Systems biology最新文献

The development of the Bioinformatics MS degree program at the University of Illinois, the challenges and opportunities associated with such a process, and the current structure of the program is described. This program has departed from earlier University practice in significant ways. Despite the existence of several interdisciplinary programs at the University, a few of which grant degrees, this is the first interdisciplinary program that grants degrees and formally recognises departmental specialisation areas. The program, which is not owned by any particular department but by the Graduate College itself, is operated in a franchise-like fashion via several departmental concentrations. With four different colleges and many more departments involved in establishing and operating the program, the logistics of the operation are of considerable complexity but result in significant interactions across the entire campus.

Biochemical systems embed complex networks and hence development and analysis of their detailed models pose a challenge for computation. Coarse-grained biochemical models, called reduced-order models (ROMs), consisting of essential biochemical mechanisms are more useful for computational analysis and for studying important features of a biochemical network. The authors present a novel method to model-reduction by identifying potentially important parameters using multidimensional sensitivity analysis. A ROM is generated for the GTPase-cycle module of m1 muscarinic acetylcholine receptor, Gq, and regulator of G-protein signalling 4 (a GTPase-activating protein or GAP) starting from a detailed model of 48 reactions. The resulting ROM has only 17 reactions. The ROM suggested that complexes of G-protein coupled receptor (GPCR) and GAP--which were proposed in the detailed model as a hypothesis--are required to fit the experimental data. Models previously published in the literature are also simulated and compared with the ROM. Through this comparison, a minimal ROM, that also requires complexes of GPCR and GAP, with just 15 parameters is generated. The proposed reduced-order modelling methodology is scalable to larger networks and provides a general framework for the reduction of models of biochemical systems.

The overexpression of secreted proteins is of critical importance to the biotechnology and biomedical fields. A common roadblock to high yields of proteins is in the endoplasmic reticulum (ER) where proofreading for properly folded proteins is often rate limiting. Heterologous expression of secreted proteins can saturate the cell's capacity to properly fold protein, initiating the unfolded protein response (UPR), and resulting in a loss of protein expression. An obvious method for overcoming this block would be to increase the capacity of the folding process (overexpressing chaperones) or decreasing the proofreading process (blocking the down-regulation by the UPR). Unfortunately, these processes are tightly interlinked, whereby modification of one mechanism has unknown effects on the other. Although some success has been achieved in improving expression via co-overexpressing ER chaperones, the results have not lead to a global method for increasing all heterologously overexpressed proteins. Further, many diseases have been linked to extended periods of stress and are not treatable by these approaches. This work utilises both experimental analysis of the interactions within the ER and modelling in order to understand how these interactions affect early secretory pathway dynamics. This study shows that overexpression of the ER chaperone binding protein does not regulate Ire1p and the UPR as predicted by a model based on the published understanding of the molecular mechanism. A new model is proposed for Ire1p regulation and the UPR that better fits the experimental data and recent studies on Ire1p.

Drugs fail in clinical studies most often from lack of efficacy or unexpected toxicities. These failures result from an inadequate understanding of drug action and follow, in part, from our dependence on drug discovery technologies that do not take into account the complexity of human disease biology. Biological systems exhibit many features of complex engineering systems, including modularity, redundancy, robustness, and emergent properties. Addressing these features has contributed to the successful design of an improved biological assay technology for inflammation drug discovery. This approach, termed Biologically Multiplexed Activity Profiling (BioMAP), involves the statistical analysis of protein datasets generated from novel complex primary human cell-based assay systems. Compound profiling in these systems has revealed that a surprisingly large number of biological mechanisms can be detected and distinguished. Features of these assays relevant to the behaviour of complex systems are described.

