Information-Theoretic Analysis of the Dynamics of an Executable Biological Model

To facilitate analysis and understanding of biological systems, large-scale data are often integrated into models using a variety of mathematical and computational approaches. Such models describe the dynamics of the biological system and can be used to study the changes in the state of the system over time. For many model classes, such as discrete or continuous dynamical systems, there exist appropriate frameworks and tools for analyzing system dynamics. However, the heterogeneous information that encodes and bridges molecular and cellular dynamics, inherent to fine-grained molecular simulation models, presents significant challenges to the study of system dynamics. In this paper, we present an algorithmic information theory based approach for the analysis and interpretation of the dynamics of such executable models of biological systems. We apply a normalized compression distance (NCD) analysis to the state representations of a model that simulates the immune decision making and immune cell behavior. We show that this analysis successfully captures the essential information in the dynamics of the system, which results from a variety of events including proliferation, differentiation, or perturbations such as gene knock-outs. We demonstrate that this approach can be used for the analysis of executable models, regardless of the modeling framework, and for making experimentally quantifiable predictions.     

Reverse engineering biomolecular systems using -omic data: challenges, progress and opportunities

Recent advances in high-throughput biotechnologies have led to the rapid growing research interest in reverse engineering of biomolecular systems (REBMS). 'Data-driven' approaches, i.e. data mining, can be used to extract patterns from large volumes of biochemical data at molecular-level resolution while 'design-driven' approaches, i.e. systems modeling, can be used to simulate emergent system properties. Consequently, both data- and design-driven approaches applied to -omic data may lead to novel insights in reverse engineering biological systems that could not be expected before using low-throughput platforms. However, there exist several challenges in this fast growing field of reverse engineering biomolecular systems: (i) to integrate heterogeneous biochemical data for data mining, (ii) to combine top-down and bottom-up approaches for systems modeling and (iii) to validate system models experimentally. In addition to reviewing progress made by the community and opportunities encountered in addressing these challenges, we explore the emerging field of synthetic biology, which is an exciting approach to validate and analyze theoretical system models directly through experimental synthesis, i.e. analysis-by-synthesis. The ultimate goal is to address the present and future challenges in reverse engineering biomolecular systems (REBMS) using integrated workflow of data mining, systems modeling and synthetic biology.     

Sequence comparison and protein structure prediction

Sequence comparison is a major step in the prediction of protein structure from existing templates in the Protein Data Bank. The identification of potentially remote homologues to be used as templates for modeling target sequences of unknown structure and their accurate alignment remain challenges, despite many years of study. The most recent advances have been in combining as many sources of information as possible--including amino acid variation in the form of profiles or hidden Markov models for both the target and template families, known and predicted secondary structures of the template and target, respectively, the combination of structure alignment for distant homologues and sequence alignment for close homologues to build better profiles, and the anchoring of certain regions of the alignment based on existing biological data. Newer technologies have been applied to the problem, including the use of support vector machines to tackle the fold classification problem for a target sequence and the alignment of hidden Markov models. Finally, using the consensus of many fold recognition methods, whether based on profile-profile alignments, threading or other approaches, continues to be one of the most successful strategies for both recognition and alignment of remote homologues. Although there is still room for improvement in identification and alignment methods, additional progress may come from model building and refinement methods that can compensate for large structural changes between remotely related targets and templates, as well as for regions of misalignment.     

Integrative top-down system metabolic modeling in experimental disease states via data-driven Bayesian methods

Multivariate metabolic profiles from biofluids such as urine and plasma are highly indicative of the biological fitness of complex organisms and can be captured analytically in order to derive top-down systems biology models. The application of currently available modeling approaches to human and animal metabolic pathway modeling is problematic because of multicompartmental cellular and tissue exchange of metabolites operating on many time scales. Hence, novel approaches are needed to analyze metabolic data obtained using minimally invasive sampling methods in order to reconstruct the patho-physiological modulations of metabolic interactions that are representative of whole system dynamics. Here, we show that spectroscopically derived metabolic data in experimental liver injury studies (induced by hydrazine and alpha-napthylisothiocyanate treatment) can be used to derive insightful probabilistic graphical models of metabolite dependencies, which we refer to as metabolic interactome maps. Using these, system level mechanistic information on homeostasis can be inferred, and the degree of reversibility of induced lesions can be related to variations in the metabolic network patterns. This approach has wider application in assessment of system level dysfunction in animal or human studies from noninvasive measurements.     

A biological approach to computational models of proteomic networks

Computational modeling is useful as a means to assemble and test what we know about proteins and networks. Models can help address key questions about the measurement, definition and function of proteomic networks. Here, we place these biological questions at the forefront in reviewing the computational strategies that are available to analyze proteomic networks. Recent examples illustrate how models can extract more information from proteomic data, test possible interactions between network proteins and link networks to cellular behavior. No single model can achieve all these goals, however, which is why it is critical to prioritize biological questions before specifying a particular modeling approach.     

Asthma, the ugly duckling of lung disease proteomics?

