CANCELLED (Multi-scale Model of CRISPR-induced Coevolutionary Dynamics -- Diversification at the Interface of Lamarck and Darwin)Wednesday, November 16, 2011 - 11:00 , Location: Skiles 006 , Lauren Childs , Biology, Georgia Tech , Organizer: Christine Heitsch
The CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) system is a recently discovered immune defense in bacteria and archaea (hosts) that functions via directed incorporation of viral DNA intohost genomes. Here, we introduce a multi-scale model of dynamic coevolution between hosts and viruses in an ecological context that incorporates CRISPR immunity principles. We analyze the model to test whether and how CRISPR immunity induces host and viral diversification and maintenance of coexisting strains. We show that hosts and viruses coevolve to form highly diverse communities through punctuated replacement of extant strains. The populations have very low similarity over long time scales. However overshort time scales, we observe evolutionary dynamics consistent with incomplete selective sweeps of novel strains, recurrence of previously rare strains, and sweeps of coalitions of dominant host strains with identical phenotypes but different genotypes. Our explicit eco-evolutionary model of CRISPR immunity can help guide efforts to understand the drivers of diversity seen in microbial communities where CRISPR systems are active.
Wednesday, November 9, 2011 - 11:00 , Location: Skiles 005 , Andrei Olifer , Georgia Gwinnett College , Organizer:
Information processing in neurons and their networks is understood incompletely, especially when neuronal inputs have indirect correlates with external stimuli as for example in the hippocampus. We study a case when all neurons in one network receive inputs from another network within a short time window. We consider it as a mapping of binary vectors of spiking activity ("spike" or "no spike") in an input network to binary vectors of spiking activity in the output network. Intuitively, if an input pattern makes a neuron spike then the neuron should also spike in response to similar patterns - otherwise, neurons would be too sensitive to noise. On the other hand, neurons should discriminate between sufficiently different input patterns and spike selectively. Our main goal was to quantify how well neurons discriminate input patterns depending on connectivity between networks, spiking threshold of neurons and other parameters. We modeled neurons with perceptrons that have binary weights. Most recent results on perceptron neuronal models are asymptotic with respect to some parameters. Here, using combinatorial analysis, we complement them by exact formulas. Those formulas in particular predict that the number of the inputs per neuron maximizes the difference between the neuronal and network responses to similar and distinct inputs. A joint work with Jean Vaillant (UAG).
“What’s Eating You?” Quantifying Proteolytic Activity in Health and Disease with Novel Assays and Computational ModelsWednesday, October 19, 2011 - 11:00 , Location: Skiles 006 , Manu O. Platt , Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University , Organizer: Christine Heitsch
Cathepsins are enzymes that can cleave collagen and elastin, major structural proteins of tissue and organs, and participate in tissue-destructive disease progression seen in osteoporosis, arthritis, atherosclerosis, and cancer metastasis. Detection of mature cathepsins and quantification of specific activity have proven difficult due to instability of the mature, active enzyme extracellularly, which has led to them being overlooked in a number of diseases. During this seminar, Dr. Platt will discuss the important development of a reliable, sensitive method to detect the activity of mature cathepsins K, L, S, and V. Then he will focus on their progress towards developing a comprehensive computational model of cathepsin-mediated degradation of extracellular matrix, based on systems of ordinary differential equations. From the computational model and experimental results, a general assumption of inertness between familial enzymes was shown to be invalid as it failed to account for the interaction of these proteases among themselves and within their microenvironment. A consequence of this was significant overestimation of total degradative potential in multiple cathepsin reaction systems. After refining the system to capture the cathepsin interactive dynamics and match the experimental degradation results, novel mechanisms of cathepsin degradation and inactivation were revealed and suggest new ways to inhibit their activity for therapeutic benefit.
Wednesday, October 12, 2011 - 11:05 , Location: Skiles 005 , Pavel Skums , CDC , Organizer:
Hepatitis C virus (HCV) infects 2.2% of the world's population and is a major cause of liver disease worldwide. There is no vaccine against HCV and current interferon and ribavirin (IFN/RBV) therapy is effective in 50%-60% of patients. Since the interferon therapy is the expansive and painful for the patient process, it is very important to predict its outcome before starting full course of treatment. HCV exists in infected patients as a large viral population of intra-host variants (quasispecies), which form the certain topological structure (sequence space) and may be differentially resistant to interferon treatment. We present a method for measuring differential interferon resistance of HCV quasispecies based on the mathematical modeling and analysis of HCV population dynamics during the first hours of interferon therapy. The analysis of the model allowed us to accurately predict the long-term outcome of the interferon therapy on the test group of patients.
Wednesday, October 5, 2011 - 11:00 , Location: Skiles 005 , Scott McKinley , University of Florida , Organizer: Christine Heitsch
In this talk, we will consider a stochastic differential equation framework for analyzing the interaction between processive molecular motors, such as kinesin and dynein, and the biomolecular cargo they tow as part of microtubule-based intracellular transport. We show that the classical experimental environment is in a parameter regime which is qualitatively distinct from conditions one expects to find in living cells. However, an asymptotic analysis of the proposed system of SDEs permits one to take "in vitro" observations of the nonlinear response by motors to forces induced on the attached cargo, and make analytical predictions for two regimes that frustrate direct experimental observation: 1) highly viscous "in vivo" transport and 2) dynamics when multiple identical motors are attached to the cargo and microtubule.
