Therapeutics, an ATP company, is discovering new disease treatments using proprietary state-of-the-art technologies for generating, visualizing, and measuring liquid-liquid phase separation and the resulting biomolecular condensates. Nereid applies leading expertise in soft matter physics and cell biology to pioneer completely new ways to fight intractable diseases. For more information, visitwww.nereidtx.com.

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Nereid Therapeutics Launches: ATP $50M Series A NewCo Co-Founded with Brangwynne, Pioneer of Biomolecular Condensates Field - Newswise

Adaptive tuning of cell sensory diversity without changes in gene expression – Science Advances

INTRODUCTION

A central question in cell biology is how a population of cells deals with an ever-changing environment. A canonical paradigm for cellular responses to environmental challenges is the genetic switch, perhaps best exemplified by the lac operon (1), where cells sense changes in environmental factors and respond by changing gene expression. The response time scale of this strategy is limited by that of transcription and translation (~1 hour), leaving cells vulnerable to rapid fluctuations in the environment. A contrasting strategy that allows cells to cope with uncertain or rapidly changing environments is bet hedging, where cell populations diversify their phenotypes even within stable environments, by exploiting inherent stochasticity in cellular processes (26). Bet hedging allows subpopulations of cells to be prepared in advance, by maintaining a heterogeneous distribution of phenotypes matched to the repertoire of environments they might encounter in the future. Although genetic switching and bet hedging provide contrasting survival strategies with distinct advantages, they are not mechanistically exclusive. Bacteria can control the degree of phenotypic diversity in an environment-dependent manner by dynamically modulating gene expression noise (79). Here, we demonstrate that cells can modulate their phenotypic diversity even in the absence of gene expression changes through posttranslational processes, thus implementing fast control of phenotypic diversity.

The chemotaxis signaling pathway of Escherichia coli detects and responds to temporal changes in the extracellular concentrations of chemoeffector molecules through receptor-kinase complexes consisting of thousands of interacting two-state chemoreceptor proteins and kinases (6, 10). An adaptation mechanism, mediated by methylation and demethylation of the chemoreceptors, modulates the sensitivity of the system. Like in many other sensory systems in biology (1113), bacteria respond to relative changes in the signal (14), thus following the Weber-Fechners law of psychophysics (1517) and fold-change detection (FCD) (18, 19). The wealth of quantitative data that has been collected for this system through population-averaged measurements (14, 2023) provides a powerful foundation for examining how individual cells differ from the average in their sensory response and whether and how such diversity is modified upon adaptation.

To quantify the sensitivity of individual cells in the presence of different background-stimulus levels, we combined single-cell fluorescence resonance energy transfer (FRET) measurements of the chemotaxis signaling activity (24, 25) with a microfluidic system for fast stimulus modulation. We found that in the absence of any background signal, the individual cells sensitivities are distributed over about one decade of concentration, but upon adaptation to a background signal, the distribution of sensitivities narrows down to about one tenth of a decade, thus focusing the populations sensitivities to the relevant signal range. Combining experiments and mathematical analyses, we show how the population of cells exploits a standing variation in the degree of allosteric receptor coupling and the environment-dependent covalent modification of the receptors to tune the diversity in signaling sensitivities, which emerges in a class of allosteric models of two-state receptor activity. Crucially, this modulation of sensory diversity does not require any changes in the expression level of proteins, and hence, this mechanism can operate rapidly and in the absence of growth. Rather, it is a network-level property that arises through an adaptive change in the nonlinear mapping between molecular states and sensory phenotype.

To quantify the sensitivity of the chemotaxis system in individual E. coli cells, we stimulated them with short pulses of -methylaspartate (MeAsp)a nonmetabolizable analog of the chemoattractant aspartate (26)while monitoring the output of the signaling pathway using an in vivo single-cell FRET measurement of the activity of the kinase CheA (Fig. 1) (24, 25). To measure instantaneous responses of cells without confounding effects due to adaptation, we developed a polydimethylsiloxane (PDMS)based microfluidic device capable of fast (~0.1 s) switching between stimulus levels (see Materials and Methods, Fig. 1A, and fig. S1), nearly 100-fold faster than the cells adaptation time scale upon a small stimulus, which is on the order of 10 s (20). Cells grown to mid-exponential phase [optical density (OD) = 0.46 0.01] were washed in motility buffer and gently loaded in the device where they attached to the surface of the coverslip. We then subjected the cells to a sequence of eight identical subsaturating step stimuli presented over zero background while measuring the FRET level in each individual cell (~100 cells per experiment) (Fig. 1B). To avoid sampling highly correlated responses from a single cell due to the relatively slow temporal fluctuation in the kinase activity [correlation time ~12 s; (25)], measurements were conducted over >100 s with 15-s intervals between consecutive stimuli. A saturating step of MeAsp (>0.5 mM) was used to determine the FRET level corresponding to zero kinase activity at the beginning of each measurement (Fig. 1B). The FRET level after subtracting this zero-kinase level, hereafter called the FRET signal, is proportional to the kinase activity [see Materials and Methods; (21)].

(A) Fast and precise control of input stimuli within our bespoke microfluidic device. Top: Temporal profile of the ligand stimulus within the device, measured using a fluorescent dye. Our stimulus protocol involves one large stimulus followed by eight subsaturating step stimuli. Bottom: Superimposing multiple stimulus time series (each in a different color) demonstrates fast and highly reproducible relaxation for both steps up (left) and steps down (right). For a drawing and more details of the device, see fig. S1. (B) Responses are highly variable both across isogenic cells from the same growth culture and over time within the same cell. Response time series (FRET signal normalized by its steady-state level) for 5 representative cells out of the 133 measured in a single experiment are shown. Blue shading indicates times at which MeAsp step stimuli were applied (4 M, except the first stimulus, which was 0.5 mM). Gray circles indicate FRET response, and red lines indicate its moving average with a 1.5-s window. (C) Poststimulus activity is defined as the median FRET signal level (black line) during the 3-s step stimulus (blue shading) relative to the steady-state FRET level. (D) Summary of response variability upon 4 M MeAsp steps for all 133 cells measured in the experiment of (B). All responses (poststimulus activities) Ri (light gray) upon repeated application of identical steps are shown for every measured cell, sorted by rank of their median response R (dark gray). Note that Ri and R can take negative values due to measurement noise (fig. S2). The cumulative distribution of median response (traced out by the R point series) is broad, indicating extensive diversity across cells. a.u., arbitrary units.

The instantaneous response of a cell to a step stimulus was quantified by the poststimulus activity defined as the FRET signal relative to the steady state: Ri = Fi/Fss, where Fi is the median of the FRET signal over the 3 s during the ith step stimulus and Fss is the steady-state FRET signal, defined as the average over the entire time series except the time points during and right after the step stimuli (see Materials and Methods; Fig. 1C). The mean level of measurement noise for each individual response was 17% of the steady-state FRET signal (fig. S2), and the response distributions were stationary during the measurements (fig. S3). Sorting cells by their median poststimulus activity reveals substantial cell-to-cell variability (Fig. 1D), consistent with previous reports of phenotypic variability in this system (6, 25, 2730). Within each cell, we also observe large variations in the responses to identical stimuli (Fig. 1D, light gray dots), consistent with previous reports of temporal (behavioral) variability in individual cells adapted to a constant environment (6, 24, 25, 31). We ruled out cell cycle phase as a source of the cell-to-cell variation in kinase responses, as the latter demonstrated no correlation with cell length (fig. S4).

