GBP34292 - GBP60011 per annum + Benefits:
Paramount Recruitment:
Scientific Consultant - Pharmaceutical - Germany
Paramount recruitment has joined forces with a global consultancy that is on the hunt for seasoned Analytical Scientists to work as Consultants across their lifesciences division. This prestigious outfit i
Munich (County), Germany
Salary - DOE:
Paramount Recruitment:
Quantitative Geneticist - Bioinformatics - Switzerland
A global biopharmaceutical giant has a unique opening for a Quantitative Geneticist to join their Bioinformatics & Data Analysis division in Switzerland.
Working alongside a team of computational b
Switzerland
Negotiable:
Stelfox:
Senior Statistician
An exciting and rare opportunity has arisen for a senior Statistician to join the Biometrics team of a global Pharmaceutical company, on a home-based contract.
France
Competitive Salary, Negociable:
Stelfox:
Senior Statistician
An excellent opportunity has arisen for a senior level Statistician at a global Pharmaceutical company, at one of their European sites.
Germany
I-Pharm Consulting Ltd: Senior CRA - Freelance - UK
Negotiable:
I-Pharm Consulting Ltd:
Senior CRA - Freelance - UK
Are you a freelance CRA coming to the end of your contract in the coming weeks or are available to start a new contract ASAP?
I am seeking CRAs to work with a leading CRO who have some of the world's most successful Pharmac
England
Negotiable:
I-Pharm Consulting Ltd:
Contract CRA- Home-based - UK
Are you a freelance CRA coming to the end of your contract in the coming weeks or are available to start a new contract ASAP?
I am seeking CRAs to work with a leading CRO who have some of the world's most successful Ph
England
South Manchester University Hospitals: Trainee Healthcare Scientists in Clinical - Uk - Wales
c.£25,500:
South Manchester University Hospitals:
The interface between computing, biology and medicine is one of the most fertile and fastest growing areas of research and development.
UK - Wales
not specified:
Life Technologies:
Job Location: San Francisco, CA This position is in the Ion Torrent Chemistry R&D group. The selected individual will be part of a high performance, cross-disciplinary team at the cutting edge of sequen
Job Location: San Francisco, CA
Wellcome Trust Sanger Institute: Research Assistant - Cellular Generation and Phenotyping
GBP17400 - GBP21750 per annum + plus excellent benefits:
Wellcome Trust Sanger Institute:
We are seeking self-motivated and enthusiastic researcher with an undergraduate degree in a biological science and cell based assay expertise to support the CGaP programme.
Cambridgeshire, England
Negotiable:
I-Pharm Consulting Ltd:
CRA / Clinical Research Associate / FREELANCE, Cardiovascular (Denmark)
JOB SUMMARY
CRA / Clinical Research / FREELANCE
CRA / Clinical Research Associate home-based in Denmark required by European CRO. Brand new exclusive opportunity has arisen, to work
Denmark
GBP25000 - GBP35000 per annum + Great Benefits:
Northpoint Recruitment:
Bioinformatician
+ Working for a growing company
+ Great career opportunities
+ Competitive Salaries
The Company
My client are a established biotechnology company located in Cambridgeshire and they specialise in providing protein engineering to pharmac
Cambridgeshire, England
Paramount Recruitment: Bioinformatician - NGS Data Analysis
Negotiable:
Paramount Recruitment:
Bioinformatician - NGS Data Analysis - Switzerland
The main purpose of the position is to analyze high-throughput experimental data and extract knowledge to achieve the goals of R&D strategies.
Switzerland
Superb benefits package includes company shares, pension, healthcare and more!:
Illumina:
Excellent opportunity to join an exciting biotech company and help develop their market leading software platform.. If you have experience developing software in C++, and want to join the biotech revolution!
Cambridgeshire
Illumina: Technical Applications Scientists (French or German or Spanish)
Excellent :
Illumina:
Technical Support across Europe to Scientific customers. Good Biology, Mol bol, Genetics or relevant subject from Degree to PhD. Excellent training and working environment.
Cambridge
Postdoctoral Fellow in Tumor Immunology, Lymphoma Immunotherapy Program, Mount Sinai School of Medicine, New York, NY, United States
The Lymphoma Immunotherapy Program at the Mount Sinai School of Medicine has an opening for a Postdoctoral Fellow available in the laboratory of Dr. Joshua Brody, Director. The position will entail a wide variety of moderately complex responsibilties...
Postdoctoral Fellow in Computational Biology, Mar Lab, Albert Einstein College of Medicine of Yeshiva University, Bronx, NY, United States
Seeking a postdoctoral fellow to join our computational biology lab. Candidate must know biostatistics, be an experienced programmer, have good communication skills and be a team player. If you meet these criteria, please send a C.V. to Dr. Jessica...
PhD student Positions, Doctoral Program in Computing and Information Science and Engineering (CISE), University of Puerto Rico at Mayaguez, mayaguez, Puerto Rico
The Doctoral Program in Computer and Information Science and Engineering (CISE) of the University of Puerto Rico at Mayaguez (UPRM) is seeking outstanding candidates for PhD student positions in the following areas: Parallel Computing for Big Data...
Post-Doctoral Fellow, Computational, The J. Craig Venter Institute, Rockville, MD, United States
J. Craig Venter Institute (JCVI) is looking for a Post-Doctoral Fellow to join their Rockville, MD location. The Fellow will work on a ?Dental Caries and Viral? metagenomics and biomarker discovery project. Responsibilities will include...
Postdoctoral position in cancer genomics and bioinformatics, Internal Medicine and The Genome Institute / Maher Lab, Washington University, St Louis, MO, United States
An NIH-funded cancer genomics post-doctoral research position focusing on integrative analysis of next generation sequencing (NGS) data is available within the Maher lab (http://x To fully translate genome-based discoveries into the clinic our group...
Freelance Medical Editor, Cactus Communications Pvt. Ltd., This is a freelance position, India
Cactus Communications Pvt. Ltd. (http://x is a pioneering language services company serving more than 33,000 clients across 95 countries. In line with its mission?growth through effective communication, Cactus offers specialist academic editing,...
