Keck Graduate Institute

Mental imagery research has weathered both disbelief of the phenomenon and inherent methodological limitations. Here we review recent behavioral, brain imaging, and clinical research that has reshaped our understanding of mental imagery. Research supports the claim that visual mental imagery is a depictive internal representation that functions like a weak form of perception. Brain imaging work has demonstrated that neural representations of mental and perceptual images resemble one another as early as the primary visual cortex (V1). Activity patterns in V1 encode mental images and perceptual images via a common set of low-level depictive visual features. Recent translational and clinical research reveals the pivotal role that imagery plays in many mental disorders and suggests how clinicians can utilize imagery in treatment.

Much recent work has explored molecular and population-genetic constraints on the rate of protein sequence evolution. The best predictor of evolutionary rate is expression level, for reasons that have remained unexplained. Here, we hypothesize that selection to reduce the burden of protein misfolding will favor protein sequences with increased robustness to translational missense errors. Pressure for translational robustness increases with expression level and constrains sequence evolution. Using several sequenced yeast genomes, global expression and protein abundance data, and sets of paralogs traceable to an ancient whole-genome duplication in yeast, we rule out several confounding effects and show that expression level explains roughly half the variation in Saccharomyces cerevisiae protein evolutionary rates. We examine causes for expression's dominant role and find that genome-wide tests favor the translational robustness explanation over existing hypotheses that invoke constraints on function or translational efficiency. Our results suggest that proteins evolve at rates largely unrelated to their functions and can explain why highly expressed proteins evolve slowly across the tree of life.

Complete inactivation of the PTEN tumor suppressor gene is extremely common in advanced cancer, including prostate cancer (CaP). However, one PTEN allele is already lost in the vast majority of CaPs at presentation. To determine the consequence of PTEN dose variations on cancer progression, we have generated by homologous recombination a hypomorphic Pten mouse mutant series with decreasing Pten activity: Pten(hy/+) > Pten(+/-) > Pten(hy/-) (mutants in which we have rescued the embryonic lethality due to complete Pten inactivation) > Pten prostate conditional knockout (Pten(pc)) mutants. In addition, we have generated and comparatively analyzed two distinct Pten(pc) mutants in which Pten is inactivated focally or throughout the entire prostatic epithelium. We find that the extent of Pten inactivation dictate in an exquisite dose-dependent fashion CaP progression, its incidence, latency, and biology. The dose of Pten affects key downstream targets such as Akt, p27(Kip1), mTOR, and FOXO3. Our results provide conclusive genetic support for the notion that PTEN is haploinsufficient in tumor suppression and that its dose is a key determinant in cancer progression.

Tumor immunotherapy is only effective in a fraction of patients due to a low response rate and severe side effects, and these challenges of immunotherapy in clinics can be addressed through induction of immunogenic cell death (ICD). ICD is elicited from many antitumor therapies to release danger associated molecular patterns (DAMPs) and tumor-associated antigens to facilitate maturation of dendritic cells (DCs) and infiltration of cytotoxic T lymphocytes (CTLs). The process can reverse the tumor immunosuppressive microenvironment to improve the sensitivity of immunotherapy. Nanostructure-based drug delivery systems (NDDSs) are explored to induce ICD by incorporating therapeutic molecules for chemotherapy, photosensitizers (PSs) for photodynamic therapy (PDT), photothermal conversion agents for photothermal therapy (PTT), and radiosensitizers for radiotherapy (RT). These NDDSs can release loaded agents at a right dose in the right place at the right time, resulting in greater effectiveness and lower toxicity. Immunotherapeutic agents can also be combined with these NDDSs to achieve the synergic antitumor effect in a multi-modality therapeutic approach. In this review, NDDSs are harnessed to load multiple agents to induce ICD by chemotherapy, PDT, PTT, and RT in combination of immunotherapy to promote the therapeutic effect and reduce side effects associated with cancer treatment.

