Cancer cell motility: Optimizing spatial search strategies

Aberrantly regulated cell motility is a hallmark of cancer cells. A hybrid agent-based model has been developed to investigate the synergistic and antagonistic cell motility-impacting effects of three microenvironment variables simultaneously: chemoattraction, haptotactic permission, and biomechanical constraint or resistance. Reflecting distinct cell-specific intracellular machinery, the cancer cells are modeled as processing a variety of spatial search strategies that respond to these three influencing factors with differential weights attached to each. While responding exclusively to chemoattraction optimizes cell displacement effectiveness, incorporating permission and resistance components becomes increasingly important with greater distance to the chemoattractant source and/or after reducing the ligand's effective diffusion coefficient. Extending this to a heterogeneous population of cells shows that displacement effectiveness increases with clonal diversity as characterized by the Shannon index. However, the resulting data can be fit best to an exponential function, suggesting that there is a level of population heterogeneity beyond which its added value to the cancer system becomes minimal as directionality ceases to increase. Possible experimental extensions and potential clinical implications are discussed.     

Spatial context and top-down strategies in visual search

Marvin M. Chun and Yuhong Jiang (1998) investigated the role of spatial context on visual search. They used two display conditions. In the Old Display condition, the spatial arrangement of items in the search display was kept constant throughout the experiment. In the New Display condition, the spatial arrangement of items was always novel from trial to trial. The results showed better performance with Old Displays than with New Displays. The authors proposed that repeated spatial context help guiding attention to the target location, thus they termed this effect Contextual Cueing. We present three attempts to reproduce this effect. Experiments 1 and 2 were near exact replications of experiments in Chun and Jiang's report, where we failed to obtain Contextual Cueing. Post-experimental interviews revealed that participants used different search strategies when performing the task: an 'active' strategy (an active effort to find the target), or a 'passive' strategy (intuitive search). In Experiment 3, we manipulated task instructions to bias participants into using active or passive strategies. A robust Contextual Cueing Effect was obtained only in the passive instruction condition.     

The energetics and scaling of search strategies in bacteria

The influence of body size on the energetic cost of movement is well studied in animals but has been rarely investigated in bacteria. Here, I calculate the cost of four chemotactic strategies for different-sized bacteria by adding the costs of their locomotion and reorientation. Size differences of 0.1 microm result in 100,000-fold changes in the energetic cost of chemotaxis. The exact cost for any given size is a nonlinear function of flagella length, the minimum speed necessary to detect and respond to a signal, and the gradient of the signal. These parameters are interlinked in such a way that body size and strategy are tightly coupled to particular environmental gradients, offering avenues for explaining and exploring diversity and competition. The analysis here has implications beyond bacteria. Power-law regression through the minimum costs of transport for different kinds of chemotaxis has the same slope as that for swimming animals, suggesting a universal allometric equation for all swimming organisms.     

Optimization strategies for metabolic networks

Background  

The increasing availability of models and data for metabolic networks poses new challenges in what concerns optimization for biological systems. Due to the high level of complexity and uncertainty associated to these networks the suggested models often lack detail and liability, required to determine the proper optimization strategies. A possible approach to overcome this limitation is the combination of both kinetic and stoichiometric models. In this paper three control optimization methods, with different levels of complexity and assuming various degrees of process information, are presented and their results compared using a prototype network.     

Optimization of interleukin-21 immunotherapeutic strategies

The recently discovered interleukin-21 (IL-21) shows strong tumor attenuation in preclinical studies, and is considered a promising cancer immunotherapy agent. Yet, to exploit its potential, therapeutic strategies must be designed to achieve adequate balance between several conflicting aspects. A mathematical model describing the IL-21-antitumor effects provided the basis for application of the optimization methodology, aimed at finding improved immunotherapeutic regimens. Both dosages and inter-dosing intervals were optimized while considering maximal efficacy, determined by reduction of tumor burden, and minimal toxicity, estimated by cumulative IL-21 doses applied. Simulations allowed to compute the optimal regimen and explore its dependence on the weights of the target function. Optimized schedules lead to substantial cancer regression even with relatively low drug concentrations. Collectively, administration times shifted towards treatment onset, and IL-21 intensities sequentially decreased. Interestingly, there was a certain window in which deviations in the total IL-21 dosage administered largely influenced tumor elimination. The findings emphasize the importance of early tumor detection and the critical consequence of the inter-dosing interval on therapeutic efficacy, as supported by similar research involving chemotherapy. Our work provides initial basis for identifying clinically applicable IL-21 therapeutic strategies with improved efficacy/toxicity ratios.     