Postdoc position in New technologies to improve the persistence and efficacy of bio-based pesticides, Edmund Mach Foundation - Fondazione Edmund Mach, San Michele All'adige, Italy
(126_CRI_BBP) A postdoc position is available in the Interactions in the agro-ecosystems unit, Sustainable Agro-Ecosystems and Bioresources department of the Research and Innovation Centre. The mission of this postdoctoral fellowship is to develop...
Postdoctoral Researcher, Department of Biological Sciences, University of Arkansas Main Campus, Fayetteville, AR, United States
A postdoctoral research position is available in the laboratory of Dr. Ravi Barabote (http://x in the Department of Biological Sciences at the University of Arkansas. The successful candidate will be involved in research focused on genome-level...
Postdoctoral fellow in stem cell biology, Stem Cell Institute, University of Minnesota-Twin Cities, Minneapolis MN, United States
A postdoctoral position is available in our lab at the Stem Cell Institute, University of Minnesota. We have recently discovered that a fusion protein between the transactivation domain of the MyoD protein and the pluripotency factor Oct4 can...
PhD position in translational research, University of Basel, Institute of Pathology, Basel, Switzerland
PhD student position in Translational Research, University of Basel We are seeking for a motivated PhD student to join our translational research group. We are located in the laboratories of the Institute of Pathology of the University Hospital Basel...
Bioinformatics of HIV immune response - Postdoctoral level, Shapiro Lab, Columbia University Medical Center, New York, NY, United States
The Shapiro Laboratory at Columbia University in New York City, working together with the NIH Vaccine Research Center (VRC), in Bethesda, MD, seeks a postdoctoral level researcher in computational biology and bioinformatics for a position starting on...
Postdoctoral position in bioinformatics and machine learning, New York State Center of Excellence in Bioinformatics and Life Sciences, The State University of New York at Buffalo, Buffalo, NY, United States
The State University of New York at Buffalo (SUNY Buffalo) is seeking several Postdoctoral Associates in the areas of machine learning and bioinformatics. The major research focus is on developing machine learning algorithms to address the...
Open rank, faculty position in biostatistics, University of Mississippi Medical Center, Jackson, MS, United States
The Center of Biostatistics and Bioinformatics at the University of Mississippi Medical Center seeks qualified applicants to join our faculty. Rank of appointment will be commensurate with experience. Candidates should have a PhD in biostatistics or...
Bioinformatics Programmer, Metagenomics Center / College of Arts and Science IT, University of Oregon, Eugene, OR, United States
The University of Oregon is looking for a bioinformatics programmer for their new metagenomics center. Details here:http://x Responsibilities include: ? Consultation with individual researchers on methods for analyzing large data sets generated by...
Post-doctoral position in biostatistics and bioinformatics for biomarker identification, Laboratory for Data Analysis Tools, CEA, Paris, France
High-throughput "omic" technologies represent promising opportunities to find new disease biomarkers because of their comprehensiveness and their complementarity. However, integration of such massive and highly heterogeneous data is a bioinformatic...
Estimation of Vaccine Efficacy and Critical Vaccination Coverage in Partially Observed Outbreaks
by Michiel van Boven, Wilhelmina L. M. Ruijs, Jacco Wallinga, Philip D. O'Neill, Susan Hahné
Classical approaches to estimate vaccine efficacy are based on the assumption that a person's risk of infection does not depend on the infection status of others. This assumption is untenable for infectious disease data where such dependencies abound. We present a novel approach to estimating vaccine efficacy in a Bayesian framework using disease transmission models. The methodology is applied to outbreaks of mumps in primary schools in the Netherlands. The total study population consisted of 2,493 children in ten primary schools, of which 510 (20%) were known to have been infected, and 832 (33%) had unknown infection status. The apparent vaccination coverage ranged from 12% to 93%, and the apparent infection attack rate varied from 1% to 76%. Our analyses show that vaccination reduces the probability of infection per contact substantially but not perfectly (?=?0.933; 95CrI: 0.908?0.954). Mumps virus appears to be moderately transmissible in the school setting, with each case yielding an estimated 2.5 secondary cases in an unvaccinated population (?=?2.49; 95%CrI: 2.36?2.63), resulting in moderate estimates of the critical vaccination coverage (64.2%; 95%CrI: 61.7?66.7%). The indirect benefits of vaccination are highest in populations with vaccination coverage just below the critical vaccination coverage. In these populations, it is estimated that almost two infections can be prevented per vaccination. We discuss the implications for the optimal control of mumps in heterogeneously vaccinated populations.
Detection of Mixed Infection from Bacterial Whole Genome Sequence Data Allows Assessment of Its Role in Clostridium difficile Transmission
by David W. Eyre, Madeleine L. Cule, David Griffiths, Derrick W. Crook, Tim E. A. Peto, A. Sarah Walker, Daniel J. Wilson
Bacterial whole genome sequencing offers the prospect of rapid and high precision investigation of infectious disease outbreaks. Close genetic relationships between microorganisms isolated from different infected cases suggest transmission is a strong possibility, whereas transmission between cases with genetically distinct bacterial isolates can be excluded. However, undetected mixed infections?infection with ?2 unrelated strains of the same species where only one is sequenced?potentially impairs exclusion of transmission with certainty, and may therefore limit the utility of this technique. We investigated the problem by developing a computationally efficient method for detecting mixed infection without the need for resource-intensive independent sequencing of multiple bacterial colonies. Given the relatively low density of single nucleotide polymorphisms within bacterial sequence data, direct reconstruction of mixed infection haplotypes from current short-read sequence data is not consistently possible. We therefore use a two-step maximum likelihood-based approach, assuming each sample contains up to two infecting strains. We jointly estimate the proportion of the infection arising from the dominant and minor strains, and the sequence divergence between these strains. In cases where mixed infection is confirmed, the dominant and minor haplotypes are then matched to a database of previously sequenced local isolates. We demonstrate the performance of our algorithm with in silico and in vitro mixed infection experiments, and apply it to transmission of an important healthcare-associated pathogen, Clostridium difficile. Using hospital ward movement data in a previously described stochastic transmission model, 15 pairs of cases enriched for likely transmission events associated with mixed infection were selected. Our method identified four previously undetected mixed infections, and a previously undetected transmission event, but no direct transmission between the pairs of cases under investigation. These results demonstrate that mixed infections can be detected without additional sequencing effort, and this will be important in assessing the extent of cryptic transmission in our hospitals.
