Guests

GUEST SPEAKER:

Leon Arriola

TBD @ 6pm (Manzanita Hall-MPR)

A Quantum Mechanics Paradigm-Part I: Feynman Diagrams & Quantum Tunneling in Single Species Population Dynamics

A quantum mechanics formal framework is created, that describes how the probabilities of having exactly n single species objects evolve in time. This quantum viewpoint, via the expected value of the stochastic generating function, yields the standard macroscopic ordinary differential equations such as the decay, growth and logistic models, etc.. In multiple interactions within single species, unexpected effects such as quantum tunneling occurs in the probabilities of the population. These tunneling effects gives specific predictions of the uncertainty in the population at the macroscopic level. This framework depicts emerging behaviors that are not seen in the current macroscopic models such as ordinary/partial differential equations.

GUEST SPEAKER:

Dr. Olcay Akman

Thursday, June 27th @ 7pm (Manzanita Hall-MPR)

• PARAMETER ESTIMATION

The foundation of modeling in exploration of dynamical systems, systems biology, or data sciences in general stands on the basic principle of minimizing estimation error. With this module, we will study the problem of fitting a parameterized model to noisy data. We will start with some properties of estimators, then turn to basic frequentist parameter estimation on our way to explore basic Bayesian parameter estimation. We will see applications of parameter estimation under Bayesian (and machine learning-if time permits) methods.

Friday, June 28th @ 7pm (Manzanita Hall-MPR)

• CLUSTER ANALYSIS WITH APPLICATIONS IN BIOLOGY AND ECOLOGY

We will study the basic concepts of cluster analysis and discover a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods and density-based methods. Then we will learn machine-learning based clustering algorithm of Self Organizing Maps. Finally, we will examine methods for clustering validation and evaluation of clustering quality with hands-on examples.

GUEST SPEAKER:

Dr. Carlo Maley

Friday, June 28th @ 11:30am (STPV 324)

High impact modeling in biology

How do you get someone, like the NIH, to pay you $1,000,000 to do modeling of biological systems? This generally depends on addressing a pressing need. I will discuss the kinds of models that have been most impactful in biology/medicine, setting up collaborations with experimentalists and clinicians, writing modeling grants, and how we carry out our modeling research.

GUEST SPEAKER:

Dr. Ryan Mills & Dr. Alan Boyle

Thursday, June 27th @ 9 (STPV 324): Genetics Lecture 1

Thursday, June 27 @ 1:30 (STPV 324): Genetics Lab 1

Friday, June 28th @ 9 (STPV 324): Genetics Lecture 2

Friday, June 28th @ 1:30 (STPV 324): Genetics Lab 2

GUEST SPEAKER:

Matthew Toro

Tuesday, June 25th @ 1:30 (STPV 324)

Geospatial Data Analysis in GIS

Geographic information systems (GIS) facilitate the processing, analysis, and visualization of spatially explicit datasets. While diverse in structure and format, all geospatial data share the common trait of being defined within horizontal and/or vertical coordinate planes. In this session, we’ll explore and manipulate geospatial data in a free and open source GIS software application called QGIS. We’ll conduct basic processing geospatial data and explore practical techniques to:

• simulate regular and randomized points,
• construct sampling grids,
• derive polygon centroids,
• define weighted mean coordinates,
• interpolate data, and
• identify clusters,
• among other standard processing and visualization functions.

Students will leave with a heightened proficiency in geospatial data processing in GIS.

GUEST SPEAKER:

Dr. Joshua Yukich

Friday, June 21st @ 6pm (Manzanita Hall-MPR)

Malaria surveillance and response: practice and theory

Malaria surveillance has relied heavily on reactive surveillance strategies in areas nearing elimination. Little is known about the effectiveness of these strategies or which variant strategies might be most effective in identifying malaria infected persons, and reducing local transmission. Data from Southern Zambia and Zanzibar are included to identify help derive and understanding of the local distribution of malaria in near elimination areas as well as the function of reactive surveillance strategies. These data are also utilized to parameterize mathematical models of reactive surveillance in malaria transmission.

