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The dynamics of biological processes are often modeled as systems of nonlinear ordinary differential equations (ODE). An important feature of nonlinear ODEs is that seemingly minor changes in initial conditions or parameters can lead to radically different behaviors. This is problematic because in general it is never possible to know/measure the precise state of any biological system due to measurement...
We present a technique for approximating the free energy of protein structures using Generalized Belief Propagation (GBP). The accuracy and utility of these estimates are then demonstrated in two different application domains. First, we show that the entropy component of our free energy estimates can be useful in distinguishing native protein structures from decoys — structures with similar internal...
We present an algorithm, called BioLab, for verifying temporal properties of rule-based models of cellular signalling networks. BioLab models are encoded in the BioNetGen language, and properties are expressed as formulae in probabilistic bounded linear temporal logic. Temporal logic is a formalism for representing and reasoning about propositions qualified in terms of time. Properties are...
Recently, there has been considerable interest in the use of Model Checking for Systems Biology. Unfortunately, the state space of stochastic biological models is often too large for classical Model Checking techniques. For these models, a statistical approach to Model Checking has been shown to be an effective alternative. Extending our earlier work, we present the first algorithm for performing...
Collective behavior involving distally separate regions in a protein is known to widely affect its function. In this paper, we present an online approach to study and characterize collective behavior in proteins as molecular dynamics simulations progress. Our representation of MD simulations as a stream of continuously evolving data allows us to succinctly capture spatial and temporal dependencies...
Parameterized probabilistic complex computational (P2C2) models are being increasingly used in computational systems biology for analyzing biological systems. A key challenge is to build mechanistic P2C2 models by combining prior knowledge and empirical data, given that certain system properties are unknown. These unknown components are incorporated into a model as parameters and determining their...
Stochastic Differential Equation (SDE) models are often used to model the dynamics of complex biological systems. The stochastic nature of these models means that some behaviors are more likely than others. It is often the case that a model's primary purpose is to study rare but interesting or important behaviors, such as the formation of a tumor, or the failure of a cyber-physical system. Unfortunately,...
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