Most of the published quantitative models in biology are lost for the community because they are either not made available or they are insufficiently characterized to allow them to be reused. The lack of a standard description format, lack of stringent reviewing and authors' carelessness are the main causes for incomplete model descriptions. With today's increased interest in detailed biochemical models, it is necessary to define a minimum quality standard for the encoding of those models. We propose a set of rules for curating quantitative models of biological systems. These rules define procedures for encoding and annotating models represented in machine-readable form. We believe their application will enable users to (i) have confidence that curated models are an accurate reflection of their associated reference descriptions, (ii) search collections of curated models with precision, (iii) quickly identify the biological phenomena that a given curated model or model constituent represents and (iv) facilitate model reuse and composition into large subcellular models.

We have devised a class of isothermal reactions for amplifying DNA. These homogeneous reactions rapidly synthesize short oligonucleotides (8-16 bases) specified by the sequence of an amplification template. Versions of the reactions can proceed in either a linear or an exponential amplification mode. Both of these reactions require simple, constant conditions, and the rate of amplification depends entirely on the molecular parameters governing the interactions of the molecules in the reaction. The exponential version of the reaction is a molecular chain reaction that uses the oligonucleotide products of each linear reaction to create producers of more of the same oligonucleotide. It is a highly sensitive chain reaction that can be specifically triggered by given DNA sequences and can achieve amplifications of >10(6)-fold. Several similar reactions in this class are described here. The robustness, speed, and sensitivity of the exponential reaction suggest it will be useful in rapidly detecting the presence of small amounts of a specific DNA sequence in a sample, and a range of other applications, including many currently making use of the PCR.

Tumor hypoxia, or low oxygen concentration, is a result of disordered vasculature that lead to distinctive hypoxic microenvironments not found in normal tissues. Many traditional anti-cancer agents are not able to penetrate into these hypoxic zones, whereas, conventional cancer therapies that work by blocking cell division are not effective to treat tumors within hypoxic zones. Under these circumstances the use of magnetic nanoparticles as a drug delivering agent system under the influence of external magnetic field has received much attention, based on their simplicity, ease of preparation, and ability to tailor their properties for specific biological applications. Hence in this review article we have reviewed current magnetic drug delivery systems, along with their application and clinical status in the field of magnetic drug delivery.

A gene's rate of sequence evolution is among the most fundamental evolutionary quantities in common use, but what determines evolutionary rates has remained unclear. Here, we carry out the first combined analysis of seven predictors (gene expression level, dispensability, protein abundance, codon adaptation index, gene length, number of protein-protein interactions, and the gene's centrality in the interaction network) previously reported to have independent influences on protein evolutionary rates. Strikingly, our analysis reveals a single dominant variable linked to the number of translation events which explains 40-fold more variation in evolutionary rate than any other, suggesting that protein evolutionary rate has a single major determinant among the seven predictors. The dominant variable explains nearly half the variation in the rate of synonymous and protein evolution. We show that the two most commonly used methods to disentangle the determinants of evolutionary rate, partial correlation analysis and ordinary multivariate regression, produce misleading or spurious results when applied to noisy biological data. We overcome these difficulties by employing principal component regression, a multivariate regression of evolutionary rate against the principal components of the predictor variables. Our results support the hypothesis that translational selection governs the rate of synonymous and protein sequence evolution in yeast.

Interest in open innovation (OI) as a field of research has grown exponentially since the phrase was coined by Chesbrough in his 2003 book, with numerous articles, special issues, books, and conference sessions. Various reviews of the literature have summarized prior work, offered new frameworks, and identified opportunities for future research. Here we summarize these opportunities, which include more research on outbound OI, the role of open innovation in services, and network forms of collaboration such as consortia, communities, ecosystems, and platforms. Research should also examine the use of OI by small, new, and not-for-profit organizations, as well as the linkage of individual actions and motivations to open innovation. Other opportunities include better measuring the costs, benefits, antecedents, mediators and moderators of the effects of OI on performance, and understanding why and how OI is rejected, abandoned, or fails. Finally, we consider how OI can be better linked to prior theoretical research, including topics such as absorptive capacity, user innovation, resources, dynamic capabilities, business models, and the definition of the firm.