The standard method for determining the sensitivity of a signaling pathway is to fit the sigmoidal K1/2H/([L]H+K1/2H) to dose-response measurements and determine 1/K1/2 as the sensitivity of the cell. This approach has been used to quantify the dose response of populations of E. coli cells using FRET-based methods (21, 23, 32) and non-adapting single cells (25). However, this approach becomes impractical for measuring the response of single cells in the presence of adaptation because of the limited photon budget in single-cell FRET (25). Therefore, we devised an alternative strategy for determining the distribution of K1/2 within a cell population without the need to measure dose-response curves from individuals (Fig. 2).

(A) Principle of extracting the K1/2 distribution, p(K1/2), without dose-response measurements. K1/2, defined as the stimulus level that yields half-maximal poststimulus activity (R = 0.5), is typically determined by measuring dose-response curves (middle), which can vary from cell to cell. Here, we instead measure the distribution of R upon a stimulus of magnitude [L]j, p(R([L]j)), because the fraction of cells with K1/2 smaller than a given stimulus magnitude [L]j (p(K1/2 < [L]j); colored at the top) is equal to the fraction of cells whose poststimulus activity R([L]j) is less than one-half (p(R([L]j) < 0.5); colored at the bottom). (B) By repeating experiments of the type depicted in Fig. 1 at different stimulus step sizes [L]j, we build up the cumulative distribution of K1/2, p(K1/2 < [L]j). Each of the three panels on the left shows the summary of responses (as in Fig. 1D) for an experiment with a different [L]j (added MeAsp concentration, given in M by bold-faced numbers within panels), where sorting cells by their median poststimulus activity R (dark gray dots) provides the cumulative distribution of R, p(R([L]j) < r), corresponding to the fraction of cells whose response to [L]j is smaller than r (0 r 1). Using the identity illustrated in (A), the cumulative distribution of K1/2 (p(K1/2 < [L]j); rightmost) can be constructed by reading off values for p(R([L]j) < r) at r = 0.5 for each applied stimulus level [L]j. Error bars in the right show 95% bootstrap CIs.

To determine the distribution of K1/2, we exploited a simple identity relating the distribution of K1/2 to that of R, the (median) poststimulus activity of individual cells, which states that the fraction of cells with K1/2 smaller than a given stimulus magnitude [L] is equal to the fraction of cells whose (median) poststimulus activity R([L]) is less than one-half (Fig. 2A)p(K1/2<[L])=p(R([L])<0.5)(1)

Thus, from the distributions of the within-cell median poststimulus activities, one can construct the cumulative distribution function (CDF) of K1/2 of the population by determining, for each step stimulus intensity [L], the relative rank of the cell whose median poststimulus activity is 0.5 (Fig. 2B). Equation 1 is valid for any monotonic dependence of R on [L] and does not assume any specific steepness of a cells response curve or variation of it across cells.

Following this approach, we first determined the median of the poststimulus activity of individual cells adapted to a uniform environment with no MeAsp in the background, by stimulating cells with step stimuli that ranged from 0 to 30 M MeAsp (Fig. 3A). From these data, we extracted the distribution of K1/2 (inverse sensitivity), which was well approximated by a log-normal distribution (see Materials and Methods; Fig. 3, B and C). We found that in zero background, the sensitivity of individual cells to MeAsp was distributed over a wide range covering about one decade (~1 M < K1/2 < ~10 M).

(A) Summary of responses to step stimulation by MeAsp (gray dots: response to individual steps Ri, colored dots: median response of each cell R). Background concentration ([L]0) and step size ([L]) are shown in M at the top and within each panel, respectively. Cells are sorted by their median response. (B) Cumulative distribution of K1/2, p(K1/2 < [L]), of responses to MeAsp in cells adapted to three different background concentrations of MeAsp, [L]0 = {0,100,200} M, constructed from the data in (A) through the procedure outlined in Fig. 2. Curves represent fits by log-normal distributions. Error bars are 95% bootstrap CIs. The concentrations of stimuli used to define saturating responses are indicated by the triangles. (C) The distributions of K1/2 computed from the fits in (B) reveal that diversity in K1/2 is strongly attenuated upon adaptation to both 100 and 200 M MeAsp. Note that in this panel, the distribution at each background concentration is centered by normalizing K1/2 by the mode of the distribution to facilitate visual comparison. (D) Cumulative distribution of K1/2, p(K1/2 < [L]), of responses to serine in cells adapted to different background concentrations of serine, [L]0 = {0,1} M.

Given the well-characterized adaptation to ambient chemoattractant concentration at the population level, we wondered whether and how the single-cell distribution of sensitivities could be affected by adaptation to a constant nonzero background of MeAsp. Consistent with the population-level FRET measurements (14, 21), the average of the K1/2 distribution shifted with the background stimulus level due to sensory adaptation when cells were adapted to 100 M MeAsp before step stimulation (Fig. 3B). Unexpectedly, the diversity in response sensitivity across cells also changed drastically, with the K1/2 distribution becoming much narrower upon adaptation (Fig. 3C). A similar collapse in the K1/2 distribution width was found to occur for cells adapted to a higher (200 M) background of MeAsp. We further determined that this sensory diversity tuning is not specific to the MeAsp receptor Tar, as the distribution for serine, the cognate ligand of the other major chemoreceptor Tsr, demonstrated a similar collapse in width upon adaptation (Fig. 3D and fig. S5). Thus, the environment-dependent tuning of response diversity is not specific to a single receptor species and appears to be a general property of the bacterial chemotaxis network.

Recent studies have shown that cell populations can control the level of phenotypic diversity in an environment-dependent manner by modulating the variance of the protein abundance distributions (79). Here, by contrast, experiments were carried out under conditions in which neither the cognate receptors nor any other protein can be synthesized (due to auxotrophic limitation; see Materials and Methods). The observed tuning of sensory diversity must therefore be attributable to a mechanism that involves posttranslational processes rather than changes in gene expression.

To understand the molecular mechanism underlying this adaptive tuning of diversity in cell response sensitivities (Fig. 3), we turned to modeling. The receptor-kinase complexes of the chemotaxis system in E. coli and other species are arranged in hexagonal arrays of trimers of dimers that respond cooperatively to signals (33, 34). The activity of such clusters can be modeled using an extension of the Monod-Wyman-Changeux (MWC) model of allostery (35) and has been shown to agree with a large body of experimental data (20, 23, 3642). In this model (Fig. 4A and Supplementary Text), allosteric interactions between n coupled receptors form signaling teams within which all n receptors (and associated kinase molecules) share the same activity states (active or inactive). The free energy difference between the two receptor states is determined not only by the ligand concentration [L] (analogous to the oxygen concentration in the classical MWC model for hemoglobin) but also by the average methylation level m of receptors. Because of feedback from downstream adaptation enzymes, the value of m at steady state, in turn, depends on the background stimulus level [L]0, i.e., m = m([L]0). Kinase activity upon a step change in input from a given background [L]0 to another stimulus level [L] depends on two parameters n and m*, where m* is the receptor methylation level in the absence of ligand stimulus. Values of the parameters n and m* for E. coli chemoreceptors have been constrained by a large body of population FRET data (20, 21, 36, 37, 39, 42) and have been shown to vary as a function of expression level ratios between key chemotaxis signaling proteins (23, 25). Given that these ratios are affected by stochastic gene expression, the values of n and m* can vary across individual cells of the population (25), whereas values of other biochemical parameters (e.g., the dissociation constants of the receptors) are intrinsic to the structure of relevant proteins, which can be assumed invariant across isogenic populations of cells (see Supplementary Text).

