Person:

Gunawardena, Jeremy

The functional impact of multisite protein phosphorylation can depend on both the numbers and the positions of phosphorylated sites—the global pattern of phosphorylation or ‘phospho-form’—giving biological systems profound capabilities for dynamic information processing. A central problem in quantitative systems biology, therefore, is to measure the ‘phospho-form distribution’: the relative amount of each of the 2(^n) phospho-forms of a protein with n-phosphorylation sites. We compared four potential methods—western blots with phospho-specific antibodies, peptide-based liquid chromatography (LC) and mass spectrometry (MS; pepMS), protein-based LC/MS (proMS) and nuclear magnetic resonance spectroscopy (NMR)—on differentially phosphorylated samples of the well-studied mitogen-activated protein kinase Erk2, with two phosphorylation sites. The MS methods were quantitatively consistent with each other and with NMR to within 10%, but western blots, while highly sensitive, showed significant discrepancies with MS. NMR also uncovered two additional phosphorylations, for which a combination of pepMS and proMS yielded an estimate of the 16-member phospho-form distribution. This combined MS strategy provides an optimal mixture of accuracy and coverage for quantifying distributions, but positional isomers remain a challenging problem.

Cellular physiology is implemented by formidably complex biochemical systems with highly nonlinear dynamics, presenting a challenge for both experiment and theory. Time-scale separation has been one of the few theoretical methods for distilling general principles from such complexity. It has provided essential insights in areas such as enzyme kinetics, allosteric enzymes, G-protein coupled receptors, ion channels, gene regulation and post-translational modification. In each case, internal molecular complexity has been eliminated, leading to rational algebraic expressions among the remaining components. This has yielded familiar formulas such as those of Michaelis-Menten in enzyme kinetics, Monod-Wyman-Changeux in allostery and Ackers-Johnson-Shea in gene regulation. Here we show that these calculations are all instances of a single graph-theoretic framework. Despite the biochemical nonlinearity to which it is applied, this framework is entirely linear, yet requires no approximation. We show that elimination of internal complexity is feasible when the relevant graph is strongly connected. The framework provides a new methodology with the potential to subdue combinatorial explosion at the molecular level.

The molecular complexity within a cell may be seen as an evolutionary response to the external complexity of the cell’s environment. This suggests that the external environment may be harnessed to interrogate the cell’s internal molecular architecture. Cells, however, are not only nonlinear and non-stationary, but also exhibit heterogeneous responses within a clonal, isogenic population. In effect, each cell undertakes its own experiment. Here, we develop a method of cellular interrogation using programmable microfluidic devices which exploits the additional information present in cell-to-cell variation, without requiring model parameters to be fitted to data. We focussed on Ca2+ signalling in response to hormone stimulation, which exhibits oscillatory spiking in many cell types and chose eight models of Ca2+ signalling networks which exhibit similar behaviour in simulation. We developed a nonlinear frequency analysis for non-stationary responses, which could classify models into groups under parameter variation, but found that this question alone was unable to distinguish critical feedback loops. We further developed a nonlinear amplitude analysis and found that the combination of both questions ruled out six of the models as inconsistent with the experimentally-observed dynamics and heterogeneity. The two models that survived the double interrogation were mathematically different but schematically identical and yielded the same unexpected predictions that we confirmed experimentally. Further analysis showed that subtle mathematical details can markedly influence non-stationary responses under parameter variation, emphasising the difficulty of finding a “correct” model. By developing questions for the pathway being studied, and designing more versatile microfluidics, cellular interrogation holds promise as a systematic strategy that can complement direct intervention by genetics or pharmacology.

In this essay I will sketch some ideas for how to think about models in biology. I will begin by trying to dispel the myth that quantitative modeling is somehow foreign to biology. I will then point out the distinction between forward and reverse modeling and focus thereafter on the former. Instead of going into mathematical technicalities about different varieties of models, I will focus on their logical structure, in terms of assumptions and conclusions. A model is a logical machine for deducing the latter from the former. If the model is correct, then, if you believe its assumptions, you must, as a matter of logic, also believe its conclusions. This leads to consideration of the assumptions underlying models. If these are based on fundamental physical laws, then it may be reasonable to treat the model as ‘predictive’, in the sense that it is not subject to falsification and we can rely on its conclusions. However, at the molecular level, models are more often derived from phenomenology and guesswork. In this case, the model is a test of its assumptions and must be falsifiable. I will discuss three models from this perspective, each of which yields biological insights, and this will lead to some guidelines for prospective model builders.

Background: Gene regulation has, for the most part, been quantitatively analysed by assuming that regulatory mechanisms operate at thermodynamic equilibrium. This formalism was originally developed to analyse the binding and unbinding of transcription factors from naked DNA in eubacteria. Although widely used, it has made it difficult to understand the role of energy-dissipating, epigenetic mechanisms, such as DNA methylation, nucleosome remodelling and post-translational modification of histones and co-regulators, which act together with transcription factors to regulate gene expression in eukaryotes. Results: Here, we introduce a graph-based framework that can accommodate non-equilibrium mechanisms. A gene-regulatory system is described as a graph, which specifies the DNA microstates (vertices), the transitions between microstates (edges) and the transition rates (edge labels). The graph yields a stochastic master equation for how microstate probabilities change over time. We show that this framework has broad scope by providing new insights into three very different ad hoc models, of steroid-hormone responsive genes, of inherently bounded chromatin domains and of the yeast PHO5 gene. We find, moreover, surprising complexity in the regulation of PHO5, which has not yet been experimentally explored, and we show that this complexity is an inherent feature of being away from equilibrium. At equilibrium, microstate probabilities do not depend on how a microstate is reached but, away from equilibrium, each path to a microstate can contribute to its steady-state probability. Systems that are far from equilibrium thereby become dependent on history and the resulting complexity is a fundamental challenge. To begin addressing this, we introduce a graph-based concept of independence, which can be applied to sub-systems that are far from equilibrium, and prove that history-dependent complexity can be circumvented when sub-systems operate independently. Conclusions: As epigenomic data become increasingly available, we anticipate that gene function will come to be represented by graphs, as gene structure has been represented by sequences, and that the methods introduced here will provide a broader foundation for understanding how genes work. Electronic supplementary material The online version of this article (doi:10.1186/s12915-014-0102-4) contains supplementary material, which is available to authorized users.

