Systems biology最新文献

High-level, mathematically precise descriptions of the global organisation of complex metabolic networks are necessary for understanding the global structure of metabolic networks, the interpretation and integration of large amounts of biologic data (sequences, various -omics) and ultimately for rational design of therapies for disease processes. Metabolic networks are highly organised to execute their function efficiently while tolerating wide variation in their environment. These networks are constrained by physical requirements (e.g. conservation of energy, redox and small moieties) but are also remarkably robust and evolvable. The authors use well-known features of the stoichiometry of bacterial metabolic networks to demonstrate how network architecture facilitates such capabilities, and to develop a minimal abstract metabolism which incorporates the known features of the stoichiometry and respects the constraints on enzymes and reactions. This model shows that the essential functionality and constraints drive the tradeoffs between robustness and fragility, as well as the large-scale structure and organisation of the whole network, particularly high variability. The authors emphasise how domain-specific constraints and tradeoffs imposed by the environment are important factors in shaping stoichiometry. Importantly, the consequence of these highly organised tradeoffs and tolerances is an architecture that has a highly structured modularity that is self-dissimilar and scale-rich.

A four-state cell-cycle model with explicit G1-phase representation, termed the quiescent-cell model (QCM), has been proposed to represent biologically the G1-phase specific effect of the chemotherapeutic tamoxifen. The QCM was used to model untreated and tamoxifen-treated tumour xenograft data from the literature with equivalent accuracy to previously developed tumour growth models. Open-loop analysis demonstrated that perturbations to the two newly introduced parameters, kG01 and kG10, significantly altered untreated tumour growth predictions. However, the sensitivity did not carry over to closed-loop simulations, where alterations to kD and kGS proved most significant in determining overall controller performance. Additional mismatch studies comparing controllers designed using the QCM to controllers designed with the Gompertz model and saturating-rate, cell-cycle model returned similar performance for a step-wise tumour reduction case study, but the quiescent-cell controller delivered a more aggressive treatment regimen. More importantly, the Gompertz and saturating-rate, cell-cycle controllers were unable to follow a reference trajectory when measurement updates were made biweekly, with both controllers returning tamoxifen dose schedules alternating between the maximum and minimum allowable dose.

The concept of systems-based strategies in medicine is emerging, with systems pathology guiding an understanding of the multidimensional aspects of disease system fingerprints and systems pharmacology providing insight into dynamic system responses upon (multiple) drug perturbations. Knowledge of the changes of system characteristics during disease progression creates a framework for the design of novel combinatorial treatment strategies. Such a systems-based, combinatorial-therapies approach readdresses the value of the synergistic actions of components of treatments based on natural products and highlights new methodology to study multidimensional intervention via reversed-pharmacology.

Analyses of different robustness aspects for models of the direct signal transduction pathway of receptor-induced apoptosis is presented. Apoptosis is a form of programmed cell death, removing unwanted cells within multicellular organisms to maintain a proper balance between cell reproduction and death. Its signalling pathway includes an activation feedback loop that generates bistable behaviour, where the two steady states can be seen as 'life' and 'death'. Inherent robustness, widely recognised in biological systems, is of major importance in apoptosis signalling, as it guarantees the same cell fate for similar conditions. First, the influence of the stochastic nature of reactions indicating a role for inhibition reactions as noise filters and justifying a deterministic approach in the further analyses is evaluated. Second, the robustness of the bistable threshold with respect to parameter changes is evaluated by statistical methods, showing the need to balance both the forward and the back part of the activation loop. These analyses can also discriminate between the models favouring the model consistent with novel biological findings. The parameter robustness analyses are also applicable to other signal transduction networks, as several have been shown to display bistable behaviour. These methods therefore have a range of possible applications in systems biology not only to measure robustness, but also for model discrimination.

The challenge of accurately predicting human clinical outcome based on preclinical data has led to a high failure rate of compounds in human clinical trials. A series of methods are described by which biosimulation can address these challenges and guide the design and evaluation of experimental and clinical protocols. Early compound development often proceeds on the basis of preclinical data from animal models. The systematic evaluation possible in a simulation can assist in the critical step of translating the preclinical outcomes to human physiology. Later in the process, clinical trials definitively establish a therapy's beneficial effects, as well as any adverse side effects. Biosimulation allows for the optimal design of clinical trials to ensure that key issues are addressed effectively and efficiently, and in doing so, improves the success rate of the trials.

