SynBio Day’21 Notes

Synthetic Biology Day’21 organized by @BilkentSynBio at 24–25 April. I attended 11 hours of talks (all!) and took 17 pages of notes. Not only enjoyed a lot but also learnt a lot. Now, it is time to share those with you. Have fun!

I added the links of the labs, the researchers presented their work (in case you are further interested in).

DISCLAIMER: I am not an expert of the field, so please don’t forget to read the notes critically. Also please feel free to share your feedback in the comments!

I would like to thank the Bilkent SynBio society, all the organizers and speakers for this great event!

Overall schedule of the talks (retrieved from twitter page of Bilkent Synthetic Biology society)

Day 1

14–14:50

The first talk was given by Dr. Friedrich Simmel from Technical University of Munich, titled as “Gene Circuits in Synthetic Biology and Real Cells”. The talk was about:

• Modelling “gene expression as a function of position&time”
• Also using time and inducer concentration to understand positional information
• 3-input AND gates
• Embedding cell-free genetic oscillator in a gel
• Switchable CRISPR activation for mammalian cells
• (Conditional) miRNA, shRNA coupled CRISPR activation
• (Displacement) switchable design of gRNA for Cas12a
• There was a nice question regarding why some crispr systems work better in mammalian cells from the audience, by Julian. If I understood correctly… — the relevant RNA might be more prone to degradation in bacterial cells — (polyA tailed based systems), there is more freedom for choosing the sequence

15–15:50

Dr. Roman Jerala from University of Ljubljana presented his work on “Coiled-coins for design of cellular logic circuits and new protein folds”. The talk was about:

• Designable dimerization building models (apart from DNA): coiled-coil (CC) dimers (w/ parallel and anti-parallel)
• De novo design of orthogonal CC pairs (to human proteome, and in mammalian cells)
• Tunable fast response by CC pairs, alternative to transcriptional regulation (which is slow, takes several hours). However CC based design reaches its saturation in ~20mins!
• Design of tetrahedral protein folds not found in nature (with “What I cannot create, I don’t understand”, R. Feynman way of deconstructing first)
• COCOPOD: pipeline of designing CCPOs (shape selection to synthesis)
• Confirming with Transmission Electron Microscopy (TEM) and Atomic Force Microscopy (AFM), also with Small-angle X-ray Scattering (SAXS) , also controlling in vivo productivity and biocompatibility.

16–16:50

Third talk was about (scientific yet fun talk) “The Power of LOV (Light Oxygen Voltage)”, presented by Dr. Barbara Di Ventura from University of Freiburg. The talk was about:

• Selected perturbations (i.e. knock-out of a gene) used to understand cell function. Similarly, protein function can be perturbed by regulating its concentration, location etc. However, proteins are dynamic entities!
• So, Light might be the best trigger.
• Light provides fast, cheap, and reversible triggering! (Remember remember the meaning of optonetics-er)
• Engineering of novel light sensitive proteins
• LOV domains (blue light sensitive) as signaling switches
• LOV2 based photo-caging of peptides (fast, reversible, genetically encoded, even smaller than GFP!)
• Caging Nuclear Localization Signals (NLS) to control nuclear import by LOVs
• LINuS(Light inducible nuclear localization signal) & LEXY(Light inducible expert system) to control the protein of interest)
• Making AraC (used to regulate reporter gene expression) light inducible
• Taking advantage of AraC working as a dimer (changes confirmation upon binding). Using DNA binding domain (AraC DBD) and
• Adding VVD (vivid as light- response dimerization system, blue light leads to dimerize) to
• Create bacterial photograhs etc.(varying light response upon linker, AraC DD domain modifications) via
• BLADE tools system with different combinations of VVD and AraC

17–18 (Break)

18–18:50

Dr. Srivatsan Raman ‘s, from University of Wisconsin-Madison, talk about “Synthetic Bacteriophages for Therapy”.

He started with a general introduction to biomolecular function.

• Then continued with genetoype-phenotype relationship by emphasizing phage- bacterial interaction (fun fact: there are 10³¹ phages on earth!).
• Phage life cycle (bind-inject-replicate-lyse)
• There is a HUGE problem of antibiotics resistance, rapid resistance to new drugs as well. So, OLD IS THE NEW! Using phages as a drug (precise, easily evolvable, auto-design-able)
• However, phage therapy is not commonly applied. Several reasons: narrow target spectrum, lower killing efficacy compared to antibiotics, inconsistency of phage cocktails, bacteria resistance to phages.
• ORACLE: high throughput phage engineering might help to overcome some of these challenges.
• By identifying sequence-function relationship and deep-mutational screening
• Phagepods as an effective device for gene-delivery

19–19:50

The last talk of the day is about “Synthetic Biology Approaches to Understand and Engineer the Gut Microbiota” by Dr. Mark Mimee from University of Chicago.

