Inference of dynamic biological networks based on responses to drug perturbations

Drugs that target specific proteins are a major paradigm in cancer research. In this article, we extend a modeling framework for drug sensitivity prediction and combination therapy design based on drug perturbation experiments. The recently proposed target inhibition map approach can infer stationary pathway models from drug perturbation experiments, but the method is limited to a steady-state snapshot of the underlying dynamical model. We consider the inverse problem of possible dynamic models that can generate the static target inhibition map model. From a deterministic viewpoint, we analyze the inference of Boolean networks that can generate the observed binarized sensitivities under different target inhibition scenarios. From a stochastic perspective, we investigate the generation of Markov chain models that satisfy the observed target inhibition sensitivities.     

Periodic oscillation in delayed gene networks with SUM regulatory logic and small perturbations

In this paper, we derive new criteria for evaluating the global stability of periodic oscillation in delayed gene networks with SUM regulatory logic and small perturbation, which appear in many biological systems at biomolecular or cellular levels due to the weak coupling and signal diffusion (or transport) process. Our results rely on the Lipschtiz conditions of Hill function, topology of gene networks and delay kernels. In particular, Our method based on the proposed model transforms the original network into matrix analysis problem, thereby not only significantly reducing the computational complexity but also making analysis of periodic oscillation tractable for even large-scale nonlinear networks.     

Modularization of biochemical networks based on classification of Petri net t-invariants

Background  

Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior.     

Identification of biochemical network modules based on shortest retroactive distances

Modularity analysis offers a route to better understand the organization of cellular biochemical networks as well as to derive practically useful, simplified models of these complex systems. While there is general agreement regarding the qualitative properties of a biochemical module, there is no clear consensus on the quantitative criteria that may be used to systematically derive these modules. In this work, we investigate cyclical interactions as the defining characteristic of a biochemical module. We utilize a round trip distance metric, termed Shortest Retroactive Distance (ShReD), to characterize the retroactive connectivity between any two reactions in a biochemical network and to group together network components that mutually influence each other. We evaluate the metric on two types of networks that feature feedback interactions: (i) epidermal growth factor receptor (EGFR) signaling and (ii) liver metabolism supporting drug transformation. For both networks, the ShReD partitions found hierarchically arranged modules that confirm biological intuition. In addition, the partitions also revealed modules that are less intuitive. In particular, ShReD-based partition of the metabolic network identified a 'redox' module that couples reactions of glucose, pyruvate, lipid and drug metabolism through shared production and consumption of NADPH. Our results suggest that retroactive interactions arising from feedback loops and metabolic cycles significantly contribute to the modularity of biochemical networks. For metabolic networks, cofactors play an important role as allosteric effectors that mediate the retroactive interactions.     

Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods

Identifying the reactions that govern a dynamical biological system is a crucial but challenging task in systems biology. In this work, we present a data-driven method to infer the underlying biochemical reaction system governing a set of observed species concentrations over time. We formulate the problem as a regression over a large, but limited, mass-action constrained reaction space and utilize sparse Bayesian inference via the regularized horseshoe prior to produce robust, interpretable biochemical reaction networks, along with uncertainty estimates of parameters. The resulting systems of chemical reactions and posteriors inform the biologist of potentially several reaction systems that can be further investigated. We demonstrate the method on two examples of recovering the dynamics of an unknown reaction system, to illustrate the benefits of improved accuracy and information obtained.     

Enzymatic synthesis of phosphatidylserine on small scale by use of a one-phase system

A modification of the phospholipase D-catalyzed synthesis of phosphatidylserine is described, which allows the handling of small quantities of lipid without the need for an ether-water system. By using octylglucoside to disperse the lipid during the enzymatic conversion, it was possible to reduce the volume of the reaction mixture to 50-100 microliters. The amount of lipid that can be handled in such small volumes is in the order of micrograms. This facilitates the synthesis of phosphatidylserine from rare or expensive phosphatidylcholine species. The yield of phosphatidylserine is increased by replacing phospholipase D from cabbage by the enzyme from Streptomyces species.     