(A) Schematic for allosteric MWC model of the receptor kinase complex. The effective number of coupled receptor dimers n affects the response of kinase activity a upon a step change in ligand concentration from [L]0 to [L], through the expression a = (1 + exp (f(n, m*, [L]0, [L])))1, where m* is the methylation level of the receptors in the absence of ligand. Both n and m* can vary across cells due to differences in gene expression. (B) Two limiting cases of cell-to-cell variation in the model parameters. Model 1 (red solid lines): m* is fixed, but n varies across cells. Model 2 (blue dotted lines): n is fixed, but m* varies across cells. (C to E) Fits of models 1 and 2 to the distribution of steady-state kinase activity a0 (C), population-averaged dose-response curves (D), and distribution of logK1/2 (E). Black corresponds [in (C) and (D)] to measured data and [in (E)] to probability density computed from model fits to cumulative distributions (see fig. S7). Error bars represent 95% bootstrap CIs.

The observed diversity in K1/2 values might thus reflect cell-to-cell differences in the value of n, m*, or both. To discriminate between these possibilities, we first considered two models that represent limiting cases (Fig. 4B and fig. S6). In model 1, the value of m* is fixed across cells, but the value of n varies across the population. In model 2, n is fixed and m* varies. Both models could fit the distribution of steady-state kinase activity (Fig. 4C) previously measured in isogenic populations (25), as well as the population-averaged dose-response data (Fig. 4D). However, the two models yield contrasting predictions for the underlying diversity in single-cell sensitivity. Whereas model 1 with variability only in the size of receptor coupling n demonstrated a tuning of K1/2 diversity upon adaptation to MeAsp in close agreement with the experimental data, model 2 with variability only in m* demonstrated little or no diversity tuning, with the width of the K1/2 distribution remaining approximately constant, with or without adaptation to MeAsp (Fig. 4E). A more general model in which both n and m* vary across cells also yielded consistent results: Fitting with this model yielded a broad distribution for n (CV(n) = 0.41) but a very narrow one for m* (CV(m*) = 0.02) (fig. S8). In a similar manner, the observed diversity tuning of response sensitivity to serine stimuli could also be explained by the variation in the number of coupled Tsr receptors while keeping m* fixed (fig. S5).

Thus, MWC modeling implicates as the predominant source of response diversity a single parameter, the degree of allosteric coupling n for the receptor cognate to the applied ligand stimulus. The model yields excellent fits to the changes in the shape of the K1/2 distribution p(K1/2) upon adaptation without assuming any changes in the underlying parameter distribution p(n). Consistently, further model-based analysis of the dose-response data (fig. S9 and Supplementary Text) did not detect significant changes in the distribution p(n) over the different backgrounds [L]0 across which diversity tuning (i.e., a change in the width of p(K1/2)) is observed. These modeling results thus suggest that while variation in n is the key ingredient for response diversity, the posttranslational mechanism that accounts for adaptive tuning of that diversity does not require a change in the degree of variation in n across cells.

To pinpoint the mechanism responsible for diversity tuning with the MWC model, we focused on the simplest variant (model 1) that reproduced the observed diversity tuning assuming cell-to-cell variation in only a single parameter, n. We first investigated how diversity in K1/2 (as quantified by its coefficient of variation, CV(K1/2)) depends on the adapted state background [L]0 in this model while holding fixed the distribution p(n). CV(K1/2) demonstrated two plateaus: At low [L]0, K1/2 is highly variable with CV(K1/2) approaching 0.5, whereas at high [L]0, diversity is strongly suppressed with CV(K1/2) attenuated by nearly an order of magnitude (Fig. 5A, gray curve). Thus, diversity in response sensitivity demonstrates two regimes: high diversity at low [L]0 and low diversity at high [L]0.

(A) The adaptive MWC model predicts a switch from high to low sensory diversity as the background stimulus level [L]0 is increased from zero. The coefficient of variation of K1/2 (CV(K1/2)) at [L]0 = 0 M and [L]0 = {100,200} M MeAsp (black points) falls within the high- and low-diversity regimes, respectively, predicted by model 1 (gray curve). To test the predicted transition regime, we measured the K1/2 distribution at the crossover point [L]0*=KI(e(m0m*)1)2.1M (dotted line). The measured CV (magenta) for cells adapted to [L]0=2M([L]0*) is in excellent agreement with the model prediction (blue point). All CV values were computed from parameters of the log-normal distributions fitted to the CDF of K1/2 (fig. S12). Error bars were computed by propagating the SE of the parameters. (B) The model accurately predicts the full distribution of K1/2 diversity and the population-level response at [L]0*. Model prediction (blue) and experimental results (magenta) for the population dose-response curve (top), CDF (middle), and probability density function (PDF, bottom) of K1/2 at [L]0 = 2 M MeAsp. Model parameters were constrained only by the data at [L]0 = {0,100,200} M data (Fig. 4), with no additional fit parameters for the [L]0 = 2 M data. Model behavior at [L]0 = {0,100,200} M backgrounds is shown for reference (gray dashed curves). Error bars represent 95% bootstrap CIs.

A key quantity that determines how the diversity in n affects diversity in K1/2 is the susceptibility of K1/2 with respect to n, defined by the absolute partial derivative n log (K1/2). In broad terms, when this susceptibility is high, variation in n contributes strongly to diversity in K1/2; when it is low, the effects of variation in n can be suppressed. We found that the susceptibility n log (K1/2) computed using the MWC model (and evaluated at the population mean, n = n) also exhibits a decreasing profile as a function of [L]0 with two plateaus (fig. S10), closely mirroring the CV(K1/2) profile (Fig. 5A, gray curve).

The existence of two regimes with contrasting susceptibilities n log (K1/2) has been predicted theoretically for chemoreceptor MWC models [fig. S11; (37)]. In this class of models, the methylation-dependent free energy difference between the active and inactive states of ligand-unbound receptors follows a linear relationship fm = (m([L]0) m0), where corresponds to the free energy per methyl group, and m0 is an offset methylation level at which fm = 0. Because of nonlinearities arising in the allosteric mechanism, the dependence of K1/2 on n changes qualitatively as the methylation level crosses m0 [fig. S11; (37)]. When the methylation level is low (i.e., m([L]0) < m0), K1/2 becomes inversely proportional to n (specifically, K1/2 KI/n, where the dissociation constant of the inactive receptor KI sets the concentration scale), and therefore, the degree of receptor coupling n strongly affects sensitivity. In this regime, the susceptibility n log (K1/2) is thus high, and we can expect cell-to-cell variation in n to cause substantial K1/2 diversity across cells. Conversely, when the methylation level is high (i.e., m([L]0) > m0), K1/2 becomes independent of n (37). In this regime, the susceptibility n log (K1/2) is thus low, and we can expect K1/2 diversity to be suppressed. The crossover between the two regimes is set by the offset methylation level m0 at which the free-energy contribution from covalent modification feedback vanishes (i.e., fm = (m([L]0) m0) = 0). Thus, the drastic difference in diversity we found at zero and high (100 and 200 M) background concentrations (Fig. 5A, black points) could be explained by the switch from high to low susceptibility n log (K1/2) as the adapted-state covalent modification level m([L]0) increases beyond m0.