The human respiratory system represents a vital but vulnerable system. It is a major target for many diseases such as cancer and asthma. The incidence of these diseases has increased dramatically in the last 40-50 years. In the search for possible new therapies, many experimental tools and methods have been developed to study these diseases, ranging from animal models to in vitro studies. In the last decades, genomic and proteomic approaches have gained a lot of attention. After the major scientific breakthroughs in the field of genomics, it is now widely accepted that to understand biological processes, large-scale protein studies through proteomics techniques are required. In the battle against lung cancer, the proteomics approach has already been successfully implemented. Surprisingly, only a few proteomics studies on the ever-increasing global asthma problem have been published so far. And although proteomics also has its limitations and experimental difficulties, in our opinion, proteomics can definitely contribute to the understanding of a complex disease such as asthma. Therefore, the additional values and possibilities of proteomics in asthma research should be thoroughly investigated. A close collaboration between the different scientific disciplines may eventually lead to the development of new therapeutic strategies against asthma.     

On the limitations of standard statistical modeling in biological systems: A full Bayesian approach for biology

One of the most important scientific challenges today is the quantitative and predictive understanding of biological function. Classical mathematical and computational approaches have been enormously successful in modeling inert matter, but they may be inadequate to address inherent features of biological systems. We address the conceptual and methodological obstacles that lie in the inverse problem in biological systems modeling. We introduce a full Bayesian approach (FBA), a theoretical framework to study biological function, in which probability distributions are conditional on biophysical information that physically resides in the biological system that is studied by the scientist.     

Mapping spatial pattern in biodiversity for regional conservation planning: where to from here?

Vast gaps in available information on the spatial distribution of biodiversity pose a major challenge for regional conservation planning in many parts of the world. This problem is often addressed by basing such planning on various biodiversity surrogates. In some situations, distributional data for selected taxa may be used as surrogates for biodiversity as a whole. However, this approach is less effective in data-poor regions, where there may be little choice but to base conservation planning on some form of remote environmental mapping, derived, for example, from interpretation of satellite imagery or from numerical classification of abiotic environmental layers. Although this alternative approach confers obvious benefits in terms of cost-effectiveness and rapidity of application, problems may arise if congruence is poor between mapped land-classes and actual biological distributions. I propose three strategies for making more effective use of available biological data and knowledge to alleviate such problems by (1) more closely integrating biological and environmental data through predictive modeling, with increased emphasis on modeling collective properties of biodiversity rather than individual entities; (2) making more rigorous use of remotely mapped surrogates in conservation planning by incorporating knowledge of heterogeneity within land-classes, and of varying levels of distinctiveness between classes, into measures of conservation priority and achievement; and (3) using relatively data-rich regions as test-beds for evaluating the performance of surrogates that can be readily applied across data-poor regions.     

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.     

Whole-genome expression analysis: challenges beyond clustering.

Measuring the expression of most or all of the genes in a biological system raises major analytic challenges. A wealth of recent reports uses microarray expression data to examine diverse biological phenomena - from basic processes in model organisms to complex aspects of human disease. After an initial flurry of methods for clustering the data on the basis of similarity, the field has recognized some longer-term challenges. Firstly, there are efforts to understand the sources of noise and variation in microarray experiments in order to increase the biological signal. Secondly, there are efforts to combine expression data with other sources of information to improve the range and quality of conclusions that can be drawn. Finally, techniques are now emerging to reconstruct networks of genetic interactions in order to create integrated and systematic models of biological systems.     

Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer

Medical oncology is in need of a mathematical modeling toolkit that can leverage clinically-available measurements to optimize treatment selection and schedules for patients. Just as the therapeutic choice has been optimized to match tumor genetics, the delivery of those therapeutics should be optimized based on patient-specific pharmacokinetic/pharmacodynamic properties. Under the current approach to treatment response planning and assessment, there does not exist an efficient method to consolidate biomarker changes into a holistic understanding of treatment response. While the majority of research on chemotherapies focus on cellular and genetic mechanisms of resistance, there are numerous patient-specific and tumor-specific measures that contribute to treatment response. New approaches that consolidate multimodal information into actionable data are needed. Mathematical modeling offers a solution to this problem. In this perspective, we first focus on the particular case of breast cancer to highlight how mathematical models have shaped the current approaches to treatment. Then we compare chemotherapy to radiation therapy. Finally, we identify opportunities to improve chemotherapy treatments using the model of radiation therapy. We posit that mathematical models can improve the application of anticancer therapeutics in the era of precision medicine. By highlighting a number of historical examples of the contributions of mathematical models to cancer therapy, we hope that this contribution serves to engage investigators who may not have previously considered how mathematical modeling can provide real insights into breast cancer therapy.     