Provably sensitive indexing strategies for biosequence similarity search.

Fast algorithms for pairwise biosequence similarity search frequently use filtering and indexing strategies to identify potential matches between a query sequence and a database. For the most part, these strategies are not informed by the substitution score matrices commonly used by comparison algorithms to assign numerical scores to pairs of aligned residues. Consequently, although many filtering strategies offer strong formal guarantees about their ability to detect pairs of sequences differing by few substitutions, these methods can make no guarantee of detecting pairs with high similarity scores. We describe a general technique, score simulation, to help resolve the tension between existing filtering techniques and the use of score matrices. Score simulation, using score matrices, maps ungapped similarity search problems to the simpler problem of finding pairs of strings that differ by few substitutions. Score simulation leads to indexing schemes for biosequences that permit efficient ungapped similarity search with arbitrary score matrices while maintaining strong formal guarantees of sensitivity. We introduce the LSH-ALL-PAIRS-SIM algorithm for finding local similarities in large biosequence collections and show that it is both computationally feasible and sensitive in practice.     

Current strategies in the search for novel antiparasitic agents

The market for antiparasitic products comprises the largest segment for sales of livestock and companion-animal healthcare agents. Despite the availability of highly effective, broad-spectrum agents, there remains a need for safer, more convenient and more environmentally friendly products that will overcome the ever-present threat of resistance development. The very high cost of discovering and developing a new drug, especially for use in livestock, is reflected in the limited number of new classes of antiparasitic agent launched on the market. New strategies are being adopted to minimise the cost of discovering potential drug candidates by maximising the chance of identifying a useful target mechanism of action and by speeding the time to discover and optimise a lead structure. These rely heavily on new technologies in target identification, screen development and lead optimisation. Examples of these will be discussed and speculation made about the possible factors that could influence the future shape of antiparasitic control.     

Lysozyme dimerization: brownian dynamics simulation

The lysozyme dimerization reaction has been studied within the framework of encounter-complex (EC) formation theory using the MacroDox software package. Two types of energetically favorite ECs were determined. In the first of them, active-center amino acids of lysozyme take part in the complex formation or the second molecule blocks accessibility to active center sterically. Epitope amino-acid residues are involved in the complex of type II. The existence of both types of complexes does not contradict experimental data. Dimer-formation rate constants for different kinds of EC were calculated. Increasing the pH from 2.0 to 10.0 decreases the total positive lysozyme charge and eliminates the unfavorable repulsive electrostatic interaction. The rate constant of EC formation is inversely proportional to the protein total charge. The association rate constant was also enhanced by an increase of ionic strength that screened repulsive electrostatic interaction between positively charged proteins.     

Mate choice and optimal search behavior: Fitness returns under the fixed sample and sequential search strategies

The behavior of a female in search of a mate determines the likelihood that she encounters a high-quality male in the search process. The fixed sample (best-of-n) search strategy and the sequential search (fixed threshold) strategy are two prominent models of search behavior. The sequential search strategy dominates the former strategy—yields an equal or higher expected net fitness return to searchers—when search costs are nontrivial and the distribution of quality among prospective mates is uniform or truncated normal. In this paper our objective is to determine whether there are any search costs or distributions of male quality for which the sequential search strategy is inferior to the fixed sample search strategy. The two search strategies are derived under general conditions in which females evaluate encountered males by inspection of an indicator character that has some functional relationship to male quality. The solutions are identical to the original models when the inspected male attribute is itself male quality. The sequential search strategy is shown to dominate the fixed sample search strategy for all search costs and distributions of male quality. Low search costs have been implicated to explain empirical observations that are consistent with the use of a fixed sample search strategy, but under conditions in which the original models were derived there is no search cost or distribution of male quality that favors the fixed sample search strategy. Plausible alternative explanations for the apparent use of this search strategy are discussed.     