The success of the adaptive MWC model in explaining the observed response diversity motivated us to further test its predictive power: Given the model calibrated by data obtained so far in the high- and low-diversity regimes, how accurately could we predict K1/2 diversity at an as-yet unmeasured background concentration? Using experimentally determined values for the parameters KI, , m0 (14, 20), and m*, the crossover background concentration [L]0* at which the adapted state modification level reaches m0 (i.e., m([L]0*)=m0) is readily computed from the model (see the Supplementary Materials) as [L]0*=KI(e(m0m*)1)2.1 M MeAsp (Fig. 5A, vertical dashed line), at which the model predicts an intermediate level of K1/2 diversity (Fig. 5A, blue point). We thus opted to measure the distribution of K1/2 at a background of 2 M (Fig. 5 and fig. S12). The results are in excellent quantitative agreement with model predictions not only at the level of CV(K1/2) (Fig. 5A, magenta point) but also for the entire shape of the distribution (Fig. 5B, middle and bottom) and population-level response (Fig. 5B, top).

Thus, the adaptive MWC model of chemoreceptors provides not only a mechanistic explanation for but also predictive power over the observed diversity tuning in the bacterial chemotaxis system, in which posttranslational receptor modification mediates the transition between two regimes of sensory diversity: When the background stimulus level is low (regime I), receptor modification falls below m0 and diversity is augmented; when the background stimulus is high (regime II), modification exceeds m0 and response diversity is attenuated.

By combining a novel microfluidic device with a single-cell FRET assay, we characterized the diversity of chemoeffector responses and its dependence on background stimulus conditions within isogenic populations of E. coli. We found that the width of the sensitivity distribution is strongly modulated in an environment-dependent manner under experimental conditions (auxotrophic limitation) that do not permit gene expression changes. Mathematical modeling provided remarkably accurate predictions and a mechanistic explanation for this diversity tuning that requires only a change in the posttranslational modification of signaling proteins. Below, we discuss the molecular requirements and functional implications of this novel mechanism for diversity tuning, as well as the significance of its implementation without changes in gene expression.

It has long been known that the intracellular variable modulating bacterial chemotactic sensitivity upon sensory adaptation is the covalent modification level m of chemoreceptors (43, 44). Naively, therefore, one might expect the diversity in sensitivity we observed across cells to be the result of cell-to-cell differences in this key internal variable. Our MWC model analysis revealed, however, that the main contribution to response diversity comes not from m but instead from n, the degree of chemoreceptor coupling. While n is a coarse-grained parameter that can be affected by both the size and composition of receptor clusters, the likely dominant contribution to its variation is the expression-level ratio between the two major receptor species Tar and Tsr, which has been shown to vary strongly across cells (45). A recent study in adaptation-deficient cells found that the diversity in dose-response parameters (K1/2 and the Hill coefficient, H) across cells could be largely explained by variation in this ratio (25). Varying the Tar/Tsr ratio determines the direction of chemotactic cell migrations when subjected to two conflicting chemoeffector gradients (46)whereas cells with high Tar/Tsr ratios migrate preferentially toward MeAsp (the cognate ligand for Tar), cells with low Tar/Tsr ratios do so toward serine (the cognate ligand for Tsr). Thus, the diversity in response sensitivity we observed in our FRET experiments can be interpreted to reflect diversity in sensory preference, which could, in turn, significantly affect population-level chemotactic performance in environments that present multiple stimuli.

Optimal strategies for biological adaptation depend on accessible information about the environment (47, 48). When environmental cues provide sufficiently accurate information, tracking strategies that accordingly adjust phenotypes can provide an advantage. When environmental cues do not carry sufficient information, bet-hedging strategies can provide readiness of different individuals to different environments.

For sensory adaptation in bacterial chemotaxis, the zero-background condition is singular in that there is no information about the nature of future environmental signals. E. coli cells express five types of chemoreceptors (Tar, Tsr, Tap, Trg, and Aer) that sense a variety of stimuli (10). Given that the relative expression levels of these receptors are highly variable across cells (45) and that different receptor species are mixed within clusters (49), the combinations of the effective degree of coupling for each receptor type realized in a cell population are numerous.

The switch-like transition in K1/2 diversity we observed (Fig. 5) enables cells to diversify their response sensitivities (and hence sensory preference) for different ligands when all ligand concentrations are near zero and uncertainty is at a maximum (Fig. 6A). This could be beneficial in improving the readiness of the isogenic population for many future signalsa sensory bet-hedging strategy. By contrast, once a relevant signal is detected, such as a gradient of aspartate, cell-to-cell variability in sensitivity can lead to detrimental desensitization (when sensitivity is too low) or sensory saturation (when sensitivity is too high) that precludes effective tracking of the signal as cells climb the gradient by chemotaxis. Our experiments revealed that the width of the distribution of sensitivities is markedly reduced upon adaptation to higher ligand concentrations, therefore focusing the population on tracking that signal. In summary, this novel mechanism of sensory diversity tuning could enable an isogenic population to be ready for any signal when the environment is uncertain but switch to tracking a specific signal once it is detected.

(A) Diversity tuning in chemotactic response sensitivity. In environments with low background signals below the crossover level ([L](x)<[L]0*), uncertainty about future signals is high, and the population diversifies its sensory preference. In environments with high background signals above the crossover level ([L](x)>[L]0*), the population can attenuate its sensory diversity and switch to tracking the perceived signal. (B) Phenotypic diversity can be tuned by environmental modulation of either gene expression or posttranslational processes. Top: Gene expressiondependent diversity tuning involves modulation of stochastic gene expression in response to environment changes (79), leading to different distributions of expressed protein counts across cells in different environments. This mechanism can tune phenotypic diversity without environmental modulation of posttranslational expression-phenotype mappings (represented here by the box labeled by f). Bottom: By contrast, the posttranslational diversity tuning mechanism we found in this study involves environmental modulation of the expression-phenotype mapping (f, implemented in bacterial chemotaxis by covalent modification of allosteric chemoreceptors). This mechanism requires no environmental modulation of gene expression and hence can achieve rapid tuning of phenotypic diversity.

Another challenge unique to the zero-background signal condition is that there is no information about the magnitude of the future signal. In dealing with the uncertainty in the signal strength, two key performance measures of a cell population as a sensory system are the width of the range of input signals over which it can respond (response range; fig. S13A) and the degree to which the input signal is amplified at the output level (gain; fig. S13A). For a homogeneous cell population with a sigmoidal stimulus-response curve, it is known that there is an inherent trade-off between these two performance measures (50, 51). A large response range requires a shallow response curve, but this inevitably reduces the response gain, and vice versa (fig. S13B). To understand whether and how the diversified sensitivity contributes to the performance of a cell population, we computed the performance measures of gain and response range in the zero-stimulus background condition using the MWC model and compared a cell population with diversity in the number of coupled receptors n to a hypothetical homogenous population (fig. S13B). As expected from the diversity in the sensitivity due to the variation in n, the diverse population exhibits a broader response range than the homogeneous population (fig. S13B). On the other hand, each individual cell in the diverse population maintains a high response gain (fig. S13B), reflecting the insensitivity of the gain to the variation in n in the low-background signal regime of the coupled two-state receptors (37). Thus, a population with diverse sensitivity can outperform homogeneous populations in dealing with the unpredictability associated with the zero background.

