Core Biological concepts explained to a Computer Scientist?

Core Biological concepts explained to a Computer Scientist?

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I am a computer scientist delving into Bioinformatics and I need to gain insight in biological phenomena. How would you explain three core concepts as: Evolution, Selection and Variation, to a Computer Scientist? I am looking for something in the middle. I read Futuyma's book and was very useful, but I would like an explanation I can relate with my background, something to start building "bridges"…

Bibliography suggestions are welcome.


Evolution is the accumulation of genetic mutations that results in phenotypic variation (physical characteristics) where surviving variations are more suited to the environment the organism lives in, thus allowing it to survive better and -- critically -- reproduce as-good-as or better-than its competing organisms.

In terms of computer science this would be like a starting with basic CPU design with all the absolute necessities. Now, let's pretend every generation of CPU after your first is going to be randomly designed by something called "Pressure OS". Pressure OS doesn't care if the CPUs it designs match the needs of the consumer. PrOS is unfeeling. An OS of pure logic. ALL it will do is make a bunch of random ones, note which ones sell the best, and make the best-selling ones to be the template for its next round of random designs.

Selection and Variation

Selection is the process by which environmental pressures (low moisture, high heat, high altitude, available food, extreme pressures at the bottom of oceanic trenches, etc.) dictate how well each successive generation of organisms survive.

In our analogy, the Pressure OS is the cause of Selection. Some CPU designs aren't going to meet the needs of consumers (first-gen Atoms which could barely operate a toaster). Those ones won't be produced again. Others will be very successful (like the Pentium-4 Series which lasted for years) and will quickly outnumber the inferior ones.

Some designs, as odd as they are, will find success in areas outside enthusiast desktops or workstations. Like ARM designs which were never part of the desktop market, but found lots of success in phones and business devices.

Market requirements/environments, as met by the random variations produced by Pressure OS, created Variation in the types of CPUs available.

ARM Snapdragons could never, ever compete in the desktop enthusiast market. That's the realm of Sandy Bridges, Haswells, Semprons, and Phenoms. Then again, they're dramatically different designs -- which stem from common designs decades ago that have been heavily modified over the years -- that don't need to compete. Snapdragons and Semprons can co-exist because they fill different niches.

Now, when consumer needs change again -- if everybody gets tired of mobile phones, tablets, and desktops in favor of cone-shaped personal assistants which go in your ear -- then designs will change again under that new pressure. Eventually CPU one design will become the standard for that particular piece of hardware, and you could say that CPU variation had evolved to get there via selective pressure from consumers.

Biology operates in the exact same way, except the selection process is instigated by environmental pressures to produce organism variations that can successfully reproduce. It's also been going on for >3 Billion Years.

Please note that these are just analogies not exact definitions.

Okay so let's imagine DNA as string of 4 letters that is the program code of an organism. Also the running environment is a good analogue to the biological environment (RAM, CPU time, disk space - as resources like food, water etc.) Genes of organism could be imagined as methods of an object like in object oriented programing. Let's define some functions:

Reproduction: copies the string and creates a new instance of the organism (like a new instance of the program) - cell or an offspring - this stands for asexual reproduction. Sexual reproduction takes two instances of the program and randomly exchange their functions and create a new instance with this shuffled code (recombination).

Mutation: induces random changes to the string at random times. This ensures that different "versions" are present of the code. You need mutation for evolution to work.

Also you need Selection - or rather selective pressure - that is like changing the environment - more efficient codes will survive, the rest die of (aka crash, run out of memory (food)) etc.) Selection drives evolution forward. Also programs can interact with each other and function as a selective pressure - think of anti-virus programs and trojans or viruses - you need better and better AV programs because new viruses come out, and bad guys need new viruses because AV virus programs keep getting better and better - they both put selective pressure on the other and drive each other's evolution.

(Genetic) Variation: think of a group of softwares with similar function: like text editing. These are like different individuals of a species because they can do the same thing but they are all a bit different. These "species" evolve by gaining new functions by mutations (the programmer adds new code sections - mutation), and after they gain enough new function they turn into a new software - new species. Evolution takes a lot of time to produce new species - as does coding a new software :).

