Systems Oncology: Bridging Pancreatic and Castrate Resistant Prostate Cancer.

Large investments by pharmaceutical companies in the development of new antineoplastic drugs have not been resulting in adequate advances of new therapies. Despite the introduction of new methods, technologies, translational medicine and bioinformatics, the usage of collected knowledge is unsatisfactory. In this paper, using examples of pancreatic ductal adenocarcinoma (PaC) and castrate-resistant prostate cancer (CRPC), we proposed a concept showing that, in order to improve applicability of current knowledge in oncology, the re-clustering of clinical and scientific data is crucial. Such an approach, based on systems oncology, would include bridging of data on biomarkers and pathways between different cancer types. Proposed concept would introduce a new matrix, which enables combining of already approved therapies between cancer types. Paper provides a (a) detailed analysis of similarities in mechanisms of etiology and progression between PaC and CRPC, (b) diabetes as common hallmark of both cancer types and (c) knowledge gaps and directions of future investigations. Proposed horizontal and vertical matrix in cancer profiling has potency to improve current antineoplastic therapy efficacy. Systems biology map using Systems Biology Graphical Notation Language is used for summarizing complex interactions and similarities of mechanisms in biology of PaC and CRPC.

The Impact of Mathematical Modeling in Understanding the Mechanisms Underlying Neurodegeneration: Evolving Dimensions and Future Directions.

Neurodegenerative diseases are a heterogeneous group of disorders that are characterized by the progressive dysfunction and loss of neurons. Here, we distil and discuss the current state of modeling in the area of neurodegeneration, and objectively compare the gaps between existing clinical knowledge and the mechanistic understanding of the major pathological processes implicated in neurodegenerative disorders. We also discuss new directions in the field of neurodegeneration that hold potential for furthering therapeutic interventions and strategies.

The promises of quantitative systems pharmacology modelling for drug development.

Recent growth in annual new therapeutic entity (NTE) approvals by the U.S. Food and Drug Administration (FDA) suggests a positive trend in current research and development (R&D) output. Prior to this, the cost of each NTE was considered to be rising exponentially, with compound failure occurring mainly in clinical phases. Quantitative systems pharmacology (QSP) modelling, as an additional tool in the drug discovery arsenal, aims to further reduce NTE costs and improve drug development success. Through in silico mathematical modelling, QSP can simulate drug activity as perturbations in biological systems and thus understand the fundamental interactions which drive disease pathology, compound pharmacology and patient response. Here we review QSP, pharmacometrics and systems biology models with respect to the diseases covered as well as their clinical relevance and applications. Overall, the majority of modelling focus was aligned with the priority of drug-discovery and clinical trials. However, a few clinically important disease categories, such as Immune System Diseases and Respiratory Tract Diseases, were poorly covered by computational models. This suggests a possible disconnect between clinical and modelling agendas. As a standard element of the drug discovery pipeline the uptake of QSP might help to increase the efficiency of drug development across all therapeutic indications.

BioModels: Content, Features, Functionality, and Use.

BioModels is a reference repository hosting mathematical models that describe the dynamic interactions of biological components at various scales. The resource provides access to over 1,200 models described in literature and over 140,000 models automatically generated from pathway resources. Most model components are cross-linked with external resources to facilitate interoperability. A large proportion of models are manually curated to ensure reproducibility of simulation results. This tutorial presents BioModels' content, features, functionality, and usage.

Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development.

The lack of a common exchange format for mathematical models in pharmacometrics has been a long-standing problem. Such a format has the potential to increase productivity and analysis quality, simplify the handling of complex workflows, ensure reproducibility of research, and facilitate the reuse of existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by the Drug Disease Model Resources (DDMoRe) consortium, is intended to become an exchange standard in pharmacometrics by providing means to encode models, trial designs, and modeling steps.

The impact of mathematical modeling on the understanding of diabetes and related complications.

Diabetes is a chronic and complex multifactorial disease caused by persistent hyperglycemia and for which underlying pathogenesis is still not completely understood. The mathematical modeling of glucose homeostasis, diabetic condition, and its associated complications is rapidly growing and provides new insights into the underlying mechanisms involved. Here, we discuss contributions to the diabetes modeling field over the past five decades, highlighting the areas where more focused research is required.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e54; doi:10.1038/psp.2013.30; advance online publication 10 July 2013.