As noted above, the high- and low-diversity regimes we found here were identified in an earlier theoretical study as regimes I and II, respectively, of cooperative chemosensing (37). In that study, it was found that cooperativity (i.e., n > 1) extends the dynamic range of sensing to lower concentrations due to the 1/n scaling of K1/2 in regime I, whereas it increases signal gain by increasing the steepness of response (i.e., the Hill coefficient, H > 1) in regime II. Subsequent studies found that when E. coli cells are adapted to higher concentrations in regime II ([L]0 KI), responses to step changes (39) and time-varying signals (18, 19) depend only on relative changes of chemoeffector concentrations (14). This property of FCD provides a robust sensory strategy in many natural contexts where absolute signal intensities tend to carry less information than relative contrast (19). By contrast, in regime I of cooperative sensing ([L]0<[L]0*), the sensory response becomes proportional to the absolute change in chemoeffector input. Although this connection between the cooperative sensing regimes and FCD is intriguing, we note that the diversity tuning we found here is not causally related to FCD. One can construct a network model that demonstrates the linear-response/FCD transition but does not demonstrate diversity tuning (see the Supplementary Materials and fig. S14). Evidently, cooperativity in E. coli chemoreceptors provides multiple benefits in sensory performance: increased dynamic range/signal gain, FCD, and diversity tuning of response sensitivity. The molecular parameters that define cooperativity and the resulting signaling regimes are thus likely under pleiotropic selection (52) and would provide fertile ground for future studies of trade-offs and optimality (53) in the design of allosteric signaling systems.

Recent pioneering studies have provided a handful of examples of how bacteria can modulate phenotypic diversity in an environment-dependent manner, by changing the distribution of protein abundance across cells [Fig. 6B, top; (79)]. By contrast, the diversity-tuning mechanism we found here is implemented by posttranslational processes (Fig. 6B, bottom). The mechanism hinges on a nonlinear relationship (represented by the box with label f in Fig. 6B, bottom) between the phenotype of interest (here, the response sensitivity or its inverse, K1/2), a gene expressiondependent parameter (here, the degree of receptor coupling, n), and a posttranslational variable that varies in response to the environment (here, the covalent modification state of chemoreceptors, m).

An important difference between gene expressiondependent and posttranslational mechanisms of diversity tuning lies in the achievable speed for environment-dependent modulation of diversity. Whereas the former is limited by the time scale of gene expression (typically measured in minutes), the latter can be implemented by much faster biochemical processes (the fastest covalent modifications occur on subsecond time scales). Another significant difference is in biochemical costs and requirements: Gene expressiondependent diversity tuning requires synthesis of new proteins and hence may be rendered useless under nutrient-limited conditions, whereas the posttranslational mechanism studied here could be operational in any environment that supports the required type of covalent modification (here, methylation). Thus, posttranslational diversity tuning could be advantageous when cell populations need to adapt to fast-switching environments such as the gut (54, 55) and/or under poor nutrient conditions such as marine environments (56). Given the ubiquity of nonlinear functions throughout cellular biochemistry, we expect that posttranslational diversity tuning could play a role in the survival of a broad range of cell types in a variety of biological contexts.

The strain used is a derivative of E. coli K-12 strain RP437 (HCB33). The FRET acceptor-donor pair (CheY-mRFP and CheZ-mYFP) is expressed in tandem from plasmid pSJAB106 (25) under an isopropyl--d-thiogalactopyranoside (IPTG)inducible promoter with induction level of 50 M IPTG. The glass-adhesive mutant of FliC (FliC*) was expressed from a sodium salicylate (NaSal)inducible pZR1 plasmid (25) with induction level of 3 M NaSal. We transformed the plasmids in VS115, a cheY cheZ fliC mutant of RP437 [a gift of V. Sourjik; (25)], referred to as wild-type strain in the main text.

Microfluidic devices were constructed from PDMS on a 24 mm 60 mm cover glass (#1.5) following standard soft lithography protocols (57). Briefly, the master molds for the device were created with a positive photoresist (AZ 9260, MicroChemicals) on a silicon wafer using a standard photolithography technique (57). Approximately 20-m-high master molds were created. To fabricate the device, the master molds were coated with a 5-mm-thick layer of degassed 10:1 PDMS-to-curing agent ratio (Sylgard 184, Dow Corning). The PDMS layer was cured at 80C for 1 hour and then cut and separated from the wafer, and holes were punched for the inlets and outlet. The PDMS device was then bonded to a cover glass. The PDMS was cleaned with transparent adhesive tape (Magic Tape, Scotch) followed by rinsing with (in order) isopropanol, methanol, and Millipore-filtered water. The glass was rinsed with acetone, isopropanol, methanol, and Millipore-filtered water. The PDMS device was tape-cleaned an additional time before the surfaces of the device and coverslip were treated with a plasma generated by a corona treater. Then, the PDMS device was laminated to the coverslip and then baked at 80C overnight.

Sample preparation in the microfluidic device was conducted as follows: Of the five inlets of the device (fig. S1A), four inlets are connected to reservoirs (liquid chromatography columns, C3669; Sigma-Aldrich) filled with motility media containing various concentrations of MeAsp through polyethylene tubing (Fine Bore Polythene Tubing, 0.58 mm inside diameter, 0.96 mm outer diameter, Smiths Medical). Another inlet (located at the extremity) is connected to a reservoir filled with motility media containing fluorescein, which enables us to observe the flow of the solution and allows us to calibrate the pressure applied to the reservoirs before each experiment. The tubing was connected to the PDMS device through stainless steel pins that were directly plugged into the inlets or outlet of the device. Cells washed and suspended in motility media were loaded in the device from the outlet of the device and attached to the glass surface in the microfluidic device by reducing the flow speed inside the chamber. The pressure inside the reservoirs connected to the inlets was controlled by computer-controlled solenoid valves (MH1, Festo) that promptly switches between atmospheric pressure and higher pressure introduced from a source of pressurized air. The pressure applied to the reservoirs was adjusted before each experiment by observing the flow of the fluorescent solution under the microscope so that all stimulus solutions are delivered to imaging areas. The FRET measurements were conducted at three different positions in a microfluidic device, and an identical stimulus protocol was repeated at every position.

Single-cell FRET microscopy and cell culture were carried out essentially as described previously (25). In brief, cells from a saturated overnight culture were grown to OD 0.45 to 0.47 in 10 ml of tryptone broth (1% bacto-tryptone and 0.5% NaCl) in the presence of ampicillin (100 g/ml), chloramphenicol (34 g/ml), 50 M IPTG, and 3 M NaSal. Cells were collected by centrifugation (5 min at 5000 rpm) and washed twice with motility media [10 mM KPO4, 0.1 mM EDTA, 1 M methionine, and 10 mM lactic acid (pH 7)] and then resuspended in 2 ml of motility media.

FRET imaging in the microfluidic device was performed using an inverted microscope (Eclipse Ti-E, Nikon) equipped with an oil immersion objective lens (CFI Apo TIRF 60 Oil, Nikon). Yellow fluorescent protein (YFP) was illuminated with a light-emitting diode illumination system (pE-4000, CoolLED, for experiments with MeAsp stimuli, and SOLA SE, Lumencor, for experiments with serine stimuli) through an excitation bandpass filter (FF01-500/24-25, Semrock) and a dichroic mirror (F01-542/27-25F, Semrock), and the fluorescence emission was led into an emission image splitter (OptoSplit II, Cairn) and further split into donor and acceptor channels with a second dichroic mirror (FF580-FDi01-25x36, Semrock) and collected through emission bandpass filters (FF520-Di02-25x36 and FF593-Di03-25x36, Semrock) with a sCMOS (scientific CMOS) camera (ORCA-Flash4.0 V2, Hamamatsu). Red fluorescent protein (RFP) was illuminated in the same way as YFP except that an excitation bandpass filter (FF01-562/40-25 for experiments with MeAsp stimuli and FF01-575/05-25 for experiments with serine; both from Semrock) and a dichroic mirror (FF593-Di03-25x36, Semrock) were used. Additional excitation filter (59026x, Chroma) was used for experiments with serine stimuli. Before each time-lapse measurement, an acceptor image (RFP excitation and RFP emission) and a donor image (YFP excitation and YFP emission) were taken to estimate the RFP expression level and cell volume of each cell used for data analysis. In time-lapse imaging, images were acquired every 0.3 to 0.5 s.