• He started with general introduction to microbiota (i.e bacteria, fungi, viruses, archae, protists), why they are important (e.g. numerous diseases linked) and how to engineer (e.g. establishing causality, engineering for therapeutics).
• Principles of it: sense-integrate-respond
• Then continued with the challenges (i.e. non-model microbe engineering, complex interactions, high heterogeneity, not-well sdudied)
• Creating “breadboard” for prototyping
• Finding representative communities
• Domesticating gut microbes via synthetic biology
• Using microbiome transcriptional response to have an idea about disease state
• Whole cell biosensors and blood sensor
• Ingestible MicroBio electronic device for multiple applications
• Future of additive microbiome eng. (additive: commensal; substractive: bacteriophage eng.)

Day 2

13–13:50

Day-2, first talk: “Large Scale SynBio, from Petri-Dish to Planet Earth” by Dr. Victor De Lorenzo from CNB-CSIC in Madrid.

• He emphasized how synbio can be used to overcome 10 big challenges of our planet has been facing.
• Such as designing ecosystem restoration agents to have sustainable soil ecosystems which depend on the balance between mineral, microbiome and plants.
• Recombineering by MAGE mutagenesis.
• CcaSR switch and optogenetic biofilm controlling
• BioPhysical engineering for different bacterial systems
• Modeling & implementing microbial communities
• Re-programming different properties of bacteria such as adhesion

14–14:50

Day-2, second talk was given by Dr. Lior Nissim from Hebrew University: “ Synthetic Immunomodulatory Gene Circuits for Cancer Immunotherapy”

• He started with the into of how to use synbio for therapeutics (simply based on disease vs. healthy state)
• Brief history of genetic circuits to biomedical applications (fun fact: 2000, first genetic circuits; 2010, Cancer targeting circuit based on two cancer markers etc.)
• How to overcome tumor mediated immunosupression (using checkpoint inhibitors and immunostimulatory cytokines are good but what about severe systematic cytotoxicity of it?) Well, use circuits! How?
• Although circuits infecting both healthy&cancer cells are used, AND gates w/increased precision will lower the error probability. Then these circuits will express the combination of immunomodulators triggering anti-tumor response.
• Short-range treatment for long-range response
• Circuits for tumor specific killing by T cells
• Combination Therapy (SCLP) to enhance treatment efficacy.
• Simultaneous cancer SCIP expression by circuits
• High Throughput Synthetic Promoter Engineering (native lacks binding specificity; synthetic, S(TF)p, more specific)
• To design these S(TF)s, they used common databases (i.e. TCGA to identify TFs) to identify cancer specific TFs, determine TF binding sites, cloned and tested. However the success rate was low! So, they created:
• Synthetic Promoters Engineering and Screening pipeline (library construction, cell infection FACS sorting, Next Generation Sequencing, analysis of promoter, specificity and activity at the end). This provides to production of Tumor Specific Synthetic Promoters, also the tissue specific versions!

15–15:50

Day-2, the third talk: “Evolution of Gene Network Activity by Tuning Strength of Negative-Feedback Regulation” by Dr. Murat Acar from Yale University.

His talk was about three main ideas:

• Understanding the evolution of gene networks
• Investigating specific components of a gene network, which helps it to evolve
• Finding possible generalizing principles guiding a gene network These require:
• well characterized components
• Identification of a phenotype that is quantitable
• Exploration of its response to relevant inducers
• Characterization of intra-specie difference of the given phenotype So, they study these on one of two main workhorses of synbio: Yeast!
• on GAL pathway (evolutionary conserved model network, works with galactose). However there are certain points to be careful:
• making sure galactose is used in the selected model strain
• the protein function etc. is preserved
• Building a famework for the network inducibility (= the fraction of ON-cells in the bimodal population)
• Measuring the activity of GAL network in two yeast strains: S. cerevisiae and S. paradoxus
• Questioning whether different GAL promoters (pGAL) are required to gave different network activity across species (not necessarily)
• Comparing different promoter activity strength in these strains
• The effect of promoter swapping for GAL network compared to will type (WT)
• How to measure maximal inducibility and galactose sensitivity of hybrid GAL networks
• Why tuning of GAL80 promoter strength is important?(required and sufficient to dictate phenotypic characteristics of the given strains)
• How to quantify relative fitness?(competition exp.)