Inference of R0 and Transmission Heterogeneity from the Size Distribution of Stuttering Chains
by Seth Blumberg, James O. Lloyd-Smith
For many infectious disease processes such as emerging zoonoses and vaccine-preventable diseases, and infections occur as self-limited stuttering transmission chains. A mechanistic understanding of transmission is essential for characterizing the risk of emerging diseases and monitoring spatio-temporal dynamics. Thus methods for inferring and the degree of heterogeneity in transmission from stuttering chain data have important applications in disease surveillance and management. Previous researchers have used chain size distributions to infer , but estimation of the degree of individual-level variation in infectiousness (as quantified by the dispersion parameter, ) has typically required contact tracing data. Utilizing branching process theory along with a negative binomial offspring distribution, we demonstrate how maximum likelihood estimation can be applied to chain size data to infer both and the dispersion parameter that characterizes heterogeneity. While the maximum likelihood value for is a simple function of the average chain size, the associated confidence intervals are dependent on the inferred degree of transmission heterogeneity. As demonstrated for monkeypox data from the Democratic Republic of Congo, this impacts when a statistically significant change in is detectable. In addition, by allowing for superspreading events, inference of shifts the threshold above which a transmission chain should be considered anomalously large for a given value of (thus reducing the probability of false alarms about pathogen adaptation). Our analysis of monkeypox also clarifies the various ways that imperfect observation can impact inference of transmission parameters, and highlights the need to quantitatively evaluate whether observation is likely to significantly bias results.
Probing the Energy Landscape of Activation Gating of the Bacterial Potassium Channel KcsA
by Tobias Linder, Bert L. de Groot, Anna Stary-Weinzinger
The bacterial potassium channel KcsA, which has been crystallized in several conformations, offers an ideal model to investigate activation gating of ion channels. In this study, essential dynamics simulations are applied to obtain insights into the transition pathways and the energy profile of KcsA pore gating. In agreement with previous hypotheses, our simulations reveal a two phasic activation gating process. In the first phase, local structural rearrangements in TM2 are observed leading to an intermediate channel conformation, followed by large structural rearrangements leading to full opening of KcsA. Conformational changes of a highly conserved phenylalanine, F114, at the bundle crossing region are crucial for the transition from a closed to an intermediate state. 3.9 µs umbrella sampling calculations reveal that there are two well-defined energy barriers dividing closed, intermediate, and open channel states. In agreement with mutational studies, the closed state was found to be energetically more favorable compared to the open state. Further, the simulations provide new insights into the dynamical coupling effects of F103 between the activation gate and the selectivity filter. Investigations on individual subunits support cooperativity of subunits during activation gating.
A Kinetic Platform to Determine the Fate of Nitric Oxide in Escherichia coli
by Jonathan L. Robinson, Mark P. Brynildsen
Nitric oxide (NO?) is generated by the innate immune response to neutralize pathogens. NO? and its autoxidation products have an extensive biochemical reaction network that includes reactions with iron-sulfur clusters, DNA, and thiols. The fate of NO? inside a pathogen depends on a kinetic competition among its many targets, and is of critical importance to infection outcomes. Due to the complexity of the NO? biochemical network, where many intermediates are short-lived and at extremely low concentrations, several species can be measured, but stable products are non-unique, and damaged biomolecules are continually repaired or regenerated, kinetic models are required to understand and predict the outcome of NO? treatment. Here, we have constructed a comprehensive kinetic model that encompasses the broad reactivity of NO? in Escherichia coli. The incorporation of spontaneous and enzymatic reactions, as well as damage and repair of biomolecules, allowed for a detailed analysis of how NO? distributes in E. coli cultures. The model was informed with experimental measurements of NO? dynamics, and used to identify control parameters of the NO? distribution. Simulations predicted that NO? dioxygenase (Hmp) functions as a dominant NO? consumption pathway at O2 concentrations as low as 35 µM (microaerobic), and interestingly, loses utility as the NO? delivery rate increases. We confirmed these predictions experimentally by measuring NO? dynamics in wild-type and mutant cultures at different NO? delivery rates and O2 concentrations. These data suggest that the kinetics of NO? metabolism must be considered when assessing the importance of cellular components to NO? tolerance, and that models such as the one described here are necessary to rigorously investigate NO? stress in microbes. This model provides a platform to identify novel strategies to potentiate the effects of NO?, and will serve as a template from which analogous models can be generated for other organisms.
by Jeffrey E. Markowitz, Elizabeth Ivie, Laura Kligler, Timothy J. Gardner
Bird songs range in form from the simple notes of a Chipping Sparrow to the rich performance of the nightingale. Non-adjacent correlations can be found in the syntax of some birdsongs, indicating that the choice of what to sing next is determined not only by the current syllable, but also by previous syllables sung. Here we examine the song of the domesticated canary, a complex singer whose song consists of syllables, grouped into phrases that are arranged in flexible sequences. Phrases are defined by a fundamental time-scale that is independent of the underlying syllable duration. We show that the ordering of phrases is governed by long-range rules: the choice of what phrase to sing next in a given context depends on the history of the song, and for some syllables, highly specific rules produce correlations in song over timescales of up to ten seconds. The neural basis of these long-range correlations may provide insight into how complex behaviors are assembled from more elementary, stereotyped modules.
Cell Patterns Emerge from Coupled Chemical and Physical Fields with Cell Proliferation Dynamics: The Arabidopsis thaliana Root as a Study System
by Rafael A. Barrio, José Roberto Romero-Arias, Marco A. Noguez, Eugenio Azpeitia, Elizabeth Ortiz-Gutiérrez, Valeria Hernández-Hernández, Yuriria Cortes-Poza, Elena R. Álvarez-Buylla
A central issue in developmental biology is to uncover the mechanisms by which stem cells maintain their capacity to regenerate, yet at the same time produce daughter cells that differentiate and attain their ultimate fate as a functional part of a tissue or an organ. In this paper we propose that, during development, cells within growing organs obtain positional information from a macroscopic physical field that is produced in space while cells are proliferating. This dynamical interaction triggers and responds to chemical and genetic processes that are specific to each biological system. We chose the root apical meristem of Arabidopsis thaliana to develop our dynamical model because this system is well studied at the molecular, genetic and cellular levels and has the key traits of multicellular stem-cell niches. We built a dynamical model that couples fundamental molecular mechanisms of the cell cycle to a tension physical field and to auxin dynamics, both of which are known to play a role in root development. We perform extensive numerical calculations that allow for quantitative comparison with experimental measurements that consider the cellular patterns at the root tip. Our model recovers, as an emergent pattern, the transition from proliferative to transition and elongation domains, characteristic of stem-cell niches in multicellular organisms. In addition, we successfully predict altered cellular patterns that are expected under various applied auxin treatments or modified physical growth conditions. Our modeling platform may be extended to explicitly consider gene regulatory networks or to treat other developmental systems.
