Informatics and computational strategies for the study of lipids

Recent advances in mass spectrometry (MS)-based techniques for lipidomic analysis have empowered us with the tools that afford studies of lipidomes at the systems level. However, these techniques pose a number of challenges for lipidomic raw data processing, lipid informatics, and the interpretation of lipidomic data in the context of lipid function and structure. Integration of lipidomic data with other systemic levels, such as genomic or proteomic, in the context of molecular pathways and biophysical processes provides a basis for the understanding of lipid function at the systems level. The present report, based on the limited literature, is an update on a young but rapidly emerging field of lipid informatics and related pathway reconstruction strategies.     

Theoretical and computational strategies for rational molecularly imprinted polymer design

The further evolution of molecularly imprinted polymer science and technology necessitates the development of robust predictive tools capable of handling the complexity of molecular imprinting systems. A combination of the rapid growth in computer power over the past decade and significant software developments have opened new possibilities for simulating aspects of the complex molecular imprinting process. We present here a survey of the current status of the use of in silico-based approaches to aspects of molecular imprinting. Finally, we highlight areas where ongoing and future efforts should yield information critical to our understanding of the underlying mechanisms sufficient to permit the rational design of molecularly imprinted polymers.     

Synergistic experimental and computational approach identifies novel strategies for polyhydroxybutyrate overproduction

Polyhydroxybutyrate (PHB) is a sustainable bioplastic produced by bacteria that is a potential replacement for conventional plastics. This study delivers an integrated experimental and computational modeling approach to decipher metabolic factors controlling PHB production and offers engineering design strategies to boost production. In the metabolically robust Rhodopseudomonas palustris CGA009, PHB production significantly increased when grown on the carbon- and electron-rich lignin breakdown product p-coumarate (C₉H₈O₃) compared to virtually no PHB titer from acetate (C₂H₃NaO₂). The maximum yield did not improve further when grown on coniferyl alcohol (C₁₀H₁₂O₃), but comparison of the PHB profiles showed that coniferyl alcohol's higher carbon content resulted in a higher rate of PHB production. Combined experimental results revealed that cytoplasmic space may be a limiting factor for maximum PHB titer. In order to obtain a systems-level understanding of factors driving PHB yield, a model-driven investigation was performed. The model yielded several engineering design strategies including utilizing reduced, high molecular weight substrates that bypass the thiolase reaction (phaA). Based on these strategies, utilization of butyrate was predicted and subsequently validated to produce PHB. Model analysis also explained why nitrogen starvation was not essential for PHB production and revealed that renewable and abundant lignin aromatics are ideal candidates for PHB production. Most importantly, the generality of the derived design rules allows them to be applied to any PHB-producing microbe with similar metabolic features.     

Experimental and computational approaches for the study of calmodulin interactions

Ca²⁺, a universal messenger in eukaryotes, plays a major role in signaling pathways that control many growth and developmental processes in plants as well as their responses to various biotic and abiotic stresses. Cellular changes in Ca²⁺ in response to diverse signals are recognized by protein sensors that either have their activity modulated or that interact with other proteins and modulate their activity. Calmodulins (CaMs) and CaM-like proteins (CMLs) are Ca²⁺ sensors that have no enzymatic activity of their own but upon binding Ca²⁺ interact and modulate the activity of other proteins involved in a large number of plant processes. Protein-protein interactions play a key role in Ca²⁺/CaM-mediated in signaling pathways. In this review, using CaM as an example, we discuss various experimental approaches and computational tools to identify protein-protein interactions. During the last two decades hundreds of CaM-binding proteins in plants have been identified using a variety of approaches ranging from simple screening of expression libraries with labeled CaM to high-throughput screens using protein chips. However, the high-throughput methods have not been applied to the entire proteome of any plant system. Nevertheless, the data provided by these screens allows the development of computational tools to predict CaM-interacting proteins. Using all known binding sites of CaM, we developed a computational method that predicted over 700 high confidence CaM interactors in the Arabidopsis proteome. Most (>600) of these are not known to bind calmodulin, suggesting that there are likely many more CaM targets than previously known. Functional analyses of some of the experimentally identified Ca²⁺ sensor target proteins have uncovered their precise role in Ca²⁺-mediated processes. Further studies on identifying novel targets of CaM and CMLs and generating their interaction network - “calcium sensor interactome” - will help us in understanding how Ca²⁺ regulates a myriad of cellular and physiological processes.     

Emerging computational approaches for the study of protein allostery

Allosteric regulation of protein function is key in controlling cellular processes so its underlying mechanisms are of primary concern to research in areas spanning protein engineering and drug design. However, due to the complex nature of allosteric mechanisms, a clear and predictive understanding of the relationship between protein structure and allosteric function remains elusive. Well established experimental approaches are available to offer a limited degree of characterization of mechanical properties within proteins, but the analytical capabilities of computational methods are evolving rapidly in their ability to accurately define the subtle and concerted structural dynamics that comprise allostery. This review includes a brief overview of allostery in proteins and an exploration of relevant experimental methods. An explanation of the transition from experimental toward computational methods for allostery is discussed, followed by a review of existing and emerging methods.     

Combined experimental and computational strategies define an expansive regulon for GcvB small RNA

The bacterial small RNA GcvB has been known as a translational repressor of mRNAs encoding amino acid transporters and has been postulated to limit uptake of unnecessary amino acids under nutrient-rich conditions. In this issue of Molecular Microbiology, Sharma et al. (2011) provide evidence for a much broader role for GcvB as a global regulator of amino acid metabolism. Using a unique combination of experimental and biocomputational approaches, the authors triple the size of the GcvB regulon, making it the largest sRNA regulon defined to date.     

