Strategies for developing protein tyrosine phosphatase inhibitors

Protein tyrosine phosphatases (PTPs) play vital roles in numerous cellular processes and are implicated in a growing number of human diseases, ranging from cancer to cardiovascular, immunological, infectious, neurological, and metabolic diseases. Here we present methods for developing small molecule inhibitors for these enzymes, starting with how to set up a high throughput chemical library screening for PTP inhibitors, how to confirm and prioritize hits, and how to circumnavigate possible pitfalls. Next, we present the relatively new hit generating method of in silico or virtual screening. We give an overview of existing software tools, describe how to choose and generate protein target structures and illustrate the procedure with examples. We then discuss how three-dimensional PTP structures can be analyzed in terms of their potential to bind small molecule inhibitors selectively over homologous proteins and how computer tools can be applied for lead optimization efforts. We finish with a perspective of how well these PTP inhibitors might perform as future drugs to treat human disease.     

The Structure of Autocatalytic Sets: Evolvability, Enablement, and Emergence

This paper presents new results from a detailed study of the structure of autocatalytic sets. We show how autocatalytic sets can be decomposed into smaller autocatalytic subsets, and how these subsets can be identified and classified. We then argue how this has important consequences for the evolvability, enablement, and emergence of autocatalytic sets. We end with some speculation on how all this might lead to a generalized theory of autocatalytic sets, which could possibly be applied to entire ecologies or even economies.     

Resource availability affects the structure of a natural bacteria-bacteriophage community

Antagonistic networks are known to be structured in the wild, but knowledge on how this structure may change as a response to environmental perturbations is scarce. We describe a natural bipartite network between bacteria and lytic bacteriophages, and investigate how it is affected by environmental productivity in the form of different resource levels for the bacteria. We report that low amounts of resource decrease phage generality and lead to less robust and less stable communities. We discuss how resource levels in nature may alter the structure of complex communities.     

Fundamental Interventions: How Clinicians Can Address the Fundamental Causes of Disease

In order to enhance the “structural competency” of medicine—the capability of clinicians to address social and institutional determinants of their patients’ health—physicians need a theoretical lens to see how social conditions influence health and how they might address them. We consider one such theoretical lens, fundamental cause theory, and propose how it might contribute to a more structurally competent medical profession. We first describe fundamental cause theory and how it makes the social causes of disease and health visible. We then outline the sorts of “fundamental interventions” that physicians might make in order to address the fundamental causes.     

Organization of receptive fields in networks with Hebbian learning: the connection between synaptic and phenomenological models

In this paper we address the question of how interactions affect the formation and organization of receptive fields in a network composed of interacting neurons with Hebbian-type learning. We show how to partially decouple single cell effects from network effects, and how some phenomenological models can be seen as approximations to these learning networks. We show that the interaction affects the structure of receptive fields. We also demonstrate how the organization of different receptive fields across the cortex is influenced by the interaction term, and that the type of singularities depends on the symmetries of the receptive fields.     

Integrating ecosystem engineering and food webs

Ecosystem engineering, the physical modification of the environment by organisms, is a common and often influential process whose significance to food web structure and dynamics is largely unknown. In the light of recent calls to expand food web studies to include non‐trophic interactions, we explore how we might best integrate ecosystem engineering and food webs. We provide rationales justifying their integration and present a provisional framework identifying how ecosystem engineering can affect the nodes and links of food webs and overall organization; how trophic interactions with the engineer can affect the engineering; and how feedbacks between engineering and trophic interactions can affect food web structure and dynamics. We use a simple integrative food chain model to illustrate how feedbacks between the engineer and the food web can alter 1) engineering effects on food web dynamics, and 2) food web responses to extrinsic environmental perturbations. We identify four general challenges to integration that we argue can readily be met, and call for studies that can achieve this integration and help pave the way to a more general understanding of interaction webs in nature. Synthesis All species are affected by their physical environment. Because ecosystem engineering species modify the physical environment and belong to food webs, such species are potentially one of the most important bridges between the trophic and non‐trophic. We examine how to integrate the so far, largely independent research areas of ecosystem engineering and food webs. We present a conceptual framework for understanding how engineering can affect food webs and vice versa, and how feedbacks between the two alter ecosystem dynamics. With appropriate empirical studies and models, integration is achievable, paving the way to a more general understanding of interaction webs in nature.     

Mathematical models of bacterial growth,inhibition and death under combined stress conditions

Summary In this report we review the history of growth theories. We show how classical growth models may be derived as special cases of a generic growth rate equation. We show how growth models may be modified to represent survival data. We use linear combinations of growth and survival models to represent complex growth/survival curves and give practical examples utilizing nonlinear regression analysis. We show that traditional methods of estimating D values are inappropriate for complex, multiphasic growth/survival data. We show how such data may be modeled mathematically and illustrate methods for estimating true D values from such data.     