16–16:50

Day-2, fourth talk was presented by Dr. Ed Boyden from MIT. The title was “Tools for Analyzing and Repairing Complex Biological Systems” .

• He started with a question: “Can we understand the brain, or other complex biological systems? (somewhat creating realistic environments?)
• 3 ways: — mapping molecules&wiring connections via Expension Microscope (ExM) — detecting high speed dynamics via controlling&observation
• To understand the brain:
• mapping by ExM (swellable polymer based material upon absorption of water etc.)
• controlling by optogenetics (bacterio/halo/channel-rhodopsins)
• observing via tools for fluorescent imaging of the dynamics (evolving molecules to have better signal)
• Possible combination of ExM with other microscope technologies to improve some aspects such as w/lattice light sheet microscopy (700x faster than classical super-resolution microscopy)
• availability of step by step application/photography tutorial of ExM.
• Light contol by optogenetics (microbial opsins, 7-transmembrane proteins, binding endogenous all-trans-retinal)
• Jaws as non-invasive optogenetic neural silencing
• Chrimson as quasi-infrared channel rhodopsin
• Evolution screen to have stable, safer, brighter molecules

(Cat enters Dr. Boyden’s room =D)

• Can we cluster signaling reporters at different points throughout a cell? (a.k.a “islands”) by:
• finding base distribution of reporters
• alive cell appearance
• reconstructing reporter identity
• Zebrafish (fun fact: it has *only 100.000 neurons in the larval state*) and worm as a model organism to understand the dynamics

17–18 (Break)

18–18:50

Day-2, the fifth speaker was Dr. Urartu Şeker from Bilkent University (UNAM). The title of his talk was “Living Drugs, Living Biosensors, Living Biomaterials”.

• Main emphasis of the talk was “sense-respond” systems by using cells & self-actuated protein delivery devices.
• How to engineer bacteria to secrete a protein (protein secretion machinery in bacteria, secretion s. to autotransporters). Basically, how to display it on the cell surface and release
• Validation of the display (i.e. AuNP labeling of Ag43)
• TEV protease secretion strategies (i.e.AND gate expression)
• A state machine for sequential release of multiple proteins (e.g. using recombinase based systems) which can be extended to self-actuated drug delivery (Wow!)
• Using these engineered self-actuated circuits to target cancer
• Living sensors: cellular biosensors such as to detect toxicity by taking advantage of environmental stress response (i.e. riboregulator mediated nano toxicity sensors, repressor mediated sensing control)
• Multi-input cellular biosensors: AND gate
• Toehold switch based detection systems (i.e. sars-cov-2)
• Living Materials (controlling the secretion of proteins via complex circuit hierarchy)
• Gene circuits for nanomaterial (i.e. CdS) synthesizing bio-devices

19–19:50

Day-2, final speaker: Dr. Savaş Tay from University of Chicago presented his work on “Understanding Complex Cellular Pathways Using Microfluidic Analysis and Mathematical Modelling” focuses on Immune Signaling (w/NF-kB), noise and single cells. (and TayLab in a nutshell)

• He started with general introduction to NF-kB (has a role in infection, cancer response and autoimmunity), and how the signal is procesed by it.
• Dose processing (NF-kB activation in single cell is digital)
• Modeling increased cytokine dose vs. NF-kB [~log(Delta(C, cytokine) +1)]
• Short-Pulse Inputs (area determines NF-kB dynamics)
• Synchronization (=entrainment) of NF-kB with periodic cytokine (TNF) input
• To study robustness of this entrainment to noise, they used microfluidic tech. by chemical perturbations.
• Turned out that there is stochastic resonance°

°if noise free, unable to change state; when noise is present, able to change

• Stochastic resonance of NF-kB!
• How about the mechanism? — rectified adaptation°°

°° “sensor of variance to process time-varying inputs”

• What is the meaning of noise dependent gene expression? — might be like a quorum sensing mechanism for the pathways that sense immune signals — biology’s way of dealing with limiting factors: exploiting environmental noise into a strategy (sounds clever!)
• So they used computer vision to reveal hidden variables behind NF-kB activation. — Is the factor controlling NF-kB activation variability stochastic (non-predictable) or deterministic (sensitive vs. tolerant cells)? (Sounds a like classical uncertainty problem)
• Using several deep learning approaches (i.e. CNN to predict, SVM learning to identify primary predictors)
• BONUS: TayLab in a nutshell: > Signaling > Devices for single cell & organoid analysis > Single cell proteomic (HT) > Pathogen-host interactions

Hope you find my notes as useful. Please do not forget to acknowledge the researchers who shared their research with us. Thanks a lot for the organizers and speakers.