Iterative approach to model identification of biological networks

Background  

Recent advances in molecular biology techniques provide an opportunity for developing detailed mathematical models of biological processes. An iterative scheme is introduced for model identification using available system knowledge and experimental measurements.     

Elementary network reconstruction: A framework for the analysis of regulatory networks in biological systems

Complexity of regulatory networks arises from the high degree of interaction between network components such as DNA, RNA, proteins, and metabolites. We have developed a modeling tool, elementary network reconstruction (ENR), to characterize these networks. ENR is a knowledge-driven, steady state, deterministic, quantitative modeling approach based on linear perturbation theory. In ENR we demonstrate a novel means of expressing control mechanisms by way of dimensionless steady state gains relating input and output variables, which are purely in terms of species abundances (extensive variables). As a result of systematic enumeration of network species in n×n matrix, the two properties of linear perturbation are manifested in graphical representations: transitive property is evident in a special L-shape structure, and additive property is evident in multiple L-shape structures arriving at the same matrix cell. Upon imposing mechanistic (lowest-level) gains, network self-assembly through transitive and additive properties results in elucidation of inherent topology and explicit cataloging of higher level gains, which in turn can be used to predict perturbation results. Application of ENR to the regulatory network behind carbon catabolite repression in Escherichia coli is presented. Through incorporation of known molecular mechanisms governing transient and permanent repressions, the ENR model correctly predicts several key features of this regulatory network, including a 50% downshift in intracellular cAMP level upon exposure to glucose. Since functional genomics studies are mainly concerned with redistribution of species abundances in perturbed systems, ENR could be exploited in the system-level analysis of biological systems.     

Advances to Bayesian network inference for generating causal networks from observational biological data

MOTIVATION: Network inference algorithms are powerful computational tools for identifying putative causal interactions among variables from observational data. Bayesian network inference algorithms hold particular promise in that they can capture linear, non-linear, combinatorial, stochastic and other types of relationships among variables across multiple levels of biological organization. However, challenges remain when applying these algorithms to limited quantities of experimental data collected from biological systems. Here, we use a simulation approach to make advances in our dynamic Bayesian network (DBN) inference algorithm, especially in the context of limited quantities of biological data. RESULTS: We test a range of scoring metrics and search heuristics to find an effective algorithm configuration for evaluating our methodological advances. We also identify sampling intervals and levels of data discretization that allow the best recovery of the simulated networks. We develop a novel influence score for DBNs that attempts to estimate both the sign (activation or repression) and relative magnitude of interactions among variables. When faced with limited quantities of observational data, combining our influence score with moderate data interpolation reduces a significant portion of false positive interactions in the recovered networks. Together, our advances allow DBN inference algorithms to be more effective in recovering biological networks from experimentally collected data. AVAILABILITY: Source code and simulated data are available upon request. SUPPLEMENTARY INFORMATION: http://www.jarvislab.net/Bioinformatics/BNAdvances/     

Erratum to: Functional model of biological neural networks

[This corrects the article DOI: 10.1007/s11571-010-9110-4.].     

The generalized flux-minimization method and its application to metabolic networks affected by enzyme deficiencies

The flux-minimization method [Holzhütter, H.G., 2004. The principle of flux-minimization and its application to calculate stationary fluxes in metabolic networks. Eur. J. Biochem. 271, 2905-2922] has been proposed as an alternative to kinetic modeling to calculate stationary fluxes in metabolic networks. Here a generalization of this method is proposed that takes into account possible limitations of internal fluxes, e.g. due to enzyme defects or partial inhibition of enzyme activities by drugs. The generalized method consists in the minimization of an objective function which expresses the compromise that has to be made between minimization of internal fluxes on one hand and maintenance of the metabolic output required for various cellular functions on the other. Fulfillment of the latter condition is measured through a fitness function, which evaluates the relative deviation of the output fluxes from demanded target values. The method is applied to assess the metabolic consequences caused by a deficiency of enzymes involved in the metabolism of erythrocytes. The obtained results are in good agreement with those obtained on the basis of a comprehensive kinetic model.     

A physical model for a fibrous network and its application to the shear modulus and other data of the fibrin gel

A physical model for a fibrous network is developed and used to calculate its shear modulus. The model is applied to the shear modulus data of the fibrin gel and compared with other data related to the fibrin gel to elucidate the physical origins for some of the interesting properties of the gel such as the concentration dependence of the shear modulus and the difference between fine and course gels.     

A theory of pragmatic information and its application to the quasi-species model of biological evolution

'Standard' information theory says nothing about the semantic content of information. Nevertheless, applications such as evolutionary theory demand consideration of precisely this aspect of information, a need that has motivated a largely unsuccessful search for a suitable measure of an 'amount of meaning'. This paper represents an attempt to move beyond this impasse, based on the observation that the meaning of a message can only be understood relative to its receiver. Positing that the semantic value of information is its usefulness in making an informed decision, we define pragmatic information as the information gain in the probability distributions of the receiver's actions, both before and after receipt of a message in some pre-defined ensemble. We then prove rigorously that our definition is the only one that satisfies obvious desiderata, such as the additivity of information from logically independent messages. This definition, when applied to the information 'learned' by the time evolution of a process, defies the intuitions of the few previous researchers thinking along these lines by being monotonic in the uncertainty that remains after receipt of the message, but non-monotonic in the Shannon entropy of the input ensemble. It also follows that the pragmatic information of the genetic 'messages' in an evolving population is a global Lyapunov function for Eigen's quasi-species model of biological evolution. A concluding section argues that a theory such as ours must explicitly acknowledge purposeful action, or 'agency', in such diverse fields as evolutionary theory and finance.     

