## Workshop 2: Control of Cellular and Molecular Systems

### Organizers

Department of Biochemistry and Biophysics, University of California, San Francisco
German Enciso
Mathematics, University of California, Irvine
Pablo Iglesias
Electrical & Computer Engineering, Johns Hopkins University
Mustafa Khammash
Biosystems Science and Engineering, ETH Zurich

The rapid development of novel experimental methods in molecular and cell biology has fed an expansion in the sophistication of models for regulatory systems in living cells. The molecular networks involved in regulation of gene expression, cell signaling, and myriad homeostatic mechanisms naturally lend themselves to analysis within the framework of control theory. Moreover, the use of nonlinear system identification methods to reverse engineer these networks is an important step along the way, as is the development of theoretical ideas such as modularity, retroactivity, and feedback design. At the same time, synthetic biology – the design of de novo cellular regulatory systems – provides an exciting testbed for systems and control ideas, and fertile ground for new interactions between the fields of mathematical biology, control engineering and genetic, regulatory and cellular biology.

Many of the open problems in systems and control analysis of biochemical networks lead to new mathematical challenges. For example, how do structural properties of networks lead to desirable features or dynamic behaviors, including stability, robustness, multistationarity and the presence of oscillations? How are networks organized to maintain homeostasis while retaining the ability to respond effectively to environmental challenges? How can one simultaneously optimize different objectives in signaling cascades such as signal amplification, noise reduction, specificity and ultrasensitivity? How is the performance of biochemical networks affected by stochastic effects due to low protein copy numbers? And how can control systems be reliably built inside a cell using biological molecules?

This workshop will consider how new mathematical ideas from control theory, stochastic processes, graph theory, information theory, optimization, and dynamical systems will help to answer these and other related questions concerning the interplay between cellular biology and control theory.

### Accepted Speakers

Murat Arcak
EECS, University of California, Berkeley
Gregory Batt
InBio, Inria Saclay - Ile-de-France and Institut Pasteur
Domitilla Del Vecchio
Department of Mechanical Engineering, Massachusetts Institute of Technology
Diego di Bernardo
Chemical, Materials and Industrial Production Engineering, Universita di Napoli Federico II''
Department of Biochemistry and Biophysics, University of California, San Francisco
German Enciso
Mathematics, University of California, Irvine
Elisa Franco
Mechanical Engineering, University of California, Riverside
Mary Ann Horn
Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University
Pablo Iglesias
Electrical & Computer Engineering, Johns Hopkins University
Brian Ingalls
Applied Mathematics, University of Waterloo
Mustafa Khammash
Biosystems Science and Engineering, ETH Zurich
Jeff Moehlis
Mechanical Engineering, University of California, Santa Barbara
Antonis Papachristodoulou
Department of Engineering Science, University of Oxford
Johan Paulsson
Dept of Systems Biology, Harvard University
Eduardo Sontag
Department of Mathematics, Rutgers University at New Brunswick
Guy-Bart Stan
Bioengineering, Imperial College London
Joerg Stelling
Biosystems Science and Engineering, ETH Zurich
Nihal Temamogullari
Cell Biology, UT Southwestern Medical Center
Jared Toettcher
Molecular Biology, Princeton University
Ophelia Venturelli
Biochemistry, University of Wisconsin
Ron Weiss
Electrical Engineering & Molecular Biology, Princeton University
Nara Yoon
Translational Hematology and Oncology Research, Cleveland Clinic Foundation
Lingchong You
Biomedical Engineering, Duke University
Monday, October 2, 2017
Time Session
08:00 AM

Shuttle to MBI

08:30 AM
09:00 AM

Breakfast

09:00 AM
09:15 AM

Welcome, Intro - MBI director

09:15 AM
09:30 AM

Introduction by Workshop Organizers

09:30 AM
10:10 AM
Mustafa Khammash - Antithetic Integral Feedback: A New Motif for Perfect Adaptation

Robust perfect adaptation in biology can be realized through the use of integral feedback control. Here we present the underlying theory and first implementation of an integral feedback control system that can achieve perfect adaptation in a living cell. The controller is based on the recently published antithetic integral feedback motif which has been analytically shown to have favorable regulation properties. The closed-loop system is highly tunable, allowing a regulated protein of interest to be driven to a desired level and maintained there with precision. Realized using a sigma/anti-sigma protein pair, the integral controller ensures that regulation is maintained in the face of perturbations that lead to the regulated protein's degradation, thus serving as a proof-of-concept prototype of integral feedback implementation in living cells.

10:15 AM
10:55 AM
Elisa Franco - Engineering Robust Molecular Feedback Controllers via Ultrasensitive Modules

I will describe a molecular reaction network that can be used to achieve robust closed loopcontrol in synthetic biology. The network relies on a motif that exhibits an ultrasensitiveinput-output mapping, and is therefore termed €œbrink controller€?. Ultrasensitivity is achievedby combining molecular titration with an activation/deactivation cycle, and requires thepresence of fast titration and switching rates, together with slow degradation rates. I willdescribe the properties of this motif, touching on the challenge of supplying simultaneouslypositive and negative action on the system to be controlled; I will also discuss potentialexperimental realizations of the circuit using RNA aptamers. Finally, I will mention ongoingcollaborative efforts to build universal PID controllers using RNA riboregulators and theCRISPR/Cas system, relying on a rapid cell-free prototyping environment.

11:00 AM
11:30 AM

Break

11:30 AM
12:10 PM
Nara Yoon - Cancer Modeling: Optimal Therapy Scheduling Based on a Pair of Collaterally Sensitive Drugs

Cancer is a disease developed by uncontrolled growth of mutated cells. To study such cancer, many different scales of researches has been carried out, from a small size of molecules to large size of organisms [1,2]. In this talk, I will give a brief overview about the range of mathematical oncology, and then talk about a recent project of a cellular scale modeling worked by my team.

In the project, we developed a model of ordinary differential equations and study the effect of sequential therapy on heterogeneous tumors comprised of resistant and sensitivity cells. Based on the model, we figured out (i) the optimal drug-switch strategy, and (ii) how composition of sensitive and resistant cell populations changes. Beyond our analytic results, we explored an individual based stochastic model and presented the distribution of extinction times for the classes of solutions found. Taken together, our results suggest opportunities to improve therapy scheduling in clinical oncology.

Reference

1. Anderson, A. R., & Quaranta, V. (2008). Integrative mathematical oncology. Nature reviews. Cancer, 8(3), 227.

2. Byrne, H. M. (2010). Dissecting cancer through mathematics: from the cell to the animal model. Nature reviews. Cancer, 10(3), 221.

12:15 PM
02:00 PM

Lunch Break

02:00 PM
02:40 PM
Antonis Papachristodoulou - Modelling and Design of Feedback Circuits in Biology

Feedback control is found extensively in many natural and technological systems. Indeed, many biological processes use feedback to regulate key processes €“ examples include bacterial chemotaxis and negative autoregulation in genetic circuits. Despite the prevalence of feedback in natural systems, its design and implementation in a Synthetic Biology context is much harder. In this talk I will give examples of how we implemented feedback systems in three different biological systems. The first one concerns the design of a synthetic recombinase-based feedback loop, which results into robust expression. The second describes the use of small RNAs to post-transcriptionally regulate gene expression through interaction with messenger RNA (mRNA). The third involves the introduction of negative feedback in a two-component signalling system through a controllable phosphatase. Closing, I will outline the challenges posed by the design of such systems, both theoretical and on their implementation.