We present a simple theory that uses thermodynamic parameters to predict the probability that a protein retains the wild-type structure after one or more random amino acid substitutions. Our theory predicts that for large numbers of substitutions the probability that a protein retains its structure will decline exponentially with the number of substitutions, with the severity of this decline determined by properties of the structure. Our theory also predicts that a protein can gain extra robustness to the first few substitutions by increasing its thermodynamic stability. We validate our theory with simulations on lattice protein models and by showing that it quantitatively predicts previously published experimental measurements on subtilisin and our own measurements on variants of TEM1 beta-lactamase. Our work unifies observations about the clustering of functional proteins in sequence space, and provides a basis for interpreting the response of proteins to substitutions in protein engineering applications.

3D bioprinting involves the combination of 3D printing technologies with cells, growth factors and biomaterials, and has been considered as one of the most advanced tools for tissue engineering and regenerative medicine (TERM). However, despite multiple breakthroughs, it is evident that numerous challenges need to be overcome before 3D bioprinting will eventually become a clinical solution for a variety of TERM applications. To produce a 3D structure that is biologically functional, cell-laden bioinks must be optimized to meet certain key characteristics including rheological properties, physico-mechanical properties, and biofunctionality; a difficult task for a single component bioink especially for extrusion based bioprinting. As such, more recent research has been centred on multicomponent bioinks consisting of a combination of two or more biomaterials to improve printability, shape fidelity and biofunctionality. In this article, multicomponent hydrogel-based bioink systems are systemically reviewed based on the inherent nature of the bioink (natural or synthetic hydrogels), including the most current examples demonstrating properties and advances in application of multicomponent bioinks, specifically for extrusion based 3D bioprinting. This review article will assist researchers in the field in identifying the most suitable bioink based on their requirements, as well as pinpointing current unmet challenges in the field.

The Arabidopsis genome encodes four Dicer-like (DCL) proteins, two of which contain putative nuclear localization signals. This suggests one or more nuclear pathways for processing double-stranded (ds) RNA in plants. To study the subcellular location of processing of nuclear-encoded dsRNA involved in transcriptional silencing, we examined short interfering (si) RNA and micro (mi) RNA accumulation in transgenic Arabidopsis expressing nuclear and cytoplasmic variants of P19, a viral protein that suppresses posttranscriptional gene silencing. P19 binds specifically to DCL-generated 21- to 25-nucleotide (nt) dsRNAs with 2-nt 3' overhangs and reportedly suppresses the accumulation of all size classes of siRNA. Nuclear P19 resulted in a significant reduction of 21- to 22-nt siRNAs and a 21-nt miRNA, but had a lesser effect on 24-nt siRNAs. Cytoplasmic P19 did not decrease the quantity but resulted in a 2-nt truncation of siRNAs and miRNA. This suggests that the direct products of DCL cleavage of dsRNA precursors of 21- to 22-nt siRNAs and miRNA are present in the nucleus, where their accumulation is partially repressed, and in the cytoplasm, where both normal sized and truncated forms accumulate. DCL1, which contains two putative nuclear localization signals, is required for miRNA production but not siRNA production. DCL1-green fluorescent protein fusion proteins localize to nuclei in transient expression assays, indicating that DCL1 is a nuclear protein. The results are consistent with a model in which dsRNA precursors of miRNAs and at least some 21- to 22-nt siRNAs are processed in the nucleus, the former by nuclear DCL1 and the latter by an unknown nuclear DCL.

