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1.1: Introduction and Goals - Biology

1.1: Introduction and Goals - Biology


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Course on computational biology

These lecture notes are aimed to be taught as a term course on computational biology, each 1.5 hour lecture covering one chapter, coupled with bi-weekly homework assignments and mentoring sessions to help students accomplish their own independent research projects. The notes grew out of MIT course 6.047/6.878, and very closely reflect the structure of the corresponding lectures.

Duality of Goals: Foundations and Frontiers

There are two goals for this course. The first goal is to introduce you to the foundations of the field of computational biology. Namely, introduce the fundamental biological problems of the field, and learn the algorithmic and machine learning techniques needed for tackling them. This goes beyond just learning how to use the programs and online tools that are popular any given year. Instead, the aim is for you to understand the underlying principles of the most successful techniques that are currently in use, and provide you with the capacity to design and implement the next generation of tools. That is the reason why an introductory algorithms class is set as a pre-req; the best way to gain a deeper understanding for the algorithms presented is to implement them yourself.

The second goal of the course is to tackle the research frontiers of computational biology, and that’s what all the advanced topics and practical assignments are really about. We’d actually like to give you a glimpse of how research works, expose you to current research directions, guide you to find the problems most interesting to you, and help you become an active practitioner in the field. This is achieved through guest lectures, problem sets, labs, and most importantly a term-long independent research project, where you carry out your independent research.

The modules of the course follow that pattern, each consisting of lectures that cover the foundations and the frontiers of each topic. The foundation lectures introduce the classical problems in the field. These problems are very well understood and elegant solutions have already been found; some have even been taught for well over a decade. The frontiers portion of the module cover advanced topics, usually by tackling central questions that still remain open in the field. These chapters frequently include guest lectures by some of the pioneers in each area speaking both about the general state of the field as well as their own lab’s research.

The assignments for the course follow the same foundation/frontiers pattern. Half of the assignments are going to be about working out the methods with pencil on paper, and diving deep into the algorithmic and machine learning notions of the problems. The other half are actually going to be practical questions consisting of programming assignments, where real data sets are provided. You will analyze this data using the techniques you have learned and interpret your results, giving you a real hands on experience. The assignments build up to the final project, where you will propose and carry out an original research project, and present your findings in conference format. Overall, the assignments are designed to give you the opportunity to apply computational biology methods to real problems in biology.

Duality of disciplines: Computation and Biology

In addition to aiming to cover both foundations and frontiers, the other important duality of this course is between computation and biology.

From the biological perspective of the course, we aim to teach topics that are fundamental to our understanding of biology, medicine, and human health. We therefore shy away from any computationally- interesting problems that are biologically-inspired, but not relevant to biology. We’re not just going to see something in biology, get inspired, and then go off into computer science and do a lot of stuff that biology will never care about. Instead, our goal is to work on problems that can make a significant change in the field of biology. We’d like you to publish papers that actually matter to the biological community and have real biological impact. This goal has therefore guided the selection of topics for the course, and each chapter focuses on a fundamental biological problem.

From the computational perspective of the course, being after all a computer science class, we focus on exploring general techniques and principles that are certainly important in computational biology, but nonetheless can be applied in any other fields that require data analysis and interpretation. Hence, if what you want is to go into cosmology, meteorology, geology, or any such, this class offers computational techniques that will likely become useful when dealing with real-world data sets related to those fields.

Why Computational Biology?

lecture1_transcript.html#Motivations

There are many reasons why Computational Biology has emerged as an important discipline in recent years, and perhaps some of these lead you to pick up this book or register for this class. Even though we have our own opinion on what these reasons are, we have asked the students year after year for their own view on what has enabled the field of Computational Biology to expand so rapidly in the last few years. Their responses fall into several broad themes, which we summarize here.