I hope this helps.

A first course in computing with applications to biology

Ran Libeskind-Hadas is the R. Michael Shanahan Professor of Computer Science at Harvey Mudd College. He received the A.B. in applied mathematics from Harvard University and the PhD in computer science from the University of Illinois at Urbana-Champaign. His research is in the area of cophylogenetics.

Eliot Bush is Assistant Professor of Biology at Harvey Mudd College. He received the A.B. in biology from Harvard University and the PhD in biology from the California Institute of Technology. His research interests in computational biology have focused on the evolution of noncoding sequences in mammals.

Ran Libeskind-Hadas, Eliot Bush, A first course in computing with applications to biology, Briefings in Bioinformatics, Volume 14, Issue 5, September 2013, Pages 610–617,

Searching for the Algorithms Underlying Life

To the computer scientist Leslie Valiant, “machine learning” is redundant. In his opinion, a toddler fumbling with a rubber ball and a deep-learning network classifying cat photos are both learning calling the latter system a “machine” is a distinction without a difference.

Valiant, a computer scientist at Harvard University, is hardly the only scientist to assume a fundamental equivalence between the capabilities of brains and computers. But he was one of the first to formalize what that relationship might look like in practice: In 1984, his “probably approximately correct” (PAC) model mathematically defined the conditions under which a mechanistic system could be said to “learn” information. Valiant won the A.M. Turing Award — often called the Nobel Prize of computing — for this contribution, which helped spawn the field of computational learning theory.

Valiant’s conceptual leaps didn’t stop there. In a 2013 book, also entitled “Probably Approximately Correct,” Valiant generalized his PAC learning framework to encompass biological evolution as well.

He broadened the concept of an algorithm into an “ecorithm,” which is a learning algorithm that “runs” on any system capable of interacting with its physical environment. Algorithms apply to computational systems, but ecorithms can apply to biological organisms or entire species. The concept draws a computational equivalence between the way that individuals learn and the way that entire ecosystems evolve. In both cases, ecorithms describe adaptive behavior in a mechanistic way.

Valiant’s self-stated goal is to find “mathematical definitions of learning and evolution which can address all ways in which information can get into systems.” If successful, the resulting “theory of everything” — a phrase Valiant himself uses, only half-jokingly — would literally fuse life science and computer science together. Furthermore, our intuitive definitions of “learning” and “intelligence” would expand to include not only non-organisms, but non-individuals as well. The “wisdom of crowds” would no longer be a mere figure of speech.

Quanta Magazine spoke with Valiant about his efforts to dissolve the distinctions between biology, computation, evolution and learning. An edited and condensed version of the interview follows.

QUANTA MAGAZINE: How did you come up with the idea of “probably approximately correct” learning?

LESLIE VALIANT: I belonged to the theoretical computer science community, specializing in computational complexity theory, but I was also interested in artificial intelligence. My first question was: Which aspect of artificial intelligence could be made into a quantitative theory? I quickly settled on the idea that it must be learning.

At the time I started working on it [in the 1980s], people were already investigating machine learning, but there was no consensus on what kind of thing “learning” was. In fact, learning was regarded with total suspicion in the theoretical computer science community as something which would never have a chance of being made a science.

On the other hand, learning is a very reproducible phenomenon — like an apple falling to the ground. Every day, children all around the world learn thousands of new words. It’s a large-scale phenomenon for which there has to be some quantitative explanation.

So I thought that learning should have some sort of theory. Since statistical inference already existed, my next question was: Why was statistics not enough to explain artificial intelligence? That was the start: Learning must be something statistical, but it’s also something computational. I needed some theory which combined both computation and statistics to explain what the phenomenon was.

So what is learning? Is it different from computing or calculating?

