Reka Albert

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  Avenues toward simplified Boolean modeling of signal transduction networks
  Reka Albert
  Over the past five years my group, in collaboration with wet-bench biologists, developed and validated asynchronous Boolean models of several signal transduction networks. Along the way we have encountered obstacles related to the lack of timing knowledge and the large size of the state space. In this talk I will present three methodologies we developed to overcome these obstacles. First, from a comparative analysis of several asynchronous update methods we concluded that updating a single, randomly selected node at each time instant offers the best combination of information and economy.
  
  Second, we developed a two-step network reduction method which was able to reduce the number of variables by 90% in two different systems without affecting their dynamic behaviors. Third, we proposed an integration of Boolean rules into graph theoretical analysis and showed that this semi-structural method can identify critical signal mediators on par with dynamic models.
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  The mathematics of biological regulatory networks
  Reka Albert
  Interaction between gene products forms the basis of essential biological processes like signal transduction, cell metabolism or embryonic development. The variety of interactions between genes, proteins and molecules are well captured by network (graph) representations. Experimental advances in the last decade helped uncover the structure of many molecular-to-cellular level networks, such as protein interaction or metabolic networks. For other types of interaction and regulation inference methods based on indirect measurements have been used to considerable success. These advances mark the first steps toward a major goal of contemporary biology: to map out, understand and model in quantifiable terms the topological and dynamic properties of the various networks that control the behavior of the cell.
  
  This talk will sample recent progress in two directions: intracellular network discovery and integration of different types of regulation (e.g. integration of metabolic and transcriptional networks), and connecting intra-cellular network structure, network dynamics and cellular behavior. A significant trust of the current research is to reveal or predict the topological or dynamic changes in the network responsible for abnormal behavior. This line of research will strenghten in time, and can be a fertile ground for mathematical biologists interested in adapting graph theory or nonlinear dynamical systems theory to biological systems.
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  Discrete Dynamic Modeling of Signaling Networks
  Reka Albert

  The use of differential equation based modeling frameworks for intra-and inter-cellular signaling networks is greatly hampered by the sparsity of known kinetic detail for the interactions and processes involved in these networks. As an alternative, discrete dynamic and algebraic methods are gaining acceptance as the basis of successful predictive models of signal transduction, and a tool for inferring regulatory mechanisms. For example, Boolean models have been fruitfully used to model signaling networks related to embryonic development, to plant responses to their environment, and to immunological disorders. The construction of a Boolean model starts with a synthesis of the nodes (components) and edges (interactions) of the signaling network, followed by a distillation of the edges incident on each node into a Boolean regulatory function. The analysis of the model consists of finding its attractors (e.g. steady states), and the basin of attraction of each attractor (the initial states that converge into that attractor). The model can be used to look at "what if" scenarios, to analyze the effects of perturbations (e.g. node disruptions), and thus to predict which nodes are critical for the normal behavior of the network.

  Part one of this presentation will review the basics of Boolean modeling, with special attention to models that allow different timescales in the system (i.e. asynchronous models). I will then present an asynchronous Boolean model of the signaling network that governs plants' response to drought conditions. This model synthesizes a large number of independent observations into a coherent system, reproduces known normal and perturbed responses, and predicts the effects of perturbations in network components. Two of these predictions were validated experimentally. Part two of this presentation will present an asynchronous Boolean model of the signaling network that is responsible for the activation induced cell death of T cells (a type of white blood cell). Perturbations of this network were identified as the root cause of the disease T-LGL leukemia, wherein T cells aberrantly survive and then attack normal cells. The model integrates interactions and information on certain components' abnormal state, explains all the observed abnormal states, and predicts manipulations that can abolish the T-LGL survival state. Several of these predictions were validated experimentally. I will finish by presenting two methods for extracting useful predictions from Boolean models of signal transduction networks without extensive simulations.

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  Control and damage mitigation in signal transduction networks
  Reka Albert
  Mutations or alterations in the expression of elements of cellular signaling networks can lead to incorrect behavioral decisions that could result in tumor development or metastasis. Thus, mitigation of the cascading effects of such dysregulations is an important control objective. My group at Penn State is collaborating with wet-bench biologists to develop and validate predictive models of various biological systems. Over the years we found that discrete dynamic modeling is very useful in molding qualitative interaction information into a predictive model. The attractors of these models can be directly related to the real system’s behaviors, and various interventions are straightforward to implement. We recently developed an efficient method to predict interventions that can drive the system toward a desired attractor or away from an undesired one . This method is based on an integration of the signal transduction network and the regulatory logic into an expanded network, and the identification of a specific type of strongly connected component, called stable motif, of this expanded network. Each stable motif corresponds to a point of no return in the dynamics of the system, and each attractor corresponds to a successive stabilization of a small set of stable motifs. Control of these stable motifs (by imposing a sustained state for a subset of their nodes) drives any initial condition of the system into the desired attractor. The predicted control sets were validated experimentally in two different systems.