Background: Cytokine-hormone network deregulations underpin pathologies ranging from autoimmune disorders to cancer, but our understanding of these networks in physiological/pathophysiological states remains patchy. We employed Bayesian networks to analyze cytokine-hormone interactions in vivo using murine lactation as a dynamic, physiological model system. Results: Circulatory levels of estrogen, progesterone, prolactin and twenty-three cytokines were profiled in post partum mice with/without pups. The resultant networks were very robust and assembled about structural hubs, with evidence that interleukin (IL)-12 (p40), IL-13 and monocyte chemoattractant protein (MCP)-1 were the primary drivers of network behavior. Network structural conservation across physiological scenarios coupled with the successful empirical validation of our approach suggested that in silico network perturbations can predict in vivo qualitative responses. In silico perturbation of network components also captured biological features of cytokine interactions (antagonism, synergy, redundancy). Conclusion: These findings highlight the potential of network-based approaches in identifying novel cytokine pharmacological targets and in predicting the effects of their exogenous manipulation in inflammatory/immune disorders. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0226-3) contains supplementary material, which is available to authorized users.

Only a small fraction of the mammalian genome codes for messenger RNAs destined to be translated into proteins, and it is generally assumed that a large portion of transcribed sequences - including introns and several classes of non-coding RNAs (ncRNAs) do not give rise to peptide products. A systematic examination of translation and physiological regulation of ncRNAs has not been conducted. Here, we use computational methods to identify the products of non-canonical translation in mouse neurons by analyzing unannotated transcripts in combination with proteomic data. This study supports the existence of non-canonical translation products from both intragenic and extragenic genomic regions, including peptides derived from anti-sense transcripts and introns. Moreover, the studied novel translation products exhibit temporal regulation similar to that of proteins known to be involved in neuronal activity processes. These observations highlight a potentially large and complex set of biologically regulated translational events from transcripts formerly thought to lack coding potential.

Background: The yeast Saccharomyces cerevisiae relies on the high-osmolarity glycerol (HOG) signaling pathway to respond to increases in external osmolarity. The HOG pathway is rapidly activated under conditions of elevated osmolarity and regulates transcriptional and metabolic changes within the cell. Under normal growth conditions, however, a three-component phospho-relay consisting of the histidine kinase Sln1, the transfer protein Ypd1, and the response regulator Ssk1 represses HOG pathway activity by phosphorylation of Ssk1. This inhibition of the HOG pathway is essential for cellular fitness in normal osmolarity. Nevertheless, the extent to and mechanisms by which inhibition is robust to fluctuations in the concentrations of the phospho-relay components has received little attention. Results: We established that the Sln1-Ypd1-Ssk1 phospho-relay is robust—it is able to maintain inhibition of the HOG pathway even after significant changes in the levels of its three components. We then developed a biochemically realistic mathematical model of the phospho-relay, which suggested that robustness is due to buffering by a large excess pool of Ypd1. We confirmed experimentally that depletion of the Ypd1 pool results in inappropriate activation of the HOG pathway. Conclusions: We identified buffering by an intermediate component in excess as a novel mechanism through which a phospho-relay can achieve robustness. This buffering requires multiple components and is therefore unavailable to two-component systems, suggesting one important advantage of multi-component relays. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0158-y) contains supplementary material, which is available to authorized users.

The DNA microstates that regulate transcription include sequence-specific transcription factors (TFs), coregulatory complexes, nucleosomes, histone modifications, DNA methylation, and parts of the three-dimensional architecture of genomes, which could create an enormous combinatorial complexity across the genome. However, many proteins and epigenetic marks are known to colocalize, suggesting that the information content encoded in these marks can be compressed. It has so far proved difficult to understand this compression in a systematic and quantitative manner. Here, we show that simple linear models can reliably predict the data generated by the ENCODE and Roadmap Epigenomics consortia. Further, we demonstrate that a small number of marks can predict all other marks with high average correlation across the genome, systematically revealing the substantial information compression that is present in different cell lines. We find that the linear models for activating marks are typically cell line-independent, while those for silencing marks are predominantly cell line-specific. Of particular note, a nuclear receptor corepressor, transducin beta-like 1 X-linked receptor 1 (TBLR1), was highly predictive of other marks in two hematopoietic cell lines. The methodology presented here shows how the potentially vast complexity of TFs, coregulators, and epigenetic marks at eukaryotic genes is highly redundant and that the information present can be compressed onto a much smaller subset of marks. These findings could be used to efficiently characterize cell lines and tissues based on a small number of diagnostic marks and suggest how the DNA microstates, which regulate the expression of individual genes, can be specified.