Information theoretical quantification of cooperativity in signalling complexes

An optimized reporter of the transcription factor hypoxia-inducible factor 1α reveals complex HIF-1α activation dynamics in single cells

Immune cells adopt a variety of metabolic states to support their many biological functions, which include fighting pathogens, removing tissue debris, and tissue remodeling. One of the key mediators of these metabolic changes is the transcription factor hypoxia-inducible factor 1α (HIF-1α). Single-cell dynamics have been shown to be an important determinant of cell behavior; however, despite the importance of HIF-1α, little is known about its single-cell dynamics or their effect on metabolism. To address this knowledge gap, here we optimized a HIF-1α fluorescent reporter and applied it to study single-cell dynamics. First, we showed that single cells are likely able to differentiate multiple levels of prolyl hydroxylase inhibition, a marker of metabolic change, via HIF-1α activity. We then applied a physiological stimulus known to trigger metabolic change, interferon-γ, and observed heterogeneous, oscillatory HIF-1α responses in single cells. Finally, we input these dynamics into a mathematical model of HIF-1α-regulated metabolism and discovered a profound difference between cells exhibiting high versus low HIF-1α activation. Specifically, we found cells with high HIF-1α activation are able to meaningfully reduce flux through the tricarboxylic acid cycle and show a notable increase in the NAD+/NADH ratio compared with cells displaying low HIF-1α activation. Altogether, this work demonstrates an optimized reporter for studying HIF-1α in single cells and reveals previously unknown principles of HIF-1α activation.

Single-Cell Information Analysis Reveals That Skeletal Muscles Incorporate Cell-to-Cell Variability as Information Not Noise

Cell-to-cell variability in signal transduction in biological systems is often considered noise. However, intercellular variation (i.e., cell-to-cell variability) has the potential to enable individual cells to encode different information. Here, we show that intercellular variation increases information transmission of skeletal muscle. We analyze the responses of multiple cultured myotubes or isolated skeletal muscle fibers as a multiple-cell channel composed of single-cell channels. We find that the multiple-cell channel, which incorporates intercellular variation as information, not noise, transmitted more information in the presence of intercellular variation than in the absence according to the "response diversity effect," increasing in the gradualness of dose response by summing the cell-to-cell variable dose responses. We quantify the information transmission of human facial muscle contraction during intraoperative neurophysiological monitoring and find that information transmission of muscle contraction is comparable to that of a multiple-cell channel. Thus, our data indicate that intercellular variation can increase the information capacity of tissues.

G-Protein coupled receptors: structure and function in drug discovery

The G-protein coupled receptors (GPCRs) superfamily comprise similar proteins arranged into families or classes thus making it one of the largest in the mammalian genome. GPCRs take part in many vital physiological functions making them targets for numerous novel drugs. GPCRs share some distinctive features, such as the seven transmembrane domains, they also differ in the number of conserved residues in their transmembrane domain. Here we provide an introductory and accessible review detailing the computational advances in GPCR pharmacology and drug discovery. An overview is provided on family A-C GPCRs; their structural differences, GPCR signalling, allosteric binding and cooperativity. The dielectric constant (relative permittivity) of proteins is also discussed in the context of site-specific environmental effects.

Cooperativity, absolute interaction, and algebraic optimization

We consider a measure of cooperativity based on the minimal interaction required to generate an observed titration behavior. We describe the corresponding algebraic optimization problem and show how it can be solved using the nonlinear algebra tool SCIP. Moreover, we compute the minimal interactions and minimal molecules for several binding polynomials that describe the oxygen binding of various hemoglobins under different conditions. We compare their minimal interaction with the maximal slope of the Hill plot, and discuss similarities and discrepancies with a view towards the shapes of the binding curves.

Adaption of human antibody λ and κ light chain architectures to CDR repertoires

Monoclonal antibodies bind with high specificity to a wide range of diverse antigens, primarily mediated by their hypervariable complementarity determining regions (CDRs). The defined antigen binding loops are supported by the structurally conserved β-sandwich framework of the light chain (LC) and heavy chain (HC) variable regions. The LC genes are encoded by two separate loci, subdividing the entity of antibodies into kappa (LC_κ) and lambda (LC_λ) isotypes that exhibit distinct sequence and conformational preferences. In this work, a diverse set of techniques were employed including machine learning, force field analysis, statistical coupling analysis and mutual information analysis of a non-redundant antibody structure collection. Thereby, it was revealed how subtle changes between the structures of LC_κ and LC_λ isotypes increase the diversity of antibodies, extending the predetermined restrictions of the general antibody fold and expanding the diversity of antigen binding. Interestingly, it was found that the characteristic framework scaffolds of κ and λ are stabilized by diverse amino acid clusters that determine the interplay between the respective fold and the embedded CDR loops. In conclusion, this work reveals how antibodies use the remarkable plasticity of the beta-sandwich Ig fold to incorporate a large diversity of CDR loops.