Modeling and Measuring Signal Relay in Noisy Directed Migration of Cell Groups
by Can Guven, Erin Rericha, Edward Ott, Wolfgang Losert
We develop a coarse-grained stochastic model for the influence of signal relay on the collective behavior of migrating Dictyostelium discoideum cells. In the experiment, cells display a range of collective migration patterns, including uncorrelated motion, formation of partially localized streams, and clumping, depending on the type of cell and the strength of the external, linear concentration gradient of the signaling molecule cyclic adenosine monophosphate (cAMP). From our model, we find that the pattern of migration can be quantitatively described by the competition of two processes, the secretion rate of cAMP by the cells and the degradation rate of cAMP in the gradient chamber. Model simulations are compared to experiments for a wide range of strengths of an external linear-gradient signal. With degradation, the model secreting cells form streams and efficiently transverse the gradient, but without degradation, we find that model secreting cells form clumps without streaming. This indicates that the observed effective collective migration in streams requires not only signal relay but also degradation of the signal. In addition, our model allows us to detect and quantify precursors of correlated motion, even when cells do not exhibit obvious streaming.
Vinculin can interact with F-actin both in recruitment of actin filaments to the growing focal adhesions and also in capping of actin filaments to regulate actin dynamics. Using molecular dynamics, both interactions are simulated using different vinculin conformations. Vinculin is simulated either with only its vinculin tail domain (Vt), with all residues in its closed conformation, with all residues in an open I conformation, and with all residues in an open II conformation. The open I conformation results from movement of domain 1 away from Vt; the open II conformation results from complete dissociation of Vt from the vinculin head domains. Simulation of vinculin binding along the actin filament showed that Vt alone can bind along the actin filaments, that vinculin in its closed conformation cannot bind along the actin filaments, and that vinculin in its open I conformation can bind along the actin filaments. The simulations confirm that movement of domain 1 away from Vt in formation of vinculin 1 is sufficient for allowing Vt to bind along the actin filament. Simulation of Vt capping actin filaments probe six possible bound structures and suggest that vinculin would cap actin filaments by interacting with both S1 and S3 of the barbed-end, using the surface of Vt normally occluded by D4 and nearby vinculin head domain residues. Simulation of D4 separation from Vt after D1 separation formed the open II conformation. Binding of open II vinculin to the barbed-end suggests this conformation allows for vinculin capping. Three binding sites on F-actin are suggested as regions that could link to vinculin. Vinculin is suggested to function as a variable switch at the focal adhesions. The conformation of vinculin and the precise F-actin binding conformation is dependent on the level of mechanical load on the focal adhesion.
Disease-causing aberrations in the normal function of a gene define that gene as a disease gene. Proving a causal link between a gene and a disease experimentally is expensive and time-consuming. Comprehensive prioritization of candidate genes prior to experimental testing drastically reduces the associated costs. Computational gene prioritization is based on various pieces of correlative evidence that associate each gene with the given disease and suggest possible causal links. A fair amount of this evidence comes from high-throughput experimentation. Thus, well-developed methods are necessary to reliably deal with the quantity of information at hand. Existing gene prioritization techniques already significantly improve the outcomes of targeted experimental studies. Faster and more reliable techniques that account for novel data types are necessary for the development of new diagnostics, treatments, and cure for many diseases.
Human Germline Antibody Gene Segments Encode Polyspecific Antibodies
by Jordan R. Willis, Bryan S. Briney, Samuel L. DeLuca, James E. Crowe, Jens Meiler
Structural flexibility in germline gene-encoded antibodies allows promiscuous binding to diverse antigens. The binding affinity and specificity for a particular epitope typically increase as antibody genes acquire somatic mutations in antigen-stimulated B cells. In this work, we investigated whether germline gene-encoded antibodies are optimal for polyspecificity by determining the basis for recognition of diverse antigens by antibodies encoded by three VH gene segments. Panels of somatically mutated antibodies encoded by a common VH gene, but each binding to a different antigen, were computationally redesigned to predict antibodies that could engage multiple antigens at once. The Rosetta multi-state design process predicted antibody sequences for the entire heavy chain variable region, including framework, CDR1, and CDR2 mutations. The predicted sequences matched the germline gene sequences to a remarkable degree, revealing by computational design the residues that are predicted to enable polyspecificity, i.e., binding of many unrelated antigens with a common sequence. The process thereby reverses antibody maturation in silico. In contrast, when designing antibodies to bind a single antigen, a sequence similar to that of the mature antibody sequence was returned, mimicking natural antibody maturation in silico. We demonstrated that the Rosetta computational design algorithm captures important aspects of antibody/antigen recognition. While the hypervariable region CDR3 often mediates much of the specificity of mature antibodies, we identified key positions in the VH gene encoding CDR1, CDR2, and the immunoglobulin framework that are critical contributors for polyspecificity in germline antibodies. Computational design of antibodies capable of binding multiple antigens may allow the rational design of antibodies that retain polyspecificity for diverse epitope binding.