  1. Perhaps the most fundamental reason why computational approaches are so well-suited to the study of biological data is that at their core, biological systems are fundamentally digital in nature. To be blunt, humans are not the first to build a digital computer – our ancestors are the first digital computer, as the earliest DNA-based life forms were already storing, copying, and processing digital information encoded in the letters A,C,G, and T. The major evolutionary advantage of a digital medium for storing genetic information is that it can persist across thousands of generations, while analog signals would be diluted from generation to generation from basic chemical diffusion.
  2. Besides DNA, many other aspects of biology are digital, such as biological switches, which ensure that only two discrete possible states are achieved by feedback loops and metastable processes, even though these are implemented by levels of molecules. Extensive feedback loops and other diverse regulatory circuits implement discrete decisions through otherwise unstable components, again with design principles similar to engineering practice, making our quest to understand biological systems from an engineering perspective more approachable.
  3. Sciences that heavily benefit from data processing, such as Computational Biology, follow a virtuous cycle involving the data available for processing. The more that can be done by processing and analyz- ing the available data, the more funding will be directed into developing technologies to obtain, process and analyze even more data. New technologies such as sequencing, and high-throughput experimental techniques like microarray, yeast two-hybrid, and ChIP-chip assays are creating enormous and in- creasing amounts of data that can be analyzed and processed using computational techniques. The $1000 and $100 genome projects are evidence of this cycle. Over ten years ago, when these projects started, it would have been ludicrous to even imagine processing such massive amounts of data. How- ever, as more potential advantages were devised from the processing of this data, more funding was dedicated into developing technologies that would make these projects feasible.
  4. The ability to process data has greatly improved in the recent years, owing to: 1) the massive compu- tational power available today (due to Moore’s law, among other things), and 2) the advances in the algorithmic techniques at hand.
  5. Optimization approaches can be used to solve, via computational techniques, that are otherwise in- tractable problems.
  6. Running time & memory considerations are critical when dealing with huge datasets. An algorithm that works well on a small genome (for example, a bacteria) might be too time or space inefficient to be applied to 1000 mammalian genomes. Also, combinatorial questions dramatically increase algorithmic complexity.
  7. Biological datasets can be noisy, and filtering signal from noise is a computational problem.
  8. Machine learning approaches are useful to make inferences, classify biological features, & identify

    robust signals.

  9. As our understanding of biological systems deepens, we have started to realize that such systems cannot be analyzed in isolation. These systems have proved to be intertwined in ways previously unheard of, and we have started to shift our analyses to techniques that consider them all as a whole.
  10. It is possible to use computational approaches to find correlations in an unbiased way, and to come up with conclusions that transform biological knowledge and facilitate active learning. This approach is called data-driven discovery.
  11. Computational studies can predict hypotheses, mechanisms, and theories to explain experimental observations. These falsifiable hypotheses can then be tested experimentally.
  12. Computational approaches can be used not only to analyze existing data but also to motivate data collection and suggest useful experiments. Also, computational filtering can narrow the experimental search space to allow more focused and efficient experimental designs.
  13. Biology has rules: Evolution is driven by two simple rules: 1) random mutation, and 2) brutal selection. Biological systems are constrained to these rules, and when analyzing data, we are looking to find and interpret the emerging behavior that these rules generate.
  14. Datasets can be combined using computational approaches, so that information collected across multiple experiments and using diverse experimental approaches can be brought to bear on questions of interest.
  15. Effective visualizations of biological data can facilitate discovery.
  16. Computational approaches can be used to simulate & model biological data.
  17. Computational approaches can be more ethical. For example, some biological experiments may be unethical to perform on live subjects but could be simulated by a computer.
  18. Large scale, systems engineering approaches are facilitated by computational technique to obtain global views into the organism that are too complex to analyze otherwise.

Finding Functional Elements: A Computational Biology Question

lecture1_transcript.html#Codons

Several computational biology problems refer to finding biological signals in DNA data (e.g. coding regions, promoters, enhancers, regulators, ...).

We then discussed a specific question that computational biology can be used to address: how can one find functional elements in a genomic sequence? Figure 1.1 shows part of the sequence of the yeast genome. Given this sequence, we can ask:

Q: What are the genes that encode proteins?

A: During translation, the start codon marks the first amino acid in a protein, and the stop codon indicates the end of the protein. However, as indicated in the “Extracting signal from noise” slide, only a few of these ATG sequences in DNA actually mark the start of a gene which will be expressed as protein. The others are “noise”; for example, they may have been part of introns (non-coding sequences which are spliced out after transcription).