The FRET level of each cell was calculated essentially in the same way as described previously (21, 25, 32). After flat-field correction of the fluorescent images, fluorescent signals, i.e., donor signal (obtained from donor channel: YFP excitation and YFP emission) and FRET-acceptor signal (obtained from FRET-acceptor channel: YFP excitation and RFP emission), were extracted from the images for each individual cell using an image segmentation technique. The extracted raw fluorescent time series were corrected for bleaching by fitting both donor and FRET-acceptor signals with a biexponential function and dividing out the decay to yield donor signal D(t) and FRET-acceptor signal A(t).

We define the FRET index as the decrease in the donor signal D(t), D (0), due to FRET between the donor (mYFP) and acceptor (mRFP1) molecule, normalized by the intensity of donor illumination reaching a cell through the donor excitation filter, D, and cell volume, VcellFRET(t)D/(DVcell)where D was extracted from the flat-field image, and Vcell was estimated from the no-binning YFP image. The FRET index was chosen because D/(DVcell) is proportional to the concentration of active CheA. To show this, we consider the following. First, D can be decomposed asD=DDEFRETQDLDSDtDDVcell[DA]where D, EFRET, QD, LD, SD, tDD, and [DA] are respectively the absorption coefficient of donor, the FRET efficiency of the complex, the quantum yield of donor, the throughput of the donor emission light-path, the quantum sensitivity of the camera for donor emission, the exposure time for the donor image, and the concentration of the donor-acceptor complex. Because D, EFRET, QD, LD, SD, and tDD are all constants once the experimental system is fixed, by introducing C = DEFRETQDLDSDtDD, we write D asD=CDVcell[DA]

So, the FRET index FRET(t) is proportional to [DA] or the concentration of CheYp-CheZ complex [CheYp CheZ]. The concentration of the complex reaches a quasisteady state on the time scale larger than the time scale of hydrolysis of phosphorylated CheY, CheYp, catalyzed by CheZ [~0.5 s; (22)] due to the balance between phosphorylation and dephosphorylation of CheY. Thus, the following holds[DA]=[CheYpCheZ]=akAkZ[CheA]=akAkZ[CheA]Twhere kA and kZ are respectively the rate constants for autophosphorylation of CheA and that for hydrolysis of CheYp by CheZ, a (0 < a < 1) is the fraction of active CheA, and [CheA]T is the total concentration of CheA (25). Given the conservation equation [CheA]T = [CheA] + [CheAp], the last step of the above equations holds when [CheAp] [CheA]T. This is achieved when sufficient amount of CheY-RFP and CheZ-YFP is present in the cell as verified previously (25), and therefore, we exclude cells from analysis whose concentrations of CheY-mRFP1 and CheZ-mYFP are low.

Together, the FRET index isFRET(t)=DDVcell=C[DA]=CkAkAa[CheA]T=Ca[CheA]Twhere C = CkA/kZ, which is invariant between cells. To increase the signal-to-noise ratio, D can be computed, rather than directly extracting from D(t), asD(t)=D0r(t)+r0+r(t)where r(t) A(t)/D(t) = r0 + r(t); r0 and D0 are the ratio r and donor signal D, respectively, in the absence of FRET, which is obtained by applying saturating stimulus to cells (21); and = A/D is the absolute value of the ratio of changes in the fluorescent signals due to FRET, which is a constant dependent on a measurement system (21, 25, 32). Fss was defined as the average FRET index excluding time points during and right after stimuli (15 s for a saturating stimuli and 6 s for subsaturating step stimuli). Fi was defined as the median FRET index during a step stimulus (10 time points). Response to each step stimulus Ri was defined as Ri = Fi/Fss.

A measured FRET time series FRET(t) can be conceptually decomposed into the true signal FRETtrue(t), which is proportional to the concentration of active CheA, and the measurement noise arising from the finite number of photons (t)FRET(t)=FRETtrue(t)+(t)

Because the true signal is also fluctuating, it is not trivial to estimate the magnitude of the measurement noise in general. However, we can exploit the fact that, when a saturating stimulus is presented, the true signal, and therefore also its variance, goes to zero (21) and henceFRET(tsat)=(t)

Thus, the variance of the FRET time series during a saturating stimulus can be equated with the measurement noiseVar(FRET(tsat))=Var()

When evaluating a FRET level upon a stimulus, we used the median value of n (=10) consecutive measurement points to mitigate the contribution of measurement noise. Because the measurement noise is delta correlated, the contribution of measurement noise is Var(FRET(tsat))/n. We estimated this quantity by computing the SE of the mean from n consecutive data points during saturating stimuli from each cell and then computed the ensemble average of the value (fig. S2).

Using the identity p(K1/2 < [L]) = p(R < 0.5) (Fig. 2), the response distributions were converted to the CDF of the response constant K1/2 as schematically shown in Fig. 2A. The error bar of the estimated p(K1/2 < [L]) was obtained by bootstrapping over single responses (95% CI). The data were fitted by the CDF of the log-normal distribution y(x)=0x1x(2)1/2exp((lnx)2/22)dx by the weighted least square fitting method. The weights were given by the inverse of the width of the 95% CI except the data points of [L] = 0 (no step stimulus) and [L] = [L]sat (saturating stimulus), which were weighted with an arbitrary high value. Extracted parameters and their SE were (, ) = (0.822 0.069,0.51 0.12) for [L]0 = 0 M, (, ) = (1.659 0.039,0.291 0.027) for [L]0 = 2 M, (, ) = (4.715 0.012,0.051 0.006) for [L]0 = 100 M, and (, ) = (5.442 0.014,0.052 0.010) for [L]0 = 200 M.

For MeAsp responses, we considered the following three different types of the MWC model, each of which has different variations in the parameter values of m* and n. Model 1: m* is fixed, but n varies. Model 2: m* varies, but n is fixed. Model 3: both m* and n vary. When the parameters are allowed to vary, we assumed the normal distribution for m*, f(m*m,m2), and the log-normal distribution for n, g(nn,n2). We determined the parameters (m*, n, n) for model 1 by the least square method, using the distribution of kinase activity a0, the dose-response curves, and the cumulative distribution of K1/2 (Fig. 4, C to E). The obtained values were (m*, n, n) = (0.445,2.018,0.387). For model 2, both the mean values of m* and n were fixed to the same values as the means of those of model 1, m* = 0.445 and n = 8.1. The SD of m*, m, was chosen to minimize the mean squared error in fitting to the distribution of kinase activity a0, which gave m = 0.02. For model 3, all the parameter values were determined in the same way as model 1. The obtained parameter values were (m, m, n, n) = (0.446,0.010,2.035,0.385), and the correlation coefficient between m* and log(n) was 0.144. For serine responses, we considered only variation in n (model 1). The best-fit parameter values were (m*, n, n) = (0.4838,2.322,0.4174). In fig. S9 (A to D), model 1 was fitted to the data, allowing the log-normal distribution of n to depend on different background conditions, i.e., the parameters m*, n,0, n,0, n,100, n,100, n,200, and n,200 were estimated simultaneously, where the numbers in the subscript indicate the corresponding background MeAsp concentrations. The estimated parameters were m* = 0.455 (0.447 to 0.464), n,0 = 2.030 (1.950 to 2.148), n,0 = 0.409 (0.254 to 0.557), n,100 = 2.206 (2.048 to 2.323), n,100 = 0.426 (0.264 to 0.670), n,200 = 2.057 (1.896 to 2.171), and n,200 = 0.603 (0.449 to 0.872), where the maximum likelihood values and 95% CIs (shown in the parentheses) were obtained from the likelihood function estimated by the Metropolis-Hastings sampling method. The log likelihood function was defined as logL=2Nai=1Na(xii())22i21Ndj=1Nd(xjj())22j21NKk=1NK(xkk())22k2+C, where Na, Nd, and NK are the numbers of data points of the kinase activity distribution (Fig. 4C), the dose-response curves (Fig. 4D), and the CDF of K1/2 (Fig. 3B), respectively; x and are the data point and its uncertainty (SD estimated as 68% CI); () is model prediction; and C is the normalization constant of the likelihood function (which need not be specified for Metropolis-Hastings sampling). The weights in front of the summations were chosen such that it computes the average value of the residuals in each experiment, which gives more equal weight across experiments that might have different statistical power, and that the two datasets (i.e., one that gives the kinase activity distribution and one that gives both the dose-response curves and the CDF of K1/2) have an equal weight.

