Optimization strategies for metabolic networks

<p>Abstract</p> <p>Background</p> <p>The increasing availability of models and data for metabolic networks poses new challenges in what concerns optimization for biological systems. Due to the high level of complexity and uncertainty associated to these networks the suggested models often lack detail and liability, required to determine the proper optimization strategies. A possible approach to overcome this limitation is the combination of both kinetic and stoichiometric models. In this paper three control optimization methods, with different levels of complexity and assuming various degrees of process information, are presented and their results compared using a prototype network.</p> <p>Results</p> <p>The results obtained show that Bi-Level optimization lead to a good approximation of the optimum attainable with the full information on the original network. Furthermore, using Pontryagin's Maximum Principle it is shown that the optimal control for the network in question, can only assume values on the extremes of the interval of its possible values.</p> <p>Conclusions</p> <p>It is shown that, for a class of networks in which the product that favors cell growth competes with the desired product yield, the optimal control that explores this trade-off assumes only extreme values. The proposed Bi-Level optimization led to a good approximation of the original network, allowing to overcome the limitation on the available information, often present in metabolic network models. Although the prototype network considered, it is stressed that the results obtained concern methods, and provide guidelines that are valid in a wider context.</p

Development of an integrated metabolic and transcriptional regulatory model for Saccharomyces cerevisiae

Saccharomyces cerevisiae (S. cerevisiae) is commonly used as a cell factory for research and industrial applications. The development of optimization strategies for S. cerevisiae was extensively driven by the establishment of genome-scale metabolic models. Additionally, to aid metabolic modeling, multiple attempts to infer transcriptional regulatory networks from expression data have been made. However, these data-based regulatory networks may show limited applicability. Therefore, we propose a general, knowledge-based approach involving a pipeline to evaluate regulatory interactions of transcription factors (TFs) and target genes from the YEASTRACT database, and form a regulatory network from these filtered datasets. So far, we filtered regulatory interactions based on two criteria: the existence of direct binding evidence of the TFs and their target genes, and the consensus over the regulatory effects identified among multiple studies that investigated each interaction, respectively. From 230 TFs and over 6000 genes contained in YEASTRACT, we obtained a regulatory network of 69 TFs and 1187 target genes with 1813 regulatory interactions, including 346 metabolic genes with 622 regulatory interactions. These interactions cover the majority of genes in the central carbon metabolism. Next, these regulatory interactions will be evaluated by identifying the networks attractor states and simulating their effect on the metabolic system via steady-state regulatory flux balance analysis. Finally, this integrated metabolic and regulatory model may be used to identify efficient optimization strategies for S. cerevisiae.info:eu-repo/semantics/publishedVersio

Asymmetries arising from the space-filling nature of vascular networks

Cardiovascular networks span the body by branching across many generations of vessels. The resulting structure delivers blood over long distances to supply all cells with oxygen via the relatively short-range process of diffusion at the capillary level. The structural features of the network that accomplish this density and ubiquity of capillaries are often called space-filling. There are multiple strategies to fill a space, but some strategies do not lead to biologically adaptive structures by requiring too much construction material or space, delivering resources too slowly, or using too much power to move blood through the system. We empirically measure the structure of real networks (18 humans and 1 mouse) and compare these observations with predictions of model networks that are space-filling and constrained by a few guiding biological principles. We devise a numerical method that enables the investigation of space-filling strategies and determination of which biological principles influence network structure. Optimization for only a single principle creates unrealistic networks that represent an extreme limit of the possible structures that could be observed in nature. We first study these extreme limits for two competing principles, minimal total material and minimal path lengths. We combine these two principles and enforce various thresholds for balance in the network hierarchy, which provides a novel approach that highlights the trade-offs faced by biological networks and yields predictions that better match our empirical data.Comment: 17 pages, 15 figure

Optimization in complex networks

Many complex systems can be described in terms of networks of interacting units. Recent studies have shown that a wide class of both natural and artificial nets display a surprisingly widespread feature: the presence of highly heterogeneous distributions of links, providing an extraordinary source of robustness against perturbations. Although most theories concerning the origin of these topologies use growing graphs, here we show that a simple optimization process can also account for the observed regularities displayed by most complex nets. Using an evolutionary algorithm involving minimization of link density and average distance, four major types of networks are encountered: (a) sparse exponential-like networks, (b) sparse scale-free networks, (c) star networks and (d) highly dense networks, apparently defining three major phases. These constraints provide a new explanation for scaling of exponent about -3. The evolutionary consequences of these results are outlined.Peer ReviewedPostprint (author's final draft

Systems approaches and algorithms for discovery of combinatorial therapies

Effective therapy of complex diseases requires control of highly non-linear complex networks that remain incompletely characterized. In particular, drug intervention can be seen as control of signaling in cellular networks. Identification of control parameters presents an extreme challenge due to the combinatorial explosion of control possibilities in combination therapy and to the incomplete knowledge of the systems biology of cells. In this review paper we describe the main current and proposed approaches to the design of combinatorial therapies, including the empirical methods used now by clinicians and alternative approaches suggested recently by several authors. New approaches for designing combinations arising from systems biology are described. We discuss in special detail the design of algorithms that identify optimal control parameters in cellular networks based on a quantitative characterization of control landscapes, maximizing utilization of incomplete knowledge of the state and structure of intracellular networks. The use of new technology for high-throughput measurements is key to these new approaches to combination therapy and essential for the characterization of control landscapes and implementation of the algorithms. Combinatorial optimization in medical therapy is also compared with the combinatorial optimization of engineering and materials science and similarities and differences are delineated.Comment: 25 page