Optimization strategies for the polymerase chain reaction

The GeneAmp polymerase chain reaction (PCR) process has now become a key procedure in molecular biology research laboratories. The PCR technique is an in vitro method in which genomic or cloned target sequences are specifically enzymatically amplified as directed by a pair of oligonucleotide primers. This technique has been quite robust in the hands of the majority of researchers and is extremely flexible, as evidenced by the increasing number of related PCR formats (i.e., inverse PCR, anchored PCR, asymmetric PCR, labeled primer PCR and RNA-PCR). Today's applications include direct sequencing, genomic cloning, DNA typing, detection of infectious microorganisms, site-directed mutagenesis, prenatal genetic disease research, and analysis of allelic sequence variations. Scientists at Cetus and Perkin-Elmer have collaborated for several years to better understand the interacting biochemical and biophysical parameters which affect PCR optimization. Following are many of the current recommendations, offered with the caveat that our understanding of the PCR process is continually evolving.     

Current strategies in the search for low penetrance genes in cancer

The genetic etiology of most cancers remains largely unclear and it has been hypothesised that common genetic variants with modest effects on disease susceptibility cause the bulk of this unexplained risk. Case-control association studies are considered the most effective strategy to identify these low-penetrance genes. While traditionally, such studies have focused on putative functional single nucleotide polymorphisms (SNPs) in candidate genes, a more comprehensive approach can now be taken, as a result of a number of recent developments: the mapping of the human genome, including the identification of almost ten million SNPs; and the development of high-throughput genotyping technologies that enable hundreds of thousands of SNPs to be genotyped in a single reaction, in multiple subjects and at an affordable cost. All common genomic variation can be captured by genotyping SNPs in gene-, pathway- or genome-wide-based strategies and these are now being applied to many diseases, including cancer. We present an outline of each of these approaches, including recent published examples, and discuss a number of challenges that remain to be addressed.     

Sequence clustering strategies improve remote homology recognitions while reducing search times

Sequence databases are rapidly growing, thereby increasing the coverage of protein sequence space, but this coverage is uneven because most sequencing efforts have concentrated on a small number of organisms. The resulting granularity of sequence space creates many problems for profile-based sequence comparison programs. In this paper, we suggest several strategies that address these problems, and at the same time speed up the searches for homologous proteins and improve the ability of profile methods to recognize distant homologies. One of our strategies combines database clustering, which removes highly redundant sequence, and a two-step PSI-BLAST (PDB-BLAST), which separates sequence spaces of profile composition and space of homology searching. The combination of these strategies improves distant homology recognitions by more than 100%, while using only 10% of the CPU time of the standard PSI-BLAST search. Another method, intermediate profile searches, allows for the exploration of additional search directions that are normally dominated by large protein sub-families within very diverse families. All methods are evaluated with a large fold-recognition benchmark.     

Optimal search strategies for detecting health services research studies in MEDLINE

Background

Evidence from health services research (HSR) is currently thinly spread through many journals, making it difficult for health services researchers, managers and policy-makers to find research on clinical practice guidelines and the appropriateness, process, outcomes, cost and economics of health care services. We undertook to develop and test search terms to retrieve from the MEDLINE database HSR articles meeting minimum quality standards.

Methods

The retrieval performance of 7445 methodologic search terms and phrases in MEDLINE (the test) were compared with a hand search of the literature (the gold standard) for each issue of 68 journal titles for the year 2000 (a total of 25 936 articles). We determined sensitivity, specificity and precision (the positive predictive value) of the MEDLINE search strategies.