Wednesday, September 28, 2011 - 11:00 , Location: Skiles 005 , Christian Reidys , Dept. of Mathematics & Computer Science, University of Southern Denmark , Organizer: Christine Heitsch
In this talk we present the natural topological classification of RNA structures in terms of irreducible components that are embedable in surfaces of fixed genus. We add to the conventional secondary structures four building blocks of genus one in order to construct certain structures of arbitrarilyhigh genus. A corresponding unambiguous multiple context free grammar provides an efficient dynamic programming approach for energy minimization, partition function, and stochastic sampling. It admits a topology-dependent parametrization of pseudoknot penalties that increases the sensitivity and positive predictive value of predicted base pairs by 10-20% compared to earlier approaches.
Wednesday, September 21, 2011 - 23:00 , Location: Skiles 005 , Franziska Hinkelmann , Mathematical Biosciences Institute, Ohio State University , Organizer: Christine Heitsch
Systems biology aims to explain how a biological system functions by investigating the interactions of its individual components from a systems perspective. Modeling is a vital tool as it helps to elucidate the underlying mechanisms of the system. My research is on methods for inference and analysis of polynomial dynamical systems (PDS). This is motivated by the fact that many discrete model types, e.g., Boolean networks or agent-based models, can be translated into the framework of PDS, that is, time- and state-discrete dynamical systems over a finite field where the transition function for each variable is given as a polynomial. This allows for using a range of theoretical and computational tools from computer algebra, which results in a powerful computational engine for model construction, parameter estimation, and analysis methods such as steady state behavior and optimal control. For model inference problems, the algebraic structure of PDS allows for efficient restriction of the model space to canalyzing functions, resulting in a subset of Boolean networks with "nice" biological properties.
A statistical model applied to 544 in vivo HIV-1 recombinants reveals that viral genomic features, especially RNA structure, promote recombinationWednesday, April 20, 2011 - 11:00 , Location: Skiles 005 , Karin Dorman , Departments of Statistics and of Genetics, Development and Cell Biology, Iowa State University , Organizer: Christine Heitsch
It has long been postulated and somewhat confirmed with limited biological experiment, that RNA structure affects the propensity of HIV-1 reverse transcriptase to undergo strand transfer, a prerequisite for recombination. Our goal was to use the large resource of in vivo recombinants isolated from patients and stored in the HIV database to determine whether there were signals in the HIV-1 genetic sequence, such as propensity to form RNA secondary structure, that promote recombination. Starting from 65,000 HIV-1 sequences at least 400 nucleotides long, we identified 2,360 recombinants involving exactly two distinct subtypes. Since we were interested in mechanistic causes, rather than selective causes, we reduced the number of recombinants to 544 verifiably unique events. We then fit a Gaussian Markov Random Field model with covariates in the mean to assess the impact of genetic features on recombination. We found SHAPE reactivities to be most strongly and negatively correlated with recombination rates, which agrees with the observation that pairing probabilities had an opposite, strong relationship with recombination. Less strongly associated, but still significant, we found G-rich stretches positively correlated, thermal stability negatively correlated, and GC content positively correlated with recombination. Interestingly, known in vitro hotspots did not explain much of the in vivo recombination.
Wednesday, March 16, 2011 - 11:00 , Location: Skiles 005 , Dr. Yi Mao , NIMBioS , email@example.com , Organizer:
Dynamic modeling of a coarse-grained elastic protein modelprovides an effective way of exploring the relationship between protein structure and function. In particular functionally important residues are identified by a variety of computational methods based on the fluctuation analysis. The results from the modeling provide great insights into how random mutagenesis of proteins can give rise to desired property (protein engineering of bioluminescence system) and how molecular physics constrains evolutionary pathways of proteins (emergence of drug resistance behaviors inHIV-1 protease).
Tuesday, March 8, 2011 - 11:00 , Location: Skiles 006 , Vadim L. Stefanuk , Russian Academy of Sciences , Organizer:
Some properties of biological memory are briefly described. The examples of short term memory and extra long term memory are drawn from psychological literature and from the personal experience. The short term memory is modeled here with the two types of mathematical models, both models being special cases of the Locally Organized Systems (LOS). The first model belongs to Prof. Mikhail Tsetlin of Moscow State University. His original ?pile of books? model was independently rediscovered a new by a number of scientists throughout the World. Tsetlin?s model demonstrates some very important properties of a natural memory organization. However mathematical study of his model turned out to be rather complicated. The second model belongs to the present author and has somewhat similar properties. However, it is organized in a completely different manner. In particular it contains some parameters, which makes the model rather interesting mathematically and pragmatically. The Stefanuk?s model has many interpretations and will be illustrated here with some biologically inspired examples. Both models founded a number of practical applications. These models demonstrate that the short term memory, which is heavily used by humans and by many biological subsystems is arranged reasonably. For humans it helps to keep the knowledge in the way facilitating its fast extraction. For biological systems the models explain the arrangement of storage of various micro organisms in a cell in an optimal manner to provide for the living.