02:45 PM
03:15 PM

Break

03:15 PM
03:55 PM
Nihal Temamogullari - Multiple Metabolic Signals Establish a Deterministic Cell Fate Decision

Single cell resolution reveals that isogenic cells exposed to the same environmental cues will choose between different fates. To understand how deterministic this process is, we need to follow various signals that cells are integrating continuously throughout the course of decision-making. To this end, we developed an experimental-theoretical framework, which enables (1) tracking multiple signals in single cells from exposure to differentiation signal to the realization of a particular fate and (2) combining these high dimensional observations into fate probabilities. More specifically, we coupled microfluidics and time-lapse microscopy, which allows for imaging up to 6 endogeneously tagged fluorophores. To merge this high dimensional data in a rigorous way, we used statistical evidence that translates these measurements into fate probabilities. This way, we can follow the €˜predisposition€™ of each cell towards a certain cell fate over the entire decision-making process. We used budding yeast meiosis as a model system, where, upon nutrient limitation, cells either commit to meiosis or become quiescent. With this framework, we followed the metabolic status of single cells and we can predict cell fates with very high accuracy well in advance the commitment point. This is a joint project with Andreas Doncic, Orlando Arguello Miranda and Yanjie Liu.

For the mathematical background of my talk, one can read "Section 4.2: Testing Binary Hypotheses with Binary Data" from the book Probability Theory - The Logic of Science by E.T. Jaynes. For cellular-decision making, the review "Cellular Decision-Making and Biological Noise: From Microbes to Mammals" by Balaszi, Oudenaarden and Collins ?and for the model system budding yeast sporulation, the review "Sporulation in Budding Yeast Saccharomyces cerevisiae" by Aaron M. Neiman are good sources.

04:00 PM
06:00 PM

Reception and Poster session in MBI Lounge

06:00 PM

Shuttle pick-up from MBI

Tuesday, October 3, 2017
Time Session
08:00 AM

Shuttle to MBI

08:15 AM
09:00 AM

Breakfast

09:00 AM
09:40 AM
Joerg Stelling - Multi-Objective and Multi-Scale Design of Synthetic Gene Circuits

Synthetic gene circuits have to operate in natural systems such as cells or organisms, with corresponding load on and cross-talk with them. These aspects are of particular relevance for biomedical applications where multiple design objectives with trade-offs (e.g., efficiency and robustness) and multiple scales (e.g., organism-wide impact of cell-based therapeutics) need to be considered. The first part of the talk will describe a Bayesian circuit design method that identifies circuit topologies whose behavior is robust to variations in parameters; it enables to reliably assess trade-offs between performance, robustness, and experimental feasibility, thus increasing the probability of success of circuit implementation. The second part will discuss a biomedical application of synthetic gene circuit design for in vivo closed-loop control, specifically for the treatment of type 1 and 2 diabetes; we achieved glucose responsiveness by a synthetic circuit that couples glycolysis-mediated calcium entry to an excitation-transcription system controlling therapeutic transgene expression. The examples help to argue that novel systems analysis methods are needed to enable efficient computational design of synthetic circuits, and how the design of synthetic systems allows us to refine our understanding of natural biological systems.

09:45 AM
10:25 AM
Gregory Batt - Balancing a genetic toggle switch using real-time control and periodic stimulation

Feedback control methods have recently been applied to successfully take control of cellular functions. This novel field of research aims to remotely pilot cellular processes in real-time with unprecedented levels of robustness and precision to leverage the biotechnological potential of synthetic biology. Yet, the control of only a small number of genetic circuits has been tested so far. In this presentation, I will present the control of a multistable gene regulatory network, which is ubiquitously found in nature and play critical roles in cell differentiation and decision-making. Using an in silico feedback control loop, we demonstrate that a bistable genetic toggle switch can be dynamically maintained near its unstable equilibrium position. Thus, single cells could be controlled to remain in an undecided state for extended periods of time. Importantly, we show that a direct method based on dual periodic forcing is sufficient to simultaneously maintain many cells in an undecided state. These findings pave the way for the control of more complex cell decision-making systems at both the single cell and the population levels, with vast fundamental and biotechnological applications.

10:30 AM
11:15 AM

Break

11:15 AM
11:55 AM
Mary Ann Horn - Using Mathematical Modeling to Understand the Role of Diacylglycerol (DAG) as a Second Messenger

Diacylgylcerol (DAG) plays a key role in cellular signaling as a second messenger. In particular, it regulates a variety of cellular processes and the breakdown of the signaling pathway that involves DAG contributes to the development of a variety of diseases, including cancer. A mathematical model of the G-protein signaling pathway in RAW 264.7 macrophages downstream of P2Y6 activation by the ubiquitous signaling nucleotide uridine 5€™-diphosphate is presented. The primary goal is to better understand the role of diacylglycerol in the signaling pathway and the underlying biological dynamics that cannot always be easily measured experimentally. The model is based on time-course measurements of P2Y6 surface receptors, inositol trisphosphate, cytosolic calcium, and with a particular focus on differential dynamics of multiple species of diacylglycerol. When using the canonical representation, the model predicted that key interactions were missing from the current pathway structure. Indeed, the model suggested that to accurately depict experimental observations, an additional branch to the signaling pathway was needed, whereby an intracellular pool of diacylglycerol is immediately phosphorylated upon stimulation of an extracellular receptor for uridine 5€™-diphosphate and subsequently used to aid replenishment of phosphatidylinositol. As a result of sensitivity analysis of the model parameters, key predictions can be made regarding which of these parameters are the most sensitive to perturbations and are therefore most responsible for output uncertainty. (Joint work with Hannah Callender, University of Portland, and the H. Alex Brown Lab, Vanderbilt.)

12:00 PM
02:00 PM

Lunch Break

02:00 PM
02:40 PM
Jared Toettcher - Optogenetics for intracellular codebreaking: how signaling stimuli is interpreted to control gene expression and cell behavior

Studies of cell signaling networks have repeatedly revealed complex patterns of protein activity: oscillations, traveling waves, and polarized spatial distributions. However, our tools to perturb and control these patterns have typically lagged far behind our ability to observe them. I will describe our recent work to develop light-sensitive proteins to control basic signaling processes in mammalian cells and Drosophila embryos, and discuss our recent work to dissect how Erk signaling controls gene expression and cell fate across these model systems.

02:45 PM
03:00 PM

Break

03:00 PM
03:40 PM
Lingchong You - Bacterial dynamics in time and space

Antibiotics are arguably the greatest medical discovery of the 20th century. However, due to over-prescription and misuse, microbes have rapidly evolved many strategies to survive drug treatment. Indeed, resistance emerges faster than new drugs can be developed, leading many to wonder whether we are returning to the €œdark ages€? of the pre-antibiotic era. Addressing the antibiotic crisis requires two complementary strategies. One is to develop novel drugs and therapeutic methods to combat bacterial infections. It is equally critical to develop effective antibiotic treatment protocols that can extend the efficacy and usability of existing antibiotics. Development of effective antibiotic treatment protocols requires a mechanistic understanding of both the short-term and long-term bacterial dynamics in response to antibiotic treatment. In this talk, I will discuss our ongoing efforts along this line of investigation, with a particular focus on dynamics of horizontal gene transfer.

03:45 PM
04:30 PM

Informal Discussion

04:30 PM

Shuttle pick-up from MBI

Wednesday, October 4, 2017
Time Session
08:00 AM

Shuttle to MBI

08:15 AM
09:15 AM

Breakfast

09:15 AM
09:55 AM
Eduardo Sontag - Dynamic Response Phenotypes in Systems Biology: Scale-Invariance and Monotone I/O Systems

Among the central questions in systems biology are those of understanding the roles of, and interactions among, signal transduction pathways and feedback loops. This talk focuses on €œdynamic phenotypes€? characterized by input/output responses to external inputs in addressing such issues, using fold-change detection and monotone architectures as case studies.

