Leah EdelsteinKeshet
Department of Mathematics University of British Columbia Vancouver, Canada 
Pattern formation, forces, and crawling cellsIn this talk, I will describe research carried out in my group on the topic of cell motility. Why do cells move? Immune cells have to move around the body to find sites of infection and kill pathogens. How do cells move? That is a fascinating question that my group has been studying and modeling for over 15 years. Biologists have found that the distribution of specific signaling proteins (Rho GTPAses) inside a motile cell govern the polarity and direction of motions, as well as regulate the machinery (actin cytoskeleton) that powers that crawling cell motion. (A high level of the GTPAse Rac leads to local actin assembly and cell edge protrusion, whereas a high level of Rho leads to myosin activation, and local cell edge contraction. In combination, protrusion of the cell front and contraction of the back leads to cell motility.) I will review mathematical models we have studied (reactiondiffusion partial differential equations) for the patterns formed by these proteins inside a cell, describe some of the mathematical methods, and an examples of biological experiments that the models could explain. I will end with recent work that depicts how mechanical forces are coupled to the signaling proteins, and what this implies about the dynamics of both single cells and multicellular tissues.

Aaron A. King
Department of Ecology & Evolutionary Biology Department of Mathematics Center for the Study of Complex Systems Center for Computational Medicine and Bioinformatics University of Michigan, USA 
Efficient scientific inference for stochastic dynamical systemsQuestions about the mechanistic operation of biological systems are naturally formulated as stochastic processes, but confronting such models with data can be challenging. In this talk, I describe the essence of the difficulty, highlighting both the technical issues and the importance of the "plugandplay property". I then illustrate some effective approaches to efficient inference based on such models. I conclude by sketching promising new developments and describing some open problems.

Johnny T. Ottesen
Department of Sciences and Environment Roskilde University, Denmark 
Modeling the Coupled LeukemicInflammatory Responses with application to treatment planning.Inflammation trigs and drives leukemia and the related Myeloproliferative Neoplasm (MPNs) diseases through the innate immune system while leukemia and MPNs stimulates the inflammatory responds of the adaptive immune system fighting the malign cells of the diseases. Where the twoway coupling of solid tumor cancer and the adaptive immune system has drawn some attention during the last decades and inspired to immuno and genetherapy, leukemia and MPNs have by the large been left unnoticed with respect to such coupling. Likewise, the twoway coupling of leukemia and MPNs, and the innate immune system is by the large left unstudied with respect to treatments and preventive measures.
We pose a novel mathematical model of development of leukemia and MPNs taking these twoway couplings into account. The model is validated against human data. It follows that the innate immune response is crucial in the development and treatment of leukemia and MPNs. Steady states and their stability are determine analytically and it is discuss how the model may be used for optimizing treatment. Geometric singular perturbation theory suggests a reduced model which shows excellent agreement with the full model. 
Dmytro Chebotarov
International Rice Research Institute Los Baños, Philippines 
Insights from 3,000 rice genomesIn this talk I will present recent and ongoing work from 3,000 rice genomes project, from an overview of variation (SNP and structural variants), population structure, to implications for history of rice domestication and utility for uncovering genotypephenotype associations. I will touch on open questions and discuss opportunities for collaboration.

Ricardo del Rosario
Stanley Center for Psychiatric Research, Broad Institute, USA 
A sensitive method for detecting chromatinaltering polymorphismsMost singlenucleotide polymorphisms (SNPs) found to be associated with disease from genomewide association studies (GWAS) lie on noncoding regions. The majority of these noncoding GWAS SNPs might not even be in in linkage disequilibrium (LD) with SNPs in coding regions, and hence it has been speculated that these variants possess regulatory functions. For example, mutations in regulatory regions such as enhancers could affect the enhancer’s activity to regulate the expression of its target gene. Identifying causal variants on a large scale poses difficulties because most GWAS SNPs are merely in LD with the causal SNP and moreover, the function of SNPs in regulatory regions are mostly unknown. We present a method that simultaneously detects SNPs in regulatory regions and quantifies their regulatory impact. The method uses only one assay, chromatin immunoprecipitationsequencing (ChIPseq), and does not require a separate genotyping of the cohort. The regulatory regions are identified using ChIPseq on histone H3 acetylated on lysine 27 (H3K27ac; a histone mark of active promoters and enhancers) and from the ChIPseq reads, we detected histone acetylation quantitative trait loci (haQTLs). We developed a statistical test called the genotypeindependent signal correlation and imbalance (GSCI) test that simultaneously scores ChIPseq peak height correlation and allelic imbalance. As proof of principle, we performed H3K27ac ChIPseq on 57 lymphoblastoid cell lines. The GSCI test detected 8,764 haQTLs, an order of magnitude larger than the number of SNPs detected by expression QTL analysis. Our method facilitates the detection of regulatory variants on a largescale, using only a moderately sized cohort and provides a simple way to identify causal variants within disease associated loci.

