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.     

Genetically Engineered Human Cancer Models Utilizing Mammalian Transgene Expression

Cancer models are vital to cancer biology research, and multiple cancer models are currently available that utilize either murine or human cells, each with particular strengths and weaknesses. The ability to transform primary human cells into tumors through the expression of specific transgenes offers many advantages as a cancer model, including genetic malleability and the ability to transform specific cell types. Until recently, the conversion of primary human cells into tumors through transgene expression required the use of viral genetic elements, which unfortunately adds uncertainty regarding which cancer pathways are affected and how they are affected. In recent years multiple reports have described the transformation of primary human cells into tumors using only mammalian transgenes. This review focuses on these five cancer models, comparing the different cell types which were transformed into tumors and which transgenes were expressed, as well as the cancer pathways affected in the disparate models. These genetically-engineered human cancer models offer a valuable tool to complement existing cancer models and further cancer research.     

3D-biohybrid systems: applications in drug screening

Biotechnology demands powerful methods for the functional characterisation and monitoring of molecular alterations in tissues in response to various stimuli. Currently, cellular biosensors provide information about cell and tissue internal transduction pathways. In this article, recent biosensor systems are briefly described and the use of 3D tissue aggregates as recognition elements is discussed. An example of an innovative approach for drug testing using 3D heart muscle aggregates, as well as tumor models, positioned in capillary systems for electrical potential recording and impedance measurement is described. The effectiveness of drugs and therapies can be tested and monitored in a short time using such biohybrid sensors.     

Evaluation of different nitrous oxide production models with four continuous long-term wastewater treatment process data series

Five activated sludge models describing N₂O production by ammonium oxidising bacteria (AOB) were compared to four different long-term process data sets. Each model considers one of the two known N₂O production pathways by AOB, namely the AOB denitrification pathway and the hydroxylamine oxidation pathway, with specific kinetic expressions. Satisfactory calibration could be obtained in most cases, but none of the models was able to describe all the N₂O data obtained in the different systems with a similar parameter set. Variability of the parameters can be related to difficulties related to undescribed local concentration heterogeneities, physiological adaptation of micro-organisms, a microbial population switch, or regulation between multiple AOB pathways. This variability could be due to a dependence of the N₂O production pathways on the nitrite (or free nitrous acid—FNA) concentrations and other operational conditions in different systems. This work gives an overview of the potentialities and limits of single AOB pathway models. Indicating in which condition each single pathway model is likely to explain the experimental observations, this work will also facilitate future work on models in which the two main N₂O pathways active in AOB are represented together.     

An overview of data models for the analysis of biochemical pathways

Biochemical pathways such as metabolic, regulatory or signal transduction pathways can be viewed as interconnected processes forming an intricate network of functional and physical interactions between molecular species in the cell. The amount of information available on such pathways for different organisms is increasing very rapidly. This is offering the possibility of performing various analyses on the structure of the full network of pathways for one organism as well as across different organisms, and has therefore generated interest in developing databases for storing and managing this information. Analysing these networks remains far from straightforward owing to the nature of the databases, which are often heterogeneous, incomplete or inconsistent. Pathway analysis is hence a challenging problem in systems biology and in bioinformatics. Various forms of data models have been devised for the analysis of biochemical pathways. This paper presents an overview of the types of models used for this purpose, concentrating on those concerned with the structural aspects of biochemical networks. In particular, the different types of data models found in the literature are classified using a unified framework. In addition, how these models have been used in the analysis of biochemical networks is described. This enables us to underline the strengths and weaknesses of the different approaches, as well as to highlight relevant future research directions.     

Practical limits for reverse engineering of dynamical systems: a statistical analysis of sensitivity and parameter inferability in systems biology models

The size and complexity of cellular systems make building predictive models an extremely difficult task. In principle dynamical time-course data can be used to elucidate the structure of the underlying molecular mechanisms, but a central and recurring problem is that many and very different models can be fitted to experimental data, especially when the latter are limited and subject to noise. Even given a model, estimating its parameters remains challenging in real-world systems. Here we present a comprehensive analysis of 180 systems biology models, which allows us to classify the parameters with respect to their contribution to the overall dynamical behaviour of the different systems. Our results reveal candidate elements of control in biochemical pathways that differentially contribute to dynamics. We introduce sensitivity profiles that concisely characterize parameter sensitivity and demonstrate how this can be connected to variability in data. Systematically linking data and model sloppiness allows us to extract features of dynamical systems that determine how well parameters can be estimated from time-course measurements, and associates the extent of data required for parameter inference with the model structure, and also with the global dynamical state of the system. The comprehensive analysis of so many systems biology models reaffirms the inability to estimate precisely most model or kinetic parameters as a generic feature of dynamical systems, and provides safe guidelines for performing better inferences and model predictions in the context of reverse engineering of mathematical models for biological systems.     