The Input Signal Step Function (ISSF), a standard method to encode input signals in SBML models with software support, applied to circadian clock models.

Time-dependent light input is an important feature of computational models of the circadian clock. However, publicly available models encoded in standard representations such as the Systems Biology Markup Language (SBML) either do not encode this input or use different mechanisms to do so, which hinders reproducibility of published results as well as model reuse. The authors describe here a numerically continuous function suitable for use in SBML for models of circadian rhythms forced by periodic light-dark cycles. The Input Signal Step Function (ISSF) is broadly applicable to encoding experimental manipulations, such as drug treatments, temperature changes, or inducible transgene expression, which may be transient, periodic, or mixed. It is highly configurable and is able to reproduce a wide range of waveforms. The authors have implemented this function in SBML and demonstrated its ability to modify the behavior of publicly available models to accurately reproduce published results. The implementation of ISSF allows standard simulation software to reproduce specialized circadian protocols, such as the phase-response curve. To facilitate the reuse of this function in public models, the authors have developed software to configure its behavior without any specialist knowledge of SBML. A community-standard approach to represent the inputs that entrain circadian clock models could particularly facilitate research in chronobiology.

SysBioMed report: advancing systems biology for medical applications.

The following report selects and summarises some of the conclusions and recommendations generated throughout a series of workshops and discussions that have lead to the publication of the Science Policy Briefing (SPB) Nr. 35, published by the European Science Foundation. (Large parts of the present text are directly based on the ESF SPB. Detailed recommendations with regard to specific application areas are not given here but can be found in the SPB. Issues related to mathematical modelling, including training and the need for an infrastructure supporting modelling are discussed in greater detail in the present text.)The numerous reports and publications about the advances within the rapidly growing field of systems biology have led to a plethora of alternative definitions for key concepts. Here, with 'mathematical modelling' the authors refer to the modelling and simulation of subcellular, cellular and macro-scale phenomena, using primarily methods from dynamical systems theory. The aim of such models is encoding and testing hypotheses about mechanisms underlying the functioning of cells. Typical examples are models for molecular networks, where the behaviour of cells is expressed in terms of quantitative changes in the levels of transcripts and gene products. Bioinformatics provides essential complementary tools, including procedures for pattern recognition, machine learning, statistical modelling (testing for differences, searching for associations and correlations) and secondary data extracted from databases.Dynamical systems theory is the natural language to investigate complex biological systems demonstrating nonlinear spatio-temporal behaviour. However, the generation of experimental data suitable to parameterise, calibrate and validate such models is often time consuming and expensive or not even possible with the technology available today. In our report, we use the term 'computational model' when mathematical models are complemented with information generated from bioinformatics resources. Hence, 'the model' is, in reality, an integrated collection of data and models from various (possibly heterogeneous) sources. The present report focuses on a selection of topics, which were identified as appropriate case studies for medical systems biology, and adopts a particular perspective which the authors consider important. We strongly believe that mathematical modelling represents a natural language with which to integrate data at various levels and, in doing so, to provide insight into complex diseases: 1. Modelling necessitates the statement of explicit hypotheses, a process which often enhances comprehension of the biological system and can uncover critical points where understanding is lacking. 2. Simulations can reveal hidden patterns and/or counter-intuitive mechanisms in complex systems. 3. Theoretical thinking and mathematical modelling constitute powerful tools to integrate and make sense of the biological and clinical information being generated and, more importantly, to generate new hypotheses that can then be tested in the laboratory.Medical Systems Biology projects carried out recently across Europe have revealed a need for action: 4. While the need for mathematical modelling and interdisciplinary collaborations is becoming widely recognised in the biological sciences, with substantial implications for the training and research funding mechanisms within this area, the medical sciences have yet to follow this lead. 5. To achieve major breakthroughs in Medical Systems Biology, existing academic funding schemes for large-scale projects need to be reconsidered. 6. The hesitant stance of the pharmaceutical industry towards major investment in systems biology research has to be addressed. 7. Leading medical journals should be encouraged to promote mathematical modelling.

Neurological disease: are systems approaches the way forward?

