Abstract: The gene networks that comprise the circadian clock modulate biological function across a range of scales, from gene expression to performance and adaptive behaviour. The clock functions by generating endogenous rhythms that can be entrained to the external 24-hour day/night cycle, enabling organisms to optimally time biochemical processes relative to dawn and dusk. In recent years, computational models based on differential equations have become useful tools for dissecting and quantifying the complex regulatory relationships underlying the clock's oscillatory dynamics. However, optimising the large parameter sets characteristic of these models places intense demands on both computational and experimental resources, limiting the scope of in silico studies. Here, we develop an approach based on Boolean logic that dramatically reduces the parametrisation, making the state and parameter spaces finite and tractable. We introduce efficient methods for fitting Boolean models to molecular data, successfully demonstrating their application to synthetic time courses generated by a number of established clock models, as well as experimental expression levels measured using luciferase imaging. Our results indicate that despite their relative simplicity, logic models can: (i) simulate circadian oscillations with the correct, experimentally-observed phase relationships amongst genes; and (ii) flexibly entrain to light stimuli, reproducing the complex responses to variations in daylength generated by more detailed differential equation
formulations. Our work also demonstrates that logic models have sufficient predictive power to identify optimal regulatory structures from experimental data. By presenting the fi rst Boolean models of circadian circuits together with general techniques for their optimisation, we hope to establish a new framework for the systematic modelling of more complex clocks, as well as other circuits with different qualitative dynamics. In particular, we anticipate that the ability of logic models to provide a computationally efficient representation of system behaviour could greatly facilitate the reverse-engineering of large-scale biochemical networks.
Abstract: The cholesterol biosynthesis pathway has recently been shown to play an important role in the innate immune response to viral infection with host protection occurring through a coordinate down regulation of the enzymes catalyzing each metabolic step. In contrast, statin based drugs, which form the principle pharmaceutical agents for decreasing the activity of this pathway, target a single enzyme. Here, we build an ordinary differential equation model of the cholesterol biosynthesis pathway in order to investigate how the two regulatory strategies impact upon the behaviour of the pathway. We employ a modest set of assumptions: that the pathway operates away from saturation, that each metabolite is involved in multiple cellular interactions and that mRNA levels reflect enzyme concentrations. Using data taken from primary bone marrow derived macrophage cells infected with murine cytomegalovirus infection or treated with IFN, we show that, under these assumptions, coordinate down regulation of enzyme activity imparts a graduated reduction in flux along the pathway. In contrast, modelling a statin-like treatment that achieves the same degree of downregulation in cholesterol production, we show that this delivers a step change in flux along the pathway. The graduated reduction mediated by physiological coordinate regulation of multiple enzymes supports a mechanism that allows a greater level of specificity, altering cholesterol levels with less impact upon interactions branching from the pathway, than pharmacological step reductions. We argue that coordinate regulation is likely to show a long-term evolutionary advantage over single enzyme regulation. Finally, the results from our models have implications for future pharmaceutical therapies intended to target cholesterol production with greater specificity and fewer off target effects, suggesting that this can be achieved by mimicking the coordinated down-regulation observed in immunological responses.
Abstract: For a long time biologists have used visual representations of biochemical networks to gain a quick overview of important structural properties. Recently SBGN, the Systems Biology Graphical Notation, has been developed to standardise the way in which such graphical maps are drawn in order to facilitate the exchange of information. Its qualitative Process Description (SBGN-PD) diagrams are based on an implicit Process Flow Abstraction (PFA) that can also be used to construct quantitative representations, which facilitate automated analyses of the system. Here we explicitly describe the PFA that underpins SBGN-PD and define attributes for SBGN-PD glyphs that make it possible to capture the quantitative details of a biochemical reaction network. Such quantitative details can be used to automatically generate an executable model. To facilitate this, we developed a textual representation for SBGN-PD called âSBGNtextâ and implemented SBGNtext2BioPEPA, a tool that demonstrates how Bio-PEPA models can be generated automatically from SBGNtext. Bio-PEPA is a process algebra that was designed for implementing quantitative models of concurrent biochemical reaction systems. The scheme developed here is general and can be easily adapted to other output formalisms. To illustrate the intended workflow, we model the metabolic pathway of the cholesterol synthesis. We use this to compute the statin dosage response of the flux through the cholesterol pathway for different concentrations of the enzyme HMGCR that is inhibited by statin.