An ubiquitous property of sensory systems is "adaptation": a step increase in stimulus triggers an initial change in a biochemical or physiological response, followed by a more gradual relaxation toward a basal, pre-stimulus level. Adaptation helps maintain essential variables within acceptable bounds and allows organisms to readjust themselves to an optimum and non-saturating sensitivity range when faced with a prolonged change in their environment. It has been recently observed that some adapting systems, ranging from bacterial chemotaxis pathways to signal transduction mechanisms in eukaryotes, enjoy a remarkable additional feature: scale invariance or "fold change detection" meaning that the initial, transient behavior remains approximately the same even when the background signal level is scaled. This talk will review the biological phenomenon, and formulate a theoretical framework leading to a general theorem characterizing scale invariant behavior by equivariant actions on sets of vector fields that satisfy appropriate Lie-algebraic nondegeneracy conditions. The theorem allows one to make experimentally testable predictions, and the presentation will discuss the validation of these predictions using genetically engineered bacteria and microfluidic devices, as well their use as a "dynamical phenotype" for model invalidation. The talk will also include some speculative remarks about the role of the shape of transient responses in immune system self/other recognition and in cancer immunotherapy, as well as a brief discussion of how control-theoretic structures such as differential positivity (monotonicity) have been experimentally employed together with experimental data in order to elucidating mechanisms for stress responses and chemosensing.

10:00 AM
10:40 AM
Murat Arcak - Spatial Patterns of Gene Expression in Multicellular Ensembles

Breaking symmetry in spatially distributed systems is a fascinating dynamical systems problem and is of fundamental interest to developmental biology. In this talk we discuss several feedback mechanisms that enable formation of gene expression patterns in multi-cellular organisms. With the help of dynamical models we reveal the key structural properties that are necessary for patterning and present novel synthetic gene networks built upon these models.

10:45 AM
11:00 AM

Break

11:00 AM
11:40 AM
Jeff Moehlis - Controlling Biological Oscillators

Nonlinear oscillators - dynamical systems with stable periodic solutions - arise in many systems of physical, technological, and biological interest. Examples from biology include pacemaker cells in the heart, the firing of action potentials in neurons, and circadian rhythms. There are situations in which it is desirable to control biological oscillators, for example changing the phase of the circadian rhythm in order to adjust to a new time zone. With this in mind, we have developed an optimal control algorithm to change the phase of a periodic orbit using a minimum energy input, which also minimizes the transversal distance to the uncontrolled periodic trajectory. Our algorithm uses a two-dimensional augmented phase reduction technique based on both isochrons and isostables. This control algorithm is effective even when a large change in time period is required or when the nontrivial Floquet multiplier of the periodic orbit is close to one; in such cases, an analogous control algorithm based on standard phase reduction fails. Inspired by deep brain stimulation treatment of Parkinson's disease, we have also developed control algorithms for desynchronizing populations of oscillators, for example by maximizing the Lyapunov exponent associated with their phase dynamics, and through optimal phase resetting.

11:45 AM
12:25 PM
Guy-Bart Stan - Design of de novo biomolecular feedbacks for improved performance and robustness in living cells

In this talk I will give an overview of some of our research activities in the "Control Engineering Synthetic Biology" group, where we focus our efforts on developing foundational forward-engineering methods to mathematically model, control, and experimentally implement synthetic gene circuits and cellular systems that aim at increasing the robustness, performance, and genetic stability of engineered cells. During the talk, I will propose some approaches to answer some of the following questions in systems and synthetic biology:

• How can we use cellular resources more efficiently to simultaneously improve growth rates and production yields?

• What is the interplay between feedback and buffering in cellular homeostasis?

• How can we create genetic oscillators for which the amplitude and period of oscillations can be tuned independently?

12:30 PM
02:00 PM

Lunch Break

02:00 PM
02:40 PM
Brian Ingalls - Synthetic biology approaches to suppression of antibiotic resistance: model-based design

Antibiotic-resistant pathogens present an increasing global health concern. Our group is investigating synthetic biology-based strategies for suppression of resistance in environmental bacterial populations. This approach involves the delivery of engineered genetic elements to target populations. We are developing models of the dynamics of this system, at both the genetic and population level, to be used for model-based design of potential implementations. Analysis of proof-of-principle scenarios and accompanying experimental results will be presented.

02:45 PM
03:00 PM

Break

03:00 PM
03:40 PM
Pablo Iglesias - Set point control, excitable systems and cell migration

In recent years, there has been considerable experimental evidence that the migration of cells is regulated by a network displaying excitable behavior. Stochastically generated firings of this excitable system can generate random actin-filled protrusions that propel cells. By altering the threshold of the excitable system is a spatially-dependent manner, external stimuli can bias this stochastic activity to direct cellular motion. In this talk we present some recent theoretical and experimental results that demonstrate that by altering the set point of this excitable system synthetically, cellular protrusions can expand and, consequently, the cell can be induced to transition between different migratory modes.

03:45 PM
04:30 PM

Informal Discussion

04:30 PM

Shuttle pick-up from MBI

Thursday, October 5, 2017
Time Session
08:30 AM

Shuttle to MBI

08:45 AM
09:30 AM

Breakfast

09:30 AM
10:10 AM
German Enciso - Absolute Robustness and Output Stabilization in Stochastic Chemical Reaction Networks

Absolute robustness is a structural property ensuring that the steady state output of a chemical reaction network is unchanged as a function of total protein concentrations. Originally proposed by Shinar and Feinberg, this property holds regardless of parameter values and can be verified by inspection of the network topology. In this talk I discuss how this property generalizes to the case of stochastic networks. I also discuss the stabilization of a given network by the addition of an appropriate absolute robustness module, incorporating recent work by Mustafa Khammash.

10:15 AM
10:55 AM
Diego di Bernardo - Applications of external control of gene expression in yeast and mammalian cells

I will discuss our recent results on the control of gene expression from chemically inducible promoters in both yeast and mammalian cells. I will discuss the peculiarities and difficulties in controlling biological systems, then I will show how the same control strategy and experimental platform can be applied to both organisms, despite the huge differences in their biology. I will also show how we are using these external controllers to gain quantitative insights in disease causing proteins such as alpha-synuclein involved in Parkinson's disease aetiology.

11:00 AM
11:30 AM

Break

11:30 AM
12:10 PM
Hana El Samad - Build to Perturb, Perturb to Understand

We discuss synthetic biology tools that interface with endogenous biological systems, perturbing them to reveal their organization and function.

12:15 PM
02:00 PM

Lunch Break

02:00 PM
02:40 PM
Johan Paulsson - Control in single cells
02:45 PM
03:00 PM

Break

03:00 PM
03:40 PM
Ophelia Venturelli - Elucidating network design principles of microbial consortia for controlling ecological states

Microbial communities are coupled networks of diverse organisms operating on multiple time and spatial scales that occupy nearly every environment on Earth. A detailed and quantitative understanding of microbial communities combined with capabilities to forecast dynamic responses will enable the design of targeted interventions to shift communities to desired states. I will describe a generalizable model-guided approach to reverse engineer microbial interactions that shape the assembly of a human gut microbiome synthetic ecology. We show that pairwise interactions are major drivers of multi-species community assembly as opposed to higher-order interactions. The inferred microbial interaction network as well as a top-down approach to community assembly pinpointed influential and ecologically responsive species that were significantly modulated by microbial inter-relationships. We identify network topologies for robust species coexistence and prolonged memory of prior history. In sum, these methods define the ecological roles of constituent members of the community and illuminate design principles of stability, robustness and adaptability of microbial ecosystems.