Q: How can we find features (genes, regulatory motifs, and other functional elements) in the genomic sequence?

A: These questions could be addressed either experimentally or computationally. An experimental approach to the problem would be creating a knockout, and seeing if the fitness of the organism is affected. We could also address the question computationally by seeing whether the sequence is conserved across the genomes of multiple species. If the sequence is significantly conserved across evolutionary time, it’s likely to perform an important function.

There are caveats to both of these approaches. Removing the element may not reveal its function–even if there is no apparent difference from the original, this could be simply because the right conditions have not been tested. Also, simply because an element is not conserved doesn’t mean it isn’t functional. (Also, note that “functional element” is an ambiguous term. Certainly, there are many types of functional elements in the genome that are not protein-encoding. Intriguingly, 90-95% of the human genome is transcribed (used as a template to make RNA). It isn’t known what the function of most of these transcribed regions are, or indeed if they are functional).


1.1: Introduction and Goals - Biology

  • Living organisms are composed of cells.
  • Cells are the smallest units of life.
  • Cells come from preexisting cells.

1.1.2: Unicellular organisms carry out all the functions of life.

  • Functions of life (MRS GREN):
  • Metabolism - all reactions in a cell
  • Response - reaction to stimuli
  • Sensitivity
  • Growth
  • Reproduction
  • Excretion
  • Nutrition - need for and ability to use nutrients

1.1.3: The surface area to volume ratio limits the size of a cell

As a cell’s size increases, its surface area to volume ratio decreases. Surface area is a square function while volume is a cubic function so surface area increases at a slower rate than the volume.

The rate of diffusion of compounds in and out of a cell is determined by the cell’s surface area. The greater the surface area, the greater the rate of diffusion. Cells need to move reactants (like water and glucose) and products (like carbon dioxide and water) in order for metabolic reactions to take place.

When the surface area to volume to ratio is low (the cell is too big), it takes a long time for reactants and products to move to and from sites of metabolic reactions (like the mitochondria). This results in the reaction taking a longer amount of time, thereby making the cell inefficient.

A low surface area to volume ratio can also result in the buildup of waste products, which can be dangerous.

So cells aim for a high surface area to volume ratio resulting smaller cells.

1.1.4: State that multicellular organisms show emergent properties.

Emergent properties arise from the interaction of component parts the whole is greater than the sum of its parts. An example of an emergent property is life. Organelles are not alive, but cells, which they make up, are.

1.1.5 Specialized tissues can exist in multicellular organisms due to differentiation

The muscle tissue in your body is made of many, many specialized cells that work together to make that muscle tissue. This is the same for your skin, the tissue in your liver, the tissues that line your stomach among many others.

1.1.6: Differentiation involves the expression of some Genes but not others

All cells in a human body have the same 25,000-30,000 genes. However, only some of these genes are expressed in each cell. This is called differential gene expression. For a gene to be ‘expressed’ its DNA base sequence must (through the processes of transcription and translation) code for a protein. Specialized cells only express genes that code for proteins that enable them to carry out their specific function. For example, cells that will differentiate into red blood cells would code for hemoglobin production. But a cell that would differentiate into a neuron would not produce hemoglobin because it doesn't need it.

Unexpressed DNA wraps around proteins called histones. 9 histones and a strand of DNA form a structure called a nucleosome. Nucleosomes are essential in gene expression because the parts of the DNA that is wrapped around the histones is not expressed because it is inaccessible. However, the DNA that is expressed floats around in the nucleus like spaghetti.

1.1.7: Stem cells divide and differentiate along different pathways, this characteristic is necessary for embryonic development and make stems cells suitable therapeutic uses

A stem cell is able to divide but has not yet expressed genes to specialize to a particular function. Under the right conditions, stem cells can be induced to express particular genes and differentiate into a particular type of cell. Embryonic stem cells, found in the placenta and the umbilical cord, are totipotent and can become any type of cell. Pluripotent stem cells are found in the blastocyst and can differentiate into almost all types of cells. Adults have some multipotent cells which are slightly specialized due to the area in which they are found. Multipotent stem cells found in the abdomen become liver cells, pancreas cells among others.

