Erwin P. Gianchandani

Dynamic Analysis of Integrated Signaling, Metabolic, and Regulatory Networks

Extracellular cues affect signaling, metabolic, and regulatory processes to elicit cellular responses. Although intracellular signaling, metabolic, and regulatory networks are highly integrated, previous analyses have largely focused on independent processes (e.g., metabolism) without considering the interplay that exists among them. However, there is evidence that many diseases arise from multifunctional components with roles throughout signaling, metabolic, and regulatory networks. Therefore, in this study, we propose a flux balance analysis (FBA)–based strategy, referred to as integrated dynamic FBA (idFBA), that dynamically simulates cellular phenotypes arising from integrated networks. The idFBA framework requires an integrated stoichiometric reconstruction of signaling, metabolic, and regulatory processes. It assumes quasi-steady-state conditions for “fast” reactions and incorporates “slow” reactions into the stoichiometric formalism in a time-delayed manner. To assess the efficacy of idFBA, we developed a prototypic integrated system comprising signaling, metabolic, and regulatory processes with network features characteristic of actual systems and incorporating kinetic parameters based on typical time scales observed in literature. idFBA was applied to the prototypic system, which was evaluated for different environments and gene regulatory rules. In addition, we applied the idFBA framework in a similar manner to a representative module of the single-cell eukaryotic organism Saccharomyces cerevisiae. Ultimately, idFBA facilitated quantitative, dynamic analysis of systemic effects of extracellular cues on cellular phenotypes and generated comparable time-course predictions when contrasted with an equivalent kinetic model. Since idFBA solves a linear programming problem and does not require an exhaustive list of detailed kinetic parameters, it may be efficiently scaled to integrated intracellular systems that incorporate signaling, metabolic, and regulatory processes at the genome scale, such as the S. cerevisiae system presented here.

The application of flux balance analysis in systems biology

An increasing number of genome‐scale reconstructions of intracellular biochemical networks are being generated. Coupled with these stoichiometric models, several systems‐based approaches for probing these reconstructions in silico have been developed. One such approach, called flux balance analysis (FBA), has been effective at predicting systemic phenotypes in the form of fluxes through a reaction network. FBA employs a linear programming (LP) strategy to generate a flux distribution that is optimized toward a particular ‘objective,’ subject to a set of underlying physicochemical and thermodynamic constraints. Although classical FBA assumes steady‐state conditions, several extensions have been proposed in recent years to constrain the allowable flux distributions and enable characterization of dynamic profiles even with minimal kinetic information. Furthermore, FBA coupled with techniques for measuring fluxes in vivo has facilitated integration of computational and experimental approaches, and is allowing pursuit of rational hypothesis‐driven research. Ultimately, as we will describe in this review, studying intracellular reaction fluxes allows us to understand network structure and function and has broad applications ranging from metabolic engineering to drug discovery. Copyright

Matrix Formalism to Describe Functional States of Transcriptional Regulatory Systems

Complex regulatory networks control the transcription state of a genome. These transcriptional regulatory networks (TRNs) have been mathematically described using a Boolean formalism, in which the state of a gene is represented as either transcribed or not transcribed in response to regulatory signals. The Boolean formalism results in a series of regulatory rules for the individual genes of a TRN that in turn can be used to link environmental cues to the transcription state of a genome, thereby forming a complete transcriptional regulatory system (TRS). Herein, we develop a formalism that represents such a set of regulatory rules in a matrix form. Matrix formalism allows for the systemic characterization of the properties of a TRS and facilitates the computation of the transcriptional state of the genome under any given set of environmental conditions. Additionally, it provides a means to incorporate mechanistic detail of a TRS as it becomes available. In this study, the regulatory network matrix, R, for a prototypic TRS is characterized and the fundamental subspaces of this matrix are described. We illustrate how the matrix representation of a TRS coupled with its environment (R*) allows for a sampling of all possible expression states of a given network, and furthermore, how the fundamental subspaces of the matrix provide a way to study key TRS features and may assist in experimental design.

Characterizing emergent properties of immunological systems with multi- cellular rule-based computational modeling

The immune system is comprised of numerous components that interact with one another to give rise to phenotypic behaviors that are sometimes unexpected. Agent-based modeling (ABM) and cellular automata (CA) belong to a class of discrete mathematical approaches in which autonomous entities detect local information and act over time according to logical rules. The power of this approach lies in the emergence of behavior that arises from interactions between agents, which would otherwise be impossible to know a priori. Recent work exploring the immune system with ABM and CA has revealed novel insights into immunological processes. Here, we summarize these applications to immunology and, particularly, how ABM can help formulate hypotheses that might drive further experimental investigations of disease mechanisms.