Information theory in systems biology. Part II: protein-protein interaction and signaling networks

By the development of information theory in 1948 by Claude Shannon to address the problems in the field of data storage and data communication over (noisy) communication channel, it has been successfully applied in many other research areas such as bioinformatics and systems biology. In this manuscript, we attempt to review some of the existing literatures in systems biology, which are using the information theory measures in their calculations. As we have reviewed most of the existing information-theoretic methods in gene regulatory and metabolic networks in the first part of the review, so in the second part of our study, the application of information theory in other types of biological networks including protein-protein interaction and signaling networks will be surveyed.

Sender-receiver systems and applying information theory for quantitative synthetic biology

Sender-receiver (S-R) systems abound in biology, with communication systems sending information in various forms. Information theory provides a quantitative basis for analysing these processes and is being applied to study natural genetic, enzymatic and neural networks. Recent advances in synthetic biology are providing us with a wealth of artificial S-R systems, giving us quantitative control over networks with a finite number of well-characterised components. Combining the two approaches can help to predict how to maximise signalling robustness, and will allow us to make increasingly complex biological computers. Ultimately, pushing the boundaries of synthetic biology will require moving beyond engineering the flow of information and towards building more sophisticated circuits that interpret biological meaning.

Assessing cytokines' talking patterns following experimental myocardial damage by applying Shannon's information theory

BACKGROUND

The simultaneous measurement of multiple cytokines in parallel by using multiplex proteome arrays (MPA) is of great interest to understanding the inflammatory response following myocardial infarction; however, since cytokines are pleiotropic and redundant, increase of information throughput (IT) attained by measuring multiple cytokines remain to be determined. We aimed this study to assess the IT of an MPA system designed to assess 8 cytokines - commercially available at the time of the study - serum levels, before (control state) and after experimental myocardial cryoinjury (activated state) in rats.

METHODS

By assuming that redundant information do not generally increase the IT, we derived Entropy (H) and Redundancy (R) of information by using formulas of Shannon modified accordingly, where a high IT (high H and low R) corresponds to a low level of correlation between cytokines and vice versa for a low IT. The maximum theoretical level of IT and the contribution of each cytokine were also estimated.

RESULTS

In control state, no significant correlations were found between cytokines showing high IT; on the contrary, in activated state, several significant correlations were found supporting a complex cross-talk pattern between cytokines with low IT. Using as reference the maximum theoretical level of IT, in activated state, H was reduced of 67.0% and R was increased of 77.4% supporting a reduction of IT. Furthermore, the contribution of individual cytokines to H value of MPA was variable: in control state, IL-2 gave the most contribution to H value, conversely during activated state IL-10 gave most contribution. Finally during activated state, IL-1β was the only cytokine strongly correlated with values of all other cytokines, suggesting a crucial role in the inflammatory cascade.

CONCLUSIONS

Paradoxically, by analyzing an MPA system designed for redundant analytes such as cytokines, translating the Shannon's information theory from the field of communication to biology, the IT system in our model deteriorates during the activation state by increasing its redundancy, showing maximum value of entropy in the control conditions. Finally, the study of the mutual interdependence between cytokines by the contribution to the IT may allow formulating alternative models to describe the inflammatory cascade after myocardial infarction.

Capturing cooperative interactions with the PSI-MI format

The complex biological processes that control cellular function are mediated by intricate networks of molecular interactions. Accumulating evidence indicates that these interactions are often interdependent, thus acting cooperatively. Cooperative interactions are prevalent in and indispensible for reliable and robust control of cell regulation, as they underlie the conditional decision-making capability of large regulatory complexes. Despite an increased focus on experimental elucidation of the molecular details of cooperative binding events, as evidenced by their growing occurrence in literature, they are currently lacking from the main bioinformatics resources. One of the contributing factors to this deficiency is the lack of a computer-readable standard representation and exchange format for cooperative interaction data. To tackle this shortcoming, we added functionality to the widely used PSI-MI interchange format for molecular interaction data by defining new controlled vocabulary terms that allow annotation of different aspects of cooperativity without making structural changes to the underlying XML schema. As a result, we are able to capture cooperative interaction data in a structured format that is backward compatible with PSI-MI–based data and applications. This will facilitate the storage, exchange and analysis of cooperative interaction data, which in turn will advance experimental research on this fundamental principle in biology.