Results

A majority of the articles that were classified as outcome assessment, but fewer than half of those in the other categories, were considered methodologically acceptable (no methodologic criteria were applied for cost studies). Combining individual search terms to maximize sensitivity, while keeping specificity at 50% or more, led to sensitivities in the range of 88.1% to 100% for several categories (specificities ranged from 52.9% to 97.4%). When terms were combined to maximize specificity while keeping sensitivity at 50% or more, specificities of 88.8% to 99.8% were achieved. When terms were combined to maximize sensitivity and specificity while minimizing the differences between the 2 measurements, most strategies for HSR categories achieved sensitivity and specificity of at least 80%.

Interpretation

Sensitive and specific search strategies were validated for retrieval of HSR literature from MEDLINE. These strategies have been made available for public use by the US National Library of Medicine at www.nlm.nih.gov/nichsr/hedges/search.html.With the increasing emphasis on “using evidence” and “value for money” in health services, it is essential that researchers, clinicians, health system managers and public policy-makers be able to retrieve relevant, high-quality reports of health services research (HSR). Efficiently retrieved research evidence can aid in decision-making about which services to provide and in the resource allocation decisions to support those services, reducing the need for arbitrary decisions and aiding collaboration with clinicians and consumers.¹ MEDLINE is a huge and expanding bibliographic resource that is freely available to all with Internet access. Yet the volume of the literature often overwhelms both clinicians and health system decision-makers.^2,3 End-users of MEDLINE and other large bibliographic databases have difficulty executing precise searches^2,3 and are often unaware of what kind of information to seek, where to find it^3,4 and how to judge its quality.³HSR has been defined as the scientific study of the effect of health care delivery; the organization and management of health care access, quality, cost and financing; and the evaluation of the impact of health services and technology (Allmang NA, Koonce TY. Health services research topic searches. Bethesda [MD]: National Library of Medicine; 2000. Unpublished report). More recently, HSR has been defined as the multidisciplinary field of scientific investigation that studies how social factors, financing systems, organizational structures and processes, health technologies and personal behaviours affect access to health care, the quality and cost of health care and, ultimately, health and well-being.⁵ HSR articles constitute only a tiny fraction of the MEDLINE database and are spread through a large number of journals; hence, MEDLINE searching is challenging. Conversely, journal browsing is impractical as a means of retrieving all relevant studies for a given question or staying abreast of the literature. Our aim was to develop methodologic search filters for MEDLINE to enable end-users to efficiently retrieve articles of relevance to clinical practice guidelines (CPGs) and the appropriateness, process, outcomes, cost and economics of health services.     

Informatic search strategies to discover analogues and variants of natural product archetypes

Natural products are a crucial source of antimicrobial agents, but reliance on low-resolution bioactivity-guided approaches has led to diminishing interest in discovery programmes. Here, we demonstrate that two in-house automated informatic platforms can be used to target classes of biologically active natural products, specifically, peptaibols. We demonstrate that mass spectrometry-based informatic approaches can be used to detect natural products with high sensitivity, identifying desired agents present in complex microbial extracts. Using our specialised software packages, we could elaborate specific branches of chemical space, uncovering new variants of trichopolyn and demonstrating a way forward in mining natural products as a valuable source of potential pharmaceutical agents.     

Bayesian model choice and search strategies for mapping interacting quantitative trait Loci

Most complex traits of animals, plants, and humans are influenced by multiple genetic and environmental factors. Interactions among multiple genes play fundamental roles in the genetic control and evolution of complex traits. Statistical modeling of interaction effects in quantitative trait loci (QTL) analysis must accommodate a very large number of potential genetic effects, which presents a major challenge to determining the genetic model with respect to the number of QTL, their positions, and their genetic effects. In this study, we use the methodology of Bayesian model and variable selection to develop strategies for identifying multiple QTL with complex epistatic patterns in experimental designs with two segregating genotypes. Specifically, we develop a reversible jump Markov chain Monte Carlo algorithm to determine the number of QTL and to select main and epistatic effects. With the proposed method, we can jointly infer the genetic model of a complex trait and the associated genetic parameters, including the number, positions, and main and epistatic effects of the identified QTL. Our method can map a large number of QTL with any combination of main and epistatic effects. Utility and flexibility of the method are demonstrated using both simulated data and a real data set. Sensitivity of posterior inference to prior specifications of the number and genetic effects of QTL is investigated.     