Prediction of the Reaction Equilibrium of Biocatalytic Reactions in Aqueous-Organic Two-Phase Systems

Biocatalytic reactions can be carried out in aqueous-organic two-phase systems. Several models to describe the thermodynamically-determined equilibrium position in such systems have appeared in the literature. Some of these models are only valid for dilute systems, whereas others can also be used for nondilute systems. In this paper, these models are described and compared. It is explained in what way the equilibrium constants of each model can be used to predict the product concentration in different organic solvents.     

Prediction of the Reaction Equilibrium of Biocatalytic Reactions in Aqueous-Organic Two-Phase Systems

Biocatalytic reactions can be carried out in aqueous-organic two-phase systems. Several models to describe the thermodynamically-determined equilibrium position in such systems have appeared in the literature. Some of these models are only valid for dilute systems, whereas others can also be used for nondilute systems. In this paper, these models are described and compared. It is explained in what way the equilibrium constants of each model can be used to predict the product concentration in different organic solvents.     

A unified mechanism for protein folding: predetermined pathways with optional errors

There is a fundamental conflict between two different views of how proteins fold. Kinetic experiments and theoretical calculations are often interpreted in terms of different population fractions folding through different intermediates in independent unrelated pathways (IUP model). However, detailed structural information indicates that all of the protein population folds through a sequence of intermediates predetermined by the foldon substructure of the target protein and a sequential stabilization principle. These contrary views can be resolved by a predetermined pathway--optional error (PPOE) hypothesis. The hypothesis is that any pathway intermediate can incorporate a chance misfolding error that blocks folding and must be reversed for productive folding to continue. Different fractions of the protein population will then block at different steps, populate different intermediates, and fold at different rates, giving the appearance of multiple unrelated pathways. A test of the hypothesis matches the two models against extensive kinetic folding results for hen lysozyme which have been widely cited in support of independent parallel pathways. The PPOE model succeeds with fewer fitting constants. The fitted PPOE reaction scheme leads to known folding behavior, whereas the IUP properties are contradicted by experiment. The appearance of a conflict with multipath theoretical models seems to be due to their different focus, namely on multitrack microscopic behavior versus cooperative macroscopic behavior. The integration of three well-documented principles in the PPOE model (cooperative foldons, sequential stabilization, optional errors) provides a unifying explanation for how proteins fold and why they fold in that way.     

Morphology-induced collective behaviors: dynamic pattern formation in water-floating elements

Complex systems involving many interacting elements often organize into patterns. Two types of pattern formation can be distinguished, static and dynamic. Static pattern formation means that the resulting structure constitutes a thermodynamic equilibrium whose pattern formation can be understood in terms of the minimization of free energy, while dynamic pattern formation indicates that the system is permanently dissipating energy and not in equilibrium. In this paper, we report experimental results showing that the morphology of elements plays a significant role in dynamic pattern formation. We prepared three different shapes of elements (circles, squares, and triangles) floating in a water-filled container, in which each of the shapes has two types: active elements that were capable of self-agitation with vibration motors, and passive elements that were mere floating tiles. The system was purely decentralized: that is, elements interacted locally, and subsequently elicited global patterns in a process called self-organized segregation. We showed that, according to the morphology of the selected elements, a different type of segregation occurs. Also, we quantitatively characterized both the local interaction regime and the resulting global behavior for each type of segregation by means of information theoretic quantities, and showed the difference for each case in detail, while offering speculation on the mechanism causing this phenomenon.     

The amplitude and phase responses of the firing rates of some motoneuron models

A vertebrate motoneuron receives an enormous amount of synaptic activity from descending pathways, from spinal cord interneurons and directly from mechanoreceptor afferents. The intrinsic characteristics of the motoneuron will determine how its output spike train will encode the activities of all its inputs. Therefore, the essence of the intrinsic motoneuron characteristics should be well studied and modelled if the roles of the motoneuron as a processing or encoding element are to be well understood. Mathematical models of motoneurons have been described in the literature and tested mostly under static conditions. To increase the reality of the validation of such models, the objective of the present work is to test a few selected models described in the literature using sinusoidal injected current of different frequencies. The resulting frequency responses are compared with data available in the literature from cat type F motoneurons. Discrepancies between some of the models' responses and real motoneuron data suggest that improvements are needed in the modelling of the afterhyperpolarization mechanism.     