Monitoring pollution in Tunisian coasts: application of a classification scale based on biochemical markers

Over the past decade, molecular, biochemical and cellular markers have been extensively used in pollution monitoring of aquatic environments. Biochemical markers have been selected among early molecular events occurring in the toxicological mechanisms of main contaminants. This paper assesses the marine environment quality along the Tunisian coasts using a statistical approach. Clams (Ruditapes decussatus) were collected during the four seasons of 2003 on seven different sites from the Tunisian coasts. Oxidative stress was evaluated in gills using catalase activity (Cat), neutral lipids and malonedialdehyde accumulation. Glutathione S-transferase activity is related to the conjugation of organic compounds and was evaluated in both, gills and digestive glands. Acetylcholinesterase activity was evaluated as the biomarker of exposure to organophosphorous, carbamate pesticides and heavy metals. For each biomarker, a discriminatory factor was calculated and a response index allocated. For each site, a global response index was calculated as the sum of the response index of each biomarker. Discriminant analysis shows significant differences between sites and seasons compared with control sample. Faroua (site 1) and Menzel Jemile (site 2) seem to be the less polluted with respect to the other sites for all seasons. Gargour (site 6) shows the highest Multimarker Pollution Index during the four seasons, indicating higher contamination level.     

Monitoring pollution in Tunisian coasts: application of a classification scale based on biochemical markers

Over the past decade, molecular, biochemical and cellular markers have been extensively used in pollution monitoring of aquatic environments. Biochemical markers have been selected among early molecular events occurring in the toxicological mechanisms of main contaminants. This paper assesses the marine environment quality along the Tunisian coasts using a statistical approach. Clams (Ruditapes decussatus) were collected during the four seasons of 2003 on seven different sites from the Tunisian coasts. Oxidative stress was evaluated in gills using catalase activity (Cat), neutral lipids and malonedialdehyde accumulation. Glutathione S-transferase activity is related to the conjugation of organic compounds and was evaluated in both, gills and digestive glands. Acetylcholinesterase activity was evaluated as the biomarker of exposure to organophosphorous, carbamate pesticides and heavy metals. For each biomarker, a discriminatory factor was calculated and a response index allocated. For each site, a global response index was calculated as the sum of the response index of each biomarker. Discriminant analysis shows significant differences between sites and seasons compared with control sample. Faroua (site 1) and Menzel Jemile (site 2) seem to be the less polluted with respect to the other sites for all seasons. Gargour (site 6) shows the highest Multimarker Pollution Index during the four seasons, indicating higher contamination level.     

Identification of catalase-producingCampylobacter species based on biochemical characteristics and on cellular fatty acid composition

A total of 50 catalase-positive campylobacters from human and animal sources were studied. The nomenclatural type strains ofCampylobacter coli, C. jejuni, C. fetus, andC. fetus subsp.venerealis, a typical strain of the nalidixic acid-resistant thermophilic group, and various clinical isolates were characterized by bacteriological tests and by gas-liquid chromatographic analysis of their cellular fatty acids. The tests most useful in the differentiation of the various catalase-positive species were growth at 25 and 42°C, H₂S production, tolerance to nalidixic acid and to 2,3,5-triphenyltetrazolium chloride, and hippurate hydrolysis. The latter test was the only reliable means to differentiate betweenC. coli andC. jejuni. Differences between.C. fetus andC. jejuni/coli were confirmed by cellular fatty acid compositions. The bacteriological results indicated thatC. fetus andC. jejuni were distinct species, although within the thermophilic campylobacters there were several related taxa that included bothC. coli andC. jejuni strains with typicalC. coli and some thermophilic strains ofC. fetus subsp.fetus at the extremes.     

Viral perturbations of host networks reflect disease etiology

Many human diseases, arising from mutations of disease susceptibility genes (genetic diseases), are also associated with viral infections (virally implicated diseases), either in a directly causal manner or by indirect associations. Here we examine whether viral perturbations of host interactome may underlie such virally implicated disease relationships. Using as models two different human viruses, Epstein-Barr virus (EBV) and human papillomavirus (HPV), we find that host targets of viral proteins reside in network proximity to products of disease susceptibility genes. Expression changes in virally implicated disease tissues and comorbidity patterns cluster significantly in the network vicinity of viral targets. The topological proximity found between cellular targets of viral proteins and disease genes was exploited to uncover a novel pathway linking HPV to Fanconi anemia.     