Functional States of the Genome-Scale Escherichia Coli Transcriptional Regulatory System

A transcriptional regulatory network (TRN) constitutes the collection of regulatory rules that link environmental cues to the transcription state of a cells genome. We recently proposed a matrix formalism that quantitatively represents a system of such rules (a transcriptional regulatory system [TRS]) and allows systemic characterization of TRS properties. The matrix formalism not only allows the computation of the transcription state of the genome but also the fundamental characterization of the input-output mapping that it represents. Furthermore, a key advantage of this “pseudo-stoichiometric” matrix formalism is its ability to easily integrate with existing stoichiometric matrix representations of signaling and metabolic networks. Here we demonstrate for the first time how this matrix formalism is extendable to large-scale systems by applying it to the genome-scale Escherichia coli TRS. We analyze the fundamental subspaces of the regulatory network matrix (R) to describe intrinsic properties of the TRS. We further use Monte Carlo sampling to evaluate the E. coli transcription state across a subset of all possible environments, comparing our results to published gene expression data as validation. Finally, we present novel in silico findings for the E. coli TRS, including (1) a gene expression correlation matrix delineating functional motifs; (2) sets of gene ontologies for which regulatory rules governing gene transcription are poorly understood and which may direct further experimental characterization; and (3) the appearance of a distributed TRN structure, which is in stark contrast to the more hierarchical organization of metabolic networks.

Systems Analysis of Small Signaling Modules Relevant to Eight Human Diseases

Using eight newly generated models relevant to addiction, Alzheimer’s disease, cancer, diabetes, HIV, heart disease, malaria, and tuberculosis, we show that systems analysis of small (4–25 species), bounded protein signaling modules rapidly generates new quantitative knowledge from published experimental research. For example, our models show that tumor sclerosis complex (TSC) inhibitors may be more effective than the rapamycin (mTOR) inhibitors currently used to treat cancer, that HIV infection could be more effectively blocked by increasing production of the human innate immune response protein APOBEC3G, rather than targeting HIV’s viral infectivity factor (Vif), and how peroxisome proliferator-activated receptor alpha (PPARα) agonists used to treat dyslipidemia would most effectively stimulate PPARα signaling if drug design were to increase agonist nucleoplasmic concentration, as opposed to increasing agonist binding affinity for PPARα. Comparative analysis of system-level properties for all eight modules showed that a significantly higher proportion of concentration parameters fall in the top 15th percentile sensitivity ranking than binding affinity parameters. In infectious disease modules, host networks were significantly more sensitive to virulence factor concentration parameters compared to all other concentration parameters. This work supports the future use of this approach for informing the next generation of experimental roadmaps for known diseases.

Dephosphorylation of β-Arrestin 1 in Glioblastomas

&bgr;-Arrestins act as signal terminators for G protein-coupled receptors; they have also been implicated as scaffolding proteins for Src and mitogen-activated protein kinase signaling pathways and transactivators of receptor tyrosine kinases, suggesting their possible role in development and oncogenic signaling. Dephosphorylation of serine 412 is necessary for Src and mitogen-activated protein kinase transactivation. We hypothesized that altered &bgr;-arrestin 1 phosphorylation and activation status could play a role in gliomagenesis. Using monoclonal anti-phospho-(serine 412)- and total &bgr;-arrestin 1 antibodies, we performed immunohistochemistry on 126 human glioma samples and 7 nonneoplastic controls and Western blot analysis on 5 glioblastomas and 5 nonneoplastic controls. We found high constitutive &bgr;-arrestin 1 phosphorylation in nonneoplastic brain tissue, particularly in neurons and neuropil. Most Grade II and III gliomas retained high &bgr;-arrestin 1 phosphorylation. By contrast, most of the glioblastoma samples (58/81) showed nearly complete &bgr;-arrestin 1 dephosphorylation by immunohistochemistry and decreased relative phosphorylation by Western blot. Expression of constitutively activated epidermal growth factor receptor vIII in U251 cells caused decreased &bgr;-arrestin 1 phosphorylation without altering total &bgr;-arrestin 1 levels. These results suggest that &bgr;-arrestin 1 dephosphorylation/inactivation is associated with aspects of the malignant behavior of glioblastomas.