Myocardial infarction rapidly depletes the endogenous cardiac progenitor cell pool, and the inefficient recruitment of exogenously administered progenitor cells limits the effectiveness of cardiac cell therapy. Recent reports indicate that interactions between the CXC chemokine stromal cell-derived factor 1 and its receptor CXC chemokine receptor 4 (CXCR4) critically mediate the ischemia-induced recruitment of bone marrow-derived circulating stem/progenitor cells, but the expression of CXCR4 in cardiac progenitor cells is very low. Here, we studied the influence of hypoxia on CXCR4 expression in cardiac progenitor cells, on the recruitment of intravenously administered cells to ischemic heart tissue, and on the preservation of heart function in a murine myocardial infarction model. We found that hypoxic preconditioning increased CXCR4 expression in CLK (cardiosphere-derived, Lin(-)c-kit(+) progenitor) cells and markedly augmented CLK cell migration (in vitro) and recruitment (in vivo) to the ischemic myocardium. Four weeks after surgically induced myocardial infarction, infarct size and heart function were significantly better in mice administered hypoxia-preconditioned CLK cells than in mice treated with cells cultured under normoxic conditions. Furthermore, these effects were largely abolished by the addition of a CXCR4 inhibitor, indicating that the benefits of hypoxic preconditioning are mediated by the stromal cell-derived factor 1/CXCR4 axis, and that therapies targeting this axis may enhance cardiac-progenitor cell-based regenerative therapy.

Are biological networks different from other large complex networks? Both large biological and nonbiological networks exhibit power-law graphs (number of nodes with degree k, N(k) approximately k(-beta)), yet the exponents, beta, fall into different ranges. This may be because duplication of the information in the genome is a dominant evolutionary force in shaping biological networks (like gene regulatory networks and protein-protein interaction networks) and is fundamentally different from the mechanisms thought to dominate the growth of most nonbiological networks (such as the Internet). The preferential choice models used for nonbiological networks like web graphs can only produce power-law graphs with exponents greater than 2. We use combinatorial probabilistic methods to examine the evolution of graphs by node duplication processes and derive exact analytical relationships between the exponent of the power law and the parameters of the model. Both full duplication of nodes (with all their connections) as well as partial duplication (with only some connections) are analyzed. We demonstrate that partial duplication can produce power-law graphs with exponents less than 2, consistent with current data on biological networks. The power-law exponent for large graphs depends only on the growth process, not on the starting graph.

The possible ways that information can be represented mentally have been discussed often over the past thousand years. However, this issue could not be addressed rigorously until late in the 20th century. Initial empirical findings spurred a debate about the heterogeneity of mental representation: Is all information stored in propositional, language-like, symbolic internal representations, or can humans use at least two different types of representations (and possibly many more)? Here, in historical context, we describe recent evidence that humans do not always rely on propositional internal representations but, instead, can also rely on at least one other format: depictive representation. We propose that the debate should now move on to characterizing all of the different forms of human mental representation.

Although frequently used as protein production host, there is only a limited set of promoters available to drive the expression of recombinant proteins in Pichia pastoris. Fine-tuning of gene expression is often needed to maximize product yield and quality. However, for efficient knowledge-based engineering, a better understanding of promoter function is indispensable. Consequently, we created a promoter library by deletion and duplication of putative transcription factor-binding sites within the AOX1 promoter (P(AOX1)) sequence. This first library initially spanned an activity range between approximately 6% and >160% of the wild-type promoter activity. After characterization of the promoter library employing a green fluorescent protein (GFP) variant, the new regulatory toolbox was successfully utilized in a 'real case', i.e. the expression of industrial enzymes. Characterization of the library under repressing, derepressing and inducing conditions displayed at least 12 cis-acting elements involved in P(AOX1)-driven high-level expression. Based on this deletion analysis, novel short artificial promoter variants were constructed by combining cis-acting elements with basal promoter. In addition to improving yields and quality of heterologous protein production, the new P(AOX1) synthetic promoter library constitutes a basic toolbox to fine-tune gene expression in metabolic engineering and sequential induction of protein expression in synthetic biology.