It is a kind of calculation, but the goal of learning is to perform well in a world that isn’t precisely modeled ahead of time. A learning algorithm takes observations of the world, and given that information, it decides what to do and is evaluated on its decision. A point made in my book is that all the knowledge an individual has must have been acquired either through learning or through the evolutionary process. And if this is so, then individual learning and evolutionary processes should have a unified theory to explain them.

And from there, you eventually arrived at the concept of an “ecorithm.” What is an ecorithm, and how is it different from an algorithm?

Katherine Taylor for Quanta Magazine

Video: Valiant explains the term “ecorithm.”

An ecorithm is an algorithm, but its performance is evaluated against input it gets from a rather uncontrolled and unpredictable world. And its goal is to perform well in that same complicated world. You think of an algorithm as something running on your computer, but it could just as easily run on a biological organism. But in either case an ecorithm lives in an external world and interacts with that world.

So the concept of an ecorithm is meant to dislodge this mistaken intuition many of us have that “machine learning” is fundamentally different from “non-machine learning”?

Yes, certainly. Scientifically, the point has been made for more than half a century that if our brains run computations, then if we could identify the algorithms producing those computations, we could simulate them on a machine, and “artificial intelligence” and “intelligence” would become the same. But the practical difficulty has been to determine exactly what these computations running on the brain are. Machine learning is proving to be an effective way of bypassing this difficulty.

Some of the biggest challenges that remain for machines are those computations which concern behaviors that we acquired through evolution, or that we learned as small children crawling around on the ground touching and sensing our environment. In these ways we have acquired knowledge that isn’t written down anywhere. For example, if I squeeze a paper cup full of hot coffee, we know what will happen, but that information is very hard to find on the Internet. If it were available that way, then we could have a machine learn this information more easily.

Can systems whose behavior we already understand well enough to simulate with algorithms — like solar systems or crystals — be said to “learn” too?

I wouldn’t regard those systems as learning. I think there needs to be some kind of minimal computational activity by the learner, and if any learning takes place, it must make the system more effective. Until a decade or two ago, when machine learning began to be something that computers could do impressively, there was no evidence of learning taking place in the universe other than in biological systems.

How can a theory of learning be applied to a phenomenon like biological evolution?

Biology is based on protein expression networks, and as evolution proceeds these networks become modified. The PAC learning model imposes some logical limitations on what could be happening to those networks to cause these modifications when they undergo Darwinian evolution. If we gather more observations from biology and analyze them within this PAC-style learning framework, we should be able to figure out how and why biological evolution succeeds, and this would make our understanding of evolution more concrete and predictive.

How far have we come?

We haven’t solved every problem we face regarding biological behavior because we have yet to identify the actual, specific ecorithms used in biology to produce these phenomena. So I think this framework sets up the right questions, but we just don’t know the right answers. I think these answers are reachable through collaboration between biologists and computer scientists. We know what we’re looking for. We are looking for a learning algorithm obeying Darwinian constraints that biology can and does support. It would explain what’s happened on this planet in the amount of time that has been available for evolution to occur.

Imagine that the specific ecorithms encoding biological evolution and learning are discovered tomorrow. Now that we have this precise knowledge, what are we able to do or understand that we couldn’t before?

Well, we would understand where we came from. But the other extrapolation is in bringing more of psychology into the realm of the computationally understandable. So understanding more about human nature would be another result if this program could be carried through successfully.

Do you mean that computers would be able to reliably predict what people will do?

That’s a very extreme scenario. What data would I need about you to predict exactly what you will be doing in one hour? From the physical sciences we know that people are made of atoms, and we know a lot about the properties of atoms, and in some theoretical sense we can predict what sets of atoms can do. But this viewpoint hasn’t gone very far in explaining human behavior, because human behavior is just an extremely complicated manifestation of too many atoms. What I’m saying is that if one has a more high-level computational explanation of how the brain works, then one would get closer to this goal of having an explanation of human behavior that matches our mechanistic understanding of other physical systems. The behavior of atoms is too far removed from human behavior, but if we understood the learning algorithms used in the brain, then this would provide mechanistic concepts much closer to human behavior. And the explanations they would give as to why you do what you do would become much more plausible and predictive.