Database URL:http://psi-mi-cooperativeinteractions.embl.de/

Robustness and compensation of information transmission of signaling pathways

Robust transmission of information despite the presence of variation is a fundamental problem in cellular functions. However, the capability and characteristics of information transmission in signaling pathways remain poorly understood. We describe robustness and compensation of information transmission of signaling pathways at the cell population level. We calculated the mutual information transmitted through signaling pathways for the growth factor-mediated gene expression. Growth factors appeared to carry only information sufficient for a binary decision. Information transmission was generally more robust than average signal intensity despite pharmacological perturbations, and compensation of information transmission occurred. Information transmission to the biological output of neurite extension appeared robust. Cells may use information entropy as information so that messages can be robustly transmitted despite variation in molecular activities among individual cells.

Information theory based approaches to cellular signaling

BACKGROUND

Cells interact with their environment and they have to react adequately to internal and external changes such changes in nutrient composition, physical properties like temperature or osmolarity and other stresses. More specifically, they must be able to evaluate whether the external change is significant or just in the range of noise. Based on multiple external parameters they have to compute an optimal response. Cellular signaling pathways are considered as the major means of information perception and transmission in cells.

SCOPE OF REVIEW

Here, we review different attempts to quantify information processing on the level of individual cells. We refer to Shannon entropy, mutual information, and informal measures of signaling pathway cross-talk and specificity.

MAJOR CONCLUSIONS

Information theory in systems biology has been successfully applied to identification of optimal pathway structures, mutual information and entropy as system response in sensitivity analysis, and quantification of input and output information.

GENERAL SIGNIFICANCE

While the study of information transmission within the framework of information theory in technical systems is an advanced field with high impact in engineering and telecommunication, its application to biological objects and processes is still restricted to specific fields such as neuroscience, structural and molecular biology. However, in systems biology dealing with a holistic understanding of biochemical systems and cellular signaling only recently a number of examples for the application of information theory have emerged. This article is part of a Special Issue entitled Systems Biology of Microorganisms.

Computing spatial information from Fourier coefficient distributions

The spatial relationships between molecules can be quantified in terms of information. In the case of membranes, the spatial organization of molecules in a bilayer is closely related to biophysically and biologically important properties. Here, we present an approach to computing spatial information based on Fourier coefficient distributions. The Fourier transform (FT) of an image contains a complete description of the image, and the values of the FT coefficients are uniquely associated with that image. For an image where the distribution of pixels is uncorrelated, the FT coefficients are normally distributed and uncorrelated. Further, the probability distribution for the FT coefficients of such an image can readily be obtained by Parseval's theorem. We take advantage of these properties to compute the spatial information in an image by determining the probability of each coefficient (both real and imaginary parts) in the FT, then using the Shannon formalism to calculate information. By using the probability distribution obtained from Parseval's theorem, an effective distance from the uncorrelated or most uncertain case is obtained. The resulting quantity is an information computed in k-space (kSI). This approach provides a robust, facile and highly flexible framework for quantifying spatial information in images and other types of data (of arbitrary dimensions). The kSI metric is tested on a 2D Ising model, frequently used as a model for lipid bilayer; and the temperature-dependent phase transition is accurately determined from the spatial information in configurations of the system.

The challenges of informatics in synthetic biology: from biomolecular networks to artificial organisms

The field of synthetic biology holds an inspiring vision for the future; it integrates computational analysis, biological data and the systems engineering paradigm in the design of new biological machines and systems. These biological machines are built from basic biomolecular components analogous to electrical devices, and the information flow among these components requires the augmentation of biological insight with the power of a formal approach to information management. Here we review the informatics challenges in synthetic biology along three dimensions: in silico, in vitro and in vivo. First, we describe state of the art of the in silico support of synthetic biology, from the specific data exchange formats, to the most popular software platforms and algorithms. Next, we cast in vitro synthetic biology in terms of information flow, and discuss genetic fidelity in DNA manipulation, development strategies of biological parts and the regulation of biomolecular networks. Finally, we explore how the engineering chassis can manipulate biological circuitries in vivo to give rise to future artificial organisms.