Systems Biology Toolbox for MATLAB: a computational platform for research in systems biology

We present a Systems Biology Toolbox for the widely used general purpose mathematical software MATLAB. The toolbox offers systems biologists an open and extensible environment, in which to explore ideas, prototype and share new algorithms, and build applications for the analysis and simulation of biological and biochemical systems. Additionally it is well suited for educational purposes. The toolbox supports the Systems Biology Markup Language (SBML) by providing an interface for import and export of SBML models. In this way the toolbox connects nicely to other SBML-enabled modelling packages. Models are represented in an internal model format and can be described either by entering ordinary differential equations or, more intuitively, by entering biochemical reaction equations. The toolbox contains a large number of analysis methods, such as deterministic and stochastic simulation, parameter estimation, network identification, parameter sensitivity analysis and bifurcation analysis.     

Effect of pathogen-resistant vectors on the transmission dynamics of a vector-borne disease

A model is introduced for the transmission dynamics of a vector-borne disease with two vector strains, one wild and one pathogen-resistant; resistance comes at the cost of reduced reproductive fitness. The model, which assumes that vector reproduction can lead to the transmission or loss of resistance (reversion), is analyzed in a particular case with specified forms for the birth and force of infection functions. The vector component can have, in the absence of disease, a coexistence equilibrium where both strains survive. In the case where reversion is possible, this coexistence equilibrium is globally asymptotically stable when it exists. This equilibrium is still present in the full vector-host system, leading to a reduction of the associated reproduction number, thereby making elimination of the disease more feasible. When reversion is not possible, there can exist an additional equilibrium with only resistant vectors.     

Dupuytren's: a systems biology disease

Dupuytren's disease (DD) is an ill-defined fibroproliferative disorder of the palm of the hands leading to digital contracture. DD commonly occurs in individuals of northern European extraction. Cellular components and processes associated with DD pathogenesis include altered gene and protein expression of cytokines, growth factors, adhesion molecules, and extracellular matrix components. Histology has shown increased but varying levels of particular types of collagen, myofibroblasts and myoglobin proteins in DD tissue. Free radicals and localised ischaemia have been suggested to trigger the proliferation of DD tissue. Although the existing available biological information on DD may contain potentially valuable (though largely uninterpreted) information, the precise aetiology of DD remains unknown. Systems biology combines mechanistic modelling with quantitative experimentation in studies of networks and better understanding of the interaction of multiple components in disease processes. Adopting systems biology may be the ideal approach for future research in order to improve understanding of complex diseases of multifactorial origin. In this review, we propose that DD is a disease of several networks rather than of a single gene, and show that this accounts for the experimental observations obtained to date from a variety of sources. We outline how DD may be investigated more effectively by employing a systems biology approach that considers the disease network as a whole rather than focusing on any specific single molecule.     

Understanding the computational methodologies of systems biology

The conventional approach to understanding biological systemsand processes employs a largely static view of loosely coupledmolecular and cellular elements. This contrasts with the basicunderstanding of a biologist that life is an inherently dynamicphenomenon. Similar to many other ontological concepts, a precisedefinition of systems biology may not be attainable for a longtime [1]. However, there seems to be a consensus that systemsbiology will progressively complement the conventional modeof study by facilitating the understanding of biological networksand mechanisms in terms of their dynamic system behavior ondifferent levels of organization. This new way of investigatingliving matter involves a tight coupling of mathematical modeling,computational analysis and simulation and biological experimentation. One     

Revealing the role of predator-dependent disease transmission in the epidemiology of a wildlife infection: a model study

It is well known that predation/harvesting on a species subjected to an infectious disease can affect both the infection prevalence and the population dynamics. In this paper, I model predator?Cprey?Cpathogen interactions in the case where the presence of a predator indirectly affects the transmission rate of the infection in its prey. I call this phenomenon the predator-dependent disease transmission. Such a scenario can arise, for example, as a consequence of anti-predator defence behaviour, debilitating the immune system of the prey. Although being well documented, the predator-dependent disease transmission has rarely been taken into account in ecoepidemiological models. Mathematically, I consider a classical S-I-P ecoepidemiological model in which the infected and/or the healthy host can be consumed by a predator where the coefficient in the mass action transmission term is predator-dependent. Investigation of the model shows that including such a predator-dependent disease transmission can have important consequences for shaping predator?Cprey?Cpathogen interactions. In particular, this can enhance the survival of the predator, restricted in a system with a predator-independent disease transmission. I demonstrate the emergence of a disease-mediated strong Allee effect for the predator population. I also show that in the system with predator-dependent disease transmission, the predator can indirectly promote epidemics of highly virulent infectious diseases, which would die out in a predator-free system. Finally, I argue that taking into account predator-dependent disease transmission can have a destabilizing effect in a eutrophic environment, which can potentially cause the extinction of both species. I also show that including the predator-dependent disease transmission may increase the infection prevalence, and this fact will question the ??keeping herds healthy?? hypothesis concerning the management of wildlife infections by natural predators.     