A human functional protein interaction network and its application to cancer data analysis

Background  

One challenge facing biologists is to tease out useful information from massive data sets for further analysis. A pathway-based analysis may shed light by projecting candidate genes onto protein functional relationship networks. We are building such a pathway-based analysis system.     

Dynamic simulation of red blood cell metabolism and its application to the analysis of a pathological condition

Background  

Cell simulation, which aims to predict the complex and dynamic behavior of living cells, is becoming a valuable tool. In silico models of human red blood cell (RBC) metabolism have been developed by several laboratories. An RBC model using the E-Cell simulation system has been developed. This prototype model consists of three major metabolic pathways, namely, the glycolytic pathway, the pentose phosphate pathway and the nucleotide metabolic pathway. Like the previous model by Joshi and Palsson, it also models physical effects such as osmotic balance. This model was used here to reconstruct the pathology arising from hereditary glucose-6-phosphate dehydrogenase (G6PD) deficiency, which is the most common deficiency in human RBC.     

A novel method of protein analysis for prediction of biological function: application to tumor toxins

Informational spectra method was applied to the analysis of lymphotoxin and tumor necrosis factor. The correlation between the information contained in primary structure of these tumor toxins and some oncogen transforming proteins was established. This correlation implies the possibility of a competitive action between these two groups of proteins. "Hot spots" positions in the primary structure of lymphotoxin and tumor necrosis factor for the functional "up" and "down" mutations were predicted.     

A calorimetric method to assess endogenous metabolism and its application to the study of bovine sperm

Calorimetry was used to assess the importance of endogenous metabolism towards total ATP synthesis in bovine sperm in the presence of extracellular glucose. Sperm were incubated in the calorimeter with d-[U-¹⁴C]glucose without or with electron transport inhibitors, rotenone and antimycin A. Steady-state heat production during the incubation was measured for 30 min, the incubations were terminated, and the cell suspensions removed for analysis of radioactive glucose and its metabolic end-products. Heat production (mean±S.E. associated with the metabolism of glucose was calculated, from enthalpies of formation of glucose and its end-product, as ?412±34 mJ/h/10⁸ cells in control incubations and ?263±18 mJ/h/10⁸ cells in incubations with electron transport inhibitors. Measured heat production was ?455±36 and ?263±17 mJ/h/10⁸ cells, respectively. Thus, heat production by endogenous pathways, the difference between measured total heat production and calculated exogenous heat production, was ?43±14 mJ/h/10⁸ cells fro control cells and about ?6 mJ/h/10⁸ cells for inhibited cells. The ration of heat produced per mol of ATP synthesized is similar for all ATP-producing pathways. Therefore, about 10% of total ATP synthesis in control cells and less than 2% in inhibited cells is provided by endogenous pathways when extracellular glucose is present.     

Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function.

Due to the recent progress of the DNA microarray technology, a large number of gene expression profile data are being produced. How to analyze gene expression data is an important topic in computational molecular biology. Several studies have been done using the Boolean network as a model of a genetic network. This paper proposes efficient algorithms for identifying Boolean networks of bounded indegree and related biological networks, where identification of a Boolean network can be formalized as a problem of identifying many Boolean functions simultaneously. For the identification of a Boolean network, an O(mnD+1) time naive algorithm and a simple O (mnD) time algorithm are known, where n denotes the number of nodes, m denotes the number of examples, and D denotes the maximum in degree. This paper presents an improved O(momega-2nD + mnD+omega-3) time Monte-Carlo type randomized algorithm, where omega is the exponent of matrix multiplication (currently, omega < 2.376). The algorithm is obtained by combining fast matrix multiplication with the randomized fingerprint function for string matching. Although the algorithm and its analysis are simple, the result is nontrivial and the technique can be applied to several related problems.     

Qxpak: a versatile mixed model application for genetical genomics and QTL analyses

MOTIVATION: Current methodology and software for quantitative trait loci (QTL) analyses do not use all available information and are inadequate to deal with the huge amount of QTL analyses to be needed in forecoming genetical genomics' studies. RESULTS: We show that a mixed model statistical framework provides a very flexible tool for QTL modeling in a variety of populations, be it a cross between inbred lines, a within population study, or experiments involving a mixture of populations or crosses. The software allows multitrait and multiQTL analyses, inclusion of infinitesimal genetic value and a batch multitrait option suitable for genetical genomics studies. It also allows massive association studies between single nucleotide polymorphisms and the trait(s) of interest. AVAILABILITY: A software (Qxpak), together with a manual and example files, is freely available for research purposes. So far, the compiled program is available for linux systems, the windows version will follow soon. See http://www.icrea.es/pag.asp?id=Miguel.Perez