Chaitanya S. Gokhale
MaxPlanckInstitute for Evolutionary Biology, Germany 
Ecoevolutionary game dynamics: from cells to societiesEvolutionary game theory has been applied to a variety of fields ranging from the origins of life to the evolution of language. Biologically, the theory might sometimes sound extremely simplistic. Simplicity is however precisely the power of abstraction that evolutionary games offer us to understand the immense complexity of biology. Keeping games as simple as possible we have extended it to include multiple interactions. This simple extension allows us to include some of the overlooked complexities in nature concerning the number of interacting partners. After presenting some general results of the extension, we will discuss the applications of our theory ranging from cell dynamics to belief systems. How, relatively simple games can address the questions of how complex communities can survive in equilibrium or how a belief system can evolve even when there exists no selection pressure on the belief itself. Evolution, however, requires an ecological context. Including ecological dynamics, both biotic (population dynamics) and abiotic can affect the resulting ecoevolutionary trajectory. In closing, we will report on the findings in this domain and close the discussion with our efforts in understanding the feedback between evolutionary games and ecological dynamics.

Robert Handsaker
Stanley Center for Psychiatric Research, Broad Institute, USA 
Understanding human genome structural variation at population scale using whole genome sequencingHuman genomes vary at small scales (changes to single DNA bases) and also at large scales involving the addition or subtraction of DNA segments containing many thousands of bases. These largescale changes, known as structural variation, are abundant in the general population and have been shown to have potent effects on many common phenotypes and diseases, including schizophrenia and cardiovascular disease. Wholegenome sequencing provides new opportunities to interrogate genomes at population scale, but reconstructing and measuring these large structural variations using currently available sequencing technologies poses mathematical and computational problems.
This talk will discuss the difficulties in measuring and interpreting human structural variation and the mathematical approaches and algorithms we have developed in our Genome STRiP software. I will describe some of the biological properties of human structural variation that we observe in population scale studies such as the 1000 Genomes Project and highlight examples of structural variation relevant to human disease phenotypes. I will also discuss some of our current work to build better populationscale maps of human structural variation and to improve our ability to understand and interpret the biological impact of these variants. 
ChaoPing Hsu
Institute of Chemistry Academia Sinica, Taiwan 
Noises and dynamics in cells: Mathematical modeling in systems biologyGene expression noise is ubiquitous in cells. One source of noises is that genes are expressed in bursts, as both mRNA and protein bursts were observed in the past. In order to simulate gene network dynamics with noise effects, a Langevin’s equation formulism is developed that is capable to account for the effects of bursts.
This Langevin equation approach is further used in the development of model organism C. elegans. In this work, we study the noise propagation in a regulatory network of C. elegans, that regulates the distal tip cell (DTC) migration. It is a genetic network composed of multiple feedforward pathways with the capability of tight timing control. Feedforward loops are known to have the potential to filter the noise, but such noisefiltering is asymmetric, i.e. it works at either the “on” or the “off” states in the source. With multiple, interlinked feed forward loops, we show that the propagated noises are largely filtered regardless of the states in the source. Positive feedback loops are also helpful in maintaining the desired activity of the target gene. I’ll also discuss our recent finding in modeling the dynamics of circadian clock for plants and implication from stochastic simulations. 
Editha C. Jose
Institute of Mathematical Sciences and Physics University of the Philippines, Los Baños 
Chemical Reaction Network Theory in Systems BiologyChemical reaction network theory (CRNT) is an area of applied mathematics that attempts to model the behavior of real world chemical systems. It aims to understand connections between network structure and system dynamics with mathematical methods from graph theory, linear algebra, group theory and the theory of ordinary differential equations (dynamical systems). Even though the modern theory of CRNs was started in the 1970’s by chemical engineers, the emergence of Systems Biology led biologists, computer scientists, mathematicians and researchers from other disciplines to pursue collaborative efforts in CRNT to understand complex biological and chemical systems. CRNT has become a tool to study complex biology independent of rate parameters, that is, certain behaviors of networks are examined by analyzing their structures only.
In this talk, we will present some basic concepts of CRNT and apply these to study some biochemical systems. We will feature some Filipino contributions to this field focusing on power law kinetic systems and biological applications. We also describe our current and future research directions. 
Eunok Jung
Department of Mathematics Konkuk University, Korea 
Dynamical Models of the 2009 A/H1N1 Influenza and Effective Intervention Strategies in the Republic of KoreaEunok Jung [1], Soyoung Kim [1], Jonggul Lee [2]
[1] Department of Mathematics, Konkuk University, Seoul 143701, Korea [2] National Institute for Mathematical Sciences, Daejeon 305811, Korea A novel influenza A/H1N1 is characterized by high transmissibility and low fatality. In this talk, we present mathematical models of the 2009 A/H1N1 influenza based on the reported data of the 2009 A/H1N1 influenza collected by the Korea Center for Disease Control (KCDC). First, a spatialtemporal pattern of the 2009 A/H1N1 influenza spread is studied using the metapopulation model. The SEIRtype of an influenza transmission model is used in each subpopulation linked by commuting flow. We find localized (provincelevel) spread by using the spatial heterogeneity such as the basic reproductive number and peak time and investigate the effect of early nonpharmaceutical interventions such as isolation and/or commuting restriction. Second, we introduce an agedependent model of the 2009 A/H1N1 influenza by considering five age groups and suggest the best way to prioritize an agedependent vaccination strategy for mitigating the epidemic. The estimated transmission matrix captures one of the main characteristics of the 2009 A/H1N1 influenza, the transmission rate of which is high among young people, unlike that of seasonal influenza. The impact of an agedependent vaccination priority on the transmission dynamics of the 2009 A/H1N1 influenza is investigated. Furthermore, we quantify and analyze the Korean government vaccination policy when the vaccination started being administered 90 days (or 120 days) after the onset of the outbreak