Research from a variety of perspectives has argued that innovation no longer takes place within a single organization, but rather is distributed across multiple stakeholders in a value network. Here we contrast the vertically integrated innovation model to open innovation, user innovation, as well as other distributed processes (cumulative innovation, communities or social production, and co‐creation), while we also discuss open source software and crowdsourcing as applications of the perspectives. We consider differences in the nature of distributed innovation, as well as its origins and its effects. From this, we contrast the predictions of the perspectives on the sources, motivation and value appropriation of external innovation, and thereby provide a framework for the strategic management of distributed innovation.

MOTIVATION: We have used state-space models (SSMs) to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T cell activation. SSMs are a class of dynamic Bayesian networks in which the observed measurements depend on some hidden state variables that evolve according to Markovian dynamics. These hidden variables can capture effects that cannot be directly measured in a gene expression profiling experiment, for example: genes that have not been included in the microarray, levels of regulatory proteins, the effects of mRNA and protein degradation, etc. RESULTS: We have approached the problem of inferring the model structure of these state-space models using both classical and Bayesian methods. In our previous work, a bootstrap procedure was used to derive classical confidence intervals for parameters representing 'gene-gene' interactions over time. In this article, variational approximations are used to perform the analogous model selection task in the Bayesian context. Certain interactions are present in both the classical and the Bayesian analyses of these regulatory networks. The resulting models place JunB and JunD at the centre of the mechanisms that control apoptosis and proliferation. These mechanisms are key for clonal expansion and for controlling the long term behavior (e.g. programmed cell death) of these cells. AVAILABILITY: Supplementary data is available at http://public.kgi.edu/wild/index.htm and Matlab source code for variational Bayesian learning of SSMs is available at http://www.cse.ebuffalo.edu/faculty/mbeal/software.html.

BACKGROUND: In animals, steroid hormones regulate gene expression by binding to nuclear receptors. Plants lack genes for nuclear receptors, yet genetic evidence from Arabidopsis suggests developmental roles for lipids/sterols analogous to those in animals. In contrast to nuclear receptors, the lipid/sterol-binding StAR-related lipid transfer (START) protein domains are conserved, making them candidates for involvement in both animal and plant lipid/sterol signal transduction. RESULTS: We surveyed putative START domains from the genomes of Arabidopsis, rice, animals, protists and bacteria. START domains are more common in plants than in animals and in plants are primarily found within homeodomain (HD) transcription factors. The largest subfamily of HD-START proteins is characterized by an HD amino-terminal to a plant-specific leucine zipper with an internal loop, whereas in a smaller subfamily the HD precedes a classic leucine zipper. The START domains in plant HD-START proteins are not closely related to those of animals, implying collateral evolution to accommodate organism-specific lipids/sterols. Using crystal structures of mammalian START proteins, we show structural conservation of the mammalian phosphatidylcholine transfer protein (PCTP) START domain in plants, consistent with a common role in lipid transport and metabolism. We also describe putative START-domain proteins from bacteria and unicellular protists. CONCLUSIONS: The majority of START domains in plants belong to a novel class of putative lipid/sterol-binding transcription factors, the HD-START family, which is conserved across the plant kingdom. HD-START proteins are confined to plants, suggesting a mechanism by which lipid/sterol ligands can directly modulate transcription in plants.

Researchers in quantitative systems biology make use of a large number of different software packages for modelling, analysis, visualization, and general data manipulation. In this paper, we describe the Systems Biology Workbench (SBW), a software framework that allows heterogeneous application components--written in diverse programming languages and running on different platforms--to communicate and use each others' capabilities via a fast binary encoded-message system. Our goal was to create a simple, high performance, opensource software infrastructure which is easy to implement and understand. SBW enables applications (potentially running on separate, distributed computers) to communicate via a simple network protocol. The interfaces to the system are encapsulated in client-side libraries that we provide for different programming languages. We describe in this paper the SBW architecture, a selection of current modules, including Jarnac, JDesigner, and SBWMeta-tool, and the close integration of SBW into BioSPICE, which enables both frameworks to share tools and compliment and strengthen each others capabilities.