Difference between revisions of "20.109(S07): Calcium signaling in vivo"

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	Biological systems are complex.		Biological systems are complex.
−	Molecular insights into cell physiology have led to amazing understanding of the factors that influence a cell's behavior. Cells can be described biochemically, using binding constants and reactions rates to calculate expected concentrations of biologically relevant molecules. Cells can be described genetically, using large deletion sets and clever screens to identify critical regions of the genome and phenotypes associated with their malfunction or loss, singularly or in groups. Cells can be described structurally, with powerful microscopes capable of viewing molecules in cells "in action" and through crystalization of cellular components to give an atomic level, three-dimensional picture of cellular proteins and complexes.	+	Molecular insights into cell physiology have led to amazing understanding of the factors that influence a cell's behavior. Cells can be described biochemically, using binding constants and reactions rates to calculate expected concentrations of biologically relevant molecules. Cells can be described genetically, using large deletion sets and clever screens to identify critical regions of the genome and phenotypes associated with their malfunction or loss, singularly or in groups. Cells can be described structurally, with powerful microscopes capable of viewing molecules in cells "in action" and through crystalization of cellular components to give an atomic level, three-dimensional picture of cellular proteins and complexes. Often the most compelling and complete story comes from an approach that combines techniques or that asks one questions different ways. [[Image:Macintosh HD-Users-nkuldell-Desktop-blindmen-elephant.jpg|thumb|left| Blind Men and an Elephant, see [http://www.wordinfo.info/words/index/info/view_unit/1/?letter=B&spage=3 poem]by John Godfrey Saxe ]]
−	Despite both the precision with which some aspects of the cell can be described and the volume of data being generated from high throughput analysis and genome sequencing projects for example, our understanding of a cell, even a simple cell like E. coli, is still incomplete. Despite a good "parts list" for a cell, and a good understanding of what many parts do, we are frustratingly far from "calculating life." The great challenge is this: to describe (or better still: to build) a quantitative and predictive model for a cell's dynamic behavior. Wouldn't it be great if we could perturb a "virtual cell" and see it react as a real cell would, even if we haven't ever tried perturbing a real cell in the same way? The possibilities for discovery and engineering expand tremendously when experiments can be correctly simulated, but cell to cell variation, as well as the evolution and complexity of living systems makes them hard to understand and even harder to model.
−	We will consider two approaches people have taken to integrate complex biological phenomenon into a useful and understandable framework. <br>	+	Yet for all the precision with which some aspects of the cell can be described and the volume of data being generated from high throughput analysis and genome sequencing projects, our understanding of a cell is still incomplete. Despite having a good parts list, and a good understanding of what many parts do, we are frustratingly far from "calculating life." Even simple cell like E. coli is hard to model. The great challenge is this: to describe (or better still: to build) a quantitative and predictive model for a cell's dynamic behavior. Wouldn't it be great if we could perturb a "virtual cell" and see it react as a real cell would, even if we haven't ever tried perturbing a real cell in the same way? The possibilities for discovery and engineering expand tremendously when experiments can be correctly simulated.
−	1. Build a "virtual cell," a synthetic proxy that models a natural, relevant one. Even building small genetically encoded is still far away.<br>	+
−	2. Build uncertainty and variation into circuitry. models	+	What is holding us back from this goal? Cell to cell variation for starters, though some efforts to model "noise" and recast it as strategy for evolution are underway, e.g. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=AbstractPlus&list_uids=15790856&query_hl=1&itool=pubmed_docsum in the Elowitz lab] and [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=AbstractPlus&list_uids=15790856&query_hl=1&itool=pubmed_docsum in the van Oudenaarden lab]. Additionally, the evolution of living systems makes them hard to understand and even harder to model...nature continues to solve environmental demands in clever and novel ways. Finally, existing methods for experimentally testing and measuring the behavior of cells are limited. Measurements of single cells can be particularly noisy and difficult to correlate with bulk measurements made on populations of cells. Moreover, the measurement methods themselves are difficult to correlate with eachother, giving meaningful data in different ranges and with different sensitivities. This last point is what we will explore today, specifically asking how two techniques for measuring Ca2+ in cells compare.
−	.	+
	==Protocols==		==Protocols==
	===Part 1: Network analysis===		===Part 1: Network analysis===
−	====synthetic networks====	+
−	====fuzzy logic to model network====	+
	===Part 2: Oral presentation instruction===		===Part 2: Oral presentation instruction===

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Revision as of 17:37, 14 January 2007

Introduction

Biological systems are complex.

Molecular insights into cell physiology have led to amazing understanding of the factors that influence a cell's behavior. Cells can be described biochemically, using binding constants and reactions rates to calculate expected concentrations of biologically relevant molecules. Cells can be described genetically, using large deletion sets and clever screens to identify critical regions of the genome and phenotypes associated with their malfunction or loss, singularly or in groups. Cells can be described structurally, with powerful microscopes capable of viewing molecules in cells "in action" and through crystalization of cellular components to give an atomic level, three-dimensional picture of cellular proteins and complexes. Often the most compelling and complete story comes from an approach that combines techniques or that asks one questions different ways.

Yet for all the precision with which some aspects of the cell can be described and the volume of data being generated from high throughput analysis and genome sequencing projects, our understanding of a cell is still incomplete. Despite having a good parts list, and a good understanding of what many parts do, we are frustratingly far from "calculating life." Even simple cell like E. coli is hard to model. The great challenge is this: to describe (or better still: to build) a quantitative and predictive model for a cell's dynamic behavior. Wouldn't it be great if we could perturb a "virtual cell" and see it react as a real cell would, even if we haven't ever tried perturbing a real cell in the same way? The possibilities for discovery and engineering expand tremendously when experiments can be correctly simulated.

What is holding us back from this goal? Cell to cell variation for starters, though some efforts to model "noise" and recast it as strategy for evolution are underway, e.g. in the Elowitz lab and in the van Oudenaarden lab. Additionally, the evolution of living systems makes them hard to understand and even harder to model...nature continues to solve environmental demands in clever and novel ways. Finally, existing methods for experimentally testing and measuring the behavior of cells are limited. Measurements of single cells can be particularly noisy and difficult to correlate with bulk measurements made on populations of cells. Moreover, the measurement methods themselves are difficult to correlate with eachother, giving meaningful data in different ranges and with different sensitivities. This last point is what we will explore today, specifically asking how two techniques for measuring Ca2+ in cells compare.

Protocols

Part 1: Network analysis

Part 2: Oral presentation instruction

DONE!

For next time

1. Please download the midsemester evaluation form File:Macintosh HD-Users-nkuldell-Desktop-MidsemesterEval 20.109.doc. Complete the questionnaire and then print it out without including your name to turn in next time. If there is something you'd like to see done differently for the rest of the course, this is your chance to lobby for that change. Similarly, if there is something you think the class has to keep doing, let us know that too.
2. Prepare a ten-minute oral presentation of a primary research paper related in topic to the experiments performed in this experimental. Some articles that are suitable for presentation are listed under the link for the next lab. These can be reserved on a first come/first served basis so email your choice as soon as you’ve decided. Alternatively, you’re also welcome to present a research idea stemming from the experiments you have performed in Module 2.

Reagents list