Jae Kyoung Kim
Korea Advanced Institute of Science and Technology (KAIST) 
When can we use MichaelisMenten equation for stochastic simulations?Biochemical reaction networks (BRNs) in a cell frequently consist of reactions with disparate timescales. The stochastic simulations of such multiscale BRNs are prohibitively slow due to high computational cost for the simulations of fast reactions. One way to resolve this problem uses the fact that fast species regulated by fast reactions quickly equilibrate to their stationary distribution while slow species are unlikely to be changed. Thus, on a slow timescale, fast species can be replaced by their quasisteady state (QSS): their stationary conditional expectation values for given slow species. As the QSS are determined solely by the state of slow species, such replacement leads to a reduced model, where fast species are eliminated. However, it is challenging to derive the QSS in the presence of nonlinear reactions. In this talk, I will describe under which condition such stochastic QSS can be accurately approximated by a deterministically derived QSS (e.g. MichaeilsMenten equation), which allows to use the nonelementary functions for the propensity functions of the Gillespie algorithm. Furthermore, I will also present two classes of multiscale BRNs which can be reduced by deriving an exact stochastic QSS rather than approximations: a feedforward network or a complex balanced network. Finally, I will illustrate how we used such accurate reduction to identify a novel molecular mechanism for robust circadian rhythms and improve the prediction of drug clearance in liver.

Felicia Maria G. Magpantay
Department of Mathematics and Statistics Queen's University, Canada 
Some challenges in modeling imperfect vaccinesThe dynamics of vaccinepreventable diseases depend on the underlying disease process and the nature of the vaccine. In this talk I will present a general model of an imperfect vaccine and the epidemiological consequences of different modes of vaccine failure. I will also discuss likelihoodbased statistical inference methods that can be used to estimate the parameters of the model even in the presence of incomplete covariate information (such as vaccine coverage). The methods used can be extended to study and fit mechanistic models of complex phenomenon beyond those in disease ecology.

Atsushi Mochizuki
Institute for Frontier Life and Medical Sciences, Kyoto University, Japan CREST, Japan Science and Technology Agency, Saitama, Japan 
Controlling cell fate specification system based on network structureBy the success of modern biology we have many examples of large networks which describe regulatory interactions between a large number of genes. On the other hand, we have a limited understanding for the dynamics of molecular activity based on such complex networks. To overcome these problems, we developed Linkage Logic theory to analyze the dynamics of complex systems based on information of the regulatory linkages alone. It assures that i) any longterm dynamical behavior of the whole system can be identified/controlled by a subset of molecules in the network, and that ii) the subset is determined from the regulatory linkage alone as a feedback vertex set (FVS) of the network. We applied this theory to the gene regulatory network for cell differentiation of ascidian embryo, which includes more than 90 genes. From the analysis, dynamical attractors possibly generated by the network should be identified/controlled by only 5 genes, if the information of the network structure is correct. We verified our prediction by combinatorial experiments of knockdown and overexpression by using ascidian embryos. We found that almost all of the expected cell types, six among seven major tissues, could be induced by experimental manipulations of these five genes.
Further Reading 1. Kobayashi., et al. iScience (2018) 