Executable cell biology

Computational modeling of biological systems is becoming increasingly important in efforts to better understand complex biological behaviors. In this review, we distinguish between two types of biological models--mathematical and computational--which differ in their representations of biological phenomena. We call the approach of constructing computational models of biological systems 'executable biology', as it focuses on the design of executable computer algorithms that mimic biological phenomena. We survey the main modeling efforts in this direction, emphasize the applicability and benefits of executable models in biological research and highlight some of the challenges that executable biology poses for biology and computer science. We claim that for executable biology to reach its full potential as a mainstream biological technique, formal and algorithmic approaches must be integrated into biological research. This will drive biology toward a more precise engineering discipline.     

Using pattern-oriented modeling for revealing hidden information: a key for reconciling ecological theory and application

We suggest that the conscious use of information that is "hidden" in distinct structures in nature itself and in data extracted from nature (=pattern) during the process of modeling (=pattern-oriented modeling) can substantially improve models in ecological application and conservation. Observed patterns, such as time-series patterns and spatial patterns of presence/absence in habitat patches, contain a great deal of data on scales, site-history, parameters and processes. Use of these data provides criteria for aggregating the biological information in the model, relates the model explicitly to the relevant scales of the system, facilitates the use of helpful techniques of indirect parameter estimation with independent data, and helps detect underlying ecological processes. Additionally, pattern-oriented models produce comparative predictions that can be tested in the field. We developed a step-by-step protocol for pattern-oriented modeling and illustrate the potential of this protocol by discussing three pattern-oriented population models: (1) a population viability analysis for brown bears ( Ursus arctos ) in northern Spain using time-series data on females with cubs of the year to adjust unknown model parameters; (2) a savanna model for detecting underlying ecological processes from spatial patterns of tree distribution; and (3) the incidence function model of metapopulation dynamics as an example of process integration and model generalization. We conclude that using the pattern-oriented approach to its full potential will require a major paradigm shift in the strategies of modeling and data collection, and we argue that more emphasis must be placed on observing and documenting relevant patterns in addition to attempts to obtain direct estimates of model parameters.     

A Pseudo-Atomic Model for the Capsid Shell of Bacteriophage Lambda Using Chemical Cross-Linking/Mass Spectrometry and Molecular Modeling

Bacteriophage lambda is one of the most exhaustively studied of the double-stranded DNA viruses. Its assembly pathway is highly conserved among the herpesviruses and many of the bacteriophages, making it an excellent model system. Despite extensive genetic and biophysical characterization of many of the lambda proteins and the assembly pathways in which they are implicated, there is a relative dearth of structural information on many of the most critical proteins involved in lambda assembly and maturation, including that of the lambda major capsid protein. Toward this end, we have utilized a combination of chemical cross-linking/mass spectrometry and computational modeling to construct a pseudo-atomic model of the lambda major capsid protein as a monomer, as well as in the context of the assembled procapsid shell. The approach described here is generalizable and can be used to provide structural models for any biological complex of interest. The procapsid structural model is in good agreement with published biochemical data indicating that procapsid expansion exposes hydrophobic surface area and that this serves to nucleate assembly of capsid decoration protein, gpD. The model further implicates additional molecular interactions that may be critical to the assembly of the capsid shell and for the stabilization of the structure by the gpD decoration protein.     

Cancer modeling in the modern era: progress and challenges

Genetically engineered mouse models have contributed extensively to the field of cancer research. The ability to manipulate the mouse germline affords numerous approaches toward understanding the complexities of this disease, possibly providing accurate preclinical models for therapeutic and diagnostic advances. This review highlights some of the current strategies for modeling cancer in the mouse, recent accomplishments, and key remaining challenges.     

Logic modeling and the ridiculome under the rug

Logic-derived modeling has been used to map biological networks and to study arbitrary functional interactions, and fine-grained kinetic modeling can accurately predict the detailed behavior of well-characterized molecular systems; at present, however, neither approach comes close to unraveling the full complexity of a cell. The current data revolution offers significant promises and challenges to both approaches - and could bring them together as it has spurred the development of new methods and tools that may help to bridge the many gaps between data, models, and mechanistic understanding.Have you used logic modeling in your research? It would not be surprising if many biologists would answer no to this hypothetical question. And it would not be true. In high school biology we already became familiar with cartoon diagrams that illustrate basic mechanisms of the molecular machinery operating inside cells. These are nothing else but simple logic models. If receptor and ligand are present, then receptor-ligand complexes form; if a receptor-ligand complex exists, then an enzyme gets activated; if the enzyme is active, then a second messenger is being produced; and so on. Such chains of causality are the essence of logic models (Figure 1a). Arbitrary events and mechanisms are abstracted; relationships are simplified and usually involve just two possible conditions and three possible consequences. The presence or absence of one or more molecule, activity, or function, [some icons in the cartoon] will determine whether another one of them will be produced (created, up-regulated, stimulated) [a 'positive' link] or destroyed (degraded, down-regulated, inhibited) [a 'negative' link], or be unaffected [there is no link]. The icons and links often do not follow a standardized format, but when we look at such a cartoon diagram, we believe that we 'understand' how the system works. Because our brain is easily able to process these relationships, these diagrams allow us to answer two fundamental types of questions related to the system: why (are certain things happening)? What if (we make some changes)?