Abstract: Macrophages function as sentinel, cell-regulatory âhubsâ capable of initiating, perpetuating and contributing to the resolution of an inflammatory response, following their activation from a resting state. Highly complex and varied gene expression programs within the macrophage enable such functional diversity. To investigate how programs of gene expression relate to the phenotypic attributes of the macrophage, the development of in silico modeling methods is needed. Such models need to cover multiple scales, from molecular pathways in cell-autonomous immunity and intercellular communication pathways in tissue inflammation to whole organism response pathways in systemic disease. Here, we highlight the potential of in silico macrophage modeling as an amenable and important yet under-exploited tool in aiding in our understanding of the immune inflammatory response. We also discuss how in silico macrophage modeling can help in future therapeutic strategies for modulating both the acute protective effects of inflammation (such as host defense and tissue repair) and the harmful chronic effects (such as autoimmune diseases).
Abstract: Cholesterol biosynthesis serves as a central metabolic hub for numerous biological processes in health and disease. An integrative knowledge representation of how the cholesterol pathway is structured and how it interacts with other pathway systems is lacking. Here we provide using Systems Biology Graphical Notation the research synthesis of a process diagram integrating the regulatory and feedback systems for cholesterol synthesis.
Abstract: Little is known about the protective role of inflammatory processes in modulating lipid metabolism in infection. Here we report an intimate link between the innate immune response to infection and regulation of the sterol metabolic network characterized by down-regulation of sterol biosynthesis by an interferon regulatory loop mechanism. In time-series experiments profiling genome-wide lipid-associated gene expression of macrophages, we show a selective and coordinated negative regulation of the complete sterol pathway upon viral infection or cytokine treatment with IFNc or b but not TNF, IL1b, or IL6. Quantitative analysis at the protein level of selected sterol metabolic enzymes upon infection shows a similar level of suppression. Experimental testing of sterol metabolite levels using lipidomic-based measurements shows a reduction in metabolic output. On the basis of pharmacologic and RNAi inhibition of the sterol pathway we show augmented protection against viral infection, and in combination with metabolite rescue experiments, we identify the requirement of the mevalonate-isoprenoid branch of the sterol metabolic network in the protective response upon statin or IFNb treatment. Conditioned media experiments from infected cells support an involvement of secreted type 1 interferon(s) to be sufficient for reducing the sterol pathway upon infection. Moreover, we show that infection of primary macrophages containing a genetic knockout of the major type I interferon, IFNb, leads to only a partial suppression of the sterol pathway, while genetic knockout of the receptor for all type I interferon family members, ifnar1, or associated signaling component, tyk2, completely abolishes the reduction of the sterol biosynthetic activity upon infection. Levels of the proteolytically cleaved nuclear forms of SREBP2, a key transcriptional regulator of sterol biosynthesis, are reduced upon infection and IFNb treatment at both the protein and de novo transcription level. The reduction in srebf2 gene transcription upon infection and IFN treatment is also found to be strictly dependent on ifnar1. Altogether these results show that type 1 IFN signaling is both necessary and sufficient for reducing the sterol metabolic network activity upon infection, thereby linking the regulation of the sterol pathway with interferon anti-viral defense responses. These findings bring a new link between sterol metabolism and interferon antiviral response and support the idea of using host metabolic modifiers of innate immunity as a potential antiviral strategy.
Abstract: This poster shows how transcriptional complex assembly can be represented in SBGN Process Description language. Example: LPS-induced TNF-alpha enhancer complex formation.
Abstract: In an effort to better understand the molecular networks that underpin macrophage activation we have been assembling a map of relevant pathways. Manual curation of the published literature was carried out in order to define the components of these pathways and the interactions between them. This information has been assembled into a large integrated directional network and represented graphically using the modified Edinburgh Pathway Notation (mEPN) scheme. Results: The diagram includes detailed views of the toll-like receptor (TLR) pathways, other pathogen recognition systems, NF-kappa-B, apoptosis, interferon signalling, MAP-kinase cascades, MHC antigen presentation and proteasome assembly, as well as selected views of the transcriptional networks they regulate. The integrated pathway includes a total of 496 unique proteins, the complexes formed between them and the processes in which they are involved. This produces a network of 2,170 nodes connected by 2,553 edges. Conclusions: The pathway diagram is a navigable visual aid for displaying a consensus view of the pathway information available for these systems. It is also a valuable resource for computational modelling and aid in the interpretation of functional genomics data. We envisage that this work will be of value to those interested in macrophage biology and also contribute to the ongoing Systems Biology community effort to develop a standard notation scheme for the graphical representation of biological pathways.
Abstract: Biological pathways link the molecular and cellular levels of biological activity
and perform complex information processing seamlessly. Systems biology aims
to combine an understanding of the causeâeffect relationships of each individual
interaction to build an understanding of the function of whole pathways. Therapies
that target the âhostâ biological processes in infectious diseases are often limited
to the use of vaccines and biologics rather than small molecules. The development
of host drug-targets for small molecules is constrained by a limited knowledge of
the underlying role of each target, particularly its potential to cause harmful side
effects after targeting. By considering the combinatorial complexity of pathways
from the outset, we can develop modeling tools that are better suited to analyzing
large pathways, enabling us to identify new causal relationships. This could lead
to new drug target strategies that beneficially disrupt hostâpathogen interactions,
minimizing the number of side effects. We introduce logic theory as part of a
pathway modeling approach that can provide a new framework for understanding
pathways and refine âhost-basedâ drug target identification strategies.