03:45 PM
04:30 PM

Informal Discussion

04:30 PM

Shuttle pick-up from MBI

06:30 PM
07:00 PM

Cash Bar

07:00 PM
09:00 PM

Banquet in the Fusion Room @ Crowne Plaza Hotel

Friday, October 6, 2017
Time Session
08:30 AM

Shuttle to MBI

08:45 AM
09:30 AM

Breakfast

09:30 AM
10:10 AM
Ron Weiss - Mammalian Synthetic Biology: Engineering Sophisticated Gene Regulation for Therapeutic Systems

Synthetic biology is revolutionizing how we conceptualize and approach the engineering of biological systems. Recent advances in the field are allowing us to expand beyond the construction and analysis of small gene networks towards the implementation of complex multicellular systems with a variety of applications. In this talk I will describe our integrated computational / experimental approach to engineering complex behavior in mammalian cells. In our research, we appropriate design principles from electrical engineering and other established fields. These principles include abstraction, standardization, modularity, and computer aided design. But we also spend considerable effort towards understanding what makes synthetic biology different from all other existing engineering disciplines and discovering new design and construction rules that are effective for this unique discipline. We will discuss experimental results with synthetic biology building blocks for intracellular sensing, processing, and actuation in mammalian cells. We will then present a genetic circuit that can detect and destroy specific cancer cells based on the presence or absence or specific biomarkers in the cell. We will also discuss preliminary experimental results for obtaining precise spatiotemporal control over stem cell differentiation for tissue engineering applications. Finally, we will discuss a new framework for creating regulatory circuits based strictly on protein-protein interactions. These protein-phosphorylation based circuits operate at much faster speeds than existing transcriptional and translational based systems, with the ability to respond to a stimulus within a few seconds, thereby creating opportunities for new synthetic biology capabilities and applications.

10:15 AM
10:55 AM
Domitilla Del Vecchio - Competition for Cellular Resources in Genetic Circuits and its Mitigation Through Decentralized Feedback Control

Genetic circuits in living cells offer tremendous opportunities for tackling a number of societal problems, from energy, to environment, to medicine. Yet, our ability to design sophisticated systems that function as intended is challenged by context-dependence, wherein the input/output behavior of a system depends on the context. Context includes direct connectivity to other modules (resulting in retroactivity) and the pure presence of other modules in the same cell. In particular, genes, modules, and systems all compete with each other for a limited supply of cellular resources, such as RNA polymerases, ribosomes, and a variety of transcriptional co-factors. This competition for resources creates subtle couplings among circuits€™ components, which can dramatically change the circuits€™ intended behavior. In this talk, I will describe a design-oriented predictive model of competition effects and a control theoretic framework to mitigate these effects. Specifically, our predictive models have the same dimension as standard Hill-function-based models, yet they capture resource competition through resource demand coefficients that can be directly tuned experimentally. By tuning these parameters, we can modulate the effective interaction graph of the circuit, the superposition of the intended regulatory graph and the graph of €œhidden€? interactions due to competition. By viewing hidden interactions as disturbance inputs to a circuit€™s nodes, we introduce a control theoretic framework for network disturbance decoupling. A key element of this framework is a quasi-integral feedback controller that rejects disturbances. We provide a concrete implementation that experimentally confirms enhanced robustness to resource competition of a genetic circuit in mammalian cells. Our results enable resource-aware rational design of genetic circuits and set the basis for future genetic circuits that work robustly despite resource limitations.

11:00 AM
11:30 AM

Break

11:30 AM
12:30 PM

Informal Discussion

12:30 PM

Shuttle pick-up (one to hotel, one to airport)

Name Email Affiliation
Angulo, Marco Tulio darkbyte@gmail.com Institute of Mathematics, National Autonomous University of Mexico (UNAM)
Arcak, Murat arcak@berkeley.edu EECS, University of California, Berkeley
Batt, Gregory Gregory.Batt@inria.fr InBio, Inria Saclay - Ile-de-France and Institut Pasteur
Bhattacharya, Sayak sbhatt11@jhu.edu Electrical and Computer Engineering, Johns Hopkins University
Bhatti, Aamer aamer987@gmail.com Electrical Engineering, Capital University of Science & Technology
Braniff, Nathan nbraniff@uwaterloo.ca Applied Mathematics, University of Waterloo
Camacho Gutierrez, Jose Ariel jose.ariel.camacho@gmail.com Basic and Applied Mathematics, Center of Investigations in Mathematics (CIMAT)
Del Vecchio, Domitilla ddv@mit.edu Department of Mechanical Engineering, Massachusetts Institute of Technology
Dhawan, Andrew adhawan@qmed.ca Oncology, University of Oxford
di Bernardo, Diego dibernardo@tigem.it Chemical, Materials and Industrial Production Engineering, Universita di Napoli Federico II''
Duncan, William wduncan@math.duke.edu Mathematics, Duke University
El Samad, Hana Hana.El-Samad@ucsf.edu Department of Biochemistry and Biophysics, University of California, San Francisco
Enciso, German enciso@uci.edu Mathematics, University of California, Irvine
Fletcher, Alvaro aarrospi@uci.edu MCSB, University of California, Irvine
Franco, Elisa efranco@engr.ucr.edu Mechanical Engineering, University of California, Riverside
Gupta, Ankit ankit.gupta@bsse.ethz.ch Department of Biosystems Science and Engineering, EidgenÃ¶ssische Technische Hochschule ZÃ¼rich
Halter, Wolfgang wolfgang.halter@ist.uni-stuttgart.de Institute for Systems Theory and Automatic Control, Universit""at Stuttgart
Horn, Mary Ann maryann.horn@case.edu Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University
Iglesias, Pablo pi@jhu.edu Electrical & Computer Engineering, Johns Hopkins University
Ingalls, Brian bingalls@uwaterloo.ca Applied Mathematics, University of Waterloo
Khaledi Nasab, Ali ak705714@ohio.edu Department of Physics and Astronomy, Ohio University
Khammash, Mustafa mustafa.khammash@bsse.ethz.ch Biosystems Science and Engineering, ETH Zurich
Konstorum, Anna konstorum@uchc.edu Center for Quantitative Medicine, UConn Health
Laubenbacher, Reinhard laubenbacher@uchc.edu Center for Quantitative Medicine, UConn Health
Lillacci, Gabriele gabriele.lillacci@bsse.ethz.ch Dept. of Biosystems Science and Engineering, Eidgen""ossische TH Z""urich
Mathew, Shibin shibin_mathew@dfci.harvard.edu Cancer Biology,
Menezes, Amor amormenezes@ufl.edu Department of Mechanical and Aerospace Engineering, University of Florida
Moehlis, Jeff moehlis@engineering.ucsb.edu Mechanical Engineering, University of California, Santa Barbara
Pantea, Casian cpantea@math.wvu.edu Mathematics, West Virginia University
Papachristodoulou, Antonis antonis@eng.ox.ac.uk Department of Engineering Science, University of Oxford
Paulsson, Johan Johan_Paulsson@hms.harvard.edu Dept of Systems Biology, Harvard University
Pybus, Hannah hannah.pybus@nottingham.ac.uk Mathematics, University of Nottingham
Shoemaker, Jason jason.shoemaker@pitt.edu Chemical and Petroleum Engineering, University of Pittsburgh
Sliwka, Piotr p.sliwka@uksw.edu.pl Faculty of Mathematics and Natural Sciences. School of Exact Sciences, Cardinal Stefan Wyszyński University
Sontag, Eduardo eduardo.sontag@gmail.com Department of Mathematics, Rutgers University at New Brunswick
Srinivasan, Manoj srinivasan.88@osu.edu Mechanical and Aerospace Engineering, The Ohio State University
Stan, Guy-Bart g.stan@imperial.ac.uk Bioengineering, Imperial College London
Steel, Harrison harrison.steel@eng.ox.ac.uk Engineering Science, University of Oxford
Stelling, Joerg joerg.stelling@bsse.ethz.ch Biosystems Science and Engineering, ETH Zurich
Temamogullari, Nihal Nihal.Temamogullari@utsouthwestern.edu Cell Biology, UT Southwestern Medical Center
Thomas, Peter pjthomas@case.edu Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University
Toettcher, Jared toettcher@princeton.edu Molecular Biology, Princeton University
Venturelli, Ophelia venturelli@wisc.edu Biochemistry, University of Wisconsin
Weiss, Ron rweiss@mit.edu Electrical Engineering & Molecular Biology, Princeton University
Xu, Bin bxu2@nd.edu Applied and Computational Mathematics and Statistics, University of Notre Dame
Yoon, Nara nxy47@case.edu Translational Hematology and Oncology Research, Cleveland Clinic Foundation
You, Lingchong you@duke.edu Biomedical Engineering, Duke University
Spatial Patterns of Gene Expression in Multicellular Ensembles