A computational framework for the study of confidence in humans and animals

Confidence judgements, self-assessments about the quality of a subject's knowledge, are considered a central example of metacognition. Prima facie, introspection and self-report appear the only way to access the subjective sense of confidence or uncertainty. Contrary to this notion, overt behavioural measures can be used to study confidence judgements by animals trained in decision-making tasks with perceptual or mnemonic uncertainty. Here, we suggest that a computational approach can clarify the issues involved in interpreting these tasks and provide a much needed springboard for advancing the scientific understanding of confidence. We first review relevant theories of probabilistic inference and decision-making. We then critically discuss behavioural tasks employed to measure confidence in animals and show how quantitative models can help to constrain the computational strategies underlying confidence-reporting behaviours. In our view, post-decision wagering tasks with continuous measures of confidence appear to offer the best available metrics of confidence. Since behavioural reports alone provide a limited window into mechanism, we argue that progress calls for measuring the neural representations and identifying the computations underlying confidence reports. We present a case study using such a computational approach to study the neural correlates of decision confidence in rats. This work shows that confidence assessments may be considered higher order, but can be generated using elementary neural computations that are available to a wide range of species. Finally, we discuss the relationship of confidence judgements to the wider behavioural uses of confidence and uncertainty.     

Informatics and the Clinical Laboratory

The nature of pathology services is changing under the combined pressures of increasing workloads, cost constraints and technological advancement. In the face of this, laboratory systems need to meet new demands for data exchange with clinical electronic record systems for test requesting and results reporting. As these needs develop, new challenges are emerging especially with respect to the format and content of the datasets which are being exchanged. If the potential for the inclusion of intelligent systems in both these areas is to be realised, the continued dialogue between clinicians and laboratory information specialists is of paramount importance. Requirements of information technology (IT) in pathology, now extend well beyond the provision of purely analytical data. With the aim of achieving seamless integration of laboratory data into the total clinical pathway, ‘Informatics’ – the art and science of turning data into useful information – is becoming increasingly important in laboratory medicine. Informatics is a powerful tool in pathology – whether in implementing processes for pathology modernisation, introducing new diagnostic modalities (e.g. proteomics, genomics), providing timely and evidence-based disease management, or enabling best use of limited and often costly resources. Providing appropriate information to empowered and interested patients – which requires critical assessment of the ever-increasing volume of information available – can also benefit greatly from appropriate use of informatics in enhancing self-management of long term conditions. The increasing demands placed on pathology information systems in the context of wider developmental change in healthcare delivery are explored in this review. General trends in medical informatics are reflected in current priorities for laboratory medicine, including the need for unified electronic records, computerised order entry, data security and recovery, and audit. We conclude that there is a need to rethink the architecture of pathology systems and in particular to address the changed environment in which electronic patient record systems are maturing rapidly. The opportunity for laboratory-based informaticians to work collaboratively with clinical systems developers to embed clinically intelligent decision support systems should not be missed.     

Establishment and validation of computational model for MT1-MMP dependent ECM degradation and intervention strategies

MT1-MMP is a potent invasion-promoting membrane protease employed by aggressive cancer cells. MT1-MMP localizes preferentially at membrane protrusions called invadopodia where it plays a central role in degradation of the surrounding extracellular matrix (ECM). Previous reports suggested a role for a continuous supply of MT1-MMP in ECM degradation. However, the turnover rate of MT1-MMP and the extent to which the turnover contributes to the ECM degradation at invadopodia have not been clarified. To approach this problem, we first performed FRAP (Fluorescence Recovery after Photobleaching) experiments with fluorescence-tagged MT1-MMP focusing on a single invadopodium and found very rapid recovery in FRAP signals, approximated by double-exponential plots with time constants of 26 s and 259 s. The recovery depended primarily on vesicle transport, but negligibly on lateral diffusion. Next we constructed a computational model employing the observed kinetics of the FRAP experiments. The simulations successfully reproduced our FRAP experiments. Next we inhibited the vesicle transport both experimentally, and in simulation. Addition of drugs inhibiting vesicle transport blocked ECM degradation experimentally, and the simulation showed no appreciable ECM degradation under conditions inhibiting vesicle transport. In addition, the degree of the reduction in ECM degradation depended on the degree of the reduction in the MT1-MMP turnover. Thus, our experiments and simulations have established the role of the rapid turnover of MT1-MMP in ECM degradation at invadopodia. Furthermore, our simulations suggested synergetic contributions of proteolytic activity and the MT1-MMP turnover to ECM degradation because there was a nonlinear and marked reduction in ECM degradation if both factors were reduced simultaneously. Thus our computational model provides a new in silico tool to design and evaluate intervention strategies in cancer cell invasion.     

CAFE: a computational tool for the study of gene family evolution

SUMMARY: We present CAFE (Computational Analysis of gene Family Evolution), a tool for the statistical analysis of the evolution of the size of gene families. It uses a stochastic birth and death process to model the evolution of gene family sizes over a phylogeny. For a specified phylogenetic tree, and given the gene family sizes in the extant species, CAFE can estimate the global birth and death rate of gene families, infer the most likely gene family size at all internal nodes, identify gene families that have accelerated rates of gain and loss (quantified by a p-value) and identify which branches cause the p-value to be small for significant families. AVAILABILITY: Software is available from http://www.bio.indiana.edu/~hahnlab/Software.html