What if the ecorithms governing evolution and learning are unlearnable?

It’s a logical possibility, but I don’t think it’s likely at all. I think it’s going to be something pretty tangible and reasonably easy to understand. We can ask the same question about fundamental unsolved problems in mathematics. Do you believe that these problems have solutions that people can understand, or do you believe that they’re beyond human comprehension? In this area I’m very confident — otherwise I wouldn’t be pursuing this. I believe that the algorithms nature uses are tangible and understandable, and won’t require intuitions that we’re incapable of having.

Many prominent scientists are voicing concerns about the potential emergence of artificial “superintelligences” that can outpace our ability to control them. If your theory of ecorithms is correct, and intelligence does emerge out of the interaction between a learning algorithm and its environment, does that mean that we ought to be just as vigilant about the environments where we deploy AI systems as we are about the programming of the systems themselves?

If you design an intelligent system that learns from its environment, then who knows — in some environments the system may manifest behavior that you really couldn’t foresee at all, and this behavior may be deleterious. So you have a point. But in general I’m not so worried about all this talk about the superintelligences somehow bringing about the end of human history. I regard intelligence as made up of tangible, mechanical and ultimately understandable processes. We will understand the intelligence we put into machines in the same way we understand the physics of explosives — that is, well enough to be able to render their behavior predictable enough that in general they don’t cause unintended damage. I’m not so concerned that artificial intelligence is different in kind from other existing powerful technologies. It has a scientific basis like the others.

Basic Problems in the Teaching of Evolution

The teaching and learning of evolution has faced difficulties ranging from pedagogical obstacles to social controversy, as noted, for example, by Smith (2010a, b). These include two distinct sets of problems. One derives from objections rooted in religion (e.g., Billingsley et al. 2015 Basel et al. 2014 Rissler et al. 2014 Basel et al. 2013 Yasri and Mancy 2012), while the other stems from the fact that many evolutionary concepts may seem, at least initially, counter-intuitive to students. An overview of these problems is given by Kampourakis (2014). In this article, we do not address the first set, but focus on the second.

Previous literature has defined the basic concepts of evolution (e.g., Mayr 1982, 1997 Anderson et al. 2002 Nehm and Reilly 2007) and strenuous efforts have been made to analyze and describe students’ difficulties in comprehending them. However, in addition to understanding the concepts constituting the theory of evolution, they must also be connected in a complex web of manifold interconnected systems to thoroughly grasp the theory. Creating these connections may be one of the main problems for learners. Students may struggle to assimilate the vast amounts of information they encounter in biology classes, and thus, fail to notice relevant connections between contents and topics, or grasp the concepts that weave them together. Consequently, contents across lessons or classes can appear ambiguous or disconnected (Tenenbaum et al. 2011) and students may not successfully develop an interconnected biological knowledge structure leading to the understanding of evolution. As biological contents can only be fully understood in the integrative framework of biological evolution, it seems essential to use this framework continuously for teaching biology from the beginning onwards (Nehm and Schonfeld 2007 Smith et al. 2009 Leopoldina 2017).

The problem of knowledge integration, i.e., how to integrate newly learned content with existing knowledge, and link, connect, distinguish, organize, and structure ideas (Clark and Linn 2003, p. 452), has been tackled by numerous studies. The effectiveness of knowledge integration depends, among other factors, on the learner’s particular knowledge structure, which has been described as efficient if structured around core ideas (e.g., Bransford et al. 2000 Pugh and Bergin 2006), i.e., the central ideas in a focal discipline, such as evolution in biology (NGSS 2013). To support knowledge integration (sensu Clark and Linn 2003) in science education, core ideas have been introduced into the science standards and curricula of several countries, e.g., the USA (NGSS 2013) and Germany (KMK 2005). As a core idea, evolution can support the learning of biology by facilitating the organization of knowledge. In addition, evolution can function as a pivotal link between biological contents and highlight similarities in the complexity of the discipline. In this manner, core ideas are thought to facilitate integration of students’ knowledge and understanding of science. However, the extent to which this goal is achieved depends on the coherence (Fortus and Krajcik 2012) with which concepts like evolution are taught across different (disciplinary) contexts (Fortus et al. 2015).

