Bone regeneration is a critical challenge in the treatment of fractures, bone loss due to tumor resection, and alveolar bone deficiencies. Currently, approximately 2.2 million bone graft procedures are performed annually worldwide. Despite significant progress in bone tissue engineering, there is an unmet need for patient-specific long-lasting bone restoration to reproduce the unique physical, chemical, and biological properties of hierarchically structured bone in a personalized manner. While bones can often naturally self-heal, critical-size bone defects lead to a failed repair process. Expanding on the current understanding of bone regeneration, I will integrate the biomechanical and immunological triggers of the healing process into an artificial bio-mimicking scaffold to specifically target critical defects. Thus, I aim to develop a conceptually new approach of personalized layered 3D-printed supramolecular scaffolds. I intend to use a bottom-up multi-component co-assembly to produce tailored, layer-by-layer printed, extracellular-matrix-mimicking scaffolds that not only fit the defect shape, but also mimic the bone composition around the defect. For this purpose, I will significantly expand the repertoire of our proprietary peptide-based hydrogel technology by chemical modifications that allow interaction with bone minerals, slow release of growth factors, and activation of the immune system to trigger healing. I will combine computerized tomography scans and computer-assisted manufacturing to design personalized scaffolds that can be studied in an alveolar bone model, and be customized to accommodate bone type, structure, gender, age, and systemic diseases. PersonalBone aims to develop customized supramolecular scaffolds that will promote personalized therapy for bone regenerative medicine, thus significantly advancing the fields of tissue engineering and materials science while offering a novel solution to a major healthcare issue.

Motivated by our recent progress in feedback information theory and its deep relations to stochastic dynamical systems, and inspired by natural phenomena such as bio-molecular interactions and human conversation, this research will explore the fundamental limits of information transfer via simple interaction. In the standard information theoretic framework, the problem of reliable communications is typically studied in an asymptotic unidirectional regime, where optimal performance is attained via complex codes employed over increasingly long time epochs. Here, we will investigate a markedly different paradigm where communicating parties are restricted to use simple finite-state rules to act and react on the fly. We will consider a broad spectrum of models ranging from feedback communications and two-way channels to multiuser setups and large homogeneous networks, and study measures of information transfer and dissipation, their relations to dynamical system contraction factors, and the fundamental tradeoffs between complexity and performance. While prominently theoretic, our investigation is expected to admit important practical applications and a cross-disciplinary impact. In communications, and especially in resource-limited systems such as wireless sensor networks where battery-life is a bottleneck, a breakthrough in the understanding of optimal interaction can lead to a paradigm shift in system design, yielding simpler, cheaper, more robust solutions. In Finance, where market behavior is a cumulative effect of local actions taken by individuals based on limited noisy observations, quantifying interaction and its relation to information propagation can enhance our ability to forecast and explain macro level phenomena. Finally, an information theoretic characterization of interaction in large networks can shed light on the underlying mechanisms governing various biological systems that are empirically amenable to cellular automata modeling.

Understanding how the adaptive immune system works is of monumental scientific importance. It is remarkable, then, that despite the sophisticated modeling technologies available today, there are no human-relevant in vitro platforms mimicking the adaptive immune system in a physiological environment. Rather, state-of-the art models involve animals or in vitro systems that capture isolated elements of the immune response (e.g., activation by tumor cells). The challenge in developing immunized in vitro models is that adaptive immune cells cannot be co-cultured with non-autologous tissue, as they become activated and destroy it. We propose a groundbreaking paradigm that tackles this challenge, while advancing the greater ideal of personalized medicine. The approach builds on my expertise with the Organ-on-a-Chip: a microfluidic platform comprising human tissue that closely mimics organ functionality. I will create a novel platform integrating six vascularized Organ-Chips, all originating from iPSCs derived from a specific individual, which have been differentiated into specific tissue types. This fully isogenic platform will accommodate the donor’s adaptive immune cells: Because they originate from the same source, they will not be activated. Indeed, preliminary results support this hypothesis. The resultant system, Immune-Me-on-a-Chip, will constitute a first-of-its kind personalized-immunized-human platform for studying human physiology and biological threats. I will use the system to explore fundamental biological questions: (i) understanding how different isogenic and non-isogenic tissues interact with the immune system, e.g., in organ transplantation; and (ii) identifying how pathogens (antibiotic-resistant E. coli), as well as antibiotic treatment, affect human physiology and the immune response. This research will revolutionize the study of human physiology in general and of immunity in particular, and will open the door to a new era of personalized medicine.

RNA therapeutics is an emerging field explored in various types of diseases such as genetic disorders, cancer, inflammation and viral infections. Currently, most of the research focuses on the delivery of mRNA molecules that will transiently express a desired protein that can replace a defective protein or manipulate gene expression in the cells. My lab was the first to show systemic, cell-specific delivery of mRNA molecules in animals. Our approach and our novel amino lipids were translated to several clinical trials in the field of infectious and monogenic diseases. In protein replacement therapy, the main hurdle of using mRNA is the relative short half life of the mRNA. To address this, I suggest an approach for long-term expression: Circular RNA (circRNA), a covalently closed loop single stranded RNA that has a significant prolonged stability compared to linear mRNA. Thus, presents an immense advantage in protein replacement therapy. Duchenne muscular dystrophy (DMD) is caused by X-linked recessive mutation in dystrophin gene, leading to lack of functional dystrophin protein. This disease affects 1:5,000 males, causes a progressive loss of muscle tissues, ultimately leading to disability and premature death. Because DMD pathology is caused by the lack of functional dystrophin, restoring the function of dystrophin is a potential therapeutic strategy. As Lipid nanoparticles (LNPs) are the most clinically advanced candidate for RNA delivery, able to entrap large RNA payloads, herein I propose an innovative multidisciplinary approach for the specific delivery of circRNA-LNPs to muscle cells that will express the dystrophin protein and replace the defective one in a DMD mouse model. The long-term expression of the circRNA will offer new hope for the treatment of monogenetic diseases such as DMD. This approach may ultimately become a novel therapeutic modality for DMD and open new avenues for implementing circRNAs for other types of genetic disorders and vaccines

Cryptography—and its basic tasks such as encryption, authentication and key exchange—is essential for ensuring privacy and security on the Internet. The question of whether “unbreakable” encryption methods exist has fascinated mathematicians and cryptographers for thousands of years, and is closely related to the famous NP vs. P problem. This question is still wide open and consequently, Internet security today relies on cryptographic constructions based on the *conjectured* hardness of some computational problems (such as e.g., the factoring problem, the discrete logarithms problem or various noisy linear algebra problems). However, these conjectured hard problems all contain significant (e.g., algebraic) *structure*, that may make them vulnerable to attacks. Furthermore, for tasks such as key exchange and public-key encryption, only a *handful* of candidate hard problems are known on which these tasks can be based. As a result, some unexpected algorithmic developments for structured problems could upend the whole infrastructure we rely on for communicating and transacting on the Internet. In this project, we propose to develop an alternative approach: a foundation for provably-secure Cryptography from *unstructured hardness* assumptions. Towards resolving this long-standing challenge, we will leverage our recently-discovered connection between Cryptography and the seemingly unrelated area of Kolmogorov Complexity. Specifically, we will develop new hardness assumptions rooted in Kolmogorov Complexity on which the cryptographic tasks (e.g., private-key encryption, key-exchange, public-key encryption) can be based. Critically, these assumptions will lack algebraic and other computational structure that may make them vulnerable to attacks. Overall, KolmoCrypt will provide a new theoretical foundation for the hardness assumptions on which Cryptography is based, and ultimately, a more secure foundation for the Internet (and beyond).