Leukemia is a cancer in which the cells in the bone marrow divide uncontrollably, producing too many white blood cells. To treat leukemia, a patient is given chemotherapy drugs to kill bone marrow cells to try to eliminate those that cause leukemia. Stem cells are then placed in the bone marrow and induced to replicate into blood cells to replace the ones that caused the leukemia in the first place

1.1.8 Question the cell theory using not-typical examples

Striated muscle cell, giant algae, and aseptate fungal hyphae

A striated muscle cell challenges the idea that a cell has one nucleus. Muscle cells have more than one nucleus per cell. Muscle Cells called fibers can be very lon g (300mm). They are surrounded by a single plasma membrane but they are mult i- nucl e at e d (many nuclei).

Giant Algae is a single-celled organism that challenges both the idea that cells must be simple in structure and small in size. Giant Algae is g igantic In siz e (5 - 100mm). And it is very c omplex, it consists of three anatomical parts

Fungal Hyphae challenges t he idea tha t a cell is a single unit. They are very large with many nuclei, they have a continuous cytoplasm with no end cell wall or membrane

1.1.9 Identify functions of life in a cell (paramecium)

The functions of life are Metabolism, Response, Homeostasis, Growth, Reproduction, and Nutrition

1.1.10 Use of Stem Cells to treat Stargardt's disease

Stargardt's disease is a degenerative disease in the eyes. Basically, the eye deteriorates over time and you lose your vision. Stem cells have been tested on animals to support and regenerate light receptors in the eye in order to combat the disease

1.1.11 The Ethical Considerations of stem cell research

1.1.12 The use of a light microscope and calculation of magnification


1.1 Welcome to Unit 1

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1.1: Introduction and Goals - Biology

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This class is aimed at people interested in understanding the basic science of plant biology. In this four lecture series, we'll first learn about the structure-function of plants and of plant cells. Then we'll try to understand how plants grow and develop, making such complex structures as flowers. Once we know how plants grow and develop, we'll then delve into understanding photosynthesis - how plants take carbon dioxide from the air and water from soil, and turn this into oxygen for us to breathe and sugars for us to eat. In the last lecture we'll learn about the fascinating, important and controversial science behind genetic engineering in agriculture. If you haven't taken it already, you may also be interested in my other course - What A Plant Knows, which examines how plants see, smell, hear and feel their environment: https://www.coursera.org/learn/plantknows. In order to receive academic credit for this course you must successfully pass the academic exam on campus. For information on how to register for the academic exam – https://tauonline.tau.ac.il/registration Additionally, you can apply to certain degrees using the grades you received on the courses. Read more on this here – https://go.tau.ac.il/b.a/mooc-acceptance Teachers interested in teaching this course in their class rooms are invited to explore our Academic High school program here – https://tauonline.tau.ac.il/online-highschool

Получаемые навыки

Plant Biology, Biology, Genetics, Plant

Рецензии

The course does very good job for providing the planned content. However if you have already studied the field and want to have more deep understanding like I do, you need to look for other courses.

An interesting and informative course. A little challenging at times for those of us without a background in biology, but well presented and carefully explained. A very positive experience.


1.1: Introduction and Goals - Biology

Organic chemistry is the study of the chemistry of carbon compounds. Carbon is singled out because it has a chemical diversity unrivaled by any other chemical element. Its diversity is based on the following:

  • Carbon atoms bond reasonably strongly with other carbon atoms.
  • Carbon atoms bond reasonably strongly with atoms of other elements.
  • Carbon atoms make a large number of covalent bonds (four).

Curiously, elemental carbon is not particularly abundant. It does not even appear in the list of the most common elements in Earth’s crust. Nevertheless, all living things consist of organic compounds.

Most organic chemicals are covalent compounds, which is why we introduce organic chemistry here. By convention, compounds containing carbonate ions and bicarbonate ions, as well as carbon dioxide and carbon monoxide, are not considered part of organic chemistry, even though they contain carbon.