Breaking symmetry in spatially distributed systems is a fascinating dynamical systems problem and is of fundamental interest to developmental biology. In this talk we discuss several feedback mechanisms that enable formation of gene expression patterns in multi-cellular organisms. With the help of dynamical models we reveal the key structural properties that are necessary for patterning and present novel synthetic gene networks built upon these models.

Balancing a genetic toggle switch using real-time control and periodic stimulation

Feedback control methods have recently been applied to successfully take control of cellular functions. This novel field of research aims to remotely pilot cellular processes in real-time with unprecedented levels of robustness and precision to leverage the biotechnological potential of synthetic biology. Yet, the control of only a small number of genetic circuits has been tested so far. In this presentation, I will present the control of a multistable gene regulatory network, which is ubiquitously found in nature and play critical roles in cell differentiation and decision-making. Using an in silico feedback control loop, we demonstrate that a bistable genetic toggle switch can be dynamically maintained near its unstable equilibrium position. Thus, single cells could be controlled to remain in an undecided state for extended periods of time. Importantly, we show that a direct method based on dual periodic forcing is sufficient to simultaneously maintain many cells in an undecided state. These findings pave the way for the control of more complex cell decision-making systems at both the single cell and the population levels, with vast fundamental and biotechnological applications.

Competition for Cellular Resources in Genetic Circuits and its Mitigation Through Decentralized Feedback Control

Genetic circuits in living cells offer tremendous opportunities for tackling a number of societal problems, from energy, to environment, to medicine. Yet, our ability to design sophisticated systems that function as intended is challenged by context-dependence, wherein the input/output behavior of a system depends on the context. Context includes direct connectivity to other modules (resulting in retroactivity) and the pure presence of other modules in the same cell. In particular, genes, modules, and systems all compete with each other for a limited supply of cellular resources, such as RNA polymerases, ribosomes, and a variety of transcriptional co-factors. This competition for resources creates subtle couplings among circuitsâ€™ components, which can dramatically change the circuitsâ€™ intended behavior. In this talk, I will describe a design-oriented predictive model of competition effects and a control theoretic framework to mitigate these effects. Specifically, our predictive models have the same dimension as standard Hill-function-based models, yet they capture resource competition through resource demand coefficients that can be directly tuned experimentally. By tuning these parameters, we can modulate the effective interaction graph of the circuit, the superposition of the intended regulatory graph and the graph of â€œhiddenâ€? interactions due to competition. By viewing hidden interactions as disturbance inputs to a circuitâ€™s nodes, we introduce a control theoretic framework for network disturbance decoupling. A key element of this framework is a quasi-integral feedback controller that rejects disturbances. We provide a concrete implementation that experimentally confirms enhanced robustness to resource competition of a genetic circuit in mammalian cells. Our results enable resource-aware rational design of genetic circuits and set the basis for future genetic circuits that work robustly despite resource limitations.

Applications of external control of gene expression in yeast and mammalian cells

I will discuss our recent results on the control of gene expression from chemically inducible promoters in both yeast and mammalian cells. I will discuss the peculiarities and difficulties in controlling biological systems, then I will show how the same control strategy and experimental platform can be applied to both organisms, despite the huge differences in their biology. I will also show how we are using these external controllers to gain quantitative insights in disease causing proteins such as alpha-synuclein involved in Parkinson's disease aetiology.

Build to Perturb, Perturb to Understand

We discuss synthetic biology tools that interface with endogenous biological systems, perturbing them to reveal their organization and function.

Absolute Robustness and Output Stabilization in Stochastic Chemical Reaction Networks

Absolute robustness is a structural property ensuring that the steady state output of a chemical reaction network is unchanged as a function of total protein concentrations. Originally proposed by Shinar and Feinberg, this property holds regardless of parameter values and can be verified by inspection of the network topology. In this talk I discuss how this property generalizes to the case of stochastic networks. I also discuss the stabilization of a given network by the addition of an appropriate absolute robustness module, incorporating recent work by Mustafa Khammash.

Engineering Robust Molecular Feedback Controllers via Ultrasensitive Modules

I will describe a molecular reaction network that can be used to achieve robust closed loopcontrol in synthetic biology. The network relies on a motif that exhibits an ultrasensitiveinput-output mapping, and is therefore termed â€œbrink controllerâ€?. Ultrasensitivity is achievedby combining molecular titration with an activation/deactivation cycle, and requires thepresence of fast titration and switching rates, together with slow degradation rates. I willdescribe the properties of this motif, touching on the challenge of supplying simultaneouslypositive and negative action on the system to be controlled; I will also discuss potentialexperimental realizations of the circuit using RNA aptamers. Finally, I will mention ongoingcollaborative efforts to build universal PID controllers using RNA riboregulators and theCRISPR/Cas system, relying on a rapid cell-free prototyping environment.

Passivity-based ensemble control for cell cycle synchronization

We investigate the problem of synchronizing a population of cellular oscillators in their cell cycle. Restrictions on the observability and controllability of the population imposed by the nature of cell biology give rise to an ensemble control problem specified by finding a broadcast input based on the distribution of the population. We solve the problem by a passivity based control law which we derive from the reduced phase model representation of the population and the aim of sending the norm of the first circular moment to one. Furthermore, we present conditions on the phase response curve and circular moments of the population which are sufficient for synchronizing a population of cellular oscillators.