Combining membrane potential imaging with L-glutamate or GABA photorelease

Combining membrane potential imaging using voltage sensitive dyes with photolysis of L-glutamate or GABA allows the monitoring of electrical activity elicited by the neurotransmitter at different sub-cellular sites. Here we describe a simple system and some basic experimental protocols to achieve these measurements. We show how to apply the neurotransmitter and how to vary the dimension of the area of photolysis. We assess the localisation of photolysis and of the recorded membrane potential changes by depolarising the dendrites of cerebellar Purkinje neurons with L-glutamate photorelease using different experimental protocols. We further show in the apical dendrites of CA1 hippocampal pyramidal neurons how L-glutamate photorelease can be used to calibrate fluorescence changes from voltage sensitive dyes in terms of membrane potential changes (in mV) and how GABA photorelease can be used to investigate the phenomenon of shunting inhibition. We also show how GABA photorelease can be used to measure chloride-mediated changes of membrane potential under physiological conditions originating from different regions of a neuron, providing important information on the local intracellular chloride concentrations. The method and the proof of principle reported here open the gateway to a variety of important applications where the advantages of this approach are necessary.     

Generalized Gene Adjacencies, Graph Bandwidth, and Clusters in Yeast Evolution

We present a parameterized definition of gene clusters that allows us to control the emphasis placed on conserved order within a cluster. Though motivated by biological rather than mathematical considerations, this parameter turns out to be closely related to the bandwidth parameter of a graph. Our focus will be on how this parameter affects the characteristics of clusters: how numerous they are, how large they are, how rearranged they are, and to what extent they are preserved from ancestor to descendant in a phylogenetic tree. We infer the latter property by dynamic programming optimization of the presence of individual edges at the ancestral nodes of the phylogeny. We apply our analysis to a set of genomes drawn from the Yeast Gene Order Browser.     

Understanding implications of detection heterogeneity in wildlife abundance estimation

Negative bias in mark-recapture abundance estimators due to heterogeneity in detection (capture) probability is a well-known problem, but we believe most biologists do not understand why heterogeneity causes bias and how bias can be reduced. We demonstrate how heterogeneity creates dependence and bias in mark-recapture approaches to abundance estimation. In comparison, heterogeneity, and hence estimator bias, is not as problematic for distance sampling and mark-resight methods because both techniques estimate detection probabilities based on a known quantity. We show how the introduction of a known number of individuals planted into a study population prior to a mark-recapture survey can reduce bias from heterogeneity in detection probability. We provide examples with simulation and an analysis of motion-sensitive camera data from a study population of introduced eastern wild turkeys (Meleagris gallopavo silvestris) of known size with a subset of telemetered birds. In choosing a method for abundance estimation, careful consideration should be given to assumptions and how heterogeneity in detection probability can be accommodated for each application.     

Heads and tails: evolution of antero-posterior patterning in insects

In spite of their varied appearances, insects share a common body plan whose layout is established by patterning genes during embryogenesis. We understand in great molecular detail how the Drosophila embryo patterns its segments. However, Drosophila has a type of embryogenesis that is highly derived and varies extensively as compared to most insects. Therefore, the study of other insects is invaluable for piecing together how the ancestor of all insects established its segmented body plan, and how this process can be plastic during evolution. In this review, we discuss the evolution of Antero-Posterior (A-P) patterning mechanisms in insects. We first describe two distinct modes of insect development - long and short germ development - and how these two modes of patterning are achieved. We then summarize how A-P patterning occurs in the long-germ Drosophila, where most of our knowledge comes from, and in the well-studied short-germ insect, Tribolium. Finally, using examples from other insects, we highlight differences in patterns of expression, which suggest foci of evolutionary change.     

Disease persistence in epidemiological models: the interplay between vaccination and migration

We consider the interplay of vaccination and migration rates on disease persistence in epidemiological systems. We show that short-term and long-term migration can inhibit disease persistence. As a result, we show how migration changes how vaccination rates should be chosen to maintain herd immunity. In a system of coupled SIR models, we analyze how disease eradication depends explicitly on vaccine distribution and migration connectivity. The analysis suggests potentially novel vaccination policies that underscore the importance of optimal placement of finite resources.     

Current issues in tropical phenology: a synthesis

We retrace the development of tropical phenology research, compare temperate phenology study to that in the tropics and highlight the advances currently being made in this flourishing discipline. The synthesis draws attention to how fundamentally different tropical phenology data can be to temperate data. Tropical plants lack a phase of winter dormancy and may grow and reproduce continually. Seasonal patterns in environmental parameters, such as rainfall, irradiance or temperature, do not necessarily coincide temporally, as they do in temperate climes. We review recent research on the drivers of phenophase cycles in individual trees, species and communities and highlight how significant innovations in biometric tools and approaches are being driven by the need to deal with circular data, the complexity of defining tropical seasons and the myriad growth and reproductive strategies used by tropical plants. We discuss how important the use of leaf phenology (or remotely‐sensed proxies of leaf phenophases) has become in tracking biome responses to climate change at the continental level and how important the phenophase of forests can be in determining local weather conditions. We also highlight how powerful analyses of plant responses are hampered at many tropical sites by a lack of contextual data on local environmental conditions. We conclude by arguing that there is a clear global benefit in increasing long term tropical phenology data collection and improving empirical collection of local climate measures, contemporary to the phenology data. Directing more resources to research in this sector will be widely beneficial.     