The simplest organic compounds are the hydrocarbons , compounds composed of carbon and hydrogen atoms only. Some hydrocarbons have only single bonds and appear as a chain (which can be a straight chain or can have branches) of carbon atoms also bonded to hydrogen atoms. These hydrocarbons are called alkanes (saturated hydrocarbons) . Each alkane has a characteristic, systematic name depending on the number of carbon atoms in the molecule. These names consist of a stem that indicates the number of carbon atoms in the chain plus the ending –ane. The stem meth– means one carbon atom, so methane is an alkane with one carbon atom. Similarly, the stem eth– means two carbon atoms ethane is an alkane with two carbon atoms. Continuing, the stem prop– means three carbon atoms, so propane is an alkane with three carbon atoms. Figure 1.1. “Formulas and Molecular Models of the Three Simplest Alkanes” gives the formulas and the molecular models of the three simplest alkanes. (For more information about alkanes, see section 3.3.)

Figure 1.1. Formulae and molecular models of the three simplest alkanes

The three smallest alkanes are methane, ethane, and propane.

Some hydrocarbons have one or more carbon–carbon double bonds (denoted C=C). These hydrocarbons are called alkenes (see section 3.2. for more information) Note that the names of alkenes have the same stem as the alkane with the same number of carbon atoms in its chain but have the ending –ene. Thus, ethene is an alkene with two carbon atoms per molecule, and propene is a compound with three carbon atoms and one double bond.

Figure 1.2. Formulas and Molecular Models of the Two Simplest Alkenes

Ethene is commonly called ethylene, while propene is commonly called propylene.

Alkynes are hydrocarbons with a carbon–carbon triple bond (denoted C≡C) as part of their carbon skeleton (see section 3.2. for more information). The names for alkynes have the same stems as for alkanes but with the ending –yne.

Figure 1.3. Formulas and Molecular Models of the Two Simplest Alkynes

Ethyne is more commonly called acetylene.

To your health: saturated and unsaturated fats

Hydrocarbons are not the only compounds that can have carbon–carbon double bonds. A group of compounds called fats can have them as well, and their presence or absence in the human diet is becoming increasingly correlated with health issues.

Fats are combinations of long-chain organic compounds (fatty acids) and glycerol (C3H8O3). The long carbon chains can have either all single bonds, in which case the fat is classified as saturated, or one or more double bonds, in which case it is a monounsaturated or a polyunsaturated fat, respectively. Saturated fats are typically solids at room temperature beef fat (tallow) is one example. Mono- or polyunsaturated fats are likely to be liquids at room temperature and are often called oils. Olive oil, flaxseed oil, and many fish oils are mono- or polyunsaturated fats.

Some studies have linked higher amounts of saturated fats in people’s diets with a greater likelihood of developing heart disease, high cholesterol, and other diet-related diseases. In contrast, increases in unsaturated fats (either mono- or polyunsaturated) have been linked to a lower incidence of certain diseases. Thus, there have been recommendations by government bodies and health associations to decrease the proportion of saturated fat and increase the proportion of unsaturated fat in the diet. Most of these organizations also recommend decreasing the total amount of fat in the diet. A difference as simple as the difference between a single and double carbon–carbon bond can have a significant impact on health.

The carbon–carbon double and triple bonds are examples of functional groups in organic chemistry. A functional group is a specific structural arrangement of atoms or bonds that imparts a characteristic chemical reactivity to a molecule. Alkanes have no functional group, and they are mostly inert (unreactive). A carbon–carbon double bond is considered a functional group because carbon–carbon double bonds chemically react in specific ways that differ from reactions of alkanes (for example, under certain circumstances, alkenes react with water) a carbon–carbon triple bond also undergoes certain specific chemical reactions. In the remainder of this section, we introduce two other common functional groups.

If an OH group (also called a hydroxyl group) is substituted for a hydrogen atom in a hydrocarbon molecule, the compound is an alcohol . Alcohols are named using the parent hydrocarbon name but with the final –e dropped and the suffix –ol attached. The two simplest alcohols are methanol and ethanol (see Figure 1.4.).

Figure 1.4. The two simplest organic alcohol compounds

Alcohols have an OH functional group in the molecule. Ethanol (also called ethyl alcohol) is the alcohol in alcoholic beverages. Other alcohols include methanol (or methyl alcohol), which is used as a solvent and a cleaner, and 2-propanol (also called isopropyl alcohol or rubbing alcohol), which is used as a medicinal disinfectant. Neither methanol nor isopropyl alcohol should be ingested, as they are toxic even in small quantities. Cholesterol is an example of a more complex alcohol.

