Novel computational biology methods and their applications to drug discovery

Computational biology methods are now firmly entrenched in the drug discovery process. These methods focus on modeling and simulations of biological systems to complement and direct conventional experimental approaches. Two important branches of computational biology include protein homology modeling and the computational biophysics method of molecular dynamics. Protein modeling methods attempt to accurately predict three-dimensional (3D) structures of uncrystallized proteins for subsequent structure-based drug design applications. Molecular dynamics methods aim to elucidate the molecular motions of the static representations of crystallized protein structures. In this review we highlight recent novel methodologies in the field of homology modeling and molecular dynamics. Selected drug discovery applications using these methods conclude the review.     

The emergence of pattern discovery techniques in computational biology

In the past few years, pattern discovery has been emerging as a generic tool of choice for tackling problems from the computational biology domain. In this presentation, and after defining the problem in its generality, we review some of the algorithms that have appeared in the literature and describe several applications of pattern discovery to problems from computational biology.     

Genomics and computational molecular biology

There has been a dramatic increase in the number of completely sequenced bacterial genomes during the past two years as a result of the efforts both of public genome agencies and the pharmaceutical industry. The availability of completely sequenced genomes permits more systematic analyses of genes, evolution and genome function than was otherwise possible. Using computational methods - which are used to identify genes and their functions including statistics, sequence similarity, motifs, profiles, protein folds and probabilistic models - it is possible to develop characteristic genome signatures, assign functions to genes, identify pathogenic genes, identify metabolic pathways, develop diagnostic probes and discover potential drug-binding sites. All of these directions are critical to understanding bacterial growth, pathogenicity and host-pathogen interactions.     

Toward computational systems biology

The development and successful application of high-throughput technologies are transforming biological research. The large quantities of data being generated by these technologies have led to the emergence of systems biology, which emphasizes large-scale, parallel characterization of biological systems and integration of fragmentary information into a coherent whole. Complementing the reductionist approach that has dominated biology for the last century, mathematical modeling is becoming a powerful tool to achieve an integrated understanding of complex biological systems and to guide experimental efforts of engineering biological systems for practical applications. Here I give an overview of current mainstream approaches in modeling biological systems, highlight specific applications of modeling in various settings, and point out future research opportunities and challenges.     

Cardiac resynchronization: insight from experimental and computational models

Cardiac resynchronization therapy (CRT) is a promising therapy for heart failure patients with a conduction disturbance, such as left bundle branch block. The aim of CRT is to resynchronize contraction between and within ventricles. However, about 30% of patients do not respond to this therapy. Therefore, a better understanding is needed for the relation between electrical and mechanical activation. In this paper, we focus on to what extent animal experiments and mathematical models can help in order to understand the pathophysiology of asynchrony to further improve CRT.     

System biology and synthetic biology modify drug discovery and development

Life Sciences are built on observations. Right now, a more systemic approach allowing to integrate the different organizational levels in Biology is emerging. Such an approach uses a set of technologies and strategies allowing to build models that appear to be more and more predictive (omics, bioinformatics, integrative biology, computational biology…). Those models accelerate the rational development of new therapies avoiding an engineering based only on trials and errors. This approach both holistic and predictive radically modifies the discovery and development modalities used today in health industries. Moreover, because of the apparition of new jobs at the interface of disciplines, of private and public sectors and of life sciences and engineering sciences, this implies to rethink the training programs in both their contents and their pedagogical tools.     

Stem cell biology and drug discovery

There are many reasons to be interested in stem cells, one of the most prominent being their potential use in finding better drugs to treat human disease. This article focuses on how this may be implemented. Recent advances in the production of reprogrammed adult cells and their regulated differentiation to disease-relevant cells are presented, and diseases that have been modeled using these methods are discussed. Remaining difficulties are highlighted, as are new therapeutic insights that have emerged.     

Structural genomics meets computational biology

A meeting recently organized by the NIH NIGMS Protein StructureInitiative (PSI, http://www.nigms.nih.gov/Initiatives/PSI) hasmade crystal clear the urgency and importance of the developmentof computational methods for the analysis of protein families,definition of protein domains and regions for expression, andannotation of protein function. No really new problems, butproblems made now even more important for the development ofthe Structural Genomics projects. PSI is now in the first year of     

Deep learning for computational biology

Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology.     

The rise of computational biology

The year 2001 saw a remarkable burst of interest in biological simulation, with several international meetings on the subject, and the inclusion, by journals, of web site references from which published models can be downloaded. So, why has all this happened so suddenly?     

Implementing vertex dynamics models of cell populations in biology within a consistent computational framework

The dynamic behaviour of epithelial cell sheets plays a central role during development, growth, disease and wound healing. These processes occur as a result of cell adhesion, migration, division, differentiation and death, and involve multiple processes acting at the cellular and molecular level. Computational models offer a useful means by which to investigate and test hypotheses about these processes, and have played a key role in the study of cell–cell interactions. However, the necessarily complex nature of such models means that it is difficult to make accurate comparison between different models, since it is often impossible to distinguish between differences in behaviour that are due to the underlying model assumptions, and those due to differences in the in silico implementation of the model. In this work, an approach is described for the implementation of vertex dynamics models, a discrete approach that represents each cell by a polygon (or polyhedron) whose vertices may move in response to forces. The implementation is undertaken in a consistent manner within a single open source computational framework, Chaste, which comprises fully tested, industrial-grade software that has been developed using an agile approach. This framework allows one to easily change assumptions regarding force generation and cell rearrangement processes within these models. The versatility and generality of this framework is illustrated using a number of biological examples. In each case we provide full details of all technical aspects of our model implementations, and in some cases provide extensions to make the models more generally applicable.     

Systems biology in drug discovery

The hope of the rapid translation of 'genes to drugs' has foundered on the reality that disease biology is complex, and that drug development must be driven by insights into biological responses. Systems biology aims to describe and to understand the operation of complex biological systems and ultimately to develop predictive models of human disease. Although meaningful molecular level models of human cell and tissue function are a distant goal, systems biology efforts are already influencing drug discovery. Large-scale gene, protein and metabolite measurements ('omics') dramatically accelerate hypothesis generation and testing in disease models. Computer simulations integrating knowledge of organ and system-level responses help prioritize targets and design clinical trials. Automation of complex primary human cell-based assay systems designed to capture emergent properties can now integrate a broad range of disease-relevant human biology into the drug discovery process, informing target and compound validation, lead optimization, and clinical indication selection. These systems biology approaches promise to improve decision making in pharmaceutical development.