Statement of Tasks and Intent of Sponsor

Transcription

1 Industrialization of Biology: A Roadmap to Accelerate Advanced Manufacturing of Chemicals Statement of Tasks and Intent of Sponsor Friedrich Srienc Program Director Biotechnology, Biochemical, and Biomass Engineering NSF Directorate for Engineering Division of Chemical, Bioengineering, Environmental, and Transport Systems (CBET) The National Academy of Sciences; 2100 C St. NW; Washington DC; Feb. 27, 2014

2 NSF SPONSORS NSF Directorate for Engineering Division of Chemical, Bioengineering, Environmental, and Transport Systems (CBET) JoAnn Lighty, Division Director Office of Emerging Frontiers in Research and Innovation (EFRI) Sohi Rastegar, Senior Advisor NSF Directorate for Biological Sciences Division of Molecular & Cellular Biosciences (MCB) Parag Chitnis, Division Director Susanne von Bodman, Program Director NSF Directorate for Mathematical & Physical Sciences Division of Chemistry (CHE) Jaquelyne Gervay-Hague, Division Director

3 INTEREST ACROSS THE

4

5 Definition of Synthetic Biology The design and construction of new biological parts and systems, and the re-design of existing, natural biological systems for useful purposes, integrating engineering and computer-assisted design approaches with biological research. National Bioeconomy Blueprint The White House April 2012

6 Statement of Tasks Why is synthetic biology not yet on the list? Should it be on the list? What needs to be done to get it on the next list? What are the bottlenecks? What needs to be funded? Why don t we have more success stories?

7 Re-design of natural biological systems for useful purposes State-of-the-art for designing bio-production of chemicals: Systems Metabolic Engineering Input (nutrients) Output/Products (biofuels, amino acids, antibiotics, drugs, chemicals, etc.)

8 Engineering Models for Metabolic Design Step 1: Sequence the DNA and annotate the DNA sequence Step 2: build the metabolic map DNA sequence contains all information of a cell coding genes and corresponding enzymes are identified using bioinformatics tools Each enzyme catalyzes a specific reaction all reactions of a cell are known The reaction network is reconstructed based on the reactions that are present Step 3: build the mathematical model Step 4: simplify the model A mass balance is set up for each metabolite in a cell resulting in a system of ODE s describing the change in metabolite concentration as a function of reaction rates A steady state assumption is applied to the system of ODE s recognizing that metabolite concentrations remain almost constant and that the system expands at a much longer time scale This results in a system of algebraic equations representing the stoichiometric model of the reaction network

9 Engineering Tools in Metabolic Design 3 ways to analyze the stoichiometric model: (1) Metabolic Flux Analysis the system of algebraic equations is solved to yield the flux distribution in the reaction network however, the system is typically highly underdetermined and a solution is not possible without measuring many rates (2) Flux Balance Analysis Linear programming is applied to find the optimal solution in the underdetermined system Computationally fast Only a single solution is found (the optimal one) Requires a subjective optimization function The result could be a local optimum (3) Metabolic Pathway Analysis (Elementary Mode Analysis): The complete set of elementary modes (fundamental pathways) is identified according to which a cell can function The most rigorous and objective approach (i.e. no optimization function is needed) Elementary modes represent the fundamental, discrete states of a metabolic network Computationally intensive (combinatorial explosion with increasing network complexity) There is a need for research into more efficient algorithms/hardware that can handle complex networks

10 Design Questions Design Objectives: (1) The highest selectivity/yield the highest yielding pathway can be identified from the set of elementary modes Knowledge of the set of elementary modes permits identification of elimination targets of reactions that forces cells to operate according to most efficient pathways (2) High reaction rates (3) Robust, stable systems Biological systems may change due to natural evolution and selection Realization of (1) (3) will typically result in the smallest and most economical equipment needed for the process Uncertainty, human behavior: the main uncertainty is related to the correctness of the model; this has to be validated by experiment and adjusted as needed The approach is not affected by human behavior as it is completely rational

11 NSF ENG Strategy Attract, stimulate, catalyze and challenge research communities to think big, enable transformational research advances, and expand national innovation capacity Maximize synergy between transformative research and innovations for society New approaches to address engineering education challenges Collaborate and partner within and outside NSF to maximize opportunity for the engineering research and education community to address major national priorities Objective: Maximize long-term societal benefit

12 NSF Investments in Synthetic Biology NSF investments in Synthetic Biology have been predominantly driven through unsolicited proposals by the research community SynBERC Largest investment from NSF; established in 2006 EFRI IDEAS lab Joint NSF/EPSRC Sandpit on Synthetic Biology CBET/BBBE Unsolicited proposals MCB/Systems and Synthetic Biol. Cluster Unsolicited proposals SBIR/STTR Unsolicited proposals INSPIRE SAVI Science Across Virtual Institutes Yeast Chromosome Synthesis and Analysis; partnership between The US, China, Europe, and India

13 Needed Expertise for Workshop Academic (5) Industry (5) Biochemical Engineering (4) Biological Sciences (4) Biochemistry (3) Synthetic Biology (8) Bio-ethics (1)

14 Recent Related Activities NSF Workshop on Advanced Biomanufacturing August, 2013; Synberc Sustainability Initiative (Report) See also related NAS website