Mechanisms of seizure propagation in a cortical model

We consider a mathematical model of mesoscopic human cortical ictal electrical activity. We compare the model results with ictal electrocortical data recorded from three human subjects and show how the two agree. We determine that, in the model system, seizures result from increased connectivity between excitatory and inhibitory cell populations, or from decreased connectivity within either excitatory or inhibitory cell populations. We compare the model results with the disinhibition and 4-AP models of epilepsy and suggest how the model may guide the development of new anticonvulsant therapies.     

Adaptive Landscape by Environment Interactions Dictate Evolutionary Dynamics in Models of Drug Resistance

The adaptive landscape analogy has found practical use in recent years, as many have explored how their understanding can inform therapeutic strategies that subvert the evolution of drug resistance. A major barrier to applications of these concepts is a lack of detail concerning how the environment affects adaptive landscape topography, and consequently, the outcome of drug treatment. Here we combine empirical data, evolutionary theory, and computer simulations towards dissecting adaptive landscape by environment interactions for the evolution of drug resistance in two dimensions—drug concentration and drug type. We do so by studying the resistance mediated by Plasmodium falciparum dihydrofolate reductase (DHFR) to two related inhibitors—pyrimethamine and cycloguanil—across a breadth of drug concentrations. We first examine whether the adaptive landscapes for the two drugs are consistent with common definitions of cross-resistance. We then reconstruct all accessible pathways across the landscape, observing how their structure changes with drug environment. We offer a mechanism for non-linearity in the topography of accessible pathways by calculating of the interaction between mutation effects and drug environment, which reveals rampant patterns of epistasis. We then simulate evolution in several different drug environments to observe how these individual mutation effects (and patterns of epistasis) influence paths taken at evolutionary “forks in the road” that dictate adaptive dynamics in silico. In doing so, we reveal how classic metrics like the IC₅₀ and minimal inhibitory concentration (MIC) are dubious proxies for understanding how evolution will occur across drug environments. We also consider how the findings reveal ambiguities in the cross-resistance concept, as subtle differences in adaptive landscape topography between otherwise equivalent drugs can drive drastically different evolutionary outcomes. Summarizing, we discuss the results with regards to their basic contribution to the study of empirical adaptive landscapes, and in terms of how they inform new models for the evolution of drug resistance.     

The Evolution of Facilitated Industrial Symbiosis

While much work has been done on the conditions surrounding the emergence and establishment of industrial symbiosis (IS), new attention is being paid to understanding the evolution of IS over time. We demonstrate empirically how a new, facilitated IS initiative developed and evolved over an 8‐year period. We explore its network evolution by considering how the facilitator's actions enabled and precluded two fundamental network processes—serendipitous and goal‐directed processes. We discuss implications for a more generalized theory of IS development by exploring why and how different evolutionary trajectories may unfold.     

2. Disorders in the Formation of Meaning

In the previous section we dealt with gross disorders in the hierarchy and mediation of motives. We showed how derangement of mediation and hierarchical structure leads to gross changes in a person's activity. We showed how a transformation of a human need into a compulsion deranges the complex system of socially developed norms and the patient's personality.     

Variation,selection and evolution of function-valued traits

We describe an emerging framework for understanding variation, selection and evolution of phenotypic traits that are mathematical functions. We use one specific empirical example – thermal performance curves (TPCs) for growth rates of caterpillars – to demonstrate how models for function-valued traits are natural extensions of more familiar, multivariate models for correlated, quantitative traits. We emphasize three main points. First, because function-valued traits are continuous functions, there are important constraints on their patterns of variation that are not captured by multivariate models. Phenotypic and genetic variation in function-valued traits can be quantified in terms of variance-covariance functions and their associated eigenfunctions: we illustrate how these are estimated as well as their biological interpretations for TPCs. Second, selection on a function-valued trait is itself a function, defined in terms of selection gradient functions. For TPCs, the selection gradient describes how the relationship between an organism's performance and its fitness varies as a function of its temperature. We show how the form of the selection gradient function for TPCs relates to the frequency distribution of environmental states (caterpillar temperatures) during selection. Third, we can predict evolutionary responses of function-valued traits in terms of the genetic variance-covariance and the selection gradient functions. We illustrate how non-linear evolutionary responses of TPCs may occur even when the mean phenotype and the selection gradient are themselves linear functions of temperature. Finally, we discuss some of the methodological and empirical challenges for future studies of the evolution of function-valued traits.