State-space search strategies gleaned from animal behavior: a traveling salesman experiment

A widespread search strategy employed by predators in both vertebrate and invertebrate phyla is the well-known area-restricted search strategy. The generality, simplicity, and effectiveness of this strategy have made it emerge many times during the course of natural selection. In this work, an artificial intelligence state-space search procedure is developed using search guidelines gleaned from the foraging behavior of predators. This procedure, which we call predatory search, has been implemented on a NP-Hard combinatorial problem: the traveling salesman problem. Numerical results are presented for a limited set of benchmark problems, and area-restricted search seems to be effective: We have been able to find the optimal solution to, among others, a 400-city Manhattan problem. Received: 9 July 1997 / Accepted in revised form: 24 November 1997     

Comment on "Unbiased statistical analysis for multi-stage proteomic search strategies"

Everett et al. recently reported on a statistical bias that arises in the target-decoy approach to false discovery rate estimation in two-pass proteomics search strategies as exemplified by X!Tandem. This bias can cause serious underestimation of the false discovery rate. We argue here that the "unbiased" solution proposed by Everett et al., however, is also biased and under certain circumstances can also result in a serious underestimate of the FDR, especially at the protein level.     

Optimization strategies for production of mammalian embryos by nuclear transfer

In order to optimize each of the individual steps in the nuclear transfer procedure, we report alternative protocols useful for producing recipient cytoplasts and for improving the success rate of nuclear transfer embryos in cattle, rhesus monkey, and hamster. Vital labeling of maternal chromatin/spindle is accomplished by long wavelength fluorochromes Sybr14 and rhodamine labeled tubulin allowing constant monitoring and verification during enucleation. The use of Chinese hamster ovary (CHO) donor cells expressing the viral influenza hemagglutinin fusion protein (HA-300a+), to adhere and induce fusion between the donor cells and enucleated cow, rhesus and hamster oocytes was examined. Cell surface hemagglutinin was activated with trypsin prior to nuclear transfer and fusion was induced by a short incubation of a newly created nuclear transfer couplet at pH 5.2 at room temperature. Donor cell cytoplasm was dynamically labeled with CMFDA, or further transfected with the green fluorescence protein (GFP) gene, so that fusion could be directly monitored using live imaging. High rates of fusion were observed between CHO donor cells and hamster (100%), rhesus (100%), and cow recipient cytoplasts (81.6%). Live imaging during fusion revealed rapid intermixing of cytoplasmic components between a recipient and a donor cell. Prelabeled donor cytoplasmic components were uniformly distributed throughout the recipient cytoplast, within minutes of fusion, while the newly introduced nucleus remained at the periphery. The fusion process did not induce activation as evidenced by unchanged distribution and density of cortical granules in the recipient cytoplasts. After artificial activation, the nuclear transfer embryos created in this manner were capable of completing several embryonic cell divisions. These procedures hold promise for enhancing the efficiency of nuclear transfer in mammals of importance for biomedical research, agriculture, biotechnology, and preserving unique, rare, and endangered species.     

Use of spatial information and search strategies in a water maze analog in Drosophila melanogaster

Learning the spatial organization of the environment is crucial to fitness in most animal species. Understanding proximate and ultimate factors underpinning spatial memory is thus a major goal in the study of animal behavior. Despite considerable interest in various aspects of its behavior and biology, the model species Drosophila melanogaster lacks a standardized apparatus to investigate spatial learning and memory. We propose here a novel apparatus, the heat maze, conceptually based on the Morris water maze used in rodents. Using the heat maze, we demonstrate that D. melanogaster flies are able to use either proximal or distal visual cues to increase their performance in navigating to a safe zone. We also show that flies are actively using the orientation of distal visual cues when relevant in targeting the safe zone, i.e., Drosophila display spatial learning. Parameter-based classification of search strategies demonstrated the progressive use of spatially precise search strategies during learning. We discuss the opportunity to unravel the mechanistic and evolutionary bases of spatial learning in Drosophila using the heat maze.