Abstract: Circuit diagrams and Unified Modeling Language diagrams are just two examples of standard visual languages that help accelerate work by promoting regularity, removing ambiguity and enabling software tool support for communication of complex information. Ironically, despite having one of the highest ratios of graphical to textual information, biology still lacks standard graphical notations. The recent deluge of biological knowledge makes addressing this deficit a pressing concern. Toward this goal, we present the Systems Biology Graphical Notation (SBGN), a visual language developed by a community of biochemists, modelers and computer scientists. SBGN consists of three complementary languages: process diagram, entity relationship diagram and activity flow diagram. Together they enable scientists to represent networks of biochemical interactions in a standard, unambiguous way. We believe that SBGN will foster efficient and accurate representation, visualization, storage, exchange and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling.
Abstract: It is shown that an exact chiral symmetry can be described for Dirac-Kahler fermions using the two complexes of the geometric discretization. This principle is extended to describe exact flavour projection and it is shown that this necessitates the introduction of a new operator and two new structures of complex. To describe simultaneous chiral and flavour projection, eight complexes are needed in all and it is shown that projection leaves a single flavour of chiral field on each.
Abstract: Living systems seamlessly perform complex information processing and control tasks using combinatorially complex sets of biochemical reactions. Drugs that therapeutically modulate the biological processes of disease are developed using single protein target strategies, often with limited knowledge of the complex underlying role of the targets. Approaches that attempt to consider the combinatorial complexity from the outset might help identify any causal relationships that could lead to undesirable or adverse side-effects earlier in the development pipeline. Such approaches, in particular logic methodologies, might also aid pathway selection and multiple target strategies during the drug discovery phase. Here we describe the use of logic as a tractable and informative approach to modelling biological pathways that can allow us to improve our understanding of the dependencies in complex biological processes.
Abstract: Due to the large number of variables required and the limited number of independent experiments, the inference of genetic regulatory networks from gene expression data is a challenge of long standing within the microarray field. This report investigates the inference of Boolean networks with perturbation (BNp) from simulated data and observed microarray data. We interpret the discrete expression levels as attractor states of the underlying network and use the sequence of attractor states to determine the model. We consider the case where a complete sequence of attractors is known and the case where the known attractor states are arrived at by sampling from an underlying sequence of attractors. In the former case, a BNp can be inferred trivially, for an arbitrary number of genes and attractors. In the latter case, we use the constraints posed by the distribution of attractor states and the need to conserve probability to arrive at one of three possible solutions: an unique, exact network; several exact networks or a most-likely network. In the case of several exact networks we use a robustness requirement to select a preferred network. In the case that an exact option is not found, we select the network that best fits the observed attractor distribution. We apply the resulting algorithm to the interferon regulatory network using expression data taken from murine bone-derived macrophage cells infected with cytomegalovirus.
Abstract: It is shown that the flavour projection of staggered fermions can be written as a projection between the fields on four separate, but parallel, lattices, where the fields on each are modified forms of the standard staggered fermion field. Because the staggered Dirac operator acts equally on each lattice, it respects this flavour projection. We show that the system can be gauged in the usual fashion and that this does not interfere with flavour projection. We also consider the path integral, showing that, prior to flavour projection, it evaluates to the same form on each lattice and that this form is equal to that used in the quarter-root trick. The flavour projection leaves a path integral for a single flavour of field on each lattice.
Abstract: This paper investigates the inference of Boolean networks with perturbation (BNp) from simulated data and observed data. We interpret the discretised gene expression levels as attractor states of the underlying network and use the sequence of attractor states to determine the model. We consider the case where a complete sequence of attractors is known and the case where the known attractor states are arrived at by sampling from an underlying sequence of attractors. We apply the resulting algorithm to the interferon regulatory network using gene expression data taken from murine bone-derived macrophage cells infected with cytomegalovirus.
Abstract: We use a specific implementation of discrete differential geometry to describe Dirac-Kahler fermions in such a way that we can separate their chiral and flavour components. The formulation introduces additional lattices so that on each lattice there is a single field of definate chirality. Within this framework, we define an non-compact Abelian gauge theory.
Abstract: A mathematical result exists that relates the work performed on a system, during a change of finite duration, to the consequent free energy difference. We considered the possibility of experimental validation of this theorem and performed preliminary simulations to test its feasibility. We found that quantitative validation was possible, but that it required an experimental precision far greater than that which we consider here. Computational evidence was acquired that the result also holds in non-equilibrium regimes.