Introductory Physics for Biological Scientists

Why do elephants have sturdier thigh bones than humans? Why can't ostriches fly? How do bacteria swim through fluids? With each chapter structured around relevant biological case studies and examples, this engaging, full-colour book introduces fundamental physical concepts essential in the study of biological phenomena. Optics is introduced within the context of butterfly wing colouration, electricity is explained through the propagation of nerve signals, and accelerated motion is conveniently illustrated using the example of the jumping armadillo. Other key physical concepts covered include waves, mechanical forces, thermodynamics and magnetism, and important biological techniques are also discussed within this context, such as gel electrophoresis and fluorescence microscopy. A detailed appendix provides further discussion of the mathematical concepts utilised within the book, and numerous exercises and quizzes allow readers to test their understanding of key concepts. This book is invaluable to students aiming to improve their quantitative and analytical skills and understand the deeper nature of biological phenomena.

  • Engagingly structured around biological examples, this essential text provides insight into key physical concepts and their influence upon biological phenomena
  • Includes an appendix covering the key mathematical concepts developed in the book
  • Contains quiz questions at the end of each chapter which enable readers to test their understanding
  • Biological case studies are illustrated in full colour throughout

Curriculum & Requirements

Biology is one of the most popular science majors since it provides a broad background in the biological sciences while allowing flexibility and specialization within the major. It integrates theoretical and practical (hands-on laboratory and field work) courses in different aspects of the biology of multicellular life. It encompasses the study of structural and functional relationships of living organisms at the molecular, cellular, and organismal level, the interactions of living systems with the environment and with each other, and the evolutionary relationships of life. Our goal is to create an environment for those with a scholarly interest in the biological sciences, and to extend their understanding, awareness, and appreciation of the diversity inherent in the biological sciences. Our major is aimed at promoting an excellent education in biological sciences by involving undergraduate students in a strong interaction with faculty both in the classroom and in research laboratories.

The biology major prepares students for post graduate degrees in the biological and medical fields, and for job opportunities in industry (environmental, biomedical, pharmaceutical, and biotechnological) and governmental research, and secondary school teaching. Completion of the four-year undergraduate program plus a fifth-year internship will be necessary for biology teaching certification. Students who plan to enter medical, dental, or related professional schools are advised to confer with their faculty adviser to work the requirements for these programs into their academic majors.

Core courses in the biology major are from departments that contribute to the biological sciences community at UNH. The core curriculum consists of introductory and upper-level science courses plus seven additional courses in the biological sciences three of these must be selected from course lists in three broad categories.

While students are advised to declare the biology major as incoming first-year students to assure adequate program planning, transfer into this major at a later stage is also possible. Several of the other biological science majors share the same biology core curriculum. For the first to two years, it is quite easy to change to or from these other majors.

Core Biological concepts explained to a Computer Scientist? - Biology

About the Biology Major

At the undergraduate level, the Department of Biology at Emory offers a diverse and extensive curriculum designed to expose students to cutting edge theory and practice in biology. Successful completion of the introductory biology sequence prepares students for advanced study in cell and molecular biology, physiology, ecology and evolutionary biology, and other biological subdisciplines. Opportunities are provided for supervised laboratory experiences, seminars, directed study, and research. Our curriculum is designed to provide the biological background necessary for post-baccalaureate training at the graduate or professional level.

After completing a Biology major, students will be able to:
1. Explain and apply major biological concepts and connect concepts from the biological and physical sciences.
2. Develop problem solving, critical thinking, and quantitative skills to address biological questions.
3. Be able to pursue successfully career or post-baccalaureate education goals.

To learn about the admission process for Emory, visit the admission office website.