Disrupting the transmission of a vector-borne plant pathogen

Approaches to control vector-borne diseases rarely focus on the interface between vector and microbial pathogen, but strategies aimed at disrupting the interactions required for transmission may lead to reductions in disease spread. We tested if the vector transmission of the plant-pathogenic bacterium Xylella fastidiosa was affected by three groups of molecules: lectins, carbohydrates, and antibodies. Although not comprehensively characterized, it is known that X. fastidiosa adhesins bind to carbohydrates, and that these interactions are important for initial cell attachment to vectors, which is required for bacterial transmission from host to host. Lectins with affinity to substrates expected to occur on the cuticular surface of vectors colonized by X. fastidiosa, such as wheat germ agglutinin, resulted in statistically significant reductions in transmission rate, as did carbohydrates with N-acetylglucosamine residues. Presumably, lectins bound to receptors on the vector required for cell adhesion/colonization, while carbohydrate-saturated adhesins on X. fastidiosa's cell surface. Furthermore, antibodies against X. fastidiosa whole cells, gum, and afimbrial adhesins also resulted in transmission blockage. However, no treatment resulted in the complete abolishment of transmission, suggesting that this is a complex biological process. This work illustrates the potential to block the transmission of vector-borne pathogens without directly affecting either organism.     

Evaluation of a simplified generic bi-substrate rate equation for computational systems biology

The evaluation of a generic simplified bi-substrate enzyme kinetic equation, whose derivation is based on the assumption of equilibrium binding of substrates and products in random order, is described. This equation is much simpler than the mechanistic (ordered and ping-pong) models, in that it contains fewer parameters (that is, no K(i) values for the substrates and products). The generic equation fits data from both the ordered and the ping-pong models well over a wide range of substrate and product concentrations. In the cases where the fit is not perfect, an improved fit can be obtained by considering the rate equation for only a single set of product concentrations. Due to its relative simplicity in comparison to the mechanistic models, this equation will be useful for modelling bi-substrate reactions in computational systems biology.     

Algorithms in nature: the convergence of systems biology and computational thinking

Computer science and biology have enjoyed a long and fruitful relationship for decades. Biologists rely on computational methods to analyze and integrate large data sets, while several computational methods were inspired by the high‐level design principles of biological systems. Recently, these two directions have been converging. In this review, we argue that thinking computationally about biological processes may lead to more accurate models, which in turn can be used to improve the design of algorithms. We discuss the similar mechanisms and requirements shared by computational and biological processes and then present several recent studies that apply this joint analysis strategy to problems related to coordination, network analysis, and tracking and vision. We also discuss additional biological processes that can be studied in a similar manner and link them to potential computational problems. With the rapid accumulation of data detailing the inner workings of biological systems, we expect this direction of coupling biological and computational studies to greatly expand in the future.     

Climate,environmental and socio-economic change: weighing up the balance in vector-borne disease transmission

Arguably one of the most important effects of climate change is the potential impact on human health. While this is likely to take many forms, the implications for future transmission of vector-borne diseases (VBDs), given their ongoing contribution to global disease burden, are both extremely important and highly uncertain. In part, this is owing not only to data limitations and methodological challenges when integrating climate-driven VBD models and climate change projections, but also, perhaps most crucially, to the multitude of epidemiological, ecological and socio-economic factors that drive VBD transmission, and this complexity has generated considerable debate over the past 10–15 years. In this review, we seek to elucidate current knowledge around this topic, identify key themes and uncertainties, evaluate ongoing challenges and open research questions and, crucially, offer some solutions for the field. Although many of these challenges are ubiquitous across multiple VBDs, more specific issues also arise in different vector–pathogen systems.     