Distinct Types of Disorder in the Human Proteome: Functional Implications for Alternative Splicing
by Recep Colak, TaeHyung Kim, Magali Michaut, Mark Sun, Manuel Irimia, Jeremy Bellay, Chad L. Myers, Benjamin J. Blencowe, Philip M. Kim
Intrinsically disordered regions have been associated with various cellular processes and are implicated in several human diseases, but their exact roles remain unclear. We previously defined two classes of conserved disordered regions in budding yeast, referred to as ?flexible? and ?constrained? conserved disorder. In flexible disorder, the property of disorder has been positionally conserved during evolution, whereas in constrained disorder, both the amino acid sequence and the property of disorder have been conserved. Here, we show that flexible and constrained disorder are widespread in the human proteome, and are particularly common in proteins with regulatory functions. Both classes of disordered sequences are highly enriched in regions of proteins that undergo tissue-specific (TS) alternative splicing (AS), but not in regions of proteins that undergo general (i.e., not tissue-regulated) AS. Flexible disorder is more highly enriched in TS alternative exons, whereas constrained disorder is more highly enriched in exons that flank TS alternative exons. These latter regions are also significantly more enriched in potential phosphosites and other short linear motifs associated with cell signaling. We further show that cancer driver mutations are significantly enriched in regions of proteins associated with TS and general AS. Collectively, our results point to distinct roles for TS alternative exons and flanking exons in the dynamic regulation of protein interaction networks in response to signaling activity, and they further suggest that alternatively spliced regions of proteins are often functionally altered by mutations responsible for cancer.
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity
by Bernhard Nessler, Michael Pfeiffer, Lars Buesing, Wolfgang Maass
The principles by which networks of neurons compute, and how spike-timing dependent plasticity (STDP) of synaptic weights generates and maintains their computational function, are unknown. Preceding work has shown that soft winner-take-all (WTA) circuits, where pyramidal neurons inhibit each other via interneurons, are a common motif of cortical microcircuits. We show through theoretical analysis and computer simulations that Bayesian computation is induced in these network motifs through STDP in combination with activity-dependent changes in the excitability of neurons. The fundamental components of this emergent Bayesian computation are priors that result from adaptation of neuronal excitability and implicit generative models for hidden causes that are created in the synaptic weights through STDP. In fact, a surprising result is that STDP is able to approximate a powerful principle for fitting such implicit generative models to high-dimensional spike inputs: Expectation Maximization. Our results suggest that the experimentally observed spontaneous activity and trial-to-trial variability of cortical neurons are essential features of their information processing capability, since their functional role is to represent probability distributions rather than static neural codes. Furthermore it suggests networks of Bayesian computation modules as a new model for distributed information processing in the cortex.
Chapter 17: Bioimage Informatics for Systems Pharmacology
by Fuhai Li, Zheng Yin, Guangxu Jin, Hong Zhao, Stephen T. C. Wong
Recent advances in automated high-resolution fluorescence microscopy and robotic handling have made the systematic and cost effective study of diverse morphological changes within a large population of cells possible under a variety of perturbations, e.g., drugs, compounds, metal catalysts, RNA interference (RNAi). Cell population-based studies deviate from conventional microscopy studies on a few cells, and could provide stronger statistical power for drawing experimental observations and conclusions. However, it is challenging to manually extract and quantify phenotypic changes from the large amounts of complex image data generated. Thus, bioimage informatics approaches are needed to rapidly and objectively quantify and analyze the image data. This paper provides an overview of the bioimage informatics challenges and approaches in image-based studies for drug and target discovery. The concepts and capabilities of image-based screening are first illustrated by a few practical examples investigating different kinds of phenotypic changes caEditorsused by drugs, compounds, or RNAi. The bioimage analysis approaches, including object detection, segmentation, and tracking, are then described. Subsequently, the quantitative features, phenotype identification, and multidimensional profile analysis for profiling the effects of drugs and targets are summarized. Moreover, a number of publicly available software packages for bioimage informatics are listed for further reference. It is expected that this review will help readers, including those without bioimage informatics expertise, understand the capabilities, approaches, and tools of bioimage informatics and apply them to advance their own studies.
Chapter 16: Text Mining for Translational Bioinformatics
by K. Bretonnel Cohen, Lawrence E. Hunter
Text mining for translational bioinformatics is a new field with tremendous research potential. It is a subfield of biomedical natural language processing that concerns itself directly with the problem of relating basic biomedical research to clinical practice, and vice versa. Applications of text mining fall both into the category of T1 translational research?translating basic science results into new interventions?and T2 translational research, or translational research for public health. Potential use cases include better phenotyping of research subjects, and pharmacogenomic research. A variety of methods for evaluating text mining applications exist, including corpora, structured test suites, and post hoc judging. Two basic principles of linguistic structure are relevant for building text mining applications. One is that linguistic structure consists of multiple levels. The other is that every level of linguistic structure is characterized by ambiguity. There are two basic approaches to text mining: rule-based, also known as knowledge-based; and machine-learning-based, also known as statistical. Many systems are hybrids of the two approaches. Shared tasks have had a strong effect on the direction of the field. Like all translational bioinformatics software, text mining software for translational bioinformatics can be considered health-critical and should be subject to the strictest standards of quality assurance and software testing.
Localization of Protein Aggregation in Escherichia coli Is Governed by Diffusion and Nucleoid Macromolecular Crowding Effect
by Anne-Sophie Coquel, Jean-Pascal Jacob, Mael Primet, Alice Demarez, Mariella Dimiccoli, Thomas Julou, Lionel Moisan, Ariel B. Lindner, Hugues Berry
Aggregates of misfolded proteins are a hallmark of many age-related diseases. Recently, they have been linked to aging of Escherichia coli (E. coli) where protein aggregates accumulate at the old pole region of the aging bacterium. Because of the potential of E. coli as a model organism, elucidating aging and protein aggregation in this bacterium may pave the way to significant advances in our global understanding of aging. A first obstacle along this path is to decipher the mechanisms by which protein aggregates are targeted to specific intercellular locations. Here, using an integrated approach based on individual-based modeling, time-lapse fluorescence microscopy and automated image analysis, we show that the movement of aging-related protein aggregates in E. coli is purely diffusive (Brownian). Using single-particle tracking of protein aggregates in live E. coli cells, we estimated the average size and diffusion constant of the aggregates. Our results provide evidence that the aggregates passively diffuse within the cell, with diffusion constants that depend on their size in agreement with the Stokes-Einstein law. However, the aggregate displacements along the cell long axis are confined to a region that roughly corresponds to the nucleoid-free space in the cell pole, thus confirming the importance of increased macromolecular crowding in the nucleoids. We thus used 3D individual-based modeling to show that these three ingredients (diffusion, aggregation and diffusion hindrance in the nucleoids) are sufficient and necessary to reproduce the available experimental data on aggregate localization in the cells. Taken together, our results strongly support the hypothesis that the localization of aging-related protein aggregates in the poles of E. coli results from the coupling of passive diffusion-aggregation with spatially non-homogeneous macromolecular crowding. They further support the importance of ?soft? intracellular structuring (based on macromolecular crowding) in diffusion-based protein localization in E. coli.