Novel pathway compendium analysis elucidates mechanism of pro-angiogenic synthetic small molecule

MOTIVATION Computational techniques have been applied to experimental datasets to identify drug mode-of-action. A shortcoming of existing approaches is the requirement of large reference databases of compound expression profiles. Here, we developed a new pathway-based compendium analysis that couples multi-timepoint, controlled microarray data for a single compound with systems-based network analysis to elucidate drug mechanism more efficiently. RESULTS We applied this approach to a transcriptional regulatory footprint of phthalimide neovascular factor 1 (PNF1)-a novel synthetic small molecule that exhibits significant in vitro endothelial potency-spanning 1-48 h post-supplementation in human micro-vascular endothelial cells (HMVEC) to comprehensively interrogate PNF1 effects. We concluded that PNF1 first induces tumor necrosis factor-alpha (TNF-alpha) signaling pathway function which in turn affects transforming growth factor-beta (TGF-beta) signaling. These results are consistent with our previous observations of PNF1-directed TGF-beta signaling at 24 h, including differential regulation of TGF-beta-induced matrix metalloproteinase 14 (MMP14/MT1-MMP) which is implicated in angiogenesis. Ultimately, we illustrate how our pathway-based compendium analysis more efficiently generates hypotheses for compound mechanism than existing techniques.

Phthalimide neovascular factor 1 (PNF1) modulates MT1-MMP activity in human microvascular endothelial cells†

We are creating synthetic pharmaceuticals with angiogenic activity and potential to promote vascular invasion. We previously demonstrated that one of these molecules, phthalimide neovascular factor 1 (PNF1), significantly expands microvascular networks in vivo following sustained release from poly(lactic‐co‐glycolic acid) (PLAGA) films. In addition, to probe PNF1 mode of action, we recently applied a novel pathway‐based compendium analysis to a multi‐timepoint, controlled microarray data set of PNF1‐treated (vs. control) human microvascular endothelial cells (HMVECs), and we identified induction of tumor necrosis factor‐alpha (TNF‐α) and, subsequently, transforming growth factor‐beta (TGF‐β) signaling networks by PNF1. Here we validate this microarray data set with quantitative real‐time polymerase chain reaction (RT‐PCR) analysis. Subsequently, we probe this data set and identify three specific TGF‐β‐induced genes with regulation by PNF1 conserved over multiple timepoints—amyloid beta (A4) precursor protein (APP), early growth response 1 (EGR‐1), and matrix metalloproteinase 14 (MMP14 or MT1‐MMP)—that are also implicated in angiogenesis. We further focus on MMP14 given its unique role in angiogenesis, and we validate MT1‐MMP modulation by PNF1 with an in vitro fluorescence assay that demonstrates the direct effects that PNF1 exerts on functional metalloproteinase activity. We also utilize endothelial cord formation in collagen gels to show that PNF1‐induced stimulation of endothelial cord network formation in vitro is in some way MT1‐MMP‐dependent. Ultimately, this new network analysis of our transcriptional footprint characterizing PNF1 activity 1–48 h post‐supplementation in HMVECs coupled with corresponding validating experiments suggests a key set of a few specific targets that are involved in PNF1 mode of action and important for successful promotion of the neovascularization that we have observed by the drug in vivo. Biotechnol. Bioeng. 2009;103: 796–807.

Application of a novel optimization-based approach to characterize integrated signalling, regulatory, and metabolic biochemical networks

Abstract Extracellular cues affect signaling, metabolic, and regulatory processes to elicit cellular responses. Although intracellular signaling, metabolic, and regulatory networks are highly integrated, previous analyses have largely focused on independent processes (e.g., metabolism) without considering the interplay that exists among them. In this paper, we present the recent development of a flux balance analysis (FBA)-based strategy, referred to as integrated dynamic FBA (idFBA), that dynamically simulates cellular phenotypes arising from integrated networks [Lee et al., 2007]. The idFBA framework solves a linear program to find the optimal fluxes of biochemical reactions in an integrated network. It assumes quasi-steady-state conditions for “fast” reactions and incorporates “slow” reactions into the stoichiometric relationships to confine the feasible solution space. We also describe its recent application to a prototypic integrated system to assess the efficacy of idFBA [Lee et al., 2007].