Most neurological diseases are multifactorial diseases, where environmental conditions combine with genetic background or somatic mutations to trigger a pathological state. In the case of Parkinson's Disease and Schizophrenia, recent research revealed that susceptibility genes coded for proteins involved at different steps of specific metabolic networks and cellular processes. Comprehension of the pathology of those diseases is therefore very likely to benefit from Systems approaches. This is also true of their symptomatology, affecting neurological systems at molecular, cellular, and microcircuit levels.

The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models.

MOTIVATION: Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. RESULTS: We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. AVAILABILITY: The specification of SBML Level 1 is freely available from http://www.sbml.org/

Acute and long-term changes in the mesolimbic dopamine pathway after systemic or local single nicotine injections.

We have examined several neurochemical and behavioural parameters related to the function of the mesolimbic dopamine (DA) pathway in animals treated with nicotine following three modes of drug administration, i.e. systemic intraperitoneal injection, intra-accumbens (Acb) infusion or intraventral tegmental area (intra-VTA) microinjection. The present modes of systemic, intra-Acb and intra-VTA nicotine administration elicited comparable acute increases in dialysate DA levels from the Acb. The increase in extracellular DA levels was paralleled by a significant enhancement of locomotion in a habituated environment in the case of systemic or intra-VTA nicotine administration, whereas unilateral or bilateral intra-Acb nicotine infusion was ineffective, showing that accumbal DA increase is not sufficient to elicit locomotion in this experimental paradigm. Intra-VTA, but not systemic or intra-Acb, nicotine administration caused a long-term (at least 24-h) increase in basal dialysate DA levels from the Acb. In addition, significant increases in tyrosine hydroxylase (TH) and GluR1 (but not dopamine transporter or NR1) mRNA levels in the VTA were detected 24 h after intra-VTA nicotine administration. Systemic nicotine injection caused only an increase in TH mRNA levels while intra-Acb infusion did not modify any of the mRNAs tested. The long-term increase in basal DA levels in the Acb and TH, and GluR1 mRNA levels in the VTA upon intra-VTA nicotine microinjection indicates that even a single nicotine injection can induce plastic changes of the mesolimbic DA pathway.

MELTING, computing the melting temperature of nucleic acid duplex.

UNLABELLED: MELTING computes the enthalpy and entropy of an oligonucleotide duplex helix-coil transition, and then its melting temperature. The program uses the method of nearest-neighbours. The set of thermodynamic parameters can be easily customized. The program provides several correction methods for the concentration of salt. MELTING is a free program, available at no cost and open-source. Perl scripts are provided to show how MELTING can be used to construct more ambitious programs. AVAILABILITY: MELTING is available for several platforms (http://www.pasteur.fr/recherche/unites/neubiomol/meltinghome.html) and is accessible via a www server (http://bioweb.pasteur.fr/seqanal/interfaces/melting.html). CONTACT: nl223@cus.cam.ac.uk

The Ligand Gated Ion Channel database: an example of a sequence database in neuroscience.

Multiple comparisons of receptor sequences, or receptor subunit sequences, has proved to be an invaluable tool in modern pharmacological investigations. Although of outstanding importance, general sequence databases suffer from several imperfections due to their size and their non-specificity. Room therefore exists for expert-maintained databases of restricted focus, where knowledge of the research field helps to filter the huge amount of data generated. Accordingly, neuroscientists have designed databases covering several types of proteins, in particular receptors for neurotransmitters. Ligand-gated ion channels are oligomeric transmembrane proteins involved in the fast response to neurotransmitters. All these receptors are formed by the assembly of homologous subunits, and an unexpected wealth of genes coding for these subunits has been revealed during the last two decades. The Ligand Gated Ion Channel database (LGICdb) has been developed to handle this growing body of information. The database aims to provide only one entry for each gene, containing annotated nucleic acid and protein sequences.

STOCHSIM: modelling of stochastic biomolecular processes.

SUMMARY: STOCHSIM is a stochastic simulator for chemical reactions. Molecules are represented as individual software objects that react according to probabilities derived from concentrations and rate constants. Version 1.2 of STOCHSIM provides a novel cross-platform graphical interface written in Perl/Tk. A simple two-dimensional spatial structure has also been implemented, in which nearest-neighbour interactions of molecules in a 2-D lattice can be simulated.

Conformational spread in a ring of proteins: a stochastic approach to allostery.