Learning Multisensory Integration and Coordinate Transformation via Density Estimation
by Joseph G. Makin, Matthew R. Fellows, Philip N. Sabes
Sensory processing in the brain includes three key operations: multisensory integration?the task of combining cues into a single estimate of a common underlying stimulus; coordinate transformations?the change of reference frame for a stimulus (e.g., retinotopic to body-centered) effected through knowledge about an intervening variable (e.g., gaze position); and the incorporation of prior information. Statistically optimal sensory processing requires that each of these operations maintains the correct posterior distribution over the stimulus. Elements of this optimality have been demonstrated in many behavioral contexts in humans and other animals, suggesting that the neural computations are indeed optimal. That the relationships between sensory modalities are complex and plastic further suggests that these computations are learned?but how? We provide a principled answer, by treating the acquisition of these mappings as a case of density estimation, a well-studied problem in machine learning and statistics, in which the distribution of observed data is modeled in terms of a set of fixed parameters and a set of latent variables. In our case, the observed data are unisensory-population activities, the fixed parameters are synaptic connections, and the latent variables are multisensory-population activities. In particular, we train a restricted Boltzmann machine with the biologically plausible contrastive-divergence rule to learn a range of neural computations not previously demonstrated under a single approach: optimal integration; encoding of priors; hierarchical integration of cues; learning when not to integrate; and coordinate transformation. The model makes testable predictions about the nature of multisensory representations.
Phosphorylation of the Retinoic Acid Receptor Alpha Induces a Mechanical Allosteric Regulation and Changes in Internal Dynamics
by Yassmine Chebaro, Ismail Amal, Natacha Rochel, Cécile Rochette-Egly, Roland H. Stote, Annick Dejaegere
Nuclear receptor proteins constitute a superfamily of proteins that function as ligand dependent transcription factors. They are implicated in the transcriptional cascades underlying many physiological phenomena, such as embryogenesis, cell growth and differentiation, and apoptosis, making them one of the major signal transduction paradigms in metazoans. Regulation of these receptors occurs through the binding of hormones, and in the case of the retinoic acid receptor (RAR), through the binding of retinoic acid (RA). In addition to this canonical scenario of RAR activity, recent discoveries have shown that RAR regulation also occurs as a result of phosphorylation. In fact, RA induces non-genomic effects, such as the activation of kinase signaling pathways, resulting in the phosphorylation of several targets including RARs themselves. In the case of RAR?, phosphorylation of Ser369 located in loop L9?10 of the ligand-binding domain leads to an increase in the affinity for the protein cyclin H, which is part of the Cdk-activating kinase complex of the general transcription factor TFIIH. The cyclin H binding site in RAR? is situated more than 40 Å from the phosphorylated serine. Using molecular dynamics simulations of the unphosphorylated and phosphorylated forms of the receptor RAR?, we analyzed the structural implications of receptor phosphorylation, which led to the identification of a structural mechanism for the allosteric coupling between the two remote sites of interest. The results show that phosphorylation leads to a reorganization of a local salt bridge network, which induces changes in helix extension and orientation that affects the cyclin H binding site. This results in changes in conformation and flexibility of the latter. The high conservation of the residues implicated in this signal transduction suggests a mechanism that could be applied to other nuclear receptor proteins.
Malaria's Missing Number: Calculating the Human Component of R0 by a Within-Host Mechanistic Model of Plasmodium falciparum Infection and Transmission
by Geoffrey L. Johnston, David L. Smith, David A. Fidock
Human infection by malarial parasites of the genus Plasmodium begins with the bite of an infected Anopheles mosquito. Current estimates place malaria mortality at over 650,000 individuals each year, mostly in African children. Efforts to reduce disease burden can benefit from the development of mathematical models of disease transmission. To date, however, comprehensive modeling of the parameters defining human infectivity to mosquitoes has remained elusive. Here, we describe a mechanistic within-host model of Plasmodium falciparum infection in humans and pathogen transmission to the mosquito vector. Our model incorporates the entire parasite lifecycle, including the intra-erythrocytic asexual forms responsible for disease, the onset of symptoms, the development and maturation of intra-erythrocytic gametocytes that are transmissible to Anopheles mosquitoes, and human-to-mosquito infectivity. These model components were parameterized from malaria therapy data and other studies to simulate individual infections, and the ensemble of outputs was found to reproduce the full range of patient responses to infection. Using this model, we assessed human infectivity over the course of untreated infections and examined the effects in relation to transmission intensity, expressed by the basic reproduction number R0 (defined as the number of secondary cases produced by a single typical infection in a completely susceptible population). Our studies predict that net human-to-mosquito infectivity from a single non-immune individual is on average equal to 32 fully infectious days. This estimate of mean infectivity is equivalent to calculating the human component of malarial R0. We also predict that mean daily infectivity exceeds five percent for approximately 138 days. The mechanistic framework described herein, made available as stand-alone software, will enable investigators to conduct detailed studies into theories of malaria control, including the effects of drug treatment and drug resistance on transmission.
Biomarker Discovery by Sparse Canonical Correlation Analysis of Complex Clinical Phenotypes of Tuberculosis and Malaria
by Juho Rousu, Daniel D. Agranoff, Olugbemiro Sodeinde, John Shawe-Taylor, Delmiro Fernandez-Reyes
Biomarker discovery aims to find small subsets of relevant variables in ?omics data that correlate with the clinical syndromes of interest. Despite the fact that clinical phenotypes are usually characterized by a complex set of clinical parameters, current computational approaches assume univariate targets, e.g. diagnostic classes, against which associations are sought for. We propose an approach based on asymmetrical sparse canonical correlation analysis (SCCA) that finds multivariate correlations between the ?omics measurements and the complex clinical phenotypes. We correlated plasma proteomics data to multivariate overlapping complex clinical phenotypes from tuberculosis and malaria datasets. We discovered relevant ?omic biomarkers that have a high correlation to profiles of clinical measurements and are remarkably sparse, containing 1.5?3% of all ?omic variables. We show that using clinical view projections we obtain remarkable improvements in diagnostic class prediction, up to 11% in tuberculosis and up to 5% in malaria. Our approach finds proteomic-biomarkers that correlate with complex combinations of clinical-biomarkers. Using the clinical-biomarkers improves the accuracy of diagnostic class prediction while not requiring the measurement plasma proteomic profiles of each subject. Our approach makes it feasible to use omics' data to build accurate diagnostic algorithms that can be deployed to community health centres lacking the expensive ?omics measurement capabilities.