Acknowledgments: We thank S. Parkinson and V. Sourjik for strains; N. Frankel, A. Waite, and Y. Dufour for help with the microfluidics; S. Boskamp and Z. Rychanavska for cell culture and technical assistance; M. Kamp for microscopy assistance; E. Clay, B. Ait Said, and M. Konijnenburg for software and electronic support; F. Avgidis and S. Grannetia for help with experiments; and H. Mattingly and J. van Zon for discussions and critical reading of the manuscript. Funding: This study was supported by the Allen Distinguished Investigator Program (grant 11562) through the Paul G. Allen Frontiers Group, NIH award R01GM106189, NWO VIDI award 680-47-515, and NWO/FOM Projectruimte grant 11PR2958. Author contributions: K.K., T.E., and T.S.S. designed the research. T.E. and T.S.S. supervised the project and secured funding. K.K., J.M.K., T.E., and T.S.S. conceived the method. K.K. performed the experiments. K.K., T.E., and T.S.S. performed the data analysis and mathematical modeling. K.K. and J.L. performed theoretical analyses in the Supplementary Materials. K.K., J.M.K., T.E., and T.S.S. wrote the manuscript with input from J.L. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.

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Adaptive tuning of cell sensory diversity without changes in gene expression - Science Advances

Frederick Health Hospital Physician Named One Of 10 Top Doctors In The Area – WFMD

November 16, 2020 - 2:05 pm

Its the third time for Dr. Meredith Wenick.

Frederick, Md (KM)) Washingtonian Magazines annual list of top doctors in the region includes a Frederick Health Hospital physician. Doctor Meredith Wernick was picked for the third time by the magazine as one of its top doctors in Maryland, Virginia and Washington DC. She was received this recognition in 2018 and 2019.

Being recognized by my colleagues in this way is truly an honor, Dr. Wernick says in a statement. Im very proud of the care we provide at Frederick Health. What I do is only possible thanks to our dedicated doctors, nurses and staff, al of whom have worked tirelessly to ensure uninterrupted treatment for our patients under extraordinary conditions since March.

Dr. Wernick is board-certified radiation oncologist who works with the Hospitals Radiation Oncology team.

She received her bachelors degrees in chemistry and molecular and cell biology at the University of California at Berkeley in 1996, and spent four yours as a researcher at the UCSF Center with a focus on the genetics of breast cancer, according to a news release from Frederick Health Hospital.

Dr. Wernick completed her medical training at Georgetown University, where she also completed an internship in internal medicine.

She currently sees patients at the James M. Stockman Cancer Institute in Frederick.

Dr. Wernick live in Potomac with husband and two sons.

By Kevin McManus

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Frederick Health Hospital Physician Named One Of 10 Top Doctors In The Area - WFMD

Insights & Outcomes: Cellular bet hedgers and a message from a magnetar – Yale News

This month, Insights & Outcomes is mindful of the mental health implications of COVID-19, the moments when cells act like portfolio managers, and a missive from a Milky Way magnetar.

As always, you can find more science and medicine research news on YaleNews Science & Technology and Health & Medicinepages.

Magnetars, a type of neutron star believed to have an extremely powerful magnetic field, could be the source of some fast radio bursts (FRBs), according to a new study in the journal Nature. FRBs are extremely bright, fast radio emissions that can release more energy in a fraction of a second than the Sun generates over many years. Astronomers discovered the existence of FRBs a decade ago and continue to debate the cause of the signals.

This is the first evidence of an astrophysical source for one FRB, tying it to a galactic neutron star with a large magnetic field and providing evidence that at least some FRBs are consistent with extragalactic magnetars, based on the brightness of this event, said co-author Laura Newburgh, an assistant professor of physics at Yale. Newburgh developed new analysis and measurement information that helped establish the brightness of an FRB emanating from a nearby magnetar located in the Milky Way. The Canadian Hydrogen Intensity Mapping Experiment, a collaboration of 50 scientists, produced the research.

Age, sex, and underlying medical issues have been recognized as major risk factors for an adverse outcome from COVID-19 infection. Now Yale psychiatrists say doctors should also consider another factor that increases risk of death in the pandemic a patients mental health. A new study shows that patients with psychiatric disorders admitted to Yale New Haven Hospital for treatment of COVID-19 were significantly more likely to die than those without a diagnosed mental health disorder. The higher mortality rate held even after controlling for other risk factors such age, sex of the patient, and pre-existing health conditions. The authors theorized that psychiatric disorders such as depression may have a harmful effect on patients immune system response to infection. We need to consider the health of the mind as well as the body when considering treatment options for people diagnosed with COVID-19, said John Krystal, chair of the Department of Psychiatry and senior author of the study. Luming Li, assistant professor of psychiatry, was lead author of the study published in thejournal JAMA Network Open.

In times of stability, cell populations act like investors with large portfolios. They hedge their bets by diversifying receptors on the surfaces of individual cells, preparing the population for sudden swings in the environment. But how can these populations respond quickly to unanticipated changes when the process that dictates composition of those receptors the regulating activity of genes is typically so time consuming?

A new study of E. coli bacteria by Yale scientists shows that when receiving new environmental signals, the diversity of cellular portfolios is reduced 10-fold, allowing the cell population to adjust to changing circumstances.

Essentially, cells instantly stopped hedging their bets and adjusted their sensitivity to focus on following the present signal, effectively consolidating assets into a winning portfolio. The mechanism we found enables a population to very rapidly switch from a bet-hedging mode to an exploitation mode, said Yales Thierry Emonet,professor of molecular, cellular, and developmental biology and of physicsand co-senior author of the study. Before this study, all mechanisms reported to do so also involved gene expression, which is orders of magnitude slower.Keita Kamino is first author of the study, published in the journal Science Advances. The research was conducted in the labs of Emonet and co-senior author Thomas S. Shimizu, group leader at the AMOLF Institute.

Sidi Chen,assistant professor in the Department of Genetics and the Systems Biology Institute and member of the Yale Cancer Center, received a $50,000 research grant from the Alliance for Cancer Gene Therapy (ACGT) to advance a versatile, scalable technology for targeting difficult-to-treat cancers. The technology Chen developed is called MAEGI Multiplexed Activation of Endogenous Genes as an Immunotherapy which leveragesthe natural power of the immune system to fight tumors.

The ACGT Scientific Advisory Council finds Dr. Chens MAEGI technology to be unique and exciting because it simultaneously targets multiple differences and activates multiple immune system responses, said Kevin Honeycutt, CEO and president of ACGT. It has proven to be very effective in animal models. We believe our support will enable its advancement into the clinic where it would have major, life-saving impact on pancreatic and other difficult-to-treat cancers, such as melanoma, glioblastoma and triple negative breast cancer.