We recently suggested that the sensitivity and range of a cluster of membrane receptors in bacteria would be enhanced by cooperative interactions between neighbouring proteins. Here, we examine the consequences of this "conformational spread" mechanism for an idealised one-dimensional system comprising a closed ring of identical allosteric protomers (protein molecules, or a group of protein domains operating as a unit). We show analytically and by means of Monte Carlo simulations that a ring of allosteric protomers can exhibit a switch-like response to changes in ligand concentration. We derive expressions for the sensitivity and cooperativity of switching and show that the maximum sensitivity is proportional to the number of protomers in the ring. A ring of this kind can reproduce the sensitivity and kinetics of the switch complex of a bacterial flagellar motor, for example, which is based on a ring of 34 FliM proteins. We also compare smaller rings of conformationally coupled protomers to classical allosteric proteins such as haemoglobin and show that the canonical MWC and KNF models arise naturally as limiting cases. Conformational spread appears to be a natural extension of the familiar mechanism of allostery: a physically realistic mechanism that should apply widely to many structures built from protein molecules.

LGICdb: the ligand-gated ion channel database.

Ligand-Gated Ion Channels (LGIC) are polymeric transmembrane proteins involved in the fast response to numerous neurotransmitters. All these receptors are formed by homologous subunits and the last two decades revealed an unexpected wealth of genes coding for these subunits. The Ligand-Gated Ion Channel database (LGICdb) has been developed to handle this increasing amount of data. The database aims to provide only one entry for each gene, containing annotated nucleic acid and protein sequences. The repository is carefully structured and the entries can be retrieved by various criteria. In addition to the sequences, the LGICdb provides multiple sequence alignments, phylogenetic analyses and atomic coordinates when available. The database is accessible via the World Wide Web (http://www.pasteur.fr/recherche/banques/LGIC /LGIC.html), where it is continuously updated. The version 16 (September 2000) available for download contained 333 entries covering 34 species.

Molecular model of a lattice of signalling proteins involved in bacterial chemotaxis.

Coliform bacteria detect chemical attractants by means of a membrane-associated cluster of receptors and signalling molecules. We have used recently determined molecular structures, in conjunction with plastic models generated by three-dimensional printer technology, to predict how the proteins of the complex are arranged in relation to the plasma membrane. The proposed structure is a regular two-dimensional lattice in which the cytoplasmic ends of chemotactic-receptor dimers are inserted into a hexagonal array of CheA and CheW molecules. This structure creates separate compartments for adaptation and downstream signalling, and indicates a possible basis for the spread of activity within the cluster.

Localization of nAChR subunit mRNAs in the brain of Macaca mulatta.

We present here a systematic mapping of nAChR subunit mRNAs in Macaca mulatta brain. A fragment, from the transmembrane segments MIII to MIV of Macaca neuronal nAChR subunits was cloned, and shown to exhibit high identity (around 95%) to the corresponding human subunits. Then, specific oligodeoxynucleotides were synthesized for in situ hybridization experiments. Both alpha4 and beta2 mRNA signals were widely distributed in the brain, being stronger in the thalamus and in the dopaminergic cells of the mesencephalon. Most brain nuclei displayed both alpha4 and beta2 signals with the exception of some basal ganglia regions and the reticular thalamic nucleus which were devoid of alpha4 signal. alpha6 and beta3 mRNA signals were selectively concentrated in the substantia nigra and the medial habenula. The strongest signals for alpha3 or beta4 mRNAs were found in the epithalamus (medial habenula and pineal gland), whereas there were no specific alpha3 or beta4 signals in mesencephalic dopaminergic nuclei. alpha5 and alpha7 mRNA signals were found in several brain areas, including cerebral cortex, thalamus and substantia nigra, although at a lower level than alpha4 and beta2. The distribution of alpha3, alpha4, alpha5, alpha6, alpha7, beta2, beta3 and beta4 subunit mRNAs in the monkey is substantially similar to that observed in rodent brain. Surprisingly, alpha2 mRNA signal was largely distributed in the Macaca brain, at levels comparable with those of alpha4 and beta2. This observation represents the main difference between rodent and Macaca subunit mRNA distribution and suggests that, besides alpha4beta2*, alpha2beta2* nAChRs constitute a main nAChR isoform in primate brain.