Antibody-directed enzyme prodrug therapy (ADEPT) can generate highly localised concentrations of cytotoxic agents directly in a tumour, thereby reducing the collateral toxicity associated with normal tissue exposure. ADEPT is a two-component approach. First, a non-toxic antibody-enzyme fusion protein is localised in the tumour matrix by binding a specific antigen expressed only on the surface of a cancer cell. Once the fusion protein is bound, an inert small molecule prodrug is administered which is the substrate for the enzyme bound to the tumour surface. When the prodrug comes into contact with the bound enzyme, an active cytotoxic agent is generated. A multiple length-scale model of ADEPT therapy in solid tumours is presented. A four-compartment pharmacokinetic (PK) model is formulated where the tumour is comprised of interstitial and cell-surface subcompartments. The macroscopic PK model which describes the biodistribution of antibody-enzyme conjugate, prodrug and active drug at the largest length scale is coupled to a reaction-diffusion tumour model. The models are qualitatively validated against current literature and experimental understanding. The relationship between tumour localisation and the affinity of the antibody-enzyme conjugate for its surface antigen is explored by simulation. The influence of pharmacokinetic and biophysical parameters such as renal elimination rate and permeability of the tumour vasculature upon tumour uptake and retention of the fusion protein are also explored. Lastly, a technique for establishing an optimal prodrug dosing schedule is formulated and initial simulation results are presented.

The concept of elementary (flux) modes provides a rigorous description of pathways in metabolic networks and proved to be valuable in a number of applications. However, the computation of elementary modes is a hard computational task that gave rise to several variants of algorithms during the last years. This work brings substantial progresses to this issue. The authors start with a brief review of results obtained from previous work regarding (a) a unified framework for elementary-mode computation, (b) network compression and redundancy removal and (c) the binary approach by which elementary modes are determined as binary patterns reducing the memory demand drastically without loss of speed. Then the authors will address herein further issues. First, a new way to perform the elementarity tests required during the computation of elementary modes which empirically improves significantly the computation time in large networks is proposed. Second, a method to compute only those elementary modes where certain reactions are involved is derived. Relying on this method, a promising approach for computing EMs in a completely distributed manner by decomposing the full problem in arbitrarity many sub-tasks is presented. The new methods have been implemented in the freely available software tools FluxAnalyzer and Metatool and benchmark tests in realistic networks emphasise the potential of our proposed algorithms.

The ubiquitous Ca2(+)-phosphoinositide pathway transduces extracellular signals to cellular effectors. Using a mathematical model, we simulated intracellular Ca2+ fluctuations in hepatocytes upon humoral stimulation. We estimated the information encoded about random humoral stimuli in these Ca2+ spike trains using an information-theoretic approach based on stimulus estimation methods. We demonstrate accurate transfer of information about random humoral signals with low temporal cutoff frequencies. In contrast, our results suggest that high-frequency stimuli are poorly transduced by the transmembrane machinery. We found that humoral signals are encoded in both the timing and amplitude of intracellular Ca2+ spikes. The information transmitted per spike is similar to that of sensory neuronal systems, in spite of several orders of magnitude difference in firing rate.

Feinberg's chemical reaction network theory (CRNT) connects the structure of a biochemical reaction network to qualitative properties of the corresponding system of ordinary differential equations. No information about parameter values is needed. As such, it seems to be well suited for application in systems biology, where parameter uncertainty is predominant. However, its application in this area is rare. To demonstrate the potential benefits from its application, different reaction networks representing a single layer of the well-studied mitogen-activated protein kinase (MAPK) cascade are analysed. Recent results from Markevich et al. (2004) show that, unexpectedly, multilayered protein kinase cascades can exhibit multistationarity, even on a single cascade level. Using CRNT, we show that their assumption of a distributive mechanism for double phosphorylation and dephosphorylation is crucial for multistationarity on the single cascade level.

A large-scale mathematical model, the Entelos Rheumatoid Arthritis (RA) PhysioLab platform, has been developed to describe the inflammatory and erosive processes in afflicted joints of people suffering from RA. The platform represents the life cycle of inflammatory cells, endothelium, synovial fibroblasts, and chondrocytes, as well as their products and interactions. The interplay between these processes culminates in clinically relevant measures for inflammation and erosion. The simulation model is deterministic, which allows tracing back the mechanism of action for a particular simulation result. Different patient phenotypes are represented by different virtual patients. The RA PhysioLab platform has been used to systematically and quantitatively study the predicted therapeutic effect of modulating several molecular targets, which resulted in a ranking of putative drug targets and a workflow to confirm the simulations experimentally. In addition, critical pathways were identified that drive the predicted disease outcome. Within these pathways, targets were identified from public literature that were not previously associated with arthritis. The model provides insights into the biology of RA and can be used as a platform for hypothesis-driven research. Case studies of therapies directed against IL-12 and IL-15 illustrate the approach, with emphasis on the analysis of system dynamics.