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Insights & Outcomes: Cellular bet hedgers and a message from a magnetar - Yale News

Can We Recreate Every Human Cell Type In The Body? This UK Startup Thinks So. – Forbes

Bit Bio, a UK synthetic biology startup backed by Silicon Valley investors, has partnered with the ... [+] London Institute for Mathematical Sciences, marking a milestone in the fusion of biology and mathematics for coding human cells.

Theres a fundamental difference between Bit Bio and most other biotechnology startups. If you just need something to work once, you can find what you need with massive amounts of screening and testing think drug development, or finding a single bacterial cell with the desired qualities.

But, when we make the leap and turn biology into an engineering discipline we can predict, reproduce, and scale, things fall apart. Our understanding of life is too weak.

Thats the fundamental challenge Bit Bio, a UK-based startup, is taking on through a commercial partnership with a non-profit research institution, the London Institute of Mathematical Sciences (LIMS).

Together, they will take on a shared moonshot goal: recreating every human cell type in the body. Not only would this be a monumental scientific milestone, but access to human cells would also accelerate the development of cell therapies, which have always been limited to testing in mice.

Cellular reprogramming is as much of a mathematical problem as it is a biological one. What we've learned about cell types is that the boundaries between cell states and sub cell states sort of blur. If you stop trying to classify them in the traditional way, and just try to map the transcriptional state that is necessary to achieve such a state - then you suddenly have a completely different view on identity, explains Dr. Mark Kotter, the founder and CEO of Bit Bio.

Mark Kotter is a stem cell biologist and neurosurgeon at the University of Cambridge, and ... [+] Founder/CEO of Bit Bio.

Bit Bios cell reprogramming platform allows them to turn on gene expression at will, and flipping the right switches could transition one cell type to another. Thats why their computational team is using neural networks to map recorded gene transcription to cell states. However, it hasnt been the one-size-fits-all toolkit that it often seems to be.

Neural networks is a common buzzword tossed around the tech space. By imitating the circuitry in our brains, neural nets have learned to do tasks ranging from voice recognition and weather forecasting to identifying dementia from EEG recordings.

However, a problem that Kotter soon identified was that the moment you go into complex [biological] data, there's some off the shelf tools that everyone uses, but there isn't really that much math that's, you know, usable. It's not that that kind of maths doesn't exist or people haven't thought about it, it's just that it hasn't crossed the boundary.

How do you discover new biology in massive amounts of data? First, you need new math, and this collision of fields has already proved fruitful for both collaborators.

The interaction with people that come from a different planet, so to speak, is extremely useful, Kotter reflects. The main reason why I'm very excited about working with LIMS is that they are really some of the smartest people in this particular field.

Kotter met Thomas Fink, the founder of LIMS during his PhD in Cambridge, but never imagined they would one day be actively collaborators. Coincidentally, LIMS had a cell identity project on the back burner since 2012 the same year that Kotter began working on the problem.

This convergence embodies the merging of two fields that were not quite as far apart as they seemed. And in this interdisciplinary world, you see the human cell after us at the moment taking shape.

Im the founder of SynBioBeta, and some of the companies that I write about are sponsors of the SynBioBeta conference andweekly digest. Thank you toDesiree Hofor additional research and reporting in this article.

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Can We Recreate Every Human Cell Type In The Body? This UK Startup Thinks So. - Forbes

McGill researchers awarded $1.5 million in NRC collaborative funding – McGill – McGill Reporter

On November 2, the National Research Council of Canada (NRC) announced the results of its collaborative funding program, with a total of over $44 million awarded to institutions across the country. McGill researchers were among the cohort with more than $1.5 million in project funds awarded. In all, 19 McGill projects were supported through these initiatives. The NRC collaborative funding program is organized into three separate themes:

The support of the NRC through this collaborative funding program partners the creativity and talent of our researchers with those at the NRC, said Martha Crago, Vice-Principal, Research and Innovation. Each of these projects has the potential to make a real impact on peoples lives.

One such example is the work of Bioengineering Professor Amine Kamen, who received support for two projects. The first leverages artificial intelligence (AI) to improve the production of biological agents such as vaccines through the pairing of virtual and physical bioreactors. The second examines at the genomic level the production of Adeno-Associated Virus (AAV) vectors. This could lead to dramatically reduced costs for the targeted delivery of gene therapy treatments.

Funding from the NRC is helping us develop these platform technologies, explained Kamen. They will help ensure our preparedness in the situation of emerging or re-emerging infectious diseases.

Another researcher to have two projects funded was Professor Odile Liboiron-Ladouceur. She has been working on methods to incorporate AI into the design of photonic components, which not only accelerates the design cycles but also pushes the performance of photonic integrated circuits a step further. These circuits have multiple applications, including fiber optic networks, satellite communications, medical diagnostics, and other areas.

Funding from NRC enables a fruitful collaboration with NRC world-class scientists who take part in the training of graduate students as next-generation leaders, said Liboiron-Ladouceur, citing another major benefit of the NRC program.

Professor Amine A. Kamen, Bioengineering, for Digital-twin of bioreactor for accelerated design and optimal operations in production of complex biologics and, Genome-wide CRISPR screen to identify genes that increase the yield and functionality of AAV vectors

Professor Odile Liboiron-Ladouceur, Electrical & Computer Engineering, for AI-assisted miniaturization of integrated photonic components and, Silicon Photonics multiplexer design with machine learning methods

Professor Yelena Simine, Science, for AI-Enabled Design of Aptamers

Professor Lawrence R. Chen, Electrical & Computer Engineering, for Terabit optical networks based on quantum dot lasers and photonic integration

Professor Sylvain Coulombe, Chemical Engineering, for Functionalized BNNTs for Energy Applications

Professor Sasha Omanovic, Chemical Engineering for Development of new composite/ functionalized cathodes for bio-electrochemical conversion of CO2 and CH4

Professor Parisa Ariya, Atmospheric & Oceanic Sciences, for Microcosm studies for improved detection, physicochemical process characterization and modelling of the transport, degradation and fate of microplastics in Canadian waters (COVID-19)

Professor Michael Strauss, Anatomy & Cell Biology, for Tracking the mechanism of antibody trafficking across the blood brain barrier with advanced 3D-structure

Professor Sylvain Coulombe, Chemical Engineering, for developing a scalable solvent-free process for functionalization of Boron Nitride Nanotubes

Professor Audrey Helene Moores-Franois, Chemistry, for functionalized chitosan nanocrystals as catalysts for organic transformation reactions

Professor Mark Driscoll, Mechanical Engineering, for full body medical image segmentation for simulation-ready finite element models

Professor Abdolhamid Shafaroud Akbarzadeh, Bioresource Engineering, for bio-inspired Architected Ceramics for High Temperature Applications

Professor Victoria Kaspi, Physics, for a time-domain digital signal processing backend for fast radio burst follow up

Professor Maryam Tabrizian, Biomedical Engineering, for one step multiplex aptamer selection and validation using magnetic nanoparticle aptamer library (aptaMAG) coupled to microfluidic surface plasmon resonance imaging biosensor

Professor Lyle Whyte, Natural Resources Sciences, for an improved bio-inorganic system to couple solar energy to microbial carbon dioxide fixation

Professor Donald Smith, Plant Science, for Core microbes for field pea farming

Professor Jeffrey Bergthorson, Mechanical Engineering, for Optimized configuration of metal energy carrier for renewable energy sources

Read the NRC press release

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McGill researchers awarded $1.5 million in NRC collaborative funding - McGill - McGill Reporter