Discovering the targets of drugs via computational systems biology

Computational systems biology is empowering the study of drug action. Studies on biological effects of chemical compounds have increased in scale and accessibility, allowing integration with other large-scale experimental data types. Here, we review computational approaches for elucidating the mechanisms of both intended and undesirable effects of drugs, with the collective potential to change the nature of drug discovery and pharmacological therapy.     

The next step in systems biology: simulating the temporospatial dynamics of molecular network

As a result of the time- and context-dependency of gene expression, gene regulatory and signaling pathways undergo dynamic changes during development. Creating a model of the dynamics of molecular interaction networks offers enormous potential for understanding how a genome orchestrates the developmental processes of an organism. The dynamic nature of pathway topology calls for new modeling strategies that can capture transient molecular links at the runtime. The aim of this paper is to present a brief and informative, but not all-inclusive, viewpoint on the computational aspects of modeling and simulation of a non-static molecular network.     

Complex systems in pulmonary medicine: a systems biology approach to lung disease

The lung is a highly complex organ that can only be understood by integrating the many aspects of its structure and function into a comprehensive view. Such a view is provided by a systems biology approach, whereby the many layers of complexity, from the molecular genetic, to the cellular, to the tissue, to the whole organ, and finally to the whole body, are synthesized into a working model of understanding. The systems biology approach therefore relies on the expertise of many disciplines, including genomics, proteomics, metabolomics, physiomics, and, ultimately, clinical medicine. The overall structure and functioning of the lung cannot be predicted from studying any one of these systems in isolation, and so this approach highlights the importance of emergence as the fundamental feature of systems biology. In this paper, we will provide an overview of a systems biology approach to lung disease by briefly reviewing the advances made at many of these levels, with special emphasis on recent work done in the realm of pulmonary physiology and the analysis of clinical phenotypes.     

A systems biology approach to the blood-aluminium problem: the application and testing of a computational model

Transport and distribution of systemic aluminium are influenced by its interaction with blood. Current understanding is centred upon the role played by the iron transport protein transferrin which has been shown to bind up to 90% of serum total aluminium. We have coined what we have called the blood-aluminium problem which states that the proportion of serum aluminium which, at any one moment in time, is bound by transferrin is more heavily influenced by kinetic constraints than thermodynamic equilibria with the result that the role played by transferrin in the transport and distribution of aluminium is likely to have been over estimated. To begin to solve the blood-aluminium problem and therewith provide a numerical solution to the aforementioned kinetic constraints we have applied and tested a simple computational model of the time-dependency of a putative transferrin ligand (L) binding aluminium to form an Al-L complex with a probability of existence, K(E), between 0% (no complex) and 100% (complex will not dissociate). The model is based upon the principles of a lattice-gas automaton which when ran for K(E) in the range 0.1-98.0% demonstrated the emergence of complex behaviour which could be defined in the terms of a set of parameters (equilibrium value, E(V), equilibrium time, E(T), peak value, P(V), peak time, P(T), area under curve, AUC) the values of which varied in a predictable way with K(E). When K(E) was set to 98% the model predicted that ca. 90% of the total aluminium would be bound by transferrin within ca. 350 simulation timesteps. We have used a systems biology approach to develop a simple model of the time-dependency of the binding of aluminium by transferrin. To use this approach to begin to solve the blood-aluminium problem we shall need to increase the complexity of the model to better reflect the heterogeneity of a biological system such as the blood.     

Neural membrane microdomains as computational systems: Toward molecular modeling in the study of neural disease

Several studies indicate that the lipid biological membrane contains discrete regions known as rafts or microdomains. These structures range in size from approximately 50 to 70nm to nearly a mum and play important roles in cell signaling. In the neuron, computational models suggest that transiently polarized microdomain ethenes may regulate ion-channel dynamics and control impulse propagation. Thus the microdomain is nominated as the fundamental unit of nervous system signaling. Based on this model, the article proposes a first-approximation design for a supported-membrane device which would mimic microdomain properties. The basic architecture would consist of an electrically addressable biotemplated nanowire crossing an artificial membrane corralled in a vertical carbon nanofiber barrier. Advantages and disadvantages of model components are discussed at length. It is proposed that artificial devices of this type would be medically useful in simulating membrane states correlated with neural disease. This possibility is examined with reference to the A-current potassium channel, implicated in epilepsy.