S-systems have been used as models of biochemical systems for over 30 years. One of their hallmarks is that, although they are highly non-linear, their steady states are characterised by linear equations. This allows streamlined analyses of stability, sensitivities and gains as well as objective, mathematically controlled comparisons of similar model designs. Regular S-systems have a unique steady state at which none of the system variables is zero. This makes it difficult to represent switching phenomena, as they occur, for instance, in the expression of genes, cell cycle phenomena and signal transduction. Previously, two strategies were proposed to account for switches. One was based on a technique called recasting, which permits the modelling of any differentiable non-linearities, including bistability, but typically does not allow steady-state analyses based on linear equations. The second strategy formulated the switching system in a piece-wise fashion, where each piece consisted of a regular S-system. A representation gleaned from a simplified form of recasting is proposed and it is possible to divide the characterisation of the steady states into two phases, the first of which is linear, whereas the other is non-linear, but easy to execute. The article discusses a representative pathway with two stable states and one unstable state. The pathway model exhibits strong separation between the stable states as well as hysteresis.

Rod photoreceptors are activated by light through activation of a cascade that includes the G protein-coupled receptor rhodopsin, the G protein transducin, its effector cyclic guanosine monophosphate (cGMP) phosphodiesterase and the second messengers cGMP and Ca2+. Signalling is localised to the particular rod outer segment disc, which is activated by absorption of a single photon. Modelling of this cascade has previously been performed mostly by assumption of a well-stirred cytoplasm. We recently published the first fully spatially resolved model that captures the local nature of light activation. The model reduces the complex geometry of the cell to a simpler one using the mathematical theories of homogenisation and concentrated capacity. The model shows that, upon activation of a single rhodopsin, changes of the second messengers cGMP and Ca2+ are local about the particular activated disc. In the current work, the homogenised model is computationally compared with the full, non-homogenised one, set in the original geometry of the rod outer segment. It is found to have an accuracy of 0.03% compared with the full model in computing the integral response and a 5200-fold reduction in computation time. The model can reconstruct the radial time-profiles of cGMP and Ca2+ in the interdiscal spaces adjacent to the activated discs. Cellular electrical responses are localised near the activation sites, and multiple photons sufficiently far apart produce essentially independent responses. This leads to a computational analysis of the notion and estimate of 'spread' and the optimum distribution of activated sites that maximises the response. Biological insights arising from the spatio-temporal model include a quantification of how variability in the response to dim light is affected by the distance between the outer segment discs capturing photons. The model is thus a simulation tool for biologists to predict the effect of various factors influencing the timing, spread and control mechanisms of this G protein-coupled, receptor-mediated cascade. It permits ease of simulation experiments across a range of conditions, for example, clamping the concentration of calcium, with results matching analogous experimental results. In addition, the model accommodates differing geometries of rod outer segments from different vertebrate species. Thus it represents a building block towards a predictive model of visual transduction.

The modelling of biochemical networks becomes delicate if kinetic parameters are varying, uncertain or unknown. Facing this situation, we quantify uncertain knowledge or beliefs about parameters by probability distributions. We show how parameter distributions can be used to infer probabilistic statements about dynamic network properties, such as steady-state fluxes and concentrations, signal characteristics or control coefficients. The parameter distributions can also serve as priors in Bayesian statistical analysis. We propose a graphical scheme, the 'dependence graph', to bring out known dependencies between parameters, for instance, due to the equilibrium constants. If a parameter distribution is narrow, the resulting distribution of the variables can be computed by expanding them around a set of mean parameter values. We compute the distributions of concentrations, fluxes and probabilities for qualitative variables such as flux directions. The probabilistic framework allows the study of metabolic correlations, and it provides simple measures of variability and stochastic sensitivity. It also shows clearly how the variability of biological systems is related to the metabolic response coefficients.

When performing system identification, we have two sources of information: experimental data and prior knowledge. Many cell-biological systems are oscillating, and sometimes we know an input where the system reaches a Hopf bifurcation. This is the case, for example, for glycolysis in yeast cells and for the Belousov-Zhabotinsky reaction, and for both of these systems there exist significant numbers of quenching data, ideal for system identification. We present a method that includes prior knowledge of the location of a Hopf bifurcation in estimation based on time-series. The main contribution is a reformulation of the prior knowledge into the standard formulation of a constrained optimisation problem. This formulation allows for any of the standard methods to be applied, including all the theories regarding the method's properties. The reformulation is carried out through an over-parametrisation of the original problem. The over-parametrisation allows for extra constraints to be formed, and the net effect is a reduction of the search space. A method that can solve the new formulation of the problem is presented, and the advantage of adding the prior knowledge is demonstrated on the Brusselator.