Using Mathematical Modeling to Understand the Role of Diacylglycerol (DAG) as a Second Messenger

Diacylgylcerol (DAG) plays a key role in cellular signaling as a second messenger. In particular, it regulates a variety of cellular processes and the breakdown of the signaling pathway that involves DAG contributes to the development of a variety of diseases, including cancer. A mathematical model of the G-protein signaling pathway in RAW 264.7 macrophages downstream of P2Y6 activation by the ubiquitous signaling nucleotide uridine 5â€™-diphosphate is presented. The primary goal is to better understand the role of diacylglycerol in the signaling pathway and the underlying biological dynamics that cannot always be easily measured experimentally. The model is based on time-course measurements of P2Y6 surface receptors, inositol trisphosphate, cytosolic calcium, and with a particular focus on differential dynamics of multiple species of diacylglycerol. When using the canonical representation, the model predicted that key interactions were missing from the current pathway structure. Indeed, the model suggested that to accurately depict experimental observations, an additional branch to the signaling pathway was needed, whereby an intracellular pool of diacylglycerol is immediately phosphorylated upon stimulation of an extracellular receptor for uridine 5â€™-diphosphate and subsequently used to aid replenishment of phosphatidylinositol. As a result of sensitivity analysis of the model parameters, key predictions can be made regarding which of these parameters are the most sensitive to perturbations and are therefore most responsible for output uncertainty. (Joint work with Hannah Callender, University of Portland, and the H. Alex Brown Lab, Vanderbilt.)

Set point control, excitable systems and cell migration

In recent years, there has been considerable experimental evidence that the migration of cells is regulated by a network displaying excitable behavior. Stochastically generated firings of this excitable system can generate random actin-filled protrusions that propel cells. By altering the threshold of the excitable system is a spatially-dependent manner, external stimuli can bias this stochastic activity to direct cellular motion. In this talk we present some recent theoretical and experimental results that demonstrate that by altering the set point of this excitable system synthetically, cellular protrusions can expand and, consequently, the cell can be induced to transition between different migratory modes.

Synthetic biology approaches to suppression of antibiotic resistance: model-based design

Antibiotic-resistant pathogens present an increasing global health concern. Our group is investigating synthetic biology-based strategies for suppression of resistance in environmental bacterial populations. This approach involves the delivery of engineered genetic elements to target populations. We are developing models of the dynamics of this system, at both the genetic and population level, to be used for model-based design of potential implementations. Analysis of proof-of-principle scenarios and accompanying experimental results will be presented.

Antithetic Integral Feedback: A New Motif for Perfect Adaptation

Robust perfect adaptation in biology can be realized through the use of integral feedback control. Here we present the underlying theory and first implementation of an integral feedback control system that can achieve perfect adaptation in a living cell. The controller is based on the recently published antithetic integral feedback motif which has been analytically shown to have favorable regulation properties. The closed-loop system is highly tunable, allowing a regulated protein of interest to be driven to a desired level and maintained there with precision. Realized using a sigma/anti-sigma protein pair, the integral controller ensures that regulation is maintained in the face of perturbations that lead to the regulated protein's degradation, thus serving as a proof-of-concept prototype of integral feedback implementation in living cells.

Controlling Biological Oscillators

Nonlinear oscillators - dynamical systems with stable periodic solutions - arise in many systems of physical, technological, and biological interest. Examples from biology include pacemaker cells in the heart, the firing of action potentials in neurons, and circadian rhythms. There are situations in which it is desirable to control biological oscillators, for example changing the phase of the circadian rhythm in order to adjust to a new time zone. With this in mind, we have developed an optimal control algorithm to change the phase of a periodic orbit using a minimum energy input, which also minimizes the transversal distance to the uncontrolled periodic trajectory. Our algorithm uses a two-dimensional augmented phase reduction technique based on both isochrons and isostables. This control algorithm is effective even when a large change in time period is required or when the nontrivial Floquet multiplier of the periodic orbit is close to one; in such cases, an analogous control algorithm based on standard phase reduction fails. Inspired by deep brain stimulation treatment of Parkinson's disease, we have also developed control algorithms for desynchronizing populations of oscillators, for example by maximizing the Lyapunov exponent associated with their phase dynamics, and through optimal phase resetting.

Modelling and Design of Feedback Circuits in Biology

Feedback control is found extensively in many natural and technological systems. Indeed, many biological processes use feedback to regulate key processes â€“ examples include bacterial chemotaxis and negative autoregulation in genetic circuits. Despite the prevalence of feedback in natural systems, its design and implementation in a Synthetic Biology context is much harder. In this talk I will give examples of how we implemented feedback systems in three different biological systems. The first one concerns the design of a synthetic recombinase-based feedback loop, which results into robust expression. The second describes the use of small RNAs to post-transcriptionally regulate gene expression through interaction with messenger RNA (mRNA). The third involves the introduction of negative feedback in a two-component signalling system through a controllable phosphatase. Closing, I will outline the challenges posed by the design of such systems, both theoretical and on their implementation.

Control in single cells
Dynamic Response Phenotypes in Systems Biology: Scale-Invariance and Monotone I/O Systems

Among the central questions in systems biology are those of understanding the roles of, and interactions among, signal transduction pathways and feedback loops. This talk focuses on â€œdynamic phenotypesâ€? characterized by input/output responses to external inputs in addressing such issues, using fold-change detection and monotone architectures as case studies.

An ubiquitous property of sensory systems is "adaptation": a step increase in stimulus triggers an initial change in a biochemical or physiological response, followed by a more gradual relaxation toward a basal, pre-stimulus level. Adaptation helps maintain essential variables within acceptable bounds and allows organisms to readjust themselves to an optimum and non-saturating sensitivity range when faced with a prolonged change in their environment. It has been recently observed that some adapting systems, ranging from bacterial chemotaxis pathways to signal transduction mechanisms in eukaryotes, enjoy a remarkable additional feature: scale invariance or "fold change detection" meaning that the initial, transient behavior remains approximately the same even when the background signal level is scaled. This talk will review the biological phenomenon, and formulate a theoretical framework leading to a general theorem characterizing scale invariant behavior by equivariant actions on sets of vector fields that satisfy appropriate Lie-algebraic nondegeneracy conditions. The theorem allows one to make experimentally testable predictions, and the presentation will discuss the validation of these predictions using genetically engineered bacteria and microfluidic devices, as well their use as a "dynamical phenotype" for model invalidation. The talk will also include some speculative remarks about the role of the shape of transient responses in immune system self/other recognition and in cancer immunotherapy, as well as a brief discussion of how control-theoretic structures such as differential positivity (monotonicity) have been experimentally employed together with experimental data in order to elucidating mechanisms for stress responses and chemosensing.

Design of de novo biomolecular feedbacks for improved performance and robustness in living cells

In this talk I will give an overview of some of our research activities in the "Control Engineering Synthetic Biology" group, where we focus our efforts on developing foundational forward-engineering methods to mathematically model, control, and experimentally implement synthetic gene circuits and cellular systems that aim at increasing the robustness, performance, and genetic stability of engineered cells. During the talk, I will propose some approaches to answer some of the following questions in systems and synthetic biology:

• How can we use cellular resources more efficiently to simultaneously improve growth rates and production yields?

• What is the interplay between feedback and buffering in cellular homeostasis?

• How can we create genetic oscillators for which the amplitude and period of oscillations can be tuned independently?

Multi-Objective and Multi-Scale Design of Synthetic Gene Circuits

Synthetic gene circuits have to operate in natural systems such as cells or organisms, with corresponding load on and cross-talk with them. These aspects are of particular relevance for biomedical applications where multiple design objectives with trade-offs (e.g., efficiency and robustness) and multiple scales (e.g., organism-wide impact of cell-based therapeutics) need to be considered. The first part of the talk will describe a Bayesian circuit design method that identifies circuit topologies whose behavior is robust to variations in parameters; it enables to reliably assess trade-offs between performance, robustness, and experimental feasibility, thus increasing the probability of success of circuit implementation. The second part will discuss a biomedical application of synthetic gene circuit design for in vivo closed-loop control, specifically for the treatment of type 1 and 2 diabetes; we achieved glucose responsiveness by a synthetic circuit that couples glycolysis-mediated calcium entry to an excitation-transcription system controlling therapeutic transgene expression. The examples help to argue that novel systems analysis methods are needed to enable efficient computational design of synthetic circuits, and how the design of synthetic systems allows us to refine our understanding of natural biological systems.