Another important family of organic compounds has a carboxyl group , in which a carbon atom is double-bonded to an oxygen atom and to an OH group. Compounds with a carboxyl functional group are called carboxylic acids , and their names end in –oic acid. The two simplest carboxylic acids are shown in Figure 1.5. They are perhaps best known by the common names formic acid (found in the stingers of ants) and acetic acid (found in vinegar). The carboxyl group is sometimes written in molecules as COOH.

Figure 1.5. The two smallest organic acids

Many organic compounds are considerably more complex than the examples described here. Many compounds contain more than one functional group. The formal names can also be quite complex. In section 1.6. we will examine functional groups in more detail, and we will learn about the system of naming (nomenclature) for hydrocarbons in chapter 3.

Example 1

Identify the functional group(s) in each molecule as a double bond, a triple bond, an alcohol, or a carboxyl.

  1. This molecule has an alcohol functional group.
  2. This molecule has a double bond and a carboxyl functional group.
  3. This molecule has an alcohol functional group.
  4. This molecule has a double bond and a carboxyl functional group.

Skill-building exercise

Identify the functional group(s) in each molecule as a double bond, a triple bond, an alcohol, or a carboxyl.

Concept review exercises

What is organic chemistry?

What is a functional group? Give at least two examples of functional groups.

Answers

Organic chemistry is the study of the chemistry of carbon compounds.

A functional group is a specific structural arrangement of atoms or bonds that imparts a characteristic chemical reactivity to the molecule alcohol group and carboxylic group (answers will vary).

Key takeaways

  • Organic chemistry is the study of the chemistry of carbon compounds.
  • Organic molecules can be classified according to the types of elements and bonds in the molecules.

Exercises

Give three reasons why carbon is the central element in organic chemistry.

Are organic compounds based more on ionic bonding or covalent bonding? Explain.

Identify the type of hydrocarbon in each structure.

Identify the type of hydrocarbon in each structure.

Identify the functional group(s) in each molecule.

Identify the functional group(s) in each molecule.

How many functional groups described in this section contain carbon and hydrogen atoms only? Name them.

What is the difference in the ways the two oxygen atoms in the carboxyl group are bonded to the carbon atom?

Answers

Carbon atoms bond reasonably strongly with other carbon atoms. Carbon atoms bond reasonably strongly with atoms of other elements. Carbon atoms make a large number of covalent bonds (four).


Making Feedback Visible: Four Levels in Action

An updated format on a feedback method I started using five years ago. Saves time, puts students in charge. Give it a go!

Five years ago I was starting to become concerned with the difference between marking and feedback. What was making a difference to my students’ learning and was the effort I was putting into detailed marking worth it in terms of their improvement? In reading Hattie’s Visible Learning for Teachers, Wiliam’s Embedded Formative Assessment and the pdf of The Power of Feedback (Hattie & Timperley), I developed a four-levels feedback template for use on student work.

This post is to share an updated version – I still really like this method of giving timely, actionable, goal-focused and student-owned feedback. It definitely saves me time , but puts the focus of feedback on what’s most important for the student to take the next step. I’ll keep updating, editing and adding to this post.


Are Goals and Objectives Really That Important?

The purpose of objectives is not to restrict spontaneity or constrain the vision of education in the discipline but to ensure that learning is focused clearly enough that both students and teacher know what is going on, and so learning can be objectively measured.

Different archers have different styles, so do different teachers. Thus, you can shoot your arrows (objectives) many ways. The important thing is that they reach your target (goals) and score that bullseye!

Image created by the author, covered under this site's CC License.

1.1 Introduction to Principles of Management

Managers make things happen through strategic and entrepreneurial leadership.

What’s in It for Me?

Reading this chapter will help you do the following:

  1. Learn who managers are and about the nature of their work.
  2. Know why you should care about leadership, entrepreneurship, and strategy.
  3. Know the dimensions of the planning-organizing-leading-controlling (P-O-L-C) framework.
  4. Learn how economic performance feeds social and environmental performance.
  5. Understand what performance means at the individual and group levels.
  6. Create your survivor’s guide to learning and developing principles of management.