Lifespan Differences in Hematopoietic Stem Cells are Due to Imperfect Repair and Unstable Mean-Reversion
by Hans B Sieburg, Giulio Cattarossi, Christa E. Muller-Sieburg
The life-long supply of blood cells depends on the long-term function of hematopoietic stem cells (HSCs). HSCs are functionally defined by their multi-potency and self-renewal capacity. Because of their self-renewal capacity, HSCs were thought to have indefinite lifespans. However, there is increasing evidence that genetically identical HSCs differ in lifespan and that the lifespan of a HSC is predetermined and HSC-intrinsic. Lifespan is here defined as the time a HSC gives rise to all mature blood cells. This raises the intriguing question: what controls the lifespan of HSCs within the same animal, exposed to the same environment? We present here a new model based on reliability theory to account for the diversity of lifespans of HSCs. Using clonal repopulation experiments and computational-mathematical modeling, we tested how small-scale, molecular level, failures are dissipated at the HSC population level. We found that the best fit of the experimental data is provided by a model, where the repopulation failure kinetics of each HSC are largely anti-persistent, or mean-reverting, processes. Thus, failure rates repeatedly increase during population-wide division events and are counteracted and decreased by repair processes. In the long-run, a crossover from anti-persistent to persistent behavior occurs. The cross-over is due to a slow increase in the mean failure rate of self-renewal and leads to rapid clonal extinction. This suggests that the repair capacity of HSCs is self-limiting. Furthermore, we show that the lifespan of each HSC depends on the amplitudes and frequencies of fluctuations in the failure rate kinetics. Shorter and longer lived HSCs differ significantly in their pre-programmed ability to dissipate perturbations. A likely interpretation of these findings is that the lifespan of HSCs is determined by preprogrammed differences in repair capacity.
Cleavage Entropy as Quantitative Measure of Protease Specificity
by Julian E. Fuchs, Susanne von Grafenstein, Roland G. Huber, Michael A. Margreiter, Gudrun M. Spitzer, Hannes G. Wallnoefer, Klaus R. Liedl
A purely information theory-guided approach to quantitatively characterize protease specificity is established. We calculate an entropy value for each protease subpocket based on sequences of cleaved substrates extracted from the MEROPS database. We compare our results with known subpocket specificity profiles for individual proteases and protease groups (e.g. serine proteases, metallo proteases) and reflect them quantitatively. Summation of subpocket-wise cleavage entropy contributions yields a measure for overall protease substrate specificity. This total cleavage entropy allows ranking of different proteases with respect to their specificity, separating unspecific digestive enzymes showing high total cleavage entropy from specific proteases involved in signaling cascades. The development of a quantitative cleavage entropy score allows an unbiased comparison of subpocket-wise and overall protease specificity. Thus, it enables assessment of relative importance of physicochemical and structural descriptors in protease recognition. We present an exemplary application of cleavage entropy in tracing substrate specificity in protease evolution. This highlights the wide range of substrate promiscuity within homologue proteases and hence the heavy impact of a limited number of mutations on individual substrate specificity.
The Evolution of Collective Restraint: Policing and Obedience among Non-conjugative Plasmids
by Kyriakos Kentzoglanakis, Diana García López, Sam P. Brown, Richard A. Goldstein
The repression of competition by mechanisms of policing is now recognized as a major force in the maintenance of cooperation. General models on the evolution of policing have focused on the interplay between individual competitiveness and mutual policing, demonstrating a positive relationship between within-group diversity and levels of policing. We expand this perspective by investigating what is possibly the simplest example of reproductive policing: copy number control (CNC) among non-conjugative plasmids, a class of extra-chromosomal vertically transmitted molecular symbionts of bacteria. Through the formulation and analysis of a multi-scale dynamical model, we show that the establishment of stable reproductive restraint among plasmids requires the co-evolution of two fundamental plasmid traits: policing, through the production of plasmid-coded trans-acting replication inhibitors, and obedience, expressed as the binding affinity of plasmid-specific targets to those inhibitors. We explain the intrinsic replication instabilities that arise in the absence of policing and we show how these instabilities are resolved by the evolution of copy number control. Increasing levels of policing and obedience lead to improvements in group performance due to tighter control of local population size (plasmid copy number), delivering benefits both to plasmids, by reducing the risk of segregational loss and to the plasmid-host partnership, by increasing the rate of cell reproduction, and therefore plasmid vertical transmission.
Semi-automated 3D Leaf Reconstruction and Analysis of Trichome Patterning from Light Microscopic Images
by Henrik Failmezger, Benjamin Jaegle, Andrea Schrader, Martin Hülskamp, Achim Tresch
Trichomes are leaf hairs that are formed by single cells on the leaf surface. They are known to be involved in pathogen resistance. Their patterning is considered to emerge from a field of initially equivalent cells through the action of a gene regulatory network involving trichome fate promoting and inhibiting factors. For a quantitative analysis of single and double mutants or the phenotypic variation of patterns in different ecotypes, it is imperative to statistically evaluate the pattern reliably on a large number of leaves. Here we present a method that enables the analysis of trichome patterns at early developmental leaf stages and the automatic analysis of various spatial parameters. We focus on the most challenging young leaf stages that require the analysis in three dimensions, as the leaves are typically not flat. Our software TrichEratops reconstructs 3D surface models from 2D stacks of conventional light-microscope pictures. It allows the GUI-based annotation of different stages of trichome development, which can be analyzed with respect to their spatial distribution to capture trichome patterning events. We show that 3D modeling removes biases of simpler 2D models and that novel trichome patterning features increase the sensitivity for inter-accession comparisons.