Declaration of a Biology Major

As a student, you are required to choose a major by the end of your second year. However, you may do so as early as the second semester of your freshman year. To declare a major in Biology, please fill out and submit the new on-line Declaration of Major/Minor (DOM) form (found on the Office of Undergraduate Education (OUE) website under "OUE Forms." Then contact Ms. Barbara Shannon at [email protected] to schedule an appointment to complete the major declaration process. At the declaration appointment, the student will be provided with the major requirements and will be assigned a Biology faculty member as their academic advisor.

Degree Programs Offered in Biology

1. BA and BS degrees in Biology

The Biology department offers both a BA and a BS in Biology. The requirements for both of these are described in the documents below. The decision as to which to pursue is dependent on several factors. Some tips on making the decision are located in the FAQ at the bottom on this page.

View a suggested course schedule for a four year plan to graduate with a BS in Biology.

3. Quantitative Sciences (QSS) Major with Biology Track:

The Institute for Quantitative Theory and Methods is now offering a QSS major with a Biology track. Read here for further information.

4. Minor: Science, Culture and Society

In conjunction with the Program in Science, Culture, and Society, students may also earn a minor in that area. To learn about the minor, visit the Institute for Liberal Arts (ILA) website.


Biology is the science of living systems, from molecular and cellular to organismal and ecological levels. Biology is also a living science that continues to make new and exciting discoveries revealing the causes of human disease, generating new therapies, improving human health, and helping to understand ourselves. Biology majors choose an area of concentration representing one of the foundational modern biological disciplines.  Majors become experts in their area of concentration and attain a breadth of knowledge preparing them for careers in medicine, research, biotech and beyond.

The study of biology made large impacts on society historically and today. Biologists discovered evolution by natural selection to explain the origin and persistence of life on Earth. They discovered the replication and decoding of DNA information to explain inherited and sporadic diseases such as birth defects and cancer. Biologists identify the nature of infectious disease and the immune system, leading to antibiotics and vaccines. Crucially, biologists develop ways to detect and modify biomolecules, leading to advanced diagnostics and therapeutics. Ongoing research in biology is essential for confronting the health challenges of today and of the future.

While Covid-19 is keeping many of us off campus, our mission continues, and our commitment to your education and well-being is unwavering.


Helpful Links


During the upper years, biology majors choose an area of concentration representing one of the foundational modern biological disciplines below. These specializations build expertise in in methodologies and analysis of distinct levels of organization in biological systems. Explore our concentrations below.

Human Health and Disease

Learn about biological aspects of both health and pathology, and the biological underpinnings of medical research.

Cell and Developmental Biology

Biochemistry & Biophysics

Dive into the chemical and physical nature of biological compounds and macromolecules, and the chemical processes that govern life.

Molecular Genetics and Genomics

Discover how cells encode, express, and pass on genetic information.

Computational and Systems Biology

Investigate the range of quantitative and other analytical techniques in biological theory and experimentation.

Ecology Evolution and Conservation

Discover the interactions between ecology and evolution, the impact of climate change, habitat fragmentation, invasive species, and other factors in biodiversity and ecosystem health.

Molecular Neurobiology

Learn the molecular, cellular, developmental, structural and functional aspects of nervous systems. 

Interdisciplinary Biology

Design your own specialization.


 Visit our concentration page to explore the possibilities.


Fall 2020 Course Information

Fall 2020 course information now available!

Fall 2020 Lab BIOL_SCI 220 can be taken online OR in-person. See course description for more details.

Open Educational Resources (OER)

From OpenStax:
" Biology 2e is designed to cover the scope and sequence requirements of a typical two-semester biology course for science majors. The text provides comprehensive coverage of foundational research and core biology concepts through an evolutionary lens. Biology includes rich features that engage students in scientific inquiry, highlight careers in the biological sciences, and offer everyday applications. The book also includes various types of practice and homework questions that help students understand&mdashand apply&mdashkey concepts.
The 2nd edition has been revised to incorporate clearer, more current, and more dynamic explanations, while maintaining the same organization as the first edition. Art and illustrations have been substantially improved, and the textbook features additional assessments and related resources."