The impact of function perturbations in Boolean networks

MOTIVATION: A network is said to be robust relative to a certain network characteristic if a small change in network structure does not significantly affect the characteristic. From the perspective of network stability, robustness is desirable; however, from the perspective of intervention to exert influence on network behavior, it is undesirable. For Boolean networks, there are two fundamental types of robustness. One type pertains to perturbing the state of the network and the other to perturbing the rule-based structure. RESULTS: This article explores the impact of function perturbations in Boolean networks from two aspects: (1) analysis: predict the impact on network state transitions and attractors via analytical approaches or identify a perturbation by observing its consequences; (2) synthesis: preserve or modify the network characteristics, especially attractors, by introducing a judicious change to the functions. The results are applied to achieve intervention that structurally alters the network to achieve a more favorable steady-state distribution and to identify the function perturbation that has led to altered observed behavior. The intervention procedure is applied to a WNT5A network to reduce the risk of metastasis in melanoma, and the identification procedure is applied to a Drosophila melanogaster segmentation polarity gene network to identify regulatory function perturbation.     

Tumor-induced perturbations of cytokines and immune cell networks

Until recently, the intrinsically high level of cross-talk between immune cells, the complexity of immune cell development, and the pleiotropic nature of cytokine signaling have hampered progress in understanding the mechanisms of immunosuppression by which tumor cells circumvent native and adaptive immune responses. One technology that has helped to shed light on this complex signaling network is the cytokine antibody array, which facilitates simultaneous screening of dozens to hundreds of secreted signal proteins in complex biological samples. The combined applications of traditional methods of molecular and cell biology with the high-content, high-throughput screening capabilities of cytokine antibody arrays and other multiplexed immunoassays have revealed a complex mechanism that involves multiple cytokine signals contributed not just by tumor cells but by stromal cells and a wide spectrum of immune cell types. This review will summarize the interactions among cancerous and immune cell types, as well as the key cytokine signals that are required for tumors to survive immunoediting in a dormant state or to grow and spread by escaping it. Additionally, it will present examples of how probing secreted cell–cell signal networks in the tumor microenvironment (TME) with cytokine screens have contributed to our current understanding of these processes and discuss the implications of this understanding to antitumor therapies.     

Quantitative modeling of biochemical networks

Today different database systems for molecular structures (genes and proteins) and metabolic pathways are available. All these systems are characterized by the static data representation. For progress in biotechnology the dynamic representation of this data is important. The metabolism can be characterized as a complex biochemical network. Different models for the quantitative simulation of biochemical networks are discussed, but no useful formalization is available. This paper shows that the theory of Petrinets is useful for the quantitative modeling of biochemical networks.     

Monitoring pollution in Tunisian coasts: application of a classification scale based on biochemical markers.

Over the past decade, molecular, biochemical and cellular markers have been extensively used in pollution monitoring of aquatic environments. Biochemical markers have been selected among early molecular events occurring in the toxicological mechanisms of main contaminants. This paper assesses the marine environment quality along the Tunisian coasts using a statistical approach. Clams (Ruditapes decussatus) were collected during the four seasons of 2003 on seven different sites from the Tunisian coasts. Oxidative stress was evaluated in gills using catalase activity (Cat), neutral lipids and malonedialdehyde accumulation. Glutathione S-transferase activity is related to the conjugation of organic compounds and was evaluated in both, gills and digestive glands. Acetylcholinesterase activity was evaluated as the biomarker of exposure to organophosphorous, carbamate pesticides and heavy metals. For each biomarker, a discriminatory factor was calculated and a response index allocated. For each site, a global response index was calculated as the sum of the response index of each biomarker. Discriminant analysis shows significant differences between sites and seasons compared with control sample. Faroua (site 1) and Menzel Jemile (site 2) seem to be the less polluted with respect to the other sites for all seasons. Gargour (site 6) shows the highest Multimarker Pollution Index during the four seasons, indicating higher contamination level.     

Designing a reusable system based on nanodiamonds for biochemical determination of urea

A reusable system including urease covalently bound to the surface of modified nanodiamonds (MNDs) has been developed for the multiple determination of urea. The immobilized enzyme exhibits functional activity and catalyzes the hydrolysis of urea to yield ammonia. The presence of ammonia is confirmed by the formation of a colored product after the addition of chemical reagents. It was shown that the MNDs-urease complex can function in a wide range of temperatures and pH as well as in deionized water. The complex provides a linear yield of the product at low analyte concentrations and allows the multiple determination of urea in vitro.