We’re betting that you already have a lot of experience with organizations, teams, and leadership. You’ve been through schools, in clubs, participated in social or religious groups, competed in sports or games, or taken on full- or part-time jobs. Some of your experience was probably pretty positive, but you were also likely wondering sometimes, “Isn’t there a better way to do this?”

After participating in this course, we hope that you find the answer to be “Yes!” While management is both art and science, with our help you can identify and develop the skills essential to better managing your and others’ behaviors where organizations are concerned.

Before getting ahead of ourselves, just what is management, let alone principles of management? A manager’s primary challenge is to solve problems creatively, and you should view management as “the art of getting things done through the efforts of other people.” 1 The principles of management , then, are the means by which you actually manage, that is, get things done through others—individually, in groups, or in organizations. Formally defined, the principles of management are the activities that “plan, organize, and control the operations of the basic elements of [people], materials, machines, methods, money and markets, providing direction and coordination, and giving leadership to human efforts, so as to achieve the sought objectives of the enterprise.” 2 For this reason, principles of management are often discussed or learned using a framework called P-O-L-C, which stands for planning, organizing, leading, and controlling.

Managers are required in all the activities of organizations: budgeting, designing, selling, creating, financing, accounting, and artistic presentation the larger the organization, the more managers are needed. Everyone employed in an organization is affected by management principles, processes, policies, and practices as they are either a manager or a subordinate to a manager, and usually they are both.

Managers do not spend all their time managing. When choreographers are dancing a part, they are not managing, nor are office managers managing when they personally check out a customer’s credit. Some employees perform only part of the functions described as managerial—and to that extent, they are mostly managers in limited areas. For example, those who are assigned the preparation of plans in an advisory capacity to a manager, to that extent, are making management decisions by deciding which of several alternatives to present to the management. However, they have no participation in the functions of organizing, staffing, and supervising and no control over the implementation of the plan selected from those recommended. Even independent consultants are managers, since they get most things done through others—those others just happen to be their clients! Of course, if advisers or consultants have their own staff of subordinates, they become a manager in the fullest sense of the definition. They must develop business plans hire, train, organize, and motivate their staff members establish internal policies that will facilitate the work and direct it and represent the group and its work to those outside of the firm.

1 We draw this definition from a biography of Mary Parker Follett (1868–1933) written by P. Graham, Mary Parker Follett: Prophet of Management (Boston: Harvard Business School Press, 1995). Follett was an American social worker, consultant, and author of books on democracy, human relations, and management. She worked as a management and political theorist, introducing such phrases as “conflict resolution,” “authority and power,” and “the task of leadership.”

2 The fundamental notion of principles of management was developed by French management theorist Henri Fayol (1841–1925). He is credited with the original planning-organizing-leading-controlling framework (P-O-L-C), which, while undergoing very important changes in content, remains the dominant management framework in the world. See H. Fayol, General and Industrial Management (Paris: Institute of Electrical and Electronics Engineering, 1916).


Objective

In the objectives section of your lesson plan, write precise and delineated goals for what you want your students to be able to accomplish after the lesson is completed. Here is an example: Let's say that you are writing a lesson plan on nutrition. For this unit plan, your objective for the lesson is for students to identify the food groups, learn about the food pyramid, and name a few examples of healthy and unhealthy foods. Your goals should be specific and use exact figures and phrasing whenever appropriate. This will help you quickly and easily determine if your students met the objectives or not after the lesson is over.


Dependencies¶

WebAssembly depends on two existing standards:

IEEE 754-2019, for the representation of floating-point data and the semantics of respective numeric operations .

Unicode, for the representation of import/export names and the text format .

However, to make this specification self-contained, relevant aspects of the aforementioned standards are defined and formalized as part of this specification, such as the binary representation and rounding of floating-point values, and the value range and UTF-8 encoding of Unicode characters.

The aforementioned standards are the authoritative source of all respective definitions. Formalizations given in this specification are intended to match these definitions. Any discrepancy in the syntax or semantics described is to be considered an error.


Watch the video: Notes for IB Biology Chapter (January 2023).