OpenStax Concepts of Biology

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Open SUNY Textbooks: Microbiology: A Laboratory Experience

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"Designed to support a course in microbiology, Microbiology: A Laboratory Experience permits a glimpse into both the good and the bad in the microscopic world. The laboratory experiences are designed to engage and support student interest in microbiology as a topic, field of study, and career.

This text provides a series of laboratory exercises compatible with a one-semester undergraduate microbiology or bacteriology course with a three- or four-hour lab period that meets once or twice a week. The design of the lab manual conforms to the American Society for Microbiology curriculum guidelines and takes a ground-up approach &mdash beginning with an introduction to biosafety and containment practices and how to work with biological hazards. From there the course moves to basic but essential microscopy skills, aseptic technique and culture methods, and builds to include more advanced lab techniques. The exercises incorporate a semester-long investigative laboratory project designed to promote the sense of discovery and encourage student engagement. "

OpenStax Microbiology

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Open Oregon: Environmental Biology

From the Introduction:
"Environmental Biology is a free and open textbook that enables students to develop a nuanced understanding of today&rsquos most pressing environmental issues. This text helps students grasp the scientific foundation of environmental topics so they can better understand the world around them and their impact upon it. This book is a collaboration between various authors and organizations that are committed to providing students with high quality and affordable textbooks. Particularly, this text draws from the following open sources, in addition to new content from the editor:

Biology by OpenStax is licensed under CC BY 3.0
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Environmental Biology is licensed under CC BY 4.0 and was edited and co-authored by Matthew R. Fisher, Biology Faculty at Oregon Coast Community College. If you have questions, suggestions, or found errors in this text, please contact him at [email protected]"

Concepts of Biology: 1st Canadian Edition

From the Description:
"In this survey text, directed at those not majoring in biology, we dispel the assumption that a little learning is a dangerous thing. We hope that by skimming the surface of a very deep subject, biology, we may inspire you to drink more deeply and make more informed choices relating to your health, the environment, politics, and the greatest subject that are all of us are entwined in, life itself.

Ancillary materials, including powerpoint slides, lab manual, and assignments available upon request.

NIH The New Genetics

From the Description:
"The New Genetics is a science education booklet that explains the role of genes in health and disease, the basics of DNA and its molecular cousin RNA, and new directions in genetic research.

​Please note the publication date of this resource. There may be more recent developments that are not captured here. We are working to update our science education content and encourage you to check our website for new resources in the future."

From the About:
"This introduction to computational biology is centered on the analysis of molecular sequence data. There are two closely connected aspects to biological sequences: (i) their relative position in the space of all other sequences, and (ii) their movement through this sequence space in evolutionary time. Accordingly, the first part of the book deals with classical methods of sequence analysis: pairwise alignment, exact string matching, multiple alignment, and hidden Markov models. In the second part evolutionary time takes center stage and phylogenetic reconstruction, the analysis of sequence variation, and the dynamics of genes in populations are explained in detail. In addition, the book contains a computer program with a graphical user interface that allows the reader to experiment with a number of key concepts developed by the authors.

Introduction to Computational Biology is intended for students enrolled in courses in computational biology or bioinformatics as well as for molecular biologists, mathematicians, and computer scientists."

From the Summary:
"Anatomy and Physiology is a dynamic textbook for the two-semester human anatomy and physiology course for life science and allied health majors. The book is organized by body system and covers standard scope and sequence requirements. Its lucid text, strategically constructed art, career features, and links to external learning tools address the critical teaching and learning challenges in the course. The web-based version of Anatomy and Physiology also features links to surgical videos, histology, and interactive diagrams."

From the Summary:
"The 3rd edition of Cell and Molecular Biology 3e: What We Know & How We Found Out (CMB3e) is the latest edition of an interactive Open Educational Resource (OER) electronic textbook, available under a Creative Commons CC-BY license. Like earlier editions (and like most introductory science textbooks), the third edition of the CMB3e iText opens with a discussion of scientific method. CMB3e retains its focus on experimental support for what we know about cell and molecular biology. Having a sense of how science is practiced and how investigators think about experimental results is essential to understanding the relationship of cell structure and function, not to mention the natural world around us.