Formation of Raft-Like Assemblies within Clusters of Influenza Hemagglutinin Observed by MD Simulations
by Daniel L. Parton, Alex Tek, Marc Baaden, Mark S. P. Sansom
The association of hemagglutinin (HA) with lipid rafts in the plasma membrane is an important feature of the assembly process of influenza virus A. Lipid rafts are thought to be small, fluctuating patches of membrane enriched in saturated phospholipids, sphingolipids, cholesterol and certain types of protein. However, raft-associating transmembrane (TM) proteins generally partition into Ld domains in model membranes, which are enriched in unsaturated lipids and depleted in saturated lipids and cholesterol. The reason for this apparent disparity in behavior is unclear, but model membranes differ from the plasma membrane in a number of ways. In particular, the higher protein concentration in the plasma membrane may influence the partitioning of membrane proteins for rafts. To investigate the effect of high local protein concentration, we have conducted coarse-grained molecular dynamics (CG MD) simulations of HA clusters in domain-forming bilayers. During the simulations, we observed a continuous increase in the proportion of raft-type lipids (saturated phospholipids and cholesterol) within the area of membrane spanned by the protein cluster. Lateral diffusion of unsaturated lipids was significantly attenuated within the cluster, while saturated lipids were relatively unaffected. On this basis, we suggest a possible explanation for the change in lipid distribution, namely that steric crowding by the slow-diffusing proteins increases the chemical potential for unsaturated lipids within the cluster region. We therefore suggest that a local aggregation of HA can be sufficient to drive association of the protein with raft-type lipids. This may also represent a general mechanism for the targeting of TM proteins to rafts in the plasma membrane, which is of functional importance in a wide range of cellular processes.
Reduced Lateral Mobility of Lipids and Proteins in Crowded Membranes
by Joseph E. Goose, Mark S. P. Sansom
Coarse-grained molecular dynamics simulations of the E. coli outer membrane proteins FhuA, LamB, NanC, OmpA and OmpF in a POPE/POPG (3?1) bilayer were performed to characterise the diffusive nature of each component of the membrane. At small observation times (<10 ns) particle vibrations dominate phospholipid diffusion elevating the calculated values from the longer time-scale bulk value (>50 ns) of 8.5×10?7 cm2 s?1. The phospholipid diffusion around each protein was found to vary based on distance from protein. An asymmetry in the diffusion of annular lipids in the inner and outer leaflets was observed and correlated with an asymmetry in charged residues in the vicinity of the inner and outer leaflet head-groups. Protein rotational and translational diffusion were also found to vary with observation time and were inversely correlated with the radius of gyration of the protein in the plane of the bilayer. As the concentration of protein within the bilayer was increased, the overall mobility of the membrane decreased reflected in reduced lipid diffusion coefficients for both lipid and protein components. The increase in protein concentration also resulted in a decrease in the anomalous diffusion exponent ? of the lipid. Formation of extended clusters and networks of proteins led to compartmentalisation of lipids in extreme cases.
Optimal Balance of the Striatal Medium Spiny Neuron Network
by Adam Ponzi, Jeffery R. Wickens
Slowly varying activity in the striatum, the main Basal Ganglia input structure, is important for the learning and execution of movement sequences. Striatal medium spiny neurons (MSNs) form cell assemblies whose population firing rates vary coherently on slow behaviourally relevant timescales. It has been shown that such activity emerges in a model of a local MSN network but only at realistic connectivities of and only when MSN generated inhibitory post-synaptic potentials (IPSPs) are realistically sized. Here we suggest a reason for this. We investigate how MSN network generated population activity interacts with temporally varying cortical driving activity, as would occur in a behavioural task. We find that at unrealistically high connectivity a stable winners-take-all type regime is found where network activity separates into fixed stimulus dependent regularly firing and quiescent components. In this regime only a small number of population firing rate components interact with cortical stimulus variations. Around connectivity a transition to a more dynamically active regime occurs where all cells constantly switch between activity and quiescence. In this low connectivity regime, MSN population components wander randomly and here too are independent of variations in cortical driving. Only in the transition regime do weak changes in cortical driving interact with many population components so that sequential cell assemblies are reproducibly activated for many hundreds of milliseconds after stimulus onset and peri-stimulus time histograms display strong stimulus and temporal specificity. We show that, remarkably, this activity is maximized at striatally realistic connectivities and IPSP sizes. Thus, we suggest the local MSN network has optimal characteristics ? it is neither too stable to respond in a dynamically complex temporally extended way to cortical variations, nor is it too unstable to respond in a consistent repeatable way. Rather, it is optimized to generate stimulus dependent activity patterns for long periods after variations in cortical excitation.
Shining a Light on Dark Sequencing: Characterising Errors in Ion Torrent PGM Data
by Lauren M. Bragg, Glenn Stone, Margaret K. Butler, Philip Hugenholtz, Gene W. Tyson
The Ion Torrent Personal Genome Machine (PGM) is a new sequencing platform that substantially differs from other sequencing technologies by measuring pH rather than light to detect polymerisation events. Using re-sequencing datasets, we comprehensively characterise the biases and errors introduced by the PGM at both the base and flow level, across a combination of factors, including chip density, sequencing kit, template species and machine. We found two distinct insertion/deletion (indel) error types that accounted for the majority of errors introduced by the PGM. The main error source was inaccurate flow-calls, which introduced indels at a raw rate of 2.84% (1.38% after quality clipping) using the OneTouch 200 bp kit. Inaccurate flow-calls typically resulted in over-called short-homopolymers and under-called long-homopolymers. Flow-call accuracy decreased with consecutive flow cycles, but we also found significant periodic fluctuations in the flow error-rate, corresponding to specific positions within the flow-cycle pattern. Another less common PGM error, high frequency indel (HFI) errors, are indels that occur at very high frequency in the reads relative to a given base position in the reference genome, but in the majority of instances were not replicated consistently across separate runs. HFI errors occur approximately once every thousand bases in the reference, and correspond to 0.06% of bases in reads. Currently, the PGM does not achieve the accuracy of competing light-based technologies. However, flow-call inaccuracy is systematic and the statistical models of flow-values developed here will enable PGM-specific bioinformatics approaches to be developed, which will account for these errors. HFI errors may prove more challenging to address, especially for polymorphism and amplicon applications, but may be overcome by sequencing the same DNA template across multiple chips.
Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons
by Nicolas Frémaux, Henning Sprekeler, Wulfram Gerstner
Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.