Multiple Metabolic Signals Establish a Deterministic Cell Fate Decision

Single cell resolution reveals that isogenic cells exposed to the same environmental cues will choose between different fates. To understand how deterministic this process is, we need to follow various signals that cells are integrating continuously throughout the course of decision-making. To this end, we developed an experimental-theoretical framework, which enables (1) tracking multiple signals in single cells from exposure to differentiation signal to the realization of a particular fate and (2) combining these high dimensional observations into fate probabilities. More specifically, we coupled microfluidics and time-lapse microscopy, which allows for imaging up to 6 endogeneously tagged fluorophores. To merge this high dimensional data in a rigorous way, we used statistical evidence that translates these measurements into fate probabilities. This way, we can follow the â€˜predispositionâ€™ of each cell towards a certain cell fate over the entire decision-making process. We used budding yeast meiosis as a model system, where, upon nutrient limitation, cells either commit to meiosis or become quiescent. With this framework, we followed the metabolic status of single cells and we can predict cell fates with very high accuracy well in advance the commitment point. This is a joint project with Andreas Doncic, Orlando Arguello Miranda and Yanjie Liu.

For the mathematical background of my talk, one can read "Section 4.2: Testing Binary Hypotheses with Binary Data" from the book Probability Theory - The Logic of Science by E.T. Jaynes. For cellular-decision making, the review "Cellular Decision-Making and Biological Noise: From Microbes to Mammals" by Balaszi, Oudenaarden and Collins ?and for the model system budding yeast sporulation, the review "Sporulation in Budding Yeast Saccharomyces cerevisiae" by Aaron M. Neiman are good sources.

Optogenetics for intracellular codebreaking: how signaling stimuli is interpreted to control gene expression and cell behavior

Studies of cell signaling networks have repeatedly revealed complex patterns of protein activity: oscillations, traveling waves, and polarized spatial distributions. However, our tools to perturb and control these patterns have typically lagged far behind our ability to observe them. I will describe our recent work to develop light-sensitive proteins to control basic signaling processes in mammalian cells and Drosophila embryos, and discuss our recent work to dissect how Erk signaling controls gene expression and cell fate across these model systems.

Elucidating network design principles of microbial consortia for controlling ecological states

Microbial communities are coupled networks of diverse organisms operating on multiple time and spatial scales that occupy nearly every environment on Earth. A detailed and quantitative understanding of microbial communities combined with capabilities to forecast dynamic responses will enable the design of targeted interventions to shift communities to desired states. I will describe a generalizable model-guided approach to reverse engineer microbial interactions that shape the assembly of a human gut microbiome synthetic ecology. We show that pairwise interactions are major drivers of multi-species community assembly as opposed to higher-order interactions. The inferred microbial interaction network as well as a top-down approach to community assembly pinpointed influential and ecologically responsive species that were significantly modulated by microbial inter-relationships. We identify network topologies for robust species coexistence and prolonged memory of prior history. In sum, these methods define the ecological roles of constituent members of the community and illuminate design principles of stability, robustness and adaptability of microbial ecosystems.

Mammalian Synthetic Biology: Engineering Sophisticated Gene Regulation for Therapeutic Systems

Synthetic biology is revolutionizing how we conceptualize and approach the engineering of biological systems. Recent advances in the field are allowing us to expand beyond the construction and analysis of small gene networks towards the implementation of complex multicellular systems with a variety of applications. In this talk I will describe our integrated computational / experimental approach to engineering complex behavior in mammalian cells. In our research, we appropriate design principles from electrical engineering and other established fields. These principles include abstraction, standardization, modularity, and computer aided design. But we also spend considerable effort towards understanding what makes synthetic biology different from all other existing engineering disciplines and discovering new design and construction rules that are effective for this unique discipline. We will discuss experimental results with synthetic biology building blocks for intracellular sensing, processing, and actuation in mammalian cells. We will then present a genetic circuit that can detect and destroy specific cancer cells based on the presence or absence or specific biomarkers in the cell. We will also discuss preliminary experimental results for obtaining precise spatiotemporal control over stem cell differentiation for tissue engineering applications. Finally, we will discuss a new framework for creating regulatory circuits based strictly on protein-protein interactions. These protein-phosphorylation based circuits operate at much faster speeds than existing transcriptional and translational based systems, with the ability to respond to a stimulus within a few seconds, thereby creating opportunities for new synthetic biology capabilities and applications.

Cancer Modeling: Optimal Therapy Scheduling Based on a Pair of Collaterally Sensitive Drugs

Cancer is a disease developed by uncontrolled growth of mutated cells. To study such cancer, many different scales of researches has been carried out, from a small size of molecules to large size of organisms [1,2]. In this talk, I will give a brief overview about the range of mathematical oncology, and then talk about a recent project of a cellular scale modeling worked by my team.

In the project, we developed a model of ordinary differential equations and study the effect of sequential therapy on heterogeneous tumors comprised of resistant and sensitivity cells. Based on the model, we figured out (i) the optimal drug-switch strategy, and (ii) how composition of sensitive and resistant cell populations changes. Beyond our analytic results, we explored an individual based stochastic model and presented the distribution of extinction times for the classes of solutions found. Taken together, our results suggest opportunities to improve therapy scheduling in clinical oncology.

Reference

1. Anderson, A. R., & Quaranta, V. (2008). Integrative mathematical oncology. Nature reviews. Cancer, 8(3), 227.

2. Byrne, H. M. (2010). Dissecting cancer through mathematics: from the cell to the animal model. Nature reviews. Cancer, 10(3), 221.

Bacterial dynamics in time and space

Antibiotics are arguably the greatest medical discovery of the 20th century. However, due to over-prescription and misuse, microbes have rapidly evolved many strategies to survive drug treatment. Indeed, resistance emerges faster than new drugs can be developed, leading many to wonder whether we are returning to the â€œdark agesâ€? of the pre-antibiotic era. Addressing the antibiotic crisis requires two complementary strategies. One is to develop novel drugs and therapeutic methods to combat bacterial infections. It is equally critical to develop effective antibiotic treatment protocols that can extend the efficacy and usability of existing antibiotics. Development of effective antibiotic treatment protocols requires a mechanistic understanding of both the short-term and long-term bacterial dynamics in response to antibiotic treatment. In this talk, I will discuss our ongoing efforts along this line of investigation, with a particular focus on dynamics of horizontal gene transfer.

### Posters

Controlling Microbial Communities

Humans, plants, and oceans are inhabited by microbes that perform biogeochemical processes that are key to the well-being of their host and environment. Increasing evidence links a disruption in the state of these microbial communities to diverse alterations, including human diseases, dysfunctional crops, and climate change. We are developing a framework for controlling these microbial communities to restore their healthy states.

Application of the the CcaS/CcaR Optogenetic System to Metabolic Engineering: a Mechanistic Model

The CcaS/CcaR optogenetic system has recently been characterized in E. coli, providing precision and flexibility in the time-varying control of this biological system. Metabolic engineering is a potential application area for this new tool. Works in this field have demonstrated the increased efficiency possible through temporal control of enzyme abundance in metabolically engineered pathways. The production of myo-inositol is one such example. In this work we have provided a mechanistic re-formulation of an empirical mathematical model of the CcaS/CcaR system. We have also constructed a dynamic model of the the myo-inositol production pathway using constraint based methods. We then coupled both models to provide behavioural predictions for an optogenetically controlled metabolic pathway. We aim to use this hybrid model, along with experimental design techniques, to better understand optimal regulation of metabolism in synthetic metabolic pathways.

