Systems Pharmacology

The slides are from a presentation given by Professor Ravi Iyengar from Mount Sinai School of Medicine at the Drug Forum Meeting #9 that took place in Washington, DC on February 20-21, 2008. The slides describe two projects: one that was published last year, and the other unpublished. These projects used network analysis to explore the relationships between FDA approved drugs and a human protein-protein interaction network

Dynamic Topology of Biological Networks

The mammalian cell can be represented as a large modular network that consists of a central signal network that interacts with and regulates multiple cellular machines that are responsible for phenotypic behavior. We have used graph-theory approaches to analyze signal flow through a network representing the hippocampal neuron and find that signal-induced connectivity results in the formation of many regulatory motifs. Information flow through the central signaling network is initiated by extra-cellular signals such as hormones binding to their receptors. The flow of information through the signaling network results in the appearance of regulatory motifs such as feedback loops, feedforward and bifan motifs. Within the large cellular networks, these regulatory motifs are juxtaposed next to each other in several formats such as the stacked configuration or the nested configuration. We have studied the dynamics of regulatory motifs by biochemical computation using ordinary differential equation models. Positive feedback loops can function as bistable switches. Nested feed-forward motifs can give rise to two emergent properties: coincidence detection and prolonged outputs for short inputs. Bifan motifs can control response times, with some configurations working as delays and others promoting rapid responses. Bifan motifs can also act as filters. Feed-forward motifs lead to signal prolongation and thus function as a switch to alter cell state. The functional consequences of organization of motifs within networks as well as the properties of feedback, feedforward and bifans motifs are presented

Specification of spatial relationships in directed graphs of cell signaling networks

Graph theory provides a useful and powerful tool for the analysis of cellular signaling networks. Intracellular components such as cytoplasmic signaling proteins, transcription factors and genes are connected by links, representing various types of chemical interactions that result in functional consequences. However, these graphs lack important information regarding the spatial distribution of cellular components. The ability of two cellular components to interact depends not only on their mutual chemical affinity but also on co-localization to the same subcellular region. Localization of components is often used as a regulatory mechanism to achieve specific effects in response to different receptor signals. Here we describe an approach for incorporating spatial distribution into graphs, and for the development of mixed graphs where links are specified by mutual chemical affinity as well as colocalization. We suggest that such mixed graphs will provide more accurate descriptions of functional cellular networks and their regulatory capabilities and aid in the development of large-scale predictive models of cellular behavior

Intracellular Regulatory Networks are close to Monotone Systems

Several meso-scale biological intracellular regulatory networks that have specified directionality of interactions have been recently assembled from experimental literature. Directed networks where links are characterized as positive or negative can be converted to systems of differential equations and analyzed as dynamical systems. Such analyses have shown that networks containing only sign-consistent loops, such as positive feed-forward and feedback loops function as monotone systems that display well-ordered behavior. Perturbations to monotone systems have unambiguous global effects and a predictability characteristic that confers advantages for robustness and adaptability. We find that three intracellular regulatory networks: bacterial and yeast transcriptional networks and a mammalian signaling network contain far more sign-consistent feedback and feed-forward loops than expected for shuffled networks. Inconsistent loops with negative links can be more easily removed from real regulatory networks as compared to shuffled networks. This topological feature in real networks emerges from the presence of hubs that are enriched for either negative or positive links, and is not due to a preference for double negative links in paths. These observations indicate that intracellular regulatory networks may be close to monotone systems and that this network topology contributes to the dynamic stability

Emergent properties of networks of biological signaling pathways

Many distinct signaling pathways allow the cell to receive, process, and respond to information. Often, components of different pathways interact, resulting in signaling networks. Biochemical signaling networks were constructed with experimentally obtained constants and analyzed by computational methods to understand their role in complex biological processes. These networks exhibit emergent properties such as integration of signals across multiple time scales, generation of distinct outputs depending on input strength and duration, and self-sustaining feedback loops. Feedback can result in bistable behavior with discrete steady-state activities, well-defined input thresholds for transition between states and prolonged signal output, and signal modulation in response to transient stimuli. These properties of signaling networks raise the possibility that information for "learned behavior" of biological systems may be stored within intracellular biochemical reactions that comprise signaling pathways

Geometric principles of second messenger dynamics in dendritic spines.

Dendritic spines are small, bulbous protrusions along dendrites in neurons and play a critical role in synaptic transmission. Dendritic spines come in a variety of shapes that depend on their developmental state. Additionally, roughly 14-19% of mature spines have a specialized endoplasmic reticulum called the spine apparatus. How does the shape of a postsynaptic spine and its internal organization affect the spatio-temporal dynamics of short timescale signaling? Answers to this question are central to our understanding the initiation of synaptic transmission, learning, and memory formation. In this work, we investigated the effect of spine and spine apparatus size and shape on the spatio-temporal dynamics of second messengers using mathematical modeling using reaction-diffusion equations in idealized geometries (ellipsoids, spheres, and mushroom-shaped). Our analyses and simulations showed that in the short timescale, spine size and shape coupled with the spine apparatus geometries govern the spatiotemporal dynamics of second messengers. We show that the curvature of the geometries gives rise to pseudo-harmonic functions, which predict the locations of maximum and minimum concentrations along the spine head. Furthermore, we showed that the lifetime of the concentration gradient can be fine-tuned by localization of fluxes on the spine head and varying the relative curvatures and distances between the spine apparatus and the spine head. Thus, we have identified several key geometric determinants of how the spine head and spine apparatus may regulate the short timescale chemical dynamics of small molecules that control synaptic plasticity

Systems pharmacology and genome medicine: a future perspective

Genome medicine uses genomic information in the diagnosis of disease and in prescribing treatment. This transdisciplinary field brings together knowledge on the relationships between genetics, pathophysiology and pharmacology. Systems pharmacology aims to understand the actions and adverse effects of drugs by considering targets in the context of the biological networks in which they exist. Genome medicine forms the base on which systems pharmacology can develop. Experimental and computational approaches enable systems pharmacology to obtain holistic, mechanistic information on disease networks and drug responses, and to identify new drug targets and specific drug combinations. Network analyses of interactions involved in pathophysiology and drug response across various scales of organization, from molecular to organismal, will allow the integration of the systems-level understanding of drug action with genome medicine. The interface of the two fields will enable drug discovery for personalized medicine. Here we provide a perspective on the questions and approaches that drive the development of these new interrelated fields