Instructors and students can freely download the CMB3e Sample Chapter, Basic CMB3e and Annotated CMB3e iText. Instructors can request the Instructors CMB3e iText. All iText users can create their own digital annotations or download and print the text and write in the margins the old-fashioned way!"

From the Summary:
"The traditional approach to teaching Organic Chemistry, taken by most of the textbooks that are currently available, is to focus primarily on the reactions of laboratory synthesis, with much less discussion - in the central chapters, at least - of biological molecules and reactions. This is despite the fact that, in many classrooms, a majority of students are majoring in Biology or Health Sciences rather than in Chemistry, and are presumably taking the course in order to learn about the chemistry that takes place in living things.

In an effort to address this disconnect, I have developed a textbook for a two-semester, sophomore-level course in Organic Chemistry in which biological chemistry takes center stage. For the most part, the text covers the core concepts of organic structure, structure determination, and reactivity in the standard order. What is different is the context: biological chemistry is fully integrated into the explanation of central principles, and as much as possible the in-chapter and end-of-chapter problems are taken from the biochemical literature. Many laboratory synthesis reactions are also covered, generally in parallel with their biochemical counterparts - but it is intentionally the biological chemistry that comes first."

From the Summary:
"This textbook has been created with several goals in mind: accessibility, customization, and student engagement&mdashall while encouraging students toward high levels of academic scholarship. Students will find that this textbook offers a strong introduction to human biology in an accessible format."

A suggested course sequence for Biological Sciences majors follows.

A suggested course sequence for full-time students follows. All students should review the Program Advising Guide and consult an advisor.

First Semester

  • MATH 181 - Calculus I4 semester hours(MATF)
  • CHEM 131 - Principles of Chemistry I4 semester hours(NSLD)
  • Behavioral and Social Sciences Distribution 3 semester hours (BSSD) **

Second Semester

  • English Foundation 3 semester hours (ENGF) ***
  • BIOL 150 - Principles of Biology I4 semester hours(NSLD)
  • CHEM 132 - Principles of Chemistry II4 semester hours

Third Semester

  • COMM 112 - Business and Professional Speech Communication3 semester hours(GEEL)
  • Arts Distribution 3 semester hours (ARTD)
  • Program Electives 4 semester hours †††

Fourth Semester

  • BIOL 222 - Principles of Genetics4 semester hours
  • Behavioral and Social Sciences Distribution 3 semester hours (BSSD) **
  • Program Elective 4 semester hours †,††
  • Program Elective 3 semester hours †,††

** Behavioral and Social Science Distribution (BSSD) courses must come from different disciplines.

*** If ENGF has already been taken, then choose an arts distribution course (ARTD).

† Program electives: (Program electives range from 2-5 credits. Students are encouraged to speak with their transfer institution when selecting program electives. It is recommended that in a 2 semester chemistry sequence, both courses be taken at the same institution, e.g. CHEM 203 and CHEM 204.) BIOL 202, BIOL 210, BIOL 212, BIOL 213, BIOL 217, BIOL 226, BIOL 228, BIOL 230, BIOL 252, BIOT 120, CHEM 203, CHEM 204, CMSC 140, CMSC 203, CMSC 204, MATH 171, MATH 182, MATH 280, MATH 282, PHYS 161, PHYS 203, PHYS 204, PHYS 233, PHYS 234, PHYS 262, PHYS 263, SCIR 297.

†† Students planning to transfer to UMCP should take MATH 170, and should choose as electives: BIOL 252, CHEM 203, CHEM 204, and MATH 171. Students that enter calculus ready should consider taking PHYS 233 and PHYS 234.

Watch the video: Πρόγραμμα MK ULTRA της CIA και έλεγχος του νου!! Μια προσέγγιση της πλύσης εγκεφάλου μας!!! (February 2023).