An Optimal Control Approach to Study Cell Dynamics Under Bone Metastasis Denosumab/Radiotherapy Treatments

Bone metastasis is the spread of cancer cells from a primary tumor (e.g. prostate, breast) to the bone tissue. Clinically, it is considered incurable. Metastatic cells trigger a vicious cycle within the bone microenvironment by manipulating bone cells (osteo-blasts, -clasts, -cytes) in order to sustain its invasion. Using ecological principles, we examine this complex interaction network with mathematical models. Applying optimal control theory we aim to study bone metastasis treatments. The optimal treatment is modeled to minimize side-effects and the cost.

Homeostasis Despite Instability

Homeostasis is a ubiquitous phenomenon that plays a crucial role in cryptic genetic variation, predisposition to disease, and precision medicine. In the simplest case there is a homeostatic variable, Z, which has a stable equilibrium that changes very little over a large range of input, I. We have discovered a new phenomenon. The equilibrium may lose stability via a Hopf bifurcation but have limit cycle amplitudes which are small enough so that homeostasis is preserved. In the case of a feedback inhibition network, we investigate how the network length effect the amplitudes and existence of these instability regions.

Variance reduction in Antithetic Integral Controller Using Proportional Action

The Antithetic Integral Controller (AIC), recently introduced by Briat, Gupta and Khammash (Cell Systems 2016), is known to ensure robust perfect adaptation and disturbance rejection for the mean dynamics of a given molecular species involved in a complex stochastic biomolecular reaction network. However, one drawback of AIC is that it enhances the variance of the controlled species when compared to an open-loop control strategy. Here we discuss how this problem can be resolved by combining AIC with an additional negative feedback in the form of proportional action. Our results show that this strategy can reduce the stationary variance, even below the level of an equivalent open-loop controller, but there is a trade-off between the settling time of the controller and the achieved reduction in the stationary variance.

This is joint work with Corentin Briat and Mustafa Khammash.

Reference: Briat , Gupta and Khammash, Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks, Cell Systems, Vol 2(1), 2016.

Synergy through Nonlinearity: a Mathematical Model for Creation of 'Supereffector T Cells' by Combination Immunotherapy

1Center for Quantitative Medicine, UConn Health, Farmington, Connecticut, USA
2Department of Immunology, UConn Health, Farmington, Connecticut, USA
3Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA

Combined agonist stimulation of the CD8+ T cell costimulatory receptors OX40 (CD137) and 4- 1BB (CD134) has been shown to generate ‘supereffector` T cells that survive longer and produce a greater quantity of cytokines that mediate tumor cell killing in vivo compared to T cells stimulated with an agonist of either costimulatory receptor individually. In order to understand the mechanisms for this synergy, we have created a mathematical model for the activation of the CD8+ T cell intracellular signaling network by mono- or dual-costimulation (DCo) and show that synergy by DCo results from nonlinear interactions between intermediate signaling components. In silico simulation of the model with and without knock-out experiments supports published experimental results. We propose that, in a more general setting, these types of nonlinear interactions can play a key role, and thus be exploited, for maximizing synergy in designing combination immunotherapies.

Monoallelic Expression: The Effects of Allelic Bias on Signaling Outputs

Abstract not submitted.

Regulating Your Immune Response: Insights from Modeling Influenza-Induced Host Responses

The threat to public health that viruses such as influenza pose are well known. Lesser known is what makes a specific strain more deadly. Several recent animal studies with very deadly viruses (e.g. the 1918 influenza virus; responsible for the death of 2% of the world’s population) have shown that the immune response to the infection is a strong factor in determining severe disease outcomes. Additional studies in immune-compromised animals have further supported the idea that potent immune responses result in severe outcomes; suggesting that the immune response could be “tuned” to promote better patient outcomes. Our research group applies systems engineering modeling and simulation, bioinformatics and network biology to (1) characterize the dynamics of the host response during infection and (2) identify potent regulators of both the host response and virus replication. Recently, we demonstrated how dynamic clustering and mathematical modeling can be combined to identify key aspects of the host response kinetics. Specifically, we showed that the kinetics of the immune response appear conserved for deadly and mild influenza infection and that the immune response is regulated by an ultrasensitive-like mechanism1. This previously unknown feature of the immune response has recently been observed in A549 human lung epithelial cells. Current projects include (1) using network contexts and control of protein-protein interaction networks to identify proteins (drug targets) essential to virus replication (under review) (2) evaluating deconvolusion algorithms for their use in developing host response models and (3) mathematical modeling to understand how feedback impacts DNA- versus RNA-induced inflammation.

1. Shoemaker, J. E., Fukuyama, S., Eisfeld, A. J., Zhao, D., Kawakami, E., Sakabe, S., Maemura, T., Gorai, T., Katsura, H., Muramoto, Y., Watanabe, S., Watanabe, T., Fuji, K., Matsuoka, Y., Kitano, H. & Kawaoka, Y. An Ultrasensitive Mechanism Regulates Influenza Virus-Induced Inflammation. PLoS Pathog 11, e1004856 (2015).

Modeling the Dynamics of Cdc42 Oscillation in Fission Yeast

Regulation of polarized cell growth is essential for many cellular processes, including spatial coordination of cell morphology changes during growth and division. We present a mathematical model of the core mechanism responsible for the regulation of polarized growth dynamics during the fission yeast cell cycle. The model is based on the competition of growth zones of Cdc42 localized at the cell tips for a common GEF distributed uniformly in the cytosol. We consider several potential ways of implementing negative feedback between Cd42 and its GEF in this model that would be consistent with the observed oscillations of Cdc42 in fission yeast. We analyze the bifurcations in this model as the cell length increases and the total amount of Cdc42 increases.

Controlling Biological Oscillators
Jeff Moehlis

Nonlinear oscillators - dynamical systems with stable periodic solutions - arise in many systems of physical, technological, and biological interest. Examples from biology include pacemaker cells in the heart, the firing of action potentials

Diacylgylcerol (DAG) plays a key role in cellular signaling as a second messenger. In particular, it regulates a variety of cellular processes and the breakdown of the signaling pathway that involves DAG contributes to the development of a v

Cancer is a disease developed by uncontrolled growth of mutated cells. To study such cancer, many different scales of researches has been carried out, from a small size of molecules to large size of organisms [1,2]. In this talk, I will give

Synthetic biology is revolutionizing how we conceptualize and approach the engineering of biological systems. Recent advances in the field are allowing us to expand beyond the construction and analysis of small gene networks towards the implementa

Feedback control methods have recently been applied to successfully take control of cellular functions. This novel field of research aims to remotely pilot cellular processes in real-time with unprecedented levels of robustness and precision

Synthetic gene circuits have to operate in natural systems such as cells or organisms, with corresponding load on and cross-talk with them. These aspects are of particular relevance for biomedical applications where multiple design objectives with

In this talk I will give an overview of some of our research activities in the "Control Engineering Synthetic Biology" group, where we focus our efforts on developing foundational forward-engineering methods to mathematically model

Antibiotic-resistant pathogens present an increasing global health concern. Our group is investigating synthetic biology-based strategies for suppression of resistance in environmental bacterial populations. This

Breaking symmetry in spatially distributed systems is a fascinating dynamical systems problem and is of fundamental interest to developmental biology. In this talk we discuss several feedback mechanisms that enable formation of gene expressi

Control in single cells
Johan Paulsson

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