Targeted transposition with Tn7 elements: safe sites,mobile plasmids,CRISPR/Cas and beyond

Transposon Tn7 is notable for the control it exercises over where transposition events are directed. One Tn7 integration pathways recognizes a highly conserved attachment (att) site in the chromosome, while a second pathway specifically recognizes mobile plasmids that facilitate transfer of the element to new hosts. In this review, I discuss newly discovered families of Tn7‐like elements with different targeting pathways. Perhaps the most exciting examples are multiple instances where Tn7‐like elements have repurposed CRISPR/Cas systems. In these cases, the CRISPR/Cas systems have lost their canonical defensive function to destroy incoming mobile elements; instead, the systems have been naturally adapted to use guide RNAs to specifically direct transposition into these mobile elements. The new families of Tn7‐like elements also include a variety of novel att sites in bacterial chromosomes where genome islands can form. Interesting families have also been revealed where proteins described in the prototypic Tn7 element are fused or otherwise repurposed for the new dual activities. This expanded understanding of Tn7‐like elements broadens our view of how genetic systems are repurposed and provides potentially exciting new tools for genome modification and genomics. Future opportunities and challenges to understanding the impact of the new families of Tn7‐like elements are discussed.     

Modeling DNA sequence-based cis-regulatory gene networks

Gene network analysis requires computationally based models which represent the functional architecture of regulatory interactions, and which provide directly testable predictions. The type of model that is useful is constrained by the particular features of developmentally active cis-regulatory systems. These systems function by processing diverse regulatory inputs, generating novel regulatory outputs. A computational model which explicitly accommodates this basic concept was developed earlier for the cis-regulatory system of the endo16 gene of the sea urchin. This model represents the genetically mandated logic functions that the system executes, but also shows how time-varying kinetic inputs are processed in different circumstances into particular kinetic outputs. The same basic design features can be utilized to construct models that connect the large number of cis-regulatory elements constituting developmental gene networks. The ultimate aim of the network models discussed here is to represent the regulatory relationships among the genomic control systems of the genes in the network, and to state their functional meaning. The target site sequences of the cis-regulatory elements of these genes constitute the physical basis of the network architecture. Useful models for developmental regulatory networks must represent the genetic logic by which the system operates, but must also be capable of explaining the real time dynamics of cis-regulatory response as kinetic input and output data become available. Most importantly, however, such models must display in a direct and transparent manner fundamental network design features such as intra- and intercellular feedback circuitry; the sources of parallel inputs into each cis-regulatory element; gene battery organization; and use of repressive spatial inputs in specification and boundary formation. Successful network models lead to direct tests of key architectural features by targeted cis-regulatory analysis.     

Logical and symbolic analysis of robust biological dynamics

Logical models provide insight about key control elements of biological networks. Based solely on the logical structure, we can determine state transition diagrams that give the allowed possible transitions in a coarse grained phase space. Attracting pathways and stable nodes in the state transition diagram correspond to robust attractors that would be found in several different types of dynamical systems that have the same logical structure. Attracting nodes in the state transition diagram correspond to stable steady states. Furthermore, the sequence of logical states appearing in biological networks with robust attracting pathways would be expected to appear also in Boolean networks, asynchronous switching networks, and differential equations having the same underlying structure. This provides a basis for investigating naturally occurring and synthetic systems, both to predict the dynamics if the structure is known, and to determine the structure if the transitions are known.     

Criteria of quality and methods of measurement in multichannel biotelemetry.

In contrast with one-channel biotelemetry, multichannel biotelemetry systems permit the analysis of biological systems by means of cybernetics. Some simple examples are given. The basic concept of a multichannel system is described. The criteria of quality of which the nonevident ones are defined by detailing the test circuits are the following (input criteria): number of channels, bandwidth, voltage range, impedance, maximum voltage outside the signal band, stability of the power supply for transducers, factor of safety against multipath propagation, distance between transmitter and receiver, shock, vibration and temperature range, weight, volume and operating time. Output criteria: signal-to-noise ratio, linearity, crosstalk transfer function, different time lags.     

Novel insights through the integration of structural and functional genomics data with protein networks

In recent years, major advances in genomics, proteomics, macromolecular structure determination, and the computational resources capable of processing and disseminating the large volumes of data generated by each have played major roles in advancing a more systems-oriented appreciation of biological organization. One product of systems biology has been the delineation of graph models for describing genome-wide protein-protein interaction networks. The network organization and topology which emerges in such models may be used to address fundamental questions in an array of cellular processes, as well as biological features intrinsic to the constituent proteins (or "nodes") themselves. However, graph models alone constitute an abstraction which neglects the underlying biological and physical reality that the network's nodes and edges are highly heterogeneous entities. Here, we explore some of the advantages of introducing a protein structural dimension to such models, as the marriage of conventional network representations with macromolecular structural data helps to place static node and edge constructs in a biologically more meaningful context. We emphasize that 3D protein structures constitute a valuable conceptual and predictive framework by discussing examples of the insights provided, such as enabling in silico predictions of protein-protein interactions, providing rational and compelling classification schemes for network elements, as well as revealing interesting intrinsic differences between distinct node types, such as disorder and evolutionary features, which may then be rationalized in light of their respective functions within networks.