GUEST SPEAKER:

Dr. Matthew Scotch

Friday, June 21st @ 11:30am (STPV 324)

Sequence-informed, real-time, virus surveillance

Sequence-informed, real-time surveillance is now recognized as an important extension to the monitoring of rapidly evolving pathogens. This includes viruses occurring naturally in the environment as well as those released through acts of bioterrorism. This presentation will discuss bioinformatics methods that utilize sequence data for surveillance of infectious disease threats.

GUEST SPEAKER:

Dr. Baltazar Espinoza

Thursday, June 20th @ 11:30am-12:30pm (STPV 324)

The patch selection problem and dynamic programming

The study of "foraging theory" (as understood in the modern context) may be traced back to 1966 the papers by MacArthur and Pianka (1966) and Emlen (1966) on the optimal use of a patchy environment. The central concern on the patch selection problem is to study the behavior that optimizes forager's fitness. Following a dynamic state variable model proposed by C. Clark in his paper "Towards a Unified Foraging Theory", we will study the best way to trade off the rewards with the costs and the risks.

GUEST SPEAKER:

Dr. Susan Holecheck

Wednesday, June 19th @ 6pm (Manzanit Hall-MPR)

A tale of math, biology and population genetics: The case of Dengue

Dengue is an endemic mosquito-borne disease that affects several countries around the world with 2.5 billion at risk and an average of 50 million cases each year. While a vaccine has recently become available, it has its limitations mostly due to the different dengue virus serotypes (DENV 1 to 4) and associated genotypes that circulate in different endemic territories. An infected person can suffer from mild dengue fever or could potentially be affected with the more severe dengue hemorrhagic fever or dengue shock syndrome due to dengue–specific antibodies during a secondary infection with a different serotype. This talk will provide some insight into how math, biology and population genetics can be used as tools to better understand this disease.

GUEST SPEAKER:

Dr. Sarah Mathew - ASU Faculty

contact: Sarah.Mathew@asu.edu

Tuesday, June 11th @ 6pm (Manzanita Hall - MPR)

Assessing whether group-level selection on cultural variation has shaped the evolution of human cooperation.

A fundamental puzzle of human evolution is how we evolved to cooperate with genetically unrelated strangers in transient interactions. Group-level selection on culturally differentiated populations is a promising theory, which has not yet been rigorously tested. Empirical studies have alluded to the potential for such selection, but a central prediction of the theory that the scale of cooperation will correspond to the scale of cultural variation has not been confirmed. To evaluate this prediction, my lab is examining the population structure of cultural variation and the patterns of cooperation among subsistence pastoralists in Kenya. We have documented the normative beliefs and cooperative dispositions of 759 individuals spanning nine clans nested within four ethnic groups—the Turkana, Samburu, Rendille and Borana. I will present our first set of results which show that the scale of cultural differentiation corresponds to the scale of cooperation, suggesting that norms governing cooperation in these societies have evolved under the influence of group-level selection on cultural variation. Such selection acting over human evolutionary history can explain why we cooperate readily with unrelated and unfamiliar individuals, and why humans’ unprecedented cooperative flexibility is nevertheless culturally parochial

GUEST SPEAKER:

Dr. Yang Kuang - ASU Faculty

contact: atyxk@asu.edu

Friday, June 7th @ 11:30am (STPV 324)

Models of Hormone Treatment for Prostate Cancer: Can Mathematical Models Predict the outcomes?

Prostate cancer is commonly treated by a form of hormone therapy called androgen suppression. This form of treatment, while successful at reducing the cancer cell population, adversely affects quality of life and typically leads to a recurrence of the cancer in an androgen-independent form. Intermittent androgen suppression aims to alleviate some of these adverse affects by cycling the patient on and off treatment. Clinical studies have suggested that intermittent therapy is capable of maintaining androgen dependence over multiple treatment cycles while increasing quality of life during off-treatment periods. We present several mathematical models of prostate cancer growth to study the dynamics of androgen suppression therapy and the production of prostate-specific antigen (PSA), a clinical marker for prostate cancer. Biologically crude preliminary models were based on the assumption of an androgen independent (AI) cell population with constant net growth rate. These models gave poor accuracy when fitting clinical data during simulation. The biologically more refined models presented hypothesizes an AI population with increased sensitivity to low levels of androgen and these models generate high levels of accuracy in fitting clinical data. In general, we